diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index c42bd06..57a325c 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -1,6 +1,4 @@ -# This workflows will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries -name: Upload Python Package to PYPI +name: Upload Python Package to PyPI on: release: @@ -8,23 +6,22 @@ on: jobs: deploy: - runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: - python-version: '3.x' - - name: Install dependencies + python-version: '3.12' + - name: Install build tools run: | python -m pip install --upgrade pip - pip install setuptools wheel twine + pip install build twine - name: Build and publish env: TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} run: | - python setup.py sdist bdist_wheel - twine upload dist/* \ No newline at end of file + python -m build + twine upload dist/* diff --git a/.github/workflows/testing-work.yml b/.github/workflows/testing-work.yml index 5e686a3..cd6d88f 100644 --- a/.github/workflows/testing-work.yml +++ b/.github/workflows/testing-work.yml @@ -3,37 +3,65 @@ name: Package Testing on: pull_request: branches: - - dev - master - - update-test + - mdf-agent jobs: - - build: + test: runs-on: ubuntu-latest timeout-minutes: 20 strategy: matrix: - python-version: [3.7, 3.8, 3.9] + python-version: ['3.10', '3.11', '3.12'] - env: - CLIENT_ID: ${{ secrets.CLIENT_ID }} - CLIENT_SECRET: ${{ secrets.CLIENT_SECRET }} - name: build + name: Client — Python ${{ matrix.python-version }} steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip - pip install -e . - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - pip install -r test_requirements.txt + pip install -e ".[extractors]" + pip install pytest - name: Test with pytest run: | - pytest -s tests/test_connect_client.py + pytest tests/ -v --ignore=tests/test_connect_client.py + + backend-test: + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + matrix: + python-version: ['3.11', '3.12'] + + name: Backend — Python ${{ matrix.python-version }} + env: + STORE_BACKEND: sqlite + AUTH_MODE: dev + STORAGE_BACKEND: local + USE_MOCK_DATACITE: "true" + USE_MOCK_SEARCH: "true" + ASYNC_DISPATCH_MODE: inline + ALLOW_ALL_CURATORS: "true" + + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r cs/aws/v2/requirements-test.txt + + - name: Test backend with pytest + working-directory: cs/aws + run: | + pytest v2/test_v2_*.py -v diff --git a/.gitignore b/.gitignore index 3f0628f..e610eed 100644 --- a/.gitignore +++ b/.gitignore @@ -17,3 +17,4 @@ Untitled*.ipynb travis.tar .coverage +.DS_Store diff --git a/20260201-MDF-PLAN.md b/20260201-MDF-PLAN.md new file mode 100644 index 0000000..f255509 --- /dev/null +++ b/20260201-MDF-PLAN.md @@ -0,0 +1,318 @@ +# MDF: Backend Reality + Experience Simplicity + +## The Vision + +```bash +mdf share ./my-experiment +``` + +One command. Scans files, infers metadata, authenticates, uploads, returns DOI. Done. + +But for this to work, the backend must be real - not mocked. + +--- + +## Part 1: Complete the Backend + +### Current State → Target State + +| Component | Current (Mock) | Target (Real) | +|-----------|----------------|---------------| +| Flow | `mock_flow.py` returns fake IDs | Real Globus AutomateManager | +| Status | `flow_simulator.py` advances locally | Flow callbacks update DynamoDB | +| Search | SQLite FTS5 | Globus Search via SIAP | +| Storage | Metadata only, no files | Globus HTTPS endpoint | +| Auth | Header-based mock | Real Globus token validation | +| Database | SQLite/TinyDB | DynamoDB with GSIs | + +### Phase 1: DynamoDB + Auth (Foundation) + +**DynamoDB Tables** - Already defined in `cs/aws/template.yaml:362-428` +```bash +cd cs/aws && sam build && sam deploy --config-env dev +``` + +**Files to modify:** +- `cs/aws/v2/stream_store.py` - Add `DynamoStreamStore` class (copy pattern from `store.py:53-119`) +- `cs/aws/v2/config.py` - Add `GLOBUS_CLIENT_ID`, `GLOBUS_CLIENT_SECRET` + +**New file:** +- `cs/aws/v2/authorizer.py` - Real Globus token validation (copy from `cs/aws/auth.py:35-86`) + +**Modify:** +- `cs/aws/v2/local_server.py:35-46` - Add `USE_REAL_AUTH` mode that calls real authorizer + +### Phase 2: Real Globus Flow + +**Files to modify:** +- `cs/aws/v2/submit.py:118-135` - Add `USE_REAL_FLOW` environment toggle + +**New file:** +- `cs/aws/v2/real_flow.py` - Wrapper around existing `cs/aws/automate_manager.py` + - Uses `AutomateManager.authenticate()` for client credentials + - Uses `AutomateManager.submit()` to trigger real Flow + - Returns real action_id + +**New file:** +- `cs/aws/v2/flow_callback.py` - Lambda handler for Flow status updates + - Called by Globus Flow to update DynamoDB status + - Replaces `flow_simulator.py` artificial advancement + +### Phase 3: Real File Storage + +The storage abstraction already exists and is well-designed: +- `cs/aws/v2/storage/globus_https.py` - Already implemented! + +**Files to modify:** +- `cs/aws/v2/storage/globus_https.py:327-340` - Replace in-memory cache with DynamoDB +- `cs/aws/v2/stream_upload.py:135-136` - Persist file metadata to DynamoDB after upload + +**Config needed:** +``` +STORAGE_BACKEND=globus +GLOBUS_ENDPOINT_ID=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec +GLOBUS_BASE_PATH=/mdf/streams +``` + +### Phase 4: Real Search (SIAP) + +**Files to modify:** +- `cs/aws/v2/search.py` - Add real Globus Search client mode +- `cs/aws/v2/curation.py:272-275` - Implement `_index_to_search()` TODO + +**New file:** +- `cs/aws/v2/search_client.py` - Dedicated Globus Search/SIAP client + - `search()` - Query Globus Search index + - `ingest()` - Index entries via SIAP + +**Config needed:** +``` +SEARCH_INDEX_UUID=1a57bbe5-5272-477f-9d31-343b8258b7a5 +TEST_SEARCH_INDEX_UUID=5acded0c-a534-45af-84be-dcf042e36412 +``` + +### Credentials Required + +| Variable | Purpose | Source | +|----------|---------|--------| +| `GLOBUS_CLIENT_ID` | API auth | Globus Developer Portal | +| `GLOBUS_CLIENT_SECRET` | API auth | Globus Developer Portal | +| `FLOW_ID` | Submission flow | Globus Flows console | +| `FLOW_SCOPE` | Flow auth scope | Flow definition | +| `SEARCH_INDEX_UUID` | Production index | Globus Search console | +| `DATACITE_USERNAME` | DOI minting | DataCite account | +| `DATACITE_PASSWORD` | DOI minting | DataCite account | + +--- + +## Part 2: Simplify the Experience + +### The `mdf share` Command + +```bash +mdf share ./my-experiment +``` + +**What happens:** +1. Scans files, infers types (existing extractors) +2. Auto-infers title from folder name +3. Auto-infers author from git config / system user +4. Shows discovery summary, asks for confirmation +5. Authenticates (browser popup if needed, then cached) +6. Uploads with real-time progress +7. Returns DOI/URL + +### New Files + +**`src/mdf_agent/core/infer.py`** - Metadata inference +```python +def infer_title(path: Path) -> str: + """my-experiment -> 'My Experiment'""" + +def infer_author() -> str: + """git config user.name or system user""" + +def infer_description(path: Path, file_summary: dict) -> str: + """'Dataset containing 47 files (CSV, JSON) totaling 2.3 MB'""" + +def scan_and_infer(path: Path) -> InferredMetadata: + """Main entry: scan + infer all metadata""" +``` + +**`src/mdf_agent/core/progress.py`** - Rich progress display +```python +class ShareProgress: + def scanning(self) -> ContextManager + def authenticating(self) -> ContextManager + def show_discovery_summary(self, metadata: InferredMetadata) + def confirm_proceed(self) -> bool + def uploading(self, total_files, total_bytes) -> ContextManager + def show_success(self, result: dict) +``` + +**`src/mdf_agent/core/share.py`** - Workflow orchestrator +```python +@dataclass +class ShareConfig: + path: Path + title: Optional[str] = None # Override inferred + author: Optional[str] = None # Override inferred + test: bool = False + skip_confirm: bool = False + dry_run: bool = False + +def share(config: ShareConfig, console: Console) -> ShareResult: + """Execute complete share workflow in direct mode (no .mdf/ created)""" +``` + +### Modified Files + +**`src/mdf_agent/cli/main.py`** - Add command +```python +@app.command() +def share( + path: str = typer.Argument(".", help="Path to data folder"), + title: Optional[str] = typer.Option(None, "--title", "-t"), + author: Optional[str] = typer.Option(None, "--author", "-a"), + test: bool = typer.Option(False, "--test"), + yes: bool = typer.Option(False, "--yes", "-y"), + dry_run: bool = typer.Option(False, "--dry-run"), +): + """Share data with MDF in one command.""" +``` + +**`src/mdf_agent/auth/globus.py`** - Add helper +```python +def ensure_authenticated(service_instance: str = "prod") -> tuple[Any, bool]: + """Ensure authenticated, return (authorizer, was_already_logged_in)""" +``` + +### User Experience + +``` +$ mdf share ./my-experiment + +Scanning files... done + + Discovered: + ├── Title: My Experiment + ├── Author: Ben Johnson (from git config) + ├── Files: 47 files (CSV, JSON, PDF) + └── Size: 2.3 MB + +Proceed? [Y/n/edit]: y + +Authenticating... ✓ (cached) + +Uploading: 100% |████████████████████| 47/47 [2.3 MB/s] + +Successfully shared! + DOI: 10.18126/my-experiment-v1 + URL: https://data.materialsdatafacility.org/my-experiment-v1 +``` + +### Backward Compatibility + +Existing commands unchanged: +- `mdf init` / `mdf add` / `mdf commit` / `mdf publish` - Full repository workflow +- `mdf stream create` / `append` / `snapshot` - Streaming workflow + +New `mdf share` is a separate code path that: +- Does NOT create `.mdf/` directory +- Does NOT write `mdf.yaml` to disk +- Works entirely in memory +- One-shot experience for simple cases + +--- + +## Implementation Order + +### Week 1: Foundation +- [ ] Deploy DynamoDB tables (`sam deploy`) +- [ ] Add `DynamoStreamStore` to `stream_store.py` +- [ ] Create `authorizer.py` with real Globus validation +- [ ] Add `USE_REAL_AUTH` mode to `local_server.py` + +### Week 2: Storage + Flow +- [ ] Configure Globus HTTPS storage backend +- [ ] Test file uploads work end-to-end +- [ ] Create `real_flow.py` wrapping AutomateManager +- [ ] Add `USE_REAL_FLOW` toggle to `submit.py` +- [ ] Create `flow_callback.py` for status updates + +### Week 3: Search + Integration +- [ ] Create `search_client.py` for Globus Search +- [ ] Implement `_index_to_search()` in curation.py +- [ ] End-to-end test: submit → flow → index → search + +### Week 4: Simplified UX +- [ ] Create `infer.py` - metadata inference +- [ ] Create `progress.py` - Rich progress display +- [ ] Create `share.py` - workflow orchestrator +- [ ] Add `mdf share` command to CLI +- [ ] Add `ensure_authenticated()` to auth module + +### Week 5: Polish +- [ ] Test `mdf share` end-to-end with real backend +- [ ] Error handling and edge cases +- [ ] Documentation and examples + +--- + +## Verification Plan + +### Backend Verification +```bash +# 1. Auth works +curl -H "Authorization: Bearer " http://localhost:8080/status + +# 2. Upload works +mdf stream create --title "Test" +mdf stream upload ./test-data/ +mdf stream status + +# 3. Flow works +mdf backend submit --payload test.json +mdf backend status --source-id --watch + +# 4. Search works +mdf search "test submission" +``` + +### UX Verification +```bash +# Basic share +mdf share ./examples/dft-alloys --test --dry-run + +# Full share +mdf share ./examples/dft-alloys --test + +# With overrides +mdf share ./my-data --title "Custom Title" --author "Jane Doe" --yes +``` + +--- + +## Critical Files Summary + +### Backend (cs/aws/v2/) +| File | Action | Lines | +|------|--------|-------| +| `template.yaml` | Deploy | 362-428 | +| `stream_store.py` | Add DynamoStreamStore | New class | +| `authorizer.py` | Create | New file | +| `local_server.py` | Add real auth mode | 35-46 | +| `real_flow.py` | Create | New file | +| `submit.py` | Add real flow toggle | 118-135 | +| `flow_callback.py` | Create | New file | +| `search_client.py` | Create | New file | +| `curation.py` | Implement indexing | 272-275 | + +### Client (src/mdf_agent/) +| File | Action | +|------|--------| +| `core/infer.py` | Create - metadata inference | +| `core/progress.py` | Create - Rich progress | +| `core/share.py` | Create - workflow orchestrator | +| `cli/main.py` | Add `share` command | +| `auth/globus.py` | Add `ensure_authenticated()` | diff --git a/20260206-completed.md b/20260206-completed.md new file mode 100644 index 0000000..9927847 --- /dev/null +++ b/20260206-completed.md @@ -0,0 +1,186 @@ +# MDF Connect v2: AWS Staging Deployment with Globus Auth + +**Date:** 2026-02-06 +**Branch:** `v2-backend-curation` +**Stack:** `mdf-connect-v2-staging` +**API:** `https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging` + +--- + +## What Was Done + +Deployed the MDF Connect v2 backend to AWS (staging) and verified the full end-to-end flow: Globus OAuth2 login, stream creation, file upload to the NCSA Globus HTTPS endpoint, snapshot, repository publish, search, dataset cards, and citations. + +--- + +## Architecture + +``` +Mac (mdf-agent CLI) AWS us-east-1 +──────────────────── ───────────────── +mdf login + └─> Browser → Globus OAuth2 + └─> Tokens cached ~/.config/mdf_agent/tokens.json + - auth.globus.org (openid, identity) + - 4d5f8e8b-... (MDF Connect) + - 82f1b5c6-... (NCSA HTTPS endpoint) + - transfer.api.globus.org + +BackendClient.authenticated() + └─> Bearer: auth.globus.org token ──> API Gateway + └─> X-Globus-Token: NCSA data token └─> Lambda (FastAPI + Mangum) + └─> AuthClient.userinfo(bearer) + └─> DynamoDB (submissions, streams) + └─> Globus HTTPS PUT (user's data token) + └─> data.materialsdatafacility.org/tmp/staging/ +``` + +### Two Globus Apps + +| Role | Type | Client ID | Purpose | +|------|------|-----------|---------| +| Client | NativeAppAuthClient | `074cebcc-19ad-4332-bbf2-78402291b659` | User authenticates in browser | +| Server | ConfidentialAppAuthClient | `86e4853e-9bdd-4ea5-9130-e4a0b0638400` | Backend identity resolution, dependent tokens | + +--- + +## Key Decisions & Fixes + +### 1. Globus HTTPS Endpoint Scope + +The NCSA MDF endpoint requires a scope tied to its **collection UUID**, not the hostname: + +``` +# Wrong (returns 401): +urn:globus:auth:scope:data.materialsdatafacility.org:all + +# Correct: +https://auth.globus.org/scopes/82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/https +``` + +### 2. Token Strategy (Two Tokens) + +A single Globus token can't serve both purposes. The solution uses two tokens: + +- **Bearer token** (`auth.globus.org`): Carries `openid` scope. Backend calls `userinfo()` to get the user's identity (`sub`, `email`, `name`). +- **X-Globus-Token** (NCSA collection): Has write access to `data.materialsdatafacility.org`. Forwarded through the backend to the Globus HTTPS endpoint for file uploads. + +### 3. Backend Auth: userinfo() Instead of introspect() + +`oauth2_token_introspect()` only reports `active: true` for tokens issued to the introspecting app's own resource server. Since the Bearer token is for `auth.globus.org` (not our confidential app), introspection always returned `active: false`. + +Fix: Use `AuthClient(authorizer=AccessTokenAuthorizer(token)).userinfo()` which works with any valid Globus token that has the `openid` scope. + +### 4. Upload Token Priority + +The Globus storage backend prefers the **user's token** (forwarded via `X-Globus-Token`) for uploads. Server credentials (client credentials flow) are only used as a fallback for background operations where no user token is available. This matters because the confidential app may not have direct write access to the endpoint. + +### 5. DynamoDB Decimal Serialization + +DynamoDB returns numbers as `decimal.Decimal`. The stream snapshot handler hit `TypeError: Object of type Decimal is not JSON serializable` when building the submission record. Fixed with a `default` handler in `json.dumps()`. + +### 6. Auto-Populate data_sources from Commits + +The repo workflow (`init` → `add` → `commit` → `publish`) doesn't require users to explicitly set `data_sources`. If `manifest.data_sources` is empty but there are committed files, `build_submission()` auto-populates from the commit history. Validation was updated to know about this. + +--- + +## Files Modified + +### Client (`src/mdf_agent/`) + +| File | Changes | +|------|---------| +| `auth/globus.py` | Fixed `DATA_MDF_SCOPE` to use collection UUID scope. Added `NCSA_MDF_COLLECTION_UUID`. `get_authorizer_for_scopes()` now always includes `auth.globus.org` authorizer. | +| `cli/main.py` | `mdf login` requests MDF Connect + NCSA HTTPS + Transfer scopes in one consent. | +| `core/backend_client.py` | Added `globus_data_token` support. `authenticated()` sends auth.globus.org token as Bearer, NCSA token as X-Globus-Token. Added staging to URL map. | +| `core/submission.py` | Auto-populates `data_sources` from committed files when empty. | +| `core/agent.py` | `validate()` passes `has_committed_files` flag to skip data_sources check. | +| `core/validation.py` | `validate_manifest()` accepts `has_committed_files` parameter. | +| `models/config.py` | Extended manifest config for v2 metadata fields. | +| `models/submission.py` | Extended submission models for v2. | + +### Backend (`cs/aws/`) + +| File | Changes | +|------|---------| +| `template.yaml` | Added `StorageBackend`, `UseMockDatacite`, `AllowAllCurators` parameters with auto-select logic. Added `GLOBUS_BASE_PATH` env var. Added S3 bucket (conditional). Added S3 policies. | +| `samconfig.toml` | Staging overrides: `AuthMode=production UseMockDatacite=true AllowAllCurators=true`. | +| `deploy.sh` | Fixed macOS Bash 3 compatibility (`${env^}` → `awk`). Fixed parameter_overrides merging for Globus credentials. | +| `v2/app/auth.py` | Switched from `oauth2_token_introspect()` to `AuthClient.userinfo()`. | +| `v2/app/routers/files.py` | Added logging for upload token diagnostics. | +| `v2/app/routers/streams.py` | Fixed `Decimal` serialization in snapshot handler. | +| `v2/storage/globus_https.py` | Fixed client credentials scope. User token preferred over server credentials for uploads. | +| `v2/storage/s3.py` | New S3 storage backend (not active, for future use). | +| `v2/storage/factory.py` | Added `s3` case to factory. | + +### New Files + +| File | Purpose | +|------|---------| +| `examples/demo_staging_globus.py` | End-to-end staging demo: health, stream workflow, repo workflow, discovery. | +| `examples/test_globus_direct_upload.py` | Isolated Globus HTTPS upload test (no server). | + +--- + +## Deployed Staging Resources + +All resources are managed by CloudFormation stack `mdf-connect-v2-staging`. Redeployments update in-place (no resource pollution). + +| Resource | Name/ID | +|----------|---------| +| API Gateway | `hjccjf3eqg` → `https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging` | +| Lambda (API) | `mdf-connect-v2-staging-ApiFunction-UciD01YjsOEN` | +| Lambda (Async) | `mdf-connect-v2-staging-AsyncWorkerFunction-3t7JsGf6A6Ci` | +| DynamoDB | `mdf-submissions-staging`, `mdf-streams-staging` | +| SQS | `mdf-async-jobs-staging` | +| S3 (deploy) | `mdf-sam-deployments-staging` | +| Globus storage | `data.materialsdatafacility.org/tmp/staging/` | + +--- + +## Verified End-to-End Flow + +``` +Health check ✓ 200 OK +Stream create ✓ stream-77670a8b... +Stream upload (CSV) ✓ 283 bytes → Globus HTTPS +Stream upload (JSON) ✓ 146 bytes → Globus HTTPS +Stream list files ✓ 2 files, globus backend +Stream status ✓ open, 2 files, 429 bytes +Stream snapshot ✓ → submission record in DynamoDB +Repo init/add/commit ✓ local repository workflow +Repo validate ✓ auto-populated data_sources from commits +Repo publish (submit) ✓ → submission record in DynamoDB +Search ✓ results returned +Dataset card ✓ title, status, authors +Citation ✓ BibTeX, APA (mock DOI) +``` + +--- + +## Deploy Commands + +```bash +# Full deploy +cd cs/aws && sam build && ./deploy.sh staging + +# Check logs +aws logs filter-log-events \ + --log-group-name "/aws/lambda/mdf-connect-v2-staging-ApiFunction-UciD01YjsOEN" \ + --start-time $(python3 -c "import time; print(int((time.time() - 300) * 1000))") \ + --query 'events[*].message' --output text + +# Teardown (when done) +./deploy.sh teardown staging +``` + +--- + +## What's Next + +- **Curation workflow**: Test status transitions (submitted → pending_curation → accepted) with `ALLOW_ALL_CURATORS=true` +- **Real DOIs**: Switch from mock DataCite to sandbox/production when ready +- **Globus Search integration**: Backend indexing of submissions into Globus Search +- **Transfer integration**: Large dataset uploads via Globus Transfer (scope already requested at login) +- **Production deploy**: `./deploy.sh prod` with `AuthMode=production`, real DataCite, curator group IDs diff --git a/MDF_BACKEND_ARCHITECTURE.md b/MDF_BACKEND_ARCHITECTURE.md new file mode 100644 index 0000000..0d66521 --- /dev/null +++ b/MDF_BACKEND_ARCHITECTURE.md @@ -0,0 +1,1750 @@ +# MDF Backend Server: Architecture Design + +--- + +## Current Architecture (Actual - cs repo) + +The **actual production system** is in the `cs` repo (not `connect_server`, which is legacy). It uses **AWS Lambda + Globus Flows** - a much cleaner architecture than the old SQS processor. + +### System Diagram + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ MDF Connect Architecture │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + Client API Gateway Lambda Globus Flow + │ │ │ │ + │ POST /submit │ │ │ + │──────────────────────>│ auth.py │ │ + │ │─────────────────────>│ │ + │ │ (Globus Auth check) │ │ + │ │<─────────────────────│ │ + │ │ │ │ + │ │ submit.py │ │ + │ │─────────────────────>│ │ + │ │ │ AutomateManager │ + │ │ │ .submit() │ + │ │ │─────────────────────>│ + │ │ │ │ + │ │ │ Flow triggered │ + │ │ │<─────────────────────│ + │ │ │ │ + │ {source_id, version} │ │ │ + │<──────────────────────│<─────────────────────│ │ + │ │ │ │ + │ │ │ ┌───────────────┴───────────────┐ + │ │ │ │ GLOBUS FLOW EXECUTION │ + │ │ │ │ (runs async, hours/days) │ + │ │ │ │ │ + │ │ │ │ 1. UserTransfer (Globus) │ + │ │ │ │ 2. Xtraction (metadata) │ + │ │ │ │ 3. Curation (optional) │ + │ │ │ │ 4. SearchIngest (SIAP) │ + │ │ │ │ 5. DataDestTransfer │ + │ │ │ │ 6. SearchUpdate │ + │ │ │ │ 7. Notify User │ + │ │ │ └───────────────────────────────┘ +``` + +### Key Components + +| Component | Location | Purpose | +|-----------|----------|---------| +| **Lambda: auth** | `cs/aws/auth.py` | Globus Auth token validation | +| **Lambda: submit** | `cs/aws/submit.py` | Validate metadata & trigger Flow | +| **Lambda: status** | `cs/aws/status.py` | Query submission status | +| **Lambda: submissions** | `cs/aws/submissions.py` | List user's submissions | +| **AutomateManager** | `cs/aws/automate_manager.py` | FlowsClient wrapper | +| **DynamoManager** | `cs/aws/dynamo_manager.py` | Database operations | +| **Flow Definition** | `cs/automate/mdf_flow_def.json` | State machine (JSON) | +| **Flow DSL** | `cs/automate/minimus_mdf_flow.py` | Python DSL to generate Flow | +| **Infra (Terraform)** | `cs/infra/mdf/` | Lambda, API Gateway, DynamoDB | + +### Globus Flow States + +``` +StartSubmission → UserPermissions → UserTransfer → CheckUserTransfer + │ + ┌──────────────────────────┴──────────────────────────┐ + │ SUCCESS FAIL │ + ▼ ▼ + Xtraction FailUserTransfer + │ │ + ▼ ▼ + ChooseCuration ───────────────────────────────> ChooseNotifyUserEnd + │ │ + ┌──────────────┴──────────────┐ │ + │ curation=true false │ │ + ▼ ▼ │ + CurateSubmission SearchIngest ◄────────────────────────────────┘ + │ │ + ▼ ▼ + ChooseAcceptance DataDestTransfer + │ │ + ▼ ▼ + SearchIngest ChoosePublish → MDFPublish → ChooseCitrine → ... + │ + ▼ + PrepareSearchUpdate → SearchUpdate → EndSubmission +``` + +### External Services + +| Service | Endpoint/ID | Purpose | +|---------|-------------|---------| +| **SIAP** | `https://siap.globuscs.info/` | Search ingest action provider | +| **Globus Search (dev)** | `ab71134d-0b36-473d-aa7e-7b19b2124c88` | Development index | +| **Globus Search (prod)** | `1a57bbe5-5272-477f-9d31-343b8258b7a5` | Production index | +| **Globus Search (test)** | `5acded0c-a534-45af-84be-dcf042e36412` | Test submissions | +| **Flow (dev)** | `0c7ee169-cefc-4a23-81e1-dc323307c863` | Development flow | +| **DataCite** | Via secrets | DOI minting | + +### Database Schema (Supabase) + +**Primary Key:** `(source_id, version)` + +```sql +datasets ( + source_id VARCHAR(255), + version VARCHAR(50), + versioned_source_id VARCHAR(255), + user_id VARCHAR(255), + organization VARCHAR(255), + status VARCHAR(50), + dataset_mdata JSONB, -- Full metadata + previous_versions TEXT[], + active BOOLEAN, + cancelled BOOLEAN, + hibernating BOOLEAN, + code VARCHAR(255), -- Legacy status tracking + messages TEXT[], + created_at TIMESTAMPTZ, + updated_at TIMESTAMPTZ +) +``` + +--- + +## Is This Overengineered? + +**Short answer: No, but it could be simpler.** + +### What's Actually Reasonable + +Given the constraints (~10 datasets/month, TB-scale transfers, 24+ hour processing), the current architecture is **appropriate**: + +| Aspect | Current Approach | Assessment | +|--------|-----------------|------------| +| **Lambdas for API** | 4 small functions | ✅ Right-sized, serverless scales to zero | +| **Globus Flows for orchestration** | State machine | ✅ Perfect for long-running transfers | +| **Globus Transfer** | Built-in action | ✅ Only option for TB-scale data | +| **Globus Search** | SIAP action provider | ✅ Federated search infrastructure | + +### What IS Overengineered + +| Issue | Current State | Recommendation | +|-------|---------------|----------------| +| **Two repos** | `cs` (active) + `connect_server` (dead) | Archive `connect_server`, it causes confusion | +| **Database migration limbo** | DynamoDB → Supabase incomplete | Pick one and finish | +| **12 status steps** | Legacy from old processor | Simplify to 4-5 steps for Flow model | +| **Curation workflow** | Complex email/approval system | Rarely used, could be optional module | +| **Multiple search indexes** | dev/test/prod separation | Good, but consider single dev+test | + +### Complexity Budget Analysis + +``` +Current complexity breakdown: + + Lambda code: ~400 lines (reasonable) + Flow definition: ~320 lines (reasonable) + DynamoManager: ~800 lines (could trim to 400) + AutomateManager: ~230 lines (clean) + SourceIDManager: ~900 lines (overcomplicated) + Terraform: ~200 lines (minimal) + ───────────────────────────────────── + Total active code: ~2,850 lines + + Legacy (connect_server): ~15,000+ lines (DELETE THIS) +``` + +--- + +## Simplification Plan + +### Phase 0: Clean House (1 week) + +- [ ] **Archive `connect_server` repo** - Move to `mdf-connect-legacy` or delete +- [ ] **Document actual architecture** - Update README in `cs` repo +- [ ] **Remove dead code paths** - Citrine, MRR, unused extractors +- [ ] **Consolidate config** - Single source of truth for env vars + +### Phase 1: Database Decision (1 week) + +**Analysis of actual access patterns:** + +| Access Pattern | Frequency | Query Type | +|----------------|-----------|------------| +| Get by `source_id` + `version` | Every request | Primary key | +| List by `user_id` | Dashboard loads | GSI query | +| List by `organization` | Admin views | GSI query | +| Update status | During processing | Key update | +| Store full metadata | On submit | JSON blob (not queried) | + +**What we DON'T need:** +- Complex JSONB queries → Globus Search handles discovery +- Real-time subscriptions → Polling is fine for ~10/month +- Row-level security → Auth handled at API layer via Globus +- SQL joins → Single-table design works + +**Database comparison:** + +| Option | Fit | Rationale | +|--------|-----|-----------| +| **DynamoDB** | ✅ Best | Key-value + GSI matches patterns exactly, AWS-native, pay-per-request scales to zero, no external deps | +| **Supabase** | ⚠️ Overkill | Adds external service dependency, SQL power unused, solves problems we don't have | +| **SQLite/Turso** | ❌ Risky | Emerging, blob size limits, not battle-tested at TB metadata scale | + +**Recommendation: Stay with DynamoDB** + +The Supabase migration was likely motivated by developer ergonomics, not actual requirements. DynamoDB is: +- Already integrated and working +- Matches access patterns perfectly +- Pay-per-request ideal for low volume +- No external service to manage/pay for +- Streaming use case is still key-value (stream_id → metadata) + +**Proper DynamoDB design for new infrastructure:** + +``` +Table: mdf-connect-v2-submissions + +Primary Key: + PK: source_id (String) + SK: version (String) + +GSI1 (user-submissions): + PK: user_id + SK: updated_at + +GSI2 (org-submissions): + PK: organization + SK: source_id + +Attributes: + source_id, version, versioned_source_id + user_id, user_email, organization + status (submitted|transferring|processing|indexing|complete|failed) + dataset_mdata (JSON string - full DataCite metadata) + action_id (Globus Flow run ID) + created_at, updated_at + test (boolean) +``` + +For streaming, add a second table: + +``` +Table: mdf-connect-v2-streams + +Primary Key: + PK: stream_id (String) + +GSI1 (lab-streams): + PK: lab_id + SK: created_at + +Attributes: + stream_id, lab_id, title + status (open|closed|archived) + file_count, total_bytes + last_append_at, created_at + user_id, organization +``` + +- [ ] Design new DynamoDB tables (above schema) +- [ ] Abandon Supabase migration (remove dead code) +- [ ] Keep existing DynamoDB for current system (parallel operation) + +### Phase 2: Simplify Status Tracking (1 week) + +Current 12 steps → New 5 steps: + +| Old Steps | New Step | +|-----------|----------| +| sub_start, old_cancel | `submitted` | +| data_download, data_transfer | `transferring` | +| extracting, curation | `processing` | +| ingest_search, ingest_backup, ingest_publish, ingest_citrine, ingest_mrr | `indexing` | +| ingest_cleanup | `complete` / `failed` | + +- [ ] Update Flow to report simplified status +- [ ] Update database schema +- [ ] Update status Lambda to translate for backward compatibility + +### Phase 3: Streamline Flow Definition (2 weeks) + +The current Flow has vestigial states. Simplify to: + +``` +StartSubmission + │ + ▼ +UserTransfer (Globus Transfer action) + │ + ├─── FAIL ──> NotifyFailure ──> End + │ + ▼ SUCCESS +SearchIngest (SIAP action) + │ + ▼ +UpdateSearchEntry (add DOI, finalize metadata) + │ + ▼ +NotifySuccess + │ + ▼ +End +``` + +**Remove:** +- Xtraction state (no longer extracting server-side) +- Curation states (make optional, trigger via separate endpoint) +- Citrine/MRR states (unused) +- Multiple destination transfers (single destination sufficient) + +### Phase 4: Add Streaming Layer (3 weeks) + +Once simplified, add streaming support: + +``` +New endpoints (Lambda functions): + +POST /stream/create → Create stream, return stream_id +POST /stream/:id/append → Record file metadata +POST /stream/:id/close → Finalize, optionally mint DOI +GET /stream/:id → Stream status + +New DynamoDB/Supabase table: + +streams ( + stream_id VARCHAR PRIMARY KEY, + lab_id VARCHAR, + title VARCHAR, + status VARCHAR, -- open, closed, archived + file_count INTEGER, + total_bytes BIGINT, + last_append_at TIMESTAMPTZ, + created_at TIMESTAMPTZ +) +``` + +--- + +## Deployability Improvements + +### Current Pain Points + +1. **Docker images for Lambdas** - Adds build complexity +2. **Separate Flow deployment** - GitHub release triggers +3. **Secrets scattered** - AWS Secrets Manager + env vars + Terraform +4. **No local development** - Hard to test without deploying + +### Recommendations + +#### 1. Switch to ZIP-based Lambdas + +Docker images are overkill for ~400 lines of Python: + +```hcl +# Instead of: +image_uri = "${var.ecr_repos["submit"]}:${var.env}" + +# Use: +filename = "submit.zip" +source_code_hash = filebase64sha256("submit.zip") +handler = "submit.lambda_handler" +runtime = "python3.11" +``` + +**Benefits:** Faster deploys, simpler CI/CD, no ECR costs. + +#### 2. Single Deployment Command + +```bash +# Current (multiple steps): +git push origin dev # Triggers Lambda deploy +gh release create v1.0.0 # Triggers Flow deploy (separate) + +# Better (one command): +make deploy ENV=dev # Deploys everything +``` + +#### 3. Local Development Setup + +```python +# local_server.py +from flask import Flask +from aws.submit import lambda_handler + +app = Flask(__name__) + +@app.route('/submit', methods=['POST']) +def submit(): + event = {"body": request.data, "headers": dict(request.headers), ...} + return lambda_handler(event, None) + +if __name__ == '__main__': + app.run(port=5000) +``` + +#### 4. Consolidated Secrets + +```yaml +# secrets.yaml (encrypted with SOPS or AWS KMS) +dev: + DATACITE_USERNAME: "..." + DATACITE_PASSWORD: "..." + API_CLIENT_ID: "..." + API_CLIENT_SECRET: "..." + SES_ACCESS_KEY: "..." + +prod: + DATACITE_USERNAME: "..." + ... +``` + +--- + +## Summary: Recommended Architecture + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ Simplified MDF Connect v2 Architecture │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + ┌─────────────────┐ + │ API Gateway │ + │ /submit │ + │ /status │ + │ /submissions │ + │ /stream/* │ ◄── NEW + └────────┬────────┘ + │ + ┌────────────────────────┼────────────────────────┐ + │ │ │ + ▼ ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ + │ submit.py │ │ status.py │ │ stream.py │ + │ (ZIP Lambda) │ │ (ZIP Lambda) │ │ (ZIP Lambda) │ + └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ + │ │ │ + │ ▼ │ + │ ┌─────────────────┐ │ + │ │ DynamoDB │◄────────────┘ + │ │ v2-submissions │ + │ │ v2-streams │ + │ └─────────────────┘ + │ + ▼ + ┌─────────────────┐ + │ Globus Flow │ + │ (simplified) │ + │ │ + │ Transfer ──────┼──> Globus Transfer + │ │ │ + │ ▼ │ + │ SearchIngest ──┼──> SIAP / Globus Search + │ │ │ + │ ▼ │ + │ Notify ────────┼──> Email (SES) + └─────────────────┘ +``` + +**Why DynamoDB over Supabase:** +- Access patterns are pure key-value (source_id → record, user_id → list) +- Complex queries handled by Globus Search, not database +- Pay-per-request scales to zero for low volume +- No external service dependency (all AWS) +- Streaming is also key-value (stream_id → metadata) + +**Total complexity:** ~1,200 lines (down from ~2,850 active + 15,000 legacy) + +--- + +## Performance Analysis: Why Current System Is Slow + +The current system takes **~1 minute for a simple status check**. This is unacceptable. + +### Root Cause Analysis + +Looking at `cs/aws/status.py`: + +```python +def lambda_handler(event, context): + dynamo_manager = DynamoManager() + automate_manager = AutomateManager(get_secret(...)) # ← Network call #1 + automate_manager.authenticate() # ← Network call #2 (OAuth!) + + status_rec = dynamo_manager.read_status_record(...) # ← Network call #3 + + result = { + "flow_status": automate_manager.get_status(...) # ← Network call #4 + } +``` + +**Every status request makes 4+ network calls:** + +| Call | Target | Latency | Purpose | +|------|--------|---------|---------| +| 1 | AWS Secrets Manager | ~100ms | Fetch OAuth credentials | +| 2 | Globus Auth | ~500-2000ms | OAuth client credentials flow | +| 3 | DynamoDB | ~10-50ms | Read status record | +| 4 | Globus Flows API | ~500-2000ms | Get Flow run status | + +**Plus cold start overhead:** + +| Lambda Type | Cold Start | +|-------------|------------| +| Docker-based (current) | 5-15 seconds | +| ZIP-based | 500ms-2s | +| With provisioned concurrency | <100ms | + +**Total worst case: 15s cold start + 5s network = 20+ seconds** +**Warm case: 4-5 seconds** (still too slow for status check) + +### Design Flaws + +1. **Fetching Flow status on every call** - The Flow status is fetched from Globus API every time, even though it only changes when the Flow progresses (rare) + +2. **No credential caching** - OAuth tokens are fetched fresh every request, but they're valid for hours + +3. **No connection reuse** - New HTTP clients created per request + +4. **Docker images** - Massive cold start penalty for simple Python functions + +### v2 Performance Requirements + +| Operation | Target Latency | Strategy | +|-----------|---------------|----------| +| GET /status | <200ms | Read from DynamoDB only, no external calls | +| POST /submit | <2s | Single Globus call (trigger Flow) | +| POST /stream/append | <100ms | DynamoDB write only | +| GET /stream/:id | <100ms | DynamoDB read only | + +### v2 Performance Design + +#### 1. Store Status in DynamoDB (Don't Fetch from Globus) + +``` +CURRENT (slow): + Client → Lambda → Globus Flows API → return status + +v2 (fast): + Client → Lambda → DynamoDB → return status + + Flow updates status via callback: + Flow step completes → Lambda action → DynamoDB update +``` + +The Flow itself updates DynamoDB as it progresses. Status endpoint just reads from DynamoDB. + +#### 2. Cache OAuth Tokens + +```python +# BAD (current) - fetch every request +def lambda_handler(event, context): + automate_manager.authenticate() # Network call every time + +# GOOD (v2) - cache in Lambda memory +_cached_token = None +_token_expires = 0 + +def get_cached_token(): + global _cached_token, _token_expires + if time.time() > _token_expires - 300: # Refresh 5 min early + _cached_token = fetch_new_token() + _token_expires = time.time() + 3600 + return _cached_token +``` + +#### 3. Use ZIP-based Lambdas + +```hcl +# v2 Terraform +resource "aws_lambda_function" "status" { + function_name = "MDF-Connect3-status" + runtime = "python3.11" + handler = "status.lambda_handler" + filename = "status.zip" + memory_size = 256 # More memory = faster CPU + timeout = 10 +} +``` + +#### 4. Minimal Dependencies + +``` +# v2 requirements.txt (status Lambda) +boto3 # Already in Lambda runtime, don't bundle +``` + +Compare to current which bundles entire `globus_sdk`, `globus_automate_client`, etc. + +#### 5. Status Lambda v2 (Target: <50 lines) + +```python +import json +import boto3 + +dynamodb = boto3.resource('dynamodb') +table = dynamodb.Table('mdf-connect-v2-submissions') + +def lambda_handler(event, context): + source_id = event['pathParameters']['source_id'] + version = event.get('queryStringParameters', {}).get('version') + + if version: + response = table.get_item(Key={'source_id': source_id, 'version': version}) + else: + # Get latest version + response = table.query( + KeyConditionExpression='source_id = :sid', + ExpressionAttributeValues={':sid': source_id}, + ScanIndexForward=False, + Limit=1 + ) + response = {'Item': response['Items'][0]} if response['Items'] else {} + + if 'Item' not in response: + return {'statusCode': 404, 'body': json.dumps({'error': 'Not found'})} + + return { + 'statusCode': 200, + 'body': json.dumps(response['Item'], default=str) + } +``` + +**Expected latency: 10-50ms** (DynamoDB only, no external calls) + +--- + +## Globus Search Schema Analysis + +### Hard Constraints + +1. **Type immutability**: Once a key is added with a type, it cannot be changed +2. **Key limit**: ~1000 keys including all nested subkeys +3. **Data doesn't move**: Files stay on Globus endpoints; only index entries change + +### v2 Schema: Clean Slate Design + +**Key insight: Record entries are no longer needed.** In v1, we indexed individual files with extracted metadata (`material`, `dft`, `crystal_structure`, etc.). This caused schema bloat and key exhaustion. + +In v2: +- **Datasets** = discoverable, citable units (what users search for) +- **Streams** = live data feeds (separate index, different use case) +- **No record-level indexing** = no `files[]`, `material`, `dft`, `custom`, etc. + +### What Actually Needs to be Discoverable? + +| Field | Purpose | Searchable? | +|-------|---------|-------------| +| Title, description | Find by topic | Yes (full-text) | +| Authors | Find by researcher | Yes | +| DOI | Citation lookup | Yes (exact) | +| Keywords/subjects | Topic filtering | Yes (faceted) | +| Organization | Filter by source | Yes (faceted) | +| Date | Time-based filtering | Yes (range) | +| Data location | Access the files | No (just stored) | +| File count, size | Informational | No (just stored) | + +Everything else (material composition, DFT parameters, crystal structure) is **domain-specific** and should be: +1. Stored in the dataset's own metadata files +2. Searchable via specialized tools (not general MDF search) +3. Or added to a future domain-specific index + +### v2 Dataset Schema (Minimal) + +**Target: ~25 keys total** + +```json +{ + "mdf": { + "source_id": "fe_al_dft_v1.0", + "source_name": "fe_al_dft", + "version": "1.0", + "organization": "MDF Open", + "acl": ["public"], + "ingest_date": "2024-01-31T12:00:00Z" + }, + + "dc": { + "title": "Fe-Al Intermetallic Formation Energies", + "creators": ["Doe, Jane", "Smith, John"], + "publisher": "Materials Data Facility", + "year": 2024, + "doi": "10.18126/abc123", + "description": "DFT calculations of formation energies...", + "subjects": ["DFT", "intermetallics", "iron", "aluminum"], + "license": "CC-BY-4.0" + }, + + "data": { + "location": "globus://endpoint-uuid/datasets/fe_al_dft/v1.0/", + "size_bytes": 1234567890, + "file_count": 42 + } +} +``` + +**Key count breakdown:** + +``` +mdf.* 6 keys +dc.* 9 keys +data.* 3 keys +────────────────── +Total: 18 keys + +Headroom: 982 keys (massive safety margin) +``` + +### What We're Removing + +| v1 Field | Why Remove | +|----------|------------| +| `mdf.scroll_id` | No records, no scrolling | +| `mdf.resource_type` | Always "dataset" | +| `mdf.versioned_source_id` | Redundant with source_id | +| `mdf.domains` | Unused | +| `dc.titles[]` | Flatten to single `dc.title` | +| `dc.creators[]` objects | Flatten to string array | +| `dc.resourceType` | Always "Dataset" | +| `dc.identifier` | Just use `dc.doi` | +| `services` | Unused | +| `mrr` | Unused | +| `custom` | Key explosion risk | +| `files[]` | No record indexing | +| `material.*` | Domain-specific, not general search | +| `dft.*` | Domain-specific | +| `crystal_structure.*` | Domain-specific | +| `calphad.*` | Domain-specific | + +### Streams: Separate Index + +Streams have fundamentally different characteristics: +- High volume (1000s of appends/day) +- Ephemeral (may be deleted) +- Different access patterns (by lab, by time range) +- Different ACLs (often private to lab) + +**Recommendation: Dedicated `mdf-streams` index** + +```json +{ + "stream": { + "id": "stream_abc123", + "lab_id": "argonne-lab-42", + "title": "Perovskite Synthesis Campaign", + "status": "open", + "created_at": "2024-01-15T00:00:00Z", + "last_append": "2024-01-31T12:34:56Z", + "append_count": 1247, + "acl": ["urn:globus:auth:identity:user-uuid"] + }, + + "data": { + "location": "globus://endpoint-uuid/streams/abc123/", + "size_bytes": 52428800 + }, + + "summary": { + "compositions": ["BaTiO3", "SrTiO3", "PbTiO3"], + "yield_range": [45.2, 98.7], + "file_types": ["xy", "csv"] + } +} +``` + +**Key count: ~15 keys** + +The `summary` block contains **aggregated** metadata (updated periodically), not per-file metadata. This keeps the index small while still enabling discovery. + +### Index Architecture + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ v2 SEARCH INDEX ARCHITECTURE │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + ┌─────────────────────────────┐ ┌─────────────────────────────┐ + │ mdf-datasets-v2 │ │ mdf-streams-v2 │ + │ │ │ │ + │ • Published datasets │ │ • Active streams │ + │ • DOI-minted │ │ • Lab data feeds │ + │ • Permanent │ │ • May be ephemeral │ + │ • ~18 keys/entry │ │ • ~15 keys/entry │ + │ • ~10 new entries/month │ │ • ~1000s entries/month │ + │ │ │ │ + │ Schema: mdf, dc, data │ │ Schema: stream, data, │ + │ │ │ summary │ + └─────────────────────────────┘ └─────────────────────────────┘ + │ │ + │ │ + ▼ ▼ + ┌─────────────────────────────────────────────────────────────────┐ + │ Globus Search Queries │ + │ │ + │ Datasets: "Find iron oxide datasets from 2024" │ + │ Streams: "Show active streams from Lab 42" │ + └─────────────────────────────────────────────────────────────────┘ +``` + +### Migration Strategy (Simplified) + +Since we're only migrating **dataset entries** (not records), migration is straightforward: + +```python +# Migration script pseudocode +def migrate_v1_to_v2(): + v1_index = "1a57bbe5-5272-477f-9d31-343b8258b7a5" + v2_index = "mdf-datasets-v2" + + # Query only dataset entries (not records) + query = "mdf.resource_type:dataset" + + for entry in paginated_search(v1_index, query): + v2_entry = { + "mdf": { + "source_id": entry["mdf"]["source_id"], + "source_name": entry["mdf"]["source_name"], + "version": str(entry["mdf"]["version"]), + "organization": entry["mdf"].get("organizations", ["MDF Open"])[0], + "acl": entry["mdf"]["acl"], + "ingest_date": entry["mdf"]["ingest_date"] + }, + "dc": { + "title": entry["dc"]["titles"][0]["title"], + "creators": [c.get("creatorName", c.get("familyName", "")) + for c in entry["dc"]["creators"]], + "publisher": entry["dc"].get("publisher", "Materials Data Facility"), + "year": int(entry["dc"].get("publicationYear", 2024)), + "doi": entry["dc"].get("identifier", {}).get("identifier"), + "description": entry["dc"].get("descriptions", [{}])[0].get("description", ""), + "subjects": [s["subject"] for s in entry["dc"].get("subjects", [])], + "license": "CC-BY-4.0" + }, + "data": { + "location": entry.get("data", {}).get("endpoint_path", ""), + "size_bytes": entry.get("data", {}).get("total_size", 0), + "file_count": 0 # Not tracked in v1 + } + } + + ingest_to_index(v2_index, v2_entry) +``` + +**Migration volume:** +- v1 has ~1 million entries, but most are **records** +- Datasets only: ~5,000-10,000 entries +- Migration time: minutes, not hours + +### Search Index Configuration (Final) + +| Index | Purpose | Keys | Volume | +|-------|---------|------|--------| +| `mdf-datasets-v2` | Published datasets | ~18 | ~10K entries, growing slowly | +| `mdf-streams-v2` | Active streams | ~15 | Variable, can be large | +| `mdf` (v1 prod) | Legacy, read-only | ~120+ | ~1M entries (frozen) | + +--- + +## Vision: What This Enables + +With the fully implemented backend, MDF becomes a **living data infrastructure** rather than just a publication archive. Traditional researchers can still publish polished, DOI-minted datasets through the familiar workflow. But now, automated laboratories can stream experimental data in real-time, making it searchable within minutes. A self-driving lab running 24/7 synthesis experiments can continuously push XRD patterns, mass spec results, and synthesis parameters to MDF, where they're automatically indexed and made discoverable. Other researchers can query this data as it's generated, enabling a new paradigm of "science as it happens" rather than "science after publication." The system handles everything from a single CSV upload to petabyte-scale continuous data streams, all through unified APIs that work equally well for humans typing CLI commands, Python scripts in Jupyter notebooks, or AI agents orchestrating complex experimental workflows. + +--- + +## Use Case Examples + +### 1. Traditional Dataset Publication (Human, CLI) + +A materials scientist publishes DFT calculation results with a DOI. + +```bash +# Initialize dataset with metadata +mdf init ./fe-al-dft \ + --title "Fe-Al Intermetallic Formation Energies" \ + --author "Jane Doe" \ + --author "John Smith" + +# Add data files with auto-extraction +mdf add calculations.csv parameters.json --discover + +# Commit and validate +mdf commit -m "Initial dataset" +mdf validate + +# Publish to MDF (mints DOI, indexes to Search) +mdf publish --submit + +# Output: +# Published successfully! +# Source ID: fe_al_intermetallic_v1.0 +# DOI: 10.18126/abc123 +``` + +--- + +### 2. Automated Lab Streaming Data (Python SDK) + +A self-driving synthesis lab continuously pushes experimental results. + +```python +from mdf_agent import MDFStream + +# Create a persistent stream for the lab +stream = MDFStream.create( + title="Autonomous Perovskite Synthesis - Lab 42", + lab_id="argonne-lab-42", + schema={"composition": "str", "xrd_file": "path", "yield": "float"} +) + +print(f"Stream created: {stream.id}") +# Stream created: stream_8f3a2b1c + +# In the lab's control loop (runs 24/7) +while lab.is_running(): + sample = lab.synthesize_next() + xrd_path = lab.measure_xrd(sample) + + # Upload XRD file directly to Globus (returns pre-signed URL) + upload_url = stream.get_upload_url(f"xrd/{sample.id}.xy") + requests.put(upload_url, data=open(xrd_path, 'rb')) + + # Record metadata (indexed every 15 min automatically) + stream.append({ + "composition": sample.composition, + "xrd_file": f"xrd/{sample.id}.xy", + "yield": sample.yield_percent, + "timestamp": datetime.now().isoformat() + }) + +# After a month of experiments, promote to permanent dataset +stream.close( + mint_doi=True, + title="Perovskite Synthesis Campaign Q1 2026", + description="1,247 samples synthesized autonomously" +) +# DOI: 10.18126/xyz789 +``` + +--- + +### 3. Real-Time Experiment Monitoring (AI Agent via Claude) + +A researcher asks Claude to monitor their running experiment. + +``` +User: I started a new synthesis run in Lab 42. Can you watch for any + anomalies in the XRD patterns as they come in? + +Claude: I'll monitor the stream for Lab 42. Let me connect to it. + + [Uses mdf_agent skill: stream.connect("argonne-lab-42")] + + Connected to stream stream_8f3a2b1c + Current status: 47 samples, last append 3 minutes ago + + I'll check every 15 minutes when new data is indexed. + I'll alert you if I see: + - Unexpected phase formation + - Peak shifts suggesting strain + - Anomalous intensity ratios + + [30 minutes later...] + + ⚠️ Alert: Sample #52 shows an unexpected peak at 2θ=28.3° + This could indicate BaTiO3 contamination. Here's the pattern: + + [displays XRD plot] + + Should I flag this sample for manual review? +``` + +--- + +### 4. Cross-Lab Data Discovery (Python Query) + +A researcher searches across all active lab streams for specific materials. + +```python +from mdf_agent import MDFSearch + +# Search across both published datasets AND active streams +results = MDFSearch.query( + "material.composition:Fe* AND measurement.type:XRD", + include_streams=True # Include live lab data +) + +print(f"Found {len(results)} results") +# Found 2,847 results + +# Filter to just streaming data from the last 24 hours +live_data = [r for r in results if r.is_stream and r.age_hours < 24] +print(f"Live data points: {len(live_data)}") +# Live data points: 156 + +# Download the XRD files for analysis +for record in live_data[:10]: + record.download_file("xrd_file", dest="./analysis/") +``` + +--- + +### 5. Derived Dataset from Stream Snapshot (CLI + Python) + +A researcher creates a curated subset from a live stream. + +```bash +# Take a snapshot of interesting samples from an active stream +mdf stream snapshot stream_8f3a2b1c \ + --filter "yield > 85" \ + --output ./high-yield-samples + +# Output: +# Snapshot created: 23 samples matching filter +# Files downloaded to ./high-yield-samples/ + +cd ./high-yield-samples +``` + +```python +# Curate and enrich the snapshot +from mdf_agent import MDFAgent + +agent = MDFAgent.from_repo(".") + +# Add derived analysis +agent.manifest.derived_from = [{ + "source_id": "stream_8f3a2b1c", + "relationship": "filtered_subset", + "description": "High-yield (>85%) perovskite samples" +}] + +# Add your analysis results +agent.add("analysis/*.json", discover=True) +agent.commit("Add phase identification analysis") + +# Publish as a new DOI-minted dataset +result = agent.publish() +print(f"Published: {result['doi']}") +# Published: 10.18126/derived-456 +``` + +--- + +## Problem Statement + +Design a new backend for MDF with these constraints: +- **Volume**: ~10 datasets/month (very low) +- **Data size**: TB-scale transfers, can take 24+ hours +- **New requirement**: Streaming datasets for automated labs +- **Budget**: Free or nearly free + +## Existing Infrastructure + +- **Current backend**: AWS Lambda + DynamoDB (working but poorly documented) +- **Storage**: 2 Globus endpoints, ~1PB of published data +- **Access**: Globus Auth (institutional credentials) + Globus Transfer or HTTPS PUT/GET +- **Pain point**: Previous developer left, documentation lacking + +## Two Distinct Workflows + +| Aspect | Legacy Publishing | Ephemeral/Streaming | +|--------|------------------|---------------------| +| **Use case** | Traditional dataset publication | Automated labs, continuous data | +| **Frequency** | ~10/month | Potentially thousands of appends/day | +| **Data lifecycle** | Permanent, versioned, DOI | May be temporary, rolling window | +| **Trigger** | Human initiates `mdf publish` | Machine continuously appends | +| **Metadata** | Rich DataCite, curated | Minimal, auto-generated | +| **Indexing** | Full extraction + Search | Lightweight, incremental | + +--- + +## Recommended Approach: Improve Existing AWS + Add Streaming Layer + +Given you already have working AWS Lambda + DynamoDB infrastructure, the pragmatic approach is: + +1. **Document and refactor** the existing Lambda backend (legacy publishing) +2. **Add a new streaming layer** for ephemeral datasets (can be same or different infra) +3. **Unify the API surface** so `mdf_agent` talks to one endpoint + +``` + ┌─────────────────────────────────────────────────────┐ + │ API Gateway │ + │ api.materialsdatafacility.org │ + └──────────────────────┬──────────────────────────────┘ + │ + ┌────────────────────────────┼────────────────────────────┐ + │ │ │ + ▼ ▼ ▼ + ┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐ + │ Legacy Publishing │ │ Streaming Ingest │ │ Status/Query │ + │ (existing Lambda) │ │ (new service) │ │ (shared) │ + │ │ │ │ │ │ + │ POST /submit │ │ POST /stream/new │ │ GET /status/:id │ + │ POST /update │ │ POST /stream/:id │ │ GET /datasets │ + │ POST /curate │ │ POST /stream/close │ │ GET /search │ + └──────────┬──────────┘ └──────────┬──────────┘ └──────────┬──────────┘ + │ │ │ + ▼ ▼ ▼ + ┌─────────────────────────────────────────────────────────────────────────┐ + │ DynamoDB │ + │ submissions | streams | status_log | stream_files │ + └─────────────────────────────────────────────────────────────────────────┘ + │ + ┌────────────────────────────┼────────────────────────────┐ + ▼ ▼ ▼ + ┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐ + │ Globus Endpoints │ │ Globus Search │ │ DataCite │ + │ (2 endpoints, 1PB) │ │ (indexing) │ │ (DOI minting) │ + └─────────────────────┘ └─────────────────────┘ └─────────────────────┘ +``` + +--- + +## Workflow 1: Legacy Publishing (Refactor Existing) + +**Goal**: Clean up and document the existing AWS Lambda backend. + +### Current Flow (to preserve) +``` +1. Client POSTs to /submit with DataCite metadata + data_sources +2. Lambda validates payload, creates submission record in DynamoDB +3. Lambda initiates Globus Transfer if needed +4. Step Functions or EventBridge polls transfer status +5. On completion: extract metadata, index to Globus Search, mint DOI +6. Client polls /status/:id for progress +``` + +### Improvements Needed +- [ ] Document the existing Lambda functions +- [ ] Add comprehensive logging +- [ ] Improve error messages +- [ ] Add unit tests +- [ ] Create local development setup + +--- + +## Workflow 2: Streaming/Ephemeral Datasets (New) + +**Goal**: Support automated labs that generate data continuously. + +### API Design +``` +POST /stream/create + → Create stream with minimal metadata (title, lab_id, schema) + → Returns stream_id + +POST /stream/:id/append + → Append file(s) to stream + → Can include inline metadata per file + → Data written directly to Globus endpoint via HTTPS PUT + +POST /stream/:id/snapshot + → Create searchable checkpoint of current data + → Indexes accumulated files to Globus Search + → Optional: triggers partial processing + +POST /stream/:id/close + → Finalize stream + → Option A: Convert to permanent dataset (mint DOI) + → Option B: Mark as ephemeral (auto-delete after N days) + +GET /stream/:id/status + → Current file count, bytes, last append time +``` + +### Data Storage Strategy +``` +Globus Endpoint (ephemeral zone) +└── streams/ + └── {stream_id}/ + ├── manifest.json # Stream metadata + ├── 2024-01-15/ + │ ├── 001.csv + │ ├── 002.csv + │ └── ... + └── 2024-01-16/ + └── ... +``` + +- **Hot data**: Recent files on fast storage +- **Cold data**: Older files can be moved to cheaper storage +- **Cleanup**: Manual deletion by lab owners (no auto-TTL) + +### Indexing Strategy ✓ DECIDED + +**Choice: Every 15 minutes via cron** + +| Trigger | Action | +|---------|--------| +| Every append | Update file count in DynamoDB only | +| **Every 15 min (cron)** | Batch index new files to Globus Search | +| On snapshot | Full re-index of stream (manual trigger) | +| On close | Final index + DOI mint (if permanent) | + +### Data Lifecycle ✓ DECIDED + +**Choice: No auto-delete (manual cleanup)** + +- Streams persist until explicitly deleted +- Labs responsible for managing their storage +- Future: Add optional quotas per lab if needed + +--- + +## Key Technical Decisions + +### 1. Data Path for Streaming ✓ DECIDED + +**Choice: HTTPS PUT directly to Globus endpoint** + +``` +1. Lab calls POST /stream/:id/append with file metadata +2. Backend returns pre-signed HTTPS URL for Globus endpoint +3. Lab PUTs file directly to Globus (no Lambda in data path) +4. Lab confirms upload complete, backend records in DynamoDB +``` + +Benefits: +- No 6MB Lambda payload limit +- Lower latency for uploads +- Backend only handles metadata, not data + +### 2. Metadata Extraction + +| Data Type | Strategy | +|-----------|----------| +| Local files | Client-side extraction via `mdf_agent` | +| Globus-only files | Defer to indexing phase, or skip | +| Streaming files | Minimal extraction, schema-based | + +### 3. DynamoDB Schema Additions + +``` +# Existing tables (preserve) +submissions, status_log, ... + +# New tables for streaming +streams + PK: stream_id + lab_id, title, schema, status (open|closed|expired) + created_at, last_append_at, file_count, total_bytes + ttl_days, close_action (permanent|ephemeral) + +stream_files + PK: stream_id, SK: file_path + uploaded_at, size_bytes, metadata (JSON) + indexed: boolean +``` + +--- + +## API Endpoints (Unified Surface) + +### Legacy Publishing (existing, keep compatible) +| Endpoint | Method | Purpose | +|----------|--------|---------| +| `/submit` | POST | Accept dataset submission | +| `/status/:source_id` | GET | Check processing status | +| `/submissions` | POST | List/filter submissions | +| `/update/:source_id` | POST | Metadata-only update | +| `/curate/:source_id` | GET/POST | Curation workflow | + +### Streaming (new) +| Endpoint | Method | Purpose | +|----------|--------|---------| +| `/stream/create` | POST | Create new stream | +| `/stream/:id/append` | POST | Add files to stream | +| `/stream/:id/snapshot` | POST | Index current state | +| `/stream/:id/close` | POST | Finalize stream | +| `/stream/:id` | GET | Stream status | + +--- + +## Implementation Phases + +### Phase 0: Understand Existing (Week 1) +- [ ] Get access to current Lambda source code +- [ ] Document existing Lambda functions and DynamoDB schema +- [ ] Identify pain points and technical debt +- [ ] Set up local development environment + +### Phase 1: Stabilize Legacy (Week 2-3) +- [ ] Add logging and error handling +- [ ] Write tests for existing endpoints +- [ ] Fix known issues +- [ ] Improve deployment process + +### Phase 2: Add Streaming Layer (Week 3-5) +- [ ] Design DynamoDB tables for streams +- [ ] Implement `/stream/create` and `/stream/:id` endpoints +- [ ] Implement `/stream/:id/append` with direct Globus upload +- [ ] Add EventBridge cron for periodic indexing + +### Phase 3: Integrate Streaming with Search (Week 5-6) +- [ ] Implement `/stream/:id/snapshot` for manual indexing +- [ ] Implement `/stream/:id/close` with DOI option +- [ ] Add TTL-based cleanup for ephemeral streams + +### Phase 4: Client Integration (Week 6-7) +- [ ] Add `mdf stream` commands to `mdf_agent` CLI +- [ ] Add streaming skill handlers for Claude +- [ ] Documentation and examples + +--- + +## Remaining Questions + +1. **Existing backend access**: Can you share the current Lambda source code so I can understand the architecture before refactoring? + +2. **Globus endpoint structure**: How are the 2 endpoints organized? + - One for legacy published data, one for streaming? + - Or partitioned differently? + +3. **HTTPS PUT capability**: Does the Globus endpoint support pre-signed URLs for direct PUT? Or do we need a proxy layer? + +--- + +## Files to Create/Modify + +### Existing Backend (refactor) +``` +mdf-connect-backend/ # Current Lambda code +├── README.md # NEW: Document architecture +├── docs/ +│ ├── api.md # NEW: API documentation +│ └── dynamo-schema.md # NEW: DynamoDB schema +├── tests/ # NEW: Test coverage +└── ... +``` + +### Streaming Extension +``` +mdf-connect-backend/ +├── streaming/ +│ ├── create.py # POST /stream/create +│ ├── append.py # POST /stream/:id/append +│ ├── snapshot.py # POST /stream/:id/snapshot +│ ├── close.py # POST /stream/:id/close +│ └── status.py # GET /stream/:id +├── cron/ +│ └── index_streams.py # Periodic indexing job +└── shared/ + ├── globus_client.py # Shared Globus utilities + └── search_client.py # Globus Search utilities +``` + +### Client Updates +``` +mdf_client/src/mdf_agent/ +├── cli/ +│ └── stream.py # NEW: mdf stream commands +├── core/ +│ └── streaming.py # NEW: Streaming API client +└── skill/ + └── handlers.py # UPDATE: Add streaming handlers +``` + +--- + +## Verification Plan + +1. **Legacy regression**: Existing `mdf publish` workflow still works +2. **Stream lifecycle**: Create → append → snapshot → close flow +3. **Indexing latency**: Measure time from append to searchable +4. **Ephemeral cleanup**: Verify TTL-based deletion works +5. **Scale test**: Simulate 1000 appends/hour for 24 hours + +--- + +## Legacy Code Review: connect_server (DEPRECATED) + +> **NOTE:** The `connect_server` repo is the **old system** and should be **archived**. The production system is in the `cs` repo. This section documents issues found during review - these do NOT need to be fixed, as this code should be retired. + +After reviewing the `connect_server` codebase at `/Users/ben/Desktop/git/connect_server`, the following issues were found. **These are documented for historical reference only** - the recommendation is to archive this repo rather than fix it. + +### Critical Issues (Historical - Do Not Fix, Archive Instead) + +#### 1. SQS Message Loss - Race Condition +**File**: `mdf_connect_server/processor/processor.py:54-116` + +Messages are deleted from SQS immediately after receipt, before processing completes: + +```python +# Current (dangerous) +submissions = utils.retrieve_from_queue(wait_time=20) +for sub in submissions["entries"]: + driver = multiprocessing.Process(target=submission_driver, kwargs=sub) + driver.start() + active_processes.append(driver) +utils.delete_from_queue(submissions["delete_info"]) # DELETED BEFORE PROCESSING DONE +sleep(40) +``` + +**Impact**: If the processor crashes during a long-running submission, the message is lost forever. + +**Fix**: Delete messages only after `submission_driver` completes successfully. + +#### 2. DOI Collision - Race Condition +**File**: `mdf_connect_server/utils/utils.py:1111-1131` + +DOI generation uses a check-then-mint pattern without locks: + +```python +# Pseudocode of current flow +existing = check_if_doi_exists(source_id) +if not existing: + doi = mint_doi(source_id) # Another process could mint between check and mint +``` + +**Impact**: Two concurrent submissions could generate the same DOI. + +**Fix**: Use atomic conditional writes in DynamoDB, or generate DOI first and handle conflicts. + +#### 3. Missing VisibilityTimeout on SQS +**File**: SQS configuration + +The FIFO queue uses the default 30-second visibility timeout, but processing can take hours (TB-scale transfers). + +**Impact**: A message becomes visible again while still being processed, causing duplicate processing. + +**Fix**: Set `VisibilityTimeout` to match maximum expected processing time, or extend it dynamically. + +#### 4. ACL Cleanup Failures Leave Security Holes +**File**: `mdf_connect_server/utils/utils.py` (backup_data function) + +Write permissions on Globus endpoints may not be revoked if cleanup fails. + +**Impact**: Leftover write ACLs could allow unauthorized data modification. + +**Fix**: Track ACL grants in database and add a cleanup cron job. + +### High Priority Issues + +#### 5. 123 Bare Exception Blocks +**Files**: Throughout codebase + +Pattern `except:` or `except Exception:` with no logging or re-raise. + +```python +try: + critical_operation() +except: + pass # Silent failure - no logging, no recovery +``` + +**Impact**: Failures are silently swallowed, making debugging impossible. + +**Fix**: Add structured logging to all exception handlers, re-raise where appropriate. + +#### 6. No Retry Logic for External Services +**Files**: DataCite API calls, Globus API calls + +External service calls have no timeout, retry, or circuit breaker. + +**Impact**: A temporary DataCite outage causes permanent submission failure. + +**Fix**: Add `tenacity` or similar retry library with exponential backoff. + +#### 7. DynamoDB Overwrites Without Conditional Checks +**File**: `mdf_connect_server/utils/utils.py:1812-1919` (update_status function) + +Full `put_item` calls without conditional expressions. + +**Impact**: Concurrent updates can overwrite each other, losing data. + +**Fix**: Use conditional writes or atomic update expressions. + +### Medium Priority Issues + +#### 8. Credentials in Error Logs +**Files**: API error handlers + +Error messages may include tokens or credentials. + +**Impact**: Security risk if logs are exposed. + +**Fix**: Sanitize error messages before logging. + +#### 9. Multiprocessing Without Resource Management +**File**: `mdf_connect_server/processor/processor.py` + +Worker processes are spawned without limits. + +**Impact**: Could exhaust system resources under load. + +**Fix**: Use a process pool with bounded concurrency. + +#### 10. 24 TODO Comments +**Files**: Throughout codebase + +Unfinished work scattered throughout the code. + +**Impact**: Potential bugs or missing functionality. + +**Fix**: Triage and address or document as known limitations. + +#### 11. Incomplete Supabase Migration +**Files**: Various + +Code references Supabase but migration appears incomplete. + +**Impact**: Confusion about data source of truth. + +**Fix**: Complete migration or revert to single database. + +### Legacy Architecture Diagram (For Reference Only) + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ LEGACY Data Flow (connect_server - DO NOT USE) │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + Client API (Flask) SQS FIFO Processor + │ │ │ │ + │ POST /submit │ │ │ + │────────────────────────>│ │ │ + │ │ validate + create │ │ + │ │ submission record │ │ + │ │ in DynamoDB │ │ + │ │ │ │ + │ │ send_to_queue() │ │ + │ │──────────────────────>│ │ + │ │ │ │ + │ {"source_id": "..."} │ │ │ + │<────────────────────────│ │ │ + │ │ │ │ + │ │ │ retrieve_from_queue │ + │ │ │<─────────────────────│ + │ │ │ │ + │ │ │ delete IMMEDIATELY │ ⚠️ BUG + │ │ │<─────────────────────│ + │ │ │ │ + │ │ ┌────────┴───────────────────────┘ + │ │ │ + │ │ ▼ submission_driver() [hours] + │ │ ┌─────────────┐ + │ │ │ 1. download │ Globus Transfer + │ │ │ 2. backup │ Copy to destination + │ │ │ 3. extract │ Parse metadata + │ │ │ 4. index │ Globus Search + │ │ │ 5. mint DOI │ DataCite API + │ │ │ 6. cleanup │ Remove ACLs + │ │ └─────────────┘ +``` + +> **Recommendation:** Archive this repo. The `cs` repo with Lambda + Globus Flows is the correct modern architecture. + +--- + +## Final Implementation Plan + +### Key Principle: Parallel Infrastructure + +**The new system MUST run on completely separate infrastructure.** The current production system continues operating unchanged until we explicitly flip the switch. This means: + +``` +┌─────────────────────────────────────────────────────────────────────────────────┐ +│ PARALLEL DEPLOYMENT STRATEGY │ +└─────────────────────────────────────────────────────────────────────────────────┘ + + CURRENT (keep running) NEW (build separately) + ────────────────────── ────────────────────── + + api.materialsdatafacility.org api-v2.materialsdatafacility.org + │ │ + ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ + │ MDF-Connect2-* │ │ MDF-Connect3-* │ + │ Lambda funcs │ │ Lambda funcs │ + └────────┬────────┘ └────────┬────────┘ + │ │ + ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ + │ prod-status-* │ │ v2-submissions │ + │ DynamoDB tables │ │ v2-streams │ + └─────────────────┘ │ DynamoDB tables │ + └─────────────────┘ + │ │ + ▼ ▼ + ┌─────────────────┐ ┌─────────────────┐ + │ Flow: 4c37a999 │ │ Flow: (new ID) │ + │ (prod) │ │ mdf-flow-v2 │ + └─────────────────┘ └─────────────────┘ + │ │ + └──────────────┬───────────────────────────┘ + │ + ▼ + ┌─────────────────┐ + │ Globus Search │ ◄── Same indexes (backwards compatible) + │ (shared) │ + └─────────────────┘ + │ + ▼ + ┌─────────────────┐ + │ Globus Endpoints│ ◄── Same storage (data doesn't move) + │ (shared) │ + └─────────────────┘ +``` + +**Shared resources** (no duplication needed): +- Globus Search indexes (same schema) +- Globus storage endpoints (same data locations) +- DataCite credentials (same DOI prefix) +- AWS Secrets Manager (add v2 secrets alongside existing) + +**Separate resources** (new instances): +- API Gateway (new domain: `api-v2.materialsdatafacility.org`) +- Lambda functions (new names: `MDF-Connect3-*`) +- DynamoDB tables (new names: `mdf-connect-v2-*`) +- Globus Flow (new flow ID, new definition) +- Terraform state (separate state file) + +**Cutover strategy:** +1. Build and test v2 completely on separate infra +2. Run v2 in shadow mode (process test submissions) +3. Validate v2 produces identical Search entries +4. DNS cutover: point `api.materialsdatafacility.org` → v2 +5. Keep v1 running (read-only) for 30 days +6. Decommission v1 after validation period + +--- + +### Phase 0: Setup Parallel Infrastructure (Week 1-2) + +- [ ] Create new Terraform workspace: `infra/mdf-v2/` +- [ ] Define new resource names (MDF-Connect3-*, mdf-connect-v2-*) +- [ ] Create new API Gateway with temporary domain (`api-v2.mdf...`) +- [ ] Create new DynamoDB tables with proper schema (see Phase 1 design) +- [ ] Create new Globus Flow (fork existing, new flow_id) +- [ ] Set up CI/CD for v2 (separate GitHub Actions workflow) +- [ ] Archive `connect_server` repo (rename to `mdf-connect-legacy`) +- [ ] Document v1 → v2 mapping for reference + +### Phase 1: Core Lambda Functions (Week 3-4) + +Build simplified Lambdas from scratch (don't copy old code): + +- [ ] `auth.py` - Globus Auth validation (minimal changes) +- [ ] `submit.py` - Validate + trigger Flow (simplified) +- [ ] `status.py` - Query status from new DynamoDB +- [ ] `submissions.py` - List user submissions + +**Simplifications:** +- Remove SourceIDManager complexity (use UUID + optional user-provided name) +- Remove 12-step status (use 5 states: submitted → transferring → processing → indexing → complete/failed) +- Remove organization complexity (single config, not per-org) +- Remove curation workflow (add back later if needed) + +### Phase 2: Simplified Globus Flow (Week 5-6) + +Create new flow definition (`mdf_flow_v2_def.json`): + +``` +StartSubmission + │ + ▼ +ValidateInputs (ExpressionEval - check required fields) + │ + ▼ +UserTransfer (Globus Transfer action) + │ + ├─── FAIL ──> UpdateStatusFailed ──> NotifyFailure ──> End + │ + ▼ SUCCESS +UpdateStatusProcessing (Lambda action - update DynamoDB) + │ + ▼ +SearchIngest (SIAP action) + │ + ├─── FAIL ──> UpdateStatusFailed ──> NotifyFailure ──> End + │ + ▼ SUCCESS +UpdateStatusComplete (Lambda action - update DynamoDB) + │ + ▼ +NotifySuccess (Email action) + │ + ▼ +End +``` + +- [ ] Write new flow definition (target: <100 lines JSON) +- [ ] Remove Xtraction state (client-side extraction only) +- [ ] Remove Curation states (optional future module) +- [ ] Remove Citrine/MRR states (dead code) +- [ ] Remove multiple destination transfers +- [ ] Add status update Lambda actions (Flow → DynamoDB) +- [ ] Deploy to new flow_id +- [ ] Test with sample submissions + +### Phase 3: Streaming Layer (Week 7-9) + +New Lambda functions for streaming: + +- [ ] `stream_create.py` - POST /stream/create +- [ ] `stream_append.py` - POST /stream/:id/append +- [ ] `stream_status.py` - GET /stream/:id +- [ ] `stream_close.py` - POST /stream/:id/close + +New DynamoDB table: `mdf-connect-v2-streams` + +- [ ] Implement file metadata tracking (no actual file storage - just references) +- [ ] Implement 15-minute batch indexing (EventBridge → Lambda → SIAP) +- [ ] Implement stream → dataset promotion (close with DOI minting) +- [ ] Add rate limiting (prevent abuse) + +### Phase 4: Client Integration (Week 10-11) + +Update `mdf_agent` to support both v1 and v2: + +```python +# mdf_agent/core/client.py +class MDFClient: + def __init__(self, api_version="v1"): + if api_version == "v2": + self.base_url = "https://api-v2.materialsdatafacility.org" + else: + self.base_url = "https://api.materialsdatafacility.org" +``` + +- [ ] Add `--api-version` flag to CLI +- [ ] Implement `mdf stream create/append/close` commands +- [ ] Add streaming support to Python SDK +- [ ] Update AI agent skill handlers +- [ ] Write migration guide for existing users + +### Phase 5: Validation & Cutover (Week 12) + +- [ ] Run v2 in shadow mode (duplicate submissions to both systems) +- [ ] Compare Search entries: v1 vs v2 (must be identical) +- [ ] Load test streaming endpoint (1000 appends/hour) +- [ ] Security review (auth, rate limiting, input validation) +- [ ] DNS cutover plan (with rollback procedure) +- [ ] Execute cutover during low-traffic window +- [ ] Monitor for 48 hours +- [ ] Begin 30-day parallel operation period + +### Phase 6: Decommission v1 (Week 16) + +- [ ] Verify no traffic to v1 API +- [ ] Export v1 DynamoDB data (archive) +- [ ] Delete v1 Lambda functions +- [ ] Delete v1 DynamoDB tables +- [ ] Delete v1 API Gateway +- [ ] Keep v1 Globus Flow (read-only, for historical run inspection) +- [ ] Update documentation to remove v1 references +- [ ] Celebrate 🎉 diff --git a/MDF_BACKEND_ARCHITECTURE_COMPLETED.md b/MDF_BACKEND_ARCHITECTURE_COMPLETED.md new file mode 100644 index 0000000..e91184f --- /dev/null +++ b/MDF_BACKEND_ARCHITECTURE_COMPLETED.md @@ -0,0 +1,192 @@ +# MDF Backend Architecture – Completed Work Summary + +This document summarizes all implementation work completed in this repo so another agent can pick up and review progress quickly. It focuses on the **local MVP implementation** and the new **v2 backend scaffolding** in `cs/`. + +--- + +## ✅ Core Goal Achieved +A local, runnable MVP exists that simulates the MDF backend **without AWS**, supports dataset submissions, streaming, status updates, and snapshotting streams into datasets. It includes: +- Local API server +- Local storage (SQLite + TinyDB option) +- Mock flow IDs +- Flow simulator that advances statuses +- CLI integration (`mdf backend` + `mdf stream`) +- Rich narrated demo for Argonne XRD + TGA workflow + +--- + +## 1. New v2 Backend (Local MVP) + +### Location +``` +cs/aws/v2/ +``` + +### Core Handlers +- `submit.py` – minimal submit handler with JSON validation, ID/version generation, storage write, mock flow action_id. +- `status.py` – fetch submission by source_id (+ optional version). +- `submissions.py` – list submissions by user/org. +- `status_update.py` – manual status update endpoint for local testing. + +### Streaming Handlers +- `stream_create.py` – create stream record. +- `stream_append.py` – append file batch or counts. +- `stream_status.py` – fetch stream by stream_id. +- `stream_close.py` – close stream. +- `stream_snapshot.py` – snapshot stream → dataset submission record. + +### Local Server +- `local_server.py` – HTTP server with routes: + - `/submit` + - `/status/:id` + - `/submissions` + - `/status/update` + - `/stream/create` + - `/stream/:id/append` + - `/stream/:id/close` + - `/stream/:id/snapshot` + - `/stream/:id` +- **ThreadingHTTPServer** used when available for concurrent requests. + +### Local Storage +- `store.py` – submissions store abstraction (SQLite + TinyDB + Dynamo stub). + - SQLite default, TinyDB optional via `STORE_BACKEND=tinydb`. + - Adds `list_by_status()` for flow simulator. +- `stream_store.py` – stream storage abstraction (SQLite + TinyDB). + +### Flow Simulation +- `mock_flow.py` – returns a fake action_id (mock flow). +- `flow_simulator.py` – background loop that advances statuses: + `submitted → processing → indexing → complete` + +--- + +## 2. Local Scripts + +### Startup & Utility +- `local_start.sh` – starts local server; supports: + - `FORCE_RESTART=true` + - `START_FLOW_SIM=true` + - local env defaults + +### Demo/Test Scripts +- `local_demo.sh` – combined dataset + stream demo using `mdf backend`. +- `local_test.sh` – dataset submission + status update flow. +- `local_test_stream.sh` – stream create/append/close/snapshot. +- `local_lab_stream_demo.py` – narrated, rich demo (Argonne XRD + TGA). +- `local_lab_stream_demo.sh` – wrapper that runs the Python demo. + +--- + +## 3. MDF Agent CLI Integration + +### Backend Client +- `src/mdf_agent/core/backend_client.py` + - HTTP client for local/v2 backend. + +### CLI Commands +- `mdf backend ...` + - submit/status/submissions/update-status + - stream-create/append/status/close/snapshot +- `mdf stream ...` + - same stream commands, convenience wrapper + +### Python API +- `MDFAgent` now has streaming helpers: + - `stream_create`, `stream_append`, `stream_status`, `stream_close`, `stream_snapshot` + +### Skill Handlers +- Streaming handlers added in `src/mdf_agent/skill/handlers.py`. + +--- + +## 4. Demo Improvements + +### Rich/Pretty Demo +- `local_lab_stream_demo.py` now: + - Prints intro panel explaining flow + streaming benefits. + - Shows XRD and TGA file tables. + - Shows stream status with metadata (sample_id/run_id/instruments). + - Snapshots stream into dataset. + +### Walkthrough Update +- `examples/WALKTHROUGH.md` now has **Example 3: Streaming (Local MVP)** with commands. + +--- + +## 5. Known Caveats / TODOs + +- **No real Globus Flow integration yet** (mock only). +- **No real Search indexing** (no SIAP calls yet). +- **No real auth enforcement** (local server accepts all requests). +- **No real schema validation** (submission JSON is only lightly checked). +- **DynamoDB in v2** not fully wired (only local stubs). + +--- + +## 6. How to Run Locally + +```bash +# start local server +cs/aws/v2/local_start.sh + +# (optional) start flow simulator +START_FLOW_SIM=true cs/aws/v2/local_start.sh + +# run Argonne demo +cs/aws/v2/local_lab_stream_demo.sh + +# run combined backend demo +cs/aws/v2/local_demo.sh +``` + +--- + +## 7. Key Files Added/Updated + +**Backend v2** +- `cs/aws/v2/submit.py` +- `cs/aws/v2/status.py` +- `cs/aws/v2/submissions.py` +- `cs/aws/v2/status_update.py` +- `cs/aws/v2/stream_create.py` +- `cs/aws/v2/stream_append.py` +- `cs/aws/v2/stream_status.py` +- `cs/aws/v2/stream_close.py` +- `cs/aws/v2/stream_snapshot.py` +- `cs/aws/v2/store.py` +- `cs/aws/v2/stream_store.py` +- `cs/aws/v2/flow_simulator.py` +- `cs/aws/v2/mock_flow.py` +- `cs/aws/v2/local_server.py` + +**CLI/SDK integration** +- `src/mdf_agent/core/backend_client.py` +- `src/mdf_agent/cli/backend.py` +- `src/mdf_agent/cli/stream.py` +- `src/mdf_agent/core/agent.py` +- `src/mdf_agent/skill/handlers.py` + +**Examples** +- `examples/WALKTHROUGH.md` +- `cs/aws/v2/local_demo.sh` +- `cs/aws/v2/local_test.sh` +- `cs/aws/v2/local_test_stream.sh` +- `cs/aws/v2/local_lab_stream_demo.py` +- `cs/aws/v2/local_lab_stream_demo.sh` + +--- + +## 8. Suggested Next Steps + +1. **Wire real Globus Flow for v2** (toggle by env for prod vs local). +2. **Add search indexing mock** (local JSON index or simple query endpoint). +3. **Add schema validation** for submissions and streams. +4. **Build pytest suite** for v2 endpoints + streaming. +5. **Implement stream → dataset promotion logic** with DOI + ingestion pipeline. + +--- + +If you need context, the original architecture plan is in: +- `MDF_BACKEND_ARCHITECTURE.md` + diff --git a/README.md b/README.md index c7bc715..abe1da9 100644 --- a/README.md +++ b/README.md @@ -1,31 +1,246 @@ -# MDF Connect Client -[![PyPI](https://img.shields.io/pypi/v/mdf_connect_client.svg)](https://pypi.python.org/pypi/mdf-connect-client) -[![Coverage Status](https://coveralls.io/repos/github/materials-data-facility/connect_client/badge.svg?branch=master)](https://coveralls.io/github/materials-data-facility/connect_client?branch=master) -![GHA](https://github.com/materials-data-facility/connect_client/actions/workflows/testing-work.yml/badge.svg) +# MDF Agent -The MDF Connect Client is the Python client to easily submit datasets to MDF Connect. +Python client and CLI for the [MDF Connect v2](https://github.com/materials-data-facility/connect_server) backend. Submit datasets, stream data from automated labs, curate submissions, and search the Materials Data Facility. -# Installation +## Install +```bash +pip install -e . + +# With metadata extractors (PDF, CSV, Excel) +pip install -e ".[extractors]" +``` + +## Quick start + +```bash +# Authenticate with Globus +mdf login + +# Publish a dataset directly +mdf publish ./data/ --title "My Dataset" --author "Jane Doe" --submit + +# Or use the repository workflow +mdf init --title "My Dataset" --author "Jane Doe" +mdf add ./data +mdf commit -m "Initial commit" +mdf publish --submit + +# Check status +mdf status + +# Browse your datasets +mdf list +mdf show my_dataset_v1 +mdf versions my_dataset_v1 ``` -pip install mdf_connect_client + +## CLI commands + +### Auth + +```bash +mdf login # Authenticate via Globus (opens browser) +mdf login --service staging # Authenticate against staging +mdf logout # Clear cached tokens +mdf whoami # Show current auth status ``` -### For Developers +### Publishing datasets + +```bash +# Direct mode (no repository needed) +mdf publish ./data/ --title "My Dataset" --author "Jane" --submit +mdf publish ./data/ --title "My Dataset" --author "Jane" --dry-run # Preview payload + +# Repository mode +mdf init --title "Title" --author "Name" # Initialize dataset (interactive if no flags) +mdf add ./data --discover # Stage files with auto-metadata extraction +mdf commit -m "message" # Commit changes +mdf validate # Check manifest +mdf publish --submit # Submit to MDF + +# Update an existing dataset +mdf update --data ./new_data/ --submit # Updates last published dataset +mdf update my_dataset_v1 --title "New" --submit # Explicit source_id ``` -git clone https://github.com/materials-data-facility/connect_client.git -cd connect_client -pip install -e . + +### Discoverability + +```bash +mdf list # List your submitted datasets +mdf list --limit 50 # More results + +mdf show my_dataset_v1 # Formatted dataset card +mdf show my_dataset_v1 --cite # Include citation +mdf show my_dataset_v1 --json # Raw JSON output + +mdf versions my_dataset_v1 # Version history table + +mdf status # Status of last published dataset +mdf status my_dataset_v1 # Status of specific dataset + +mdf search "perovskite" # Search all datasets and streams +mdf search "XRD" --type streams # Search only streams +``` + +### Curation + +```bash +mdf pending # List datasets awaiting review +mdf pending --organization argonne # Filter by org +mdf approve my_dataset_v1 # Approve for publication +mdf approve my_dataset_v1 --notes "LGTM" # With curator notes +mdf reject my_dataset_v1 --reason "Missing methods" # Reject with reason +``` + +### Streaming (automated labs) + +```bash +mdf stream create --title "Lab 42 XRD Run" +mdf stream upload --stream-id ID data.csv # Upload files (progress bar for >6MB) +mdf stream status --stream-id ID +mdf stream snapshot --stream-id ID # Snapshot to dataset +mdf stream close --stream-id ID --mint-doi # Close and mint DOI +mdf stream files --stream-id ID # List uploaded files +``` + +### Configuration + +```bash +mdf config show # Show all settings +mdf config set defaults.service staging # Set default service +mdf config set user.email me@example.com # Set user email (validated) +mdf config get defaults.service # Get a value +mdf config path # Show config file location +``` + +### Backend operations + +Low-level backend API commands. All accept `--json` for raw JSON output (useful for scripting). + +```bash +mdf backend health # Health check +mdf backend status --source-id X # Submission status +mdf backend status --source-id X --json # Raw JSON (for scripts) +mdf backend submissions # List submissions +mdf backend card my_dataset_v1 # Dataset preview card +mdf backend cite my_dataset_v1 -f bibtex # Citation in BibTeX +mdf backend preview my_dataset_v1 # Dataset preview +mdf backend search "iron oxide" # Search ``` -# Documentation and examples -Documentation is available on [Read the Docs](https://mdf-connect-client.readthedocs.io/) and [GitHub](https://github.com/materials-data-facility/connect_client/tree/master/docs/). [Examples and tutorials](https://github.com/materials-data-facility/connect_client/tree/master/docs/tutorials) are provided as Jupyter notebooks, which can optionally be run interactively with [Jupyter](http://jupyter.org/). +### Service targeting + +All commands that talk to the backend accept `--service` to choose the target: + +```bash +--service prod # Production +--service staging # Staging (default) +--service local # Local dev server (http://127.0.0.1:8080) +``` + +Or set `MDF_API_URL` to point to any backend URL. + +## Python SDK + +### MDFAgent (high-level API) + +```python +from mdf_agent import MDFAgent + +agent = MDFAgent() + +# Search +results = agent.search("perovskite", service_instance="staging") + +# Dataset info +card = agent.show("my_dataset_v1", service_instance="staging") +versions = agent.versions("my_dataset_v1", service_instance="staging") +citation = agent.cite("my_dataset_v1", format="bibtex", service_instance="staging") + +# Curation +pending = agent.pending(service_instance="staging") +agent.approve("my_dataset_v1", notes="LGTM", service_instance="staging") +agent.reject("my_dataset_v1", reason="Missing methods", service_instance="staging") + +# Publishing (repository mode) +agent = MDFAgent.init("./my_data", title="My Dataset", authors=["Jane Doe"]) +agent.add("data/*.csv", discover=True) +agent.commit("Add experimental data") +result = agent.publish(service_instance="staging", dry_run=False) + +# Streaming +stream = agent.stream_create("Lab Run", service_instance="staging") +agent.stream_close(stream["stream_id"], mint_doi=True, service_instance="staging") +``` + +### BackendClient (low-level API) + +```python +from mdf_agent import BackendClient + +client = BackendClient.authenticated(service_instance="staging") + +# Submit, status, search +result = client.submit({"title": "My Dataset", "authors": [{"name": "Jane"}], ...}) +status = client.status(result["source_id"]) +results = client.search("iron oxide") + +# Versions and citations +versions = client.versions("my_dataset_v1") +citation = client.get_citation("my_dataset_v1", format="bibtex") + +# Streaming +stream = client.stream_create("Lab Run") +client.stream_upload(stream["stream_id"], "data.csv", content) +client.stream_close(stream["stream_id"], mint_doi=True) + +client.close() +``` + +### Auth resolution + +`BackendClient.authenticated()` resolves credentials in this order: + +1. Explicit `token` parameter +2. `MDF_CONNECT_TOKEN` environment variable +3. `MDF_CLIENT_ID` + `MDF_CLIENT_SECRET` (confidential client credentials) +4. `MDF_DEV_USER_ID` (dev mode, no real auth) +5. Interactive Globus OAuth login (opens browser, caches tokens) + +### Error handling + +All HTTP requests automatically retry on transient errors: +- **429** (rate limited): respects `Retry-After` header +- **502, 503, 504** (server errors): exponential backoff +- **Connection errors**: 3 retries with backoff + +File uploads (`_https_put_file`) also retry on 502/503/504 and connection errors. SSL verification for the Globus HTTPS endpoint is configurable via `MDF_SSL_VERIFY` (default: `false`, as the Globus endpoint uses a private CA). + +## Connecting to the backend + +| Environment | API URL | How to deploy | +|-------------|---------|---------------| +| dev | `./deploy.sh status dev` for URL | `cd cs/aws && sam build && ./deploy.sh dev` | +| staging | `https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging` | `cd cs/aws && sam build && ./deploy.sh staging` | +| prod | `./deploy.sh status prod` for URL | `cd cs/aws && sam build && ./deploy.sh prod` | +| local | `http://127.0.0.1:8080` | `cd cs/aws && ./deploy.sh local` | + +## Running tests + +```bash +# Client tests +python -m pytest tests/ -v --ignore=tests/test_connect_client.py + +# Backend tests (no AWS credentials needed) +cd cs/aws && python -m pytest v2/test_v2_*.py -v +``` +## Legacy -# Requirements -* The Connect Client requires Python 3.5 or greater. -* To submit data to MDF Connect, you must have an account recognized by Globus Auth (including Google, ORCiD, many academic institutions, or a [free Globus ID](https://www.globusid.org/create)), and be a member of the [MDF Connect Convert Globus Group](https://app.globus.org/groups/cc192dca-3751-11e8-90c1-0a7c735d220a). +The original `mdf_forge` and `mdf_connect_client` code is preserved in `legacy/` for reference. +## Support -# Support This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the [Center for Hierarchical Material Design (CHiMaD)](http://chimad.northwestern.edu). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the [Midwest Big Data Hub](http://midwestbigdatahub.org) under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate". diff --git a/converted_datasets.json b/converted_datasets.json new file mode 100644 index 0000000..17c1cec --- /dev/null +++ b/converted_datasets.json @@ -0,0 +1,74210 @@ +{ + "source": "datasets.json", + "count": 904, + "errors": [], + "records": [ + { + "source_id": "levine_abo2179_database_v2.1", + "source_name": "levine_abo2179_database", + "version": 2, + "ingest_date": "2022-12-01T21:13:21.635945Z", + "doi": "10.18126/jx14-t0v8", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/levine_abo2179_database_v2.1/", + "metadata": { + "title": "A mechanics-based approach to realize high-force capacity electroadhesives for robots", + "authors": [ + { + "name": "Levine, David J.", + "given_name": "David J.", + "family_name": "Levine", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "name": "Iyer, Gokulanand M.", + "given_name": "Gokulanand M.", + "family_name": "Iyer", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "name": "Turner, Kevin T.", + "given_name": "Kevin T.", + "family_name": "Turner", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "name": "Pikul, James H.", + "given_name": "James H.", + "family_name": "Pikul", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "name": "Roosa, R. Daelan", + "given_name": "R. Daelan", + "family_name": "Roosa", + "affiliations": [ + "University of Pennsylvania" + ] + } + ], + "description": "All data in this file accompanies the manuscript 'A mechanics-based approach to high-force capacity electroadhesives for robots', D.J. Levine, G.M. Iyer, R.D. Roosa, K.T. Turner & J.H. Pikul, 2022. All authors are associated with the University of Pennsylvania, Department of Mechanical Engineering & Applied Mechanics, Philadelphia, PA. All data in this file are represented in Figures 3 & 6 in the manuscript. All electroadhesive force capacity tests were run with clutch designs with a Parylene-C dielectric layer of thickness 14 microns, with a dielectric constant of 3.1. 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"authors": [ + { + "name": "Bauser, Martin", + "given_name": "Martin", + "family_name": "Bauser" + } + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_781_v1.1", + "mdf_source_name": "mdr_item_781" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_781_v1/", + "version": "1.0", + "root_version": "mdr_item_781_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_783_v1.1", + "source_name": "mdr_item_783", + "version": 1, + "ingest_date": "2018-11-15T19:17:56.476713Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_783_v1/", + "metadata": { + "title": "Aluminum", + "authors": [ + { + "name": "Campbell, FC", + "given_name": "FC", + "family_name": "Campbell" + } + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_783_v1.1", + "mdf_source_name": "mdr_item_783" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_783_v1/", + "version": "1.0", + "root_version": "mdr_item_783_v1.1", + "latest": true + } + }, + { + "source_id": "surface_diffusion_bcc011_v1.1", + "source_name": "surface_diffusion_bcc011", + "version": 1, + "ingest_date": "2018-11-02T19:44:46.339540Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/surface_diffusion_bcc011_v1/", + "metadata": { + "title": "Surface Mass Diffusion on bcc(011) Planes; V, Nb, Ta and Mo", + "authors": [ + { + "name": "Ondrejcek, Michal", + "given_name": "Michal", + "family_name": "Ondrejcek", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Rajappan, Mahesh", + "given_name": "Mahesh", + "family_name": "Rajappan", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Swiech, Wacek", + "given_name": "Wacek", + "family_name": "Swiech", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Swiech, Wacek", + "given_name": "Wacek", + "family_name": "Swiech", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + } + ], + "description": "Surface mass diffusion on a smooth surfaces provide basic quantities, such as diffusion coefficients Ds relevant to surface kinetics. By step fluctuation experiments on ~300 nm thick films of four bcc(011) metals grown on (11\u201420) alpha-Al2O3, using Low Energy Electron Microscopy (LEEM), we determine the coefficient Ds for V, Nb, Ta and Mo in the temperature range above 0.5 Tm, with Tm being the melting temperature. The fluctuation amplitudes and step relaxation times are obtained as function of wave number q by calculating the Fourier components of the step edge displacement. The temperature dependence for Mo and Nb reveals both surface self-diffusion process and bulk vacancy diffusion also. The diffusion coefficients of V, Nb overlap well as functions of T/Tm but are an order of magnitude higher than that of Mo(011). The difference and a factor ~10+3 in prefactor probably arise from the native submonolayer oxidation of the column 5 metals.", + "publisher": "University of Illinois at Urbana-Champaign", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1103/PhysRevB.73.035418", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1063/1.2356108", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1080/14786430500398433", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1016/j.susc.2006.07.035", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1080/09500830310001646671", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "extensions": { + "mdf_source_id": "surface_diffusion_bcc011_v1.1", + "mdf_source_name": "surface_diffusion_bcc011" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/surface_diffusion_bcc011_v1/", + "version": "1.0", + "root_version": "surface_diffusion_bcc011_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_558_v1.1", + "source_name": "mdr_item_558", + "version": 1, + "ingest_date": "2018-11-15T18:32:26.097901Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_558_v1/", + "metadata": { + "title": "Al-Cr-Ti Assessment of diffusion mobility for the bcc phase", + "authors": [ + { + "name": "Cui, Y.W.", + "given_name": "Y.W.", + "family_name": "Cui", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ] + }, + { + "name": "Hu, Rui", + "given_name": "Rui", + "family_name": "Hu", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ] + }, + { + "name": "Li, Weibang", + "given_name": "Weibang", + "family_name": "Li", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ] + }, + { + "name": "Tang, Bin", + "given_name": "Bin", + "family_name": "Tang", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ] + }, + { + "name": "Zhou, Lian", + "given_name": "Lian", + "family_name": "Zhou", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ] + } + ], + "keywords": [ + "Al-Cr-Ti" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "license": { + "name": "Attribution-ShareAlike 3.0 United States", + "url": "http://creativecommons.org/licenses/by-sa/3.0/us/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_558_v1.1", + "mdf_source_name": "mdr_item_558" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_558_v1/", + "version": "1.0", + "root_version": "mdr_item_558_v1.1", + "latest": true + } + }, + { + "source_id": "si_ti_oxidation_v1.1", + "source_name": "si_ti_oxidation", + "version": 1, + "ingest_date": "2018-10-30T21:44:27.860096Z", + "doi": "https://dx.doi.org/10.13011/M3B36G", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/si_ti_oxidation_v1/", + "metadata": { + "title": "Early oxidation behavior of Si-coated titanium", + "authors": [ + { + "name": "Chou, Kathleen", + "given_name": "Kathleen", + "family_name": "Chou", + "affiliations": [ + "University of Michigan" + ] + }, + { + "name": "Chu, Peng-Wei", + "given_name": "Peng-Wei", + "family_name": "Chu", + "affiliations": [ + "University of Michigan" + ] + }, + { + "name": "Marquis, Emmanuelle", + "given_name": "Emmanuelle", + "family_name": "Marquis", + "affiliations": [ + "University of Michigan" + ] + } + ], + "description": "This dataset compiles images and data from oxidation studies of pure titanium coated with a pure Si coating at 800 degrees C that supplement submitted manuscript 'Early oxidation behavior of Si-coated titanium'. Please note, the meta-data provided may be updated over time. We reserve the right to update this data without notification. If you would like to be notified of changes, please email Kathleen Chou at kachou@umich.edu.", + "keywords": [ + "Titanium", + "Oxidation", + "Silicon coatings", + "Transmission electron microscopy" + ], + "publisher": "Materials Commons", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "MCPub", + "extensions": { + "mdf_source_id": "si_ti_oxidation_v1.1", + "mdf_source_name": "si_ti_oxidation" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/si_ti_oxidation_v1/", + "version": "1.0", + "root_version": "si_ti_oxidation_v1.1", + "latest": true + } + }, + { + "source_id": "ta_melting_v1.1", + "source_name": "ta_melting", + "version": 1, + "ingest_date": "2018-11-06T16:46:32.187585Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ta_melting_v1/", + "metadata": { + "title": "Ta Melting Point Calculation by Small-cell Coexistence Method", + "authors": [ + { + "name": "Hong, Qi-jun", + "given_name": "Qi-jun", + "family_name": "Hong", + "affiliations": [ + "California Institute of Technology", + "Brown University" + ] + }, + { + "name": "van de Walle, Axel", + "given_name": "Axel", + "family_name": "van de Walle", + "affiliations": [ + "California Institute of Technology", + "Brown University" + ] + } + ], + "description": "We calculate the melting temperature of Tantalum, by employing the small-size coexistence solid-liquid coexistence method. Two pseudo-potentials are employed, namely Ta_v (5d^3 6s^2) and Ta_pv (5p^6 5d^3 6s^2). Results show the inner 5p electrons are necessary for melting properties calculations of Tantalum. The theoretical melting temperature agrees closely with experiment.", + "publisher": "MDF (placeholder)", + "publication_year": 2013, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1063/1.4819792", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "http://creativecommons.org/licenses/by/3.0/us/", + "url": "http://creativecommons.org/licenses/by/3.0/us/" + }, + "extensions": { + "mdf_source_id": "ta_melting_v1.1", + "mdf_source_name": "ta_melting" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/ta_melting_v1/", + "version": "1.0", + "root_version": "ta_melting_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_177_v1.1", + "source_name": "mdr_item_177", + "version": 1, + "ingest_date": "2018-11-15T17:10:42.412843Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_177_v1/", + "metadata": { + "title": "Li-Ni-Mn-Co-Ti-O Voltage Profile", + "authors": [ + { + "name": "Markus, Isaac M", + "given_name": "Isaac M", + "family_name": "Markus", + "affiliations": [ + "University of California Berkeley" + ] + }, + { + "name": "Asta, Mark", + "given_name": "Mark", + "family_name": "Asta", + "affiliations": [ + "University of California Berkeley" + ] + } + ], + "keywords": [ + "VASP" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2014, + "resource_type": "Dataset", + "license": { + "name": "CC0 1.0 Universal", + "url": "http://creativecommons.org/publicdomain/zero/1.0/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_177_v1.1", + "mdf_source_name": "mdr_item_177" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_177_v1/", + "version": "1.0", + "root_version": "mdr_item_177_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_192_v1.1", + "source_name": "mdr_item_192", + "version": 1, + "ingest_date": "2018-11-15T17:12:48.265230Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_192_v1/", + "metadata": { + "title": "Temperature Variation of the Elastic Constants of Cubic Elements. I. Copper", + "authors": [ + { + "name": "Overton, W.C.", + "given_name": "W.C.", + "family_name": "Overton", + "affiliations": [ + "United States Naval Research Labratory, Washington 25, D.C." + ] + }, + { + "name": "Gaffney, John", + "given_name": "John", + "family_name": "Gaffney", + "affiliations": [ + "United States Naval Research Labratory, Washington 25, D.C." + ] + } + ], + "keywords": [ + "Computational File Repository Categories::ALL CHEMICAL SYSTEMS::Cu (Copper)" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2014, + "resource_type": "Dataset", + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_192_v1.1", + "mdf_source_name": "mdr_item_192" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_192_v1/", + "version": "1.0", + "root_version": "mdr_item_192_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_1327_v1.1", + "source_name": "mdr_item_1327", + "version": 1, + "ingest_date": "2018-11-15T20:21:03.459242Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_1327_v1/", + "metadata": { + "title": "Modeling of Fe\u2013W phase diagram using first principles and phonons calculations", + "authors": [ + { + "name": "Jacob, Aur\u00e9lie", + "given_name": "Aur\u00e9lie", + "family_name": "Jacob" + }, + { + "name": "Schmetterer, Clemens", + "given_name": "Clemens", + "family_name": "Schmetterer" + }, + { + "name": "Singheiser, Lorenz", + "given_name": "Lorenz", + "family_name": "Singheiser" + }, + { + "name": "Gray-Weale, Angus", + "given_name": "Angus", + "family_name": "Gray-Weale" + }, + { + "name": "Hallstedt, Bengt", + "given_name": "Bengt", + "family_name": "Hallstedt" + }, + { + "name": "Watson, Andrew", + "given_name": "Andrew", + "family_name": "Watson" + } + ], + "keywords": [ + "Phase diagram" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2016, + "resource_type": "Dataset", + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_1327_v1.1", + "mdf_source_name": "mdr_item_1327" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_1327_v1/", + "version": "1.0", + "root_version": "mdr_item_1327_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_642_v1.1", + "source_name": "mdr_item_642", + "version": 1, + "ingest_date": "2018-11-15T18:52:51.342088Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_642_v1/", + "metadata": { + "title": "Solidification During Casting of Metal-Matrix Composites", + "authors": [ + { + "name": "Rohatgi, P.", + "given_name": "P.", + "family_name": "Rohatgi", + "affiliations": [ + "Polytechnic Institute of New York University", + "University of Wisconsin", + "Central Metallurgical Research and Development Institute" + ] + }, + { + "name": "Schultz, B.", + "given_name": "B.", + "family_name": "Schultz", + "affiliations": [ + "Polytechnic Institute of New York University", + "University of Wisconsin", + "Central Metallurgical Research and Development Institute" + ] + }, + { + "name": "Gupta, N.", + "given_name": "N.", + "family_name": "Gupta", + "affiliations": [ + "Polytechnic Institute of New York University", + "University of Wisconsin", + "Central Metallurgical Research and Development Institute" + ] + }, + { + "name": "Daoud, A.", + "given_name": "A.", + "family_name": "Daoud", + "affiliations": [ + "Polytechnic Institute of New York University", + "University of Wisconsin", + "Central Metallurgical Research and Development Institute" + ] + } + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_642_v1.1", + "mdf_source_name": "mdr_item_642" + }, + "download_url": 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"source_id": "mdr_item_562_v1.1", + "source_name": "mdr_item_562", + "version": 1, + "ingest_date": "2018-11-15T18:36:26.281960Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_562_v1/", + "metadata": { + "title": "Ag-Au-Cu Atomic mobilities and diffusion characteristics", + "authors": [ + { + "name": "Du, Y.", + "given_name": "Y.", + "family_name": "Du", + "affiliations": [ + "Western Transportation Institute, Montana State University, Bozeman, MT USA" + ] + }, + { + "name": "Liang, D.", + "given_name": "D.", + "family_name": "Liang", + "affiliations": [ + "EMPA, Swiss Federal Laboratories for Materials Testing and Research, Laboratory for Joining and Interface Technology, D\u00fcbendorf, Switzerland" + ] + }, + { + "name": "Liu, Yajun", + "given_name": "Yajun", + "family_name": "Liu", + "affiliations": [ + "State Key Laboratory of Powder Metallurgy, Central South University, Changsha China" + ] + }, + { + "name": "Sheng, G.", + 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Few-layer graphene flakes are produced by liquid-phase exfoliation and stabilized by nitrocellulose to formulate inks suitable for inkjet printing, blade coating, and spray coating. Graphene thin films exhibit high conductivity and mechanical flexibility following a thermal annealing step. Characterization of the materials using atomic force microscopy, Raman spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, thermal gravimetric analysis, and electrical measurements reveals suitable properties for application in printed and flexible electronics, with environmental and water immersion tests showing robust stability. DOI: 10.1021/acs.chemmater.7b00029", + "keywords": [ + "graphene", + "carbon", + "electrode", + "exfoliation", + "nanomaterial", + "inkjet printing", + "printed electronics", + "flexible electronics", + "colloid", + "ink" + ], + "publisher": "Chemistry of Materials", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.chemmater.7b00029", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/abs/10.1021/acs.chemmater.7b00029", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_105_secor_enhanced_v1.2", + "mdf_source_name": "pub_105_secor_enhanced", + "funding_details": "['Air Force Research Laboratory (FA8650-15-2-5518)', 'Office of Naval Research MURI Program (N0014-11-1-0690)', 'NSF-MRSEC (DMR-1121262)']", + "all_materials_included": "['graphene', 'carbon']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_821/", + "version": "1.0", + "root_version": "pub_105_secor_enhanced_v1.2", + "latest": true + } + }, + { + "source_id": "pub_106_bergeron_chemical_v1.2", + "source_name": "pub_106_bergeron_chemical", + "version": 1, + "ingest_date": "2019-03-08T20:09:12.374705Z", + "doi": "10.18126/M2K041", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_824/", + "metadata": { + "title": "Chemical vapor deposition of monolayer MoS2 directly on ultrathin Al2O3 for low-power electronics", + "authors": [ + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "McMorrow, Julian J.", + "given_name": "Julian J.", + "family_name": "McMorrow", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Campbell, Gavin P.", + "given_name": "Gavin P.", + "family_name": "Campbell", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Liu, Xiaolong", + "given_name": "Xiaolong", + "family_name": "Liu", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bedzyk, Michael J.", + "given_name": "Michael J.", + "family_name": "Bedzyk", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data corresponds to the characterization of a transfer-free ultrathin heterostructure of a 2D semiconductor and high-k dielectric, and the resulting field-effect transistors. 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Few-layer graphene flakes are produced by liquid-phase exfoliation and stabilized by ethyl cellulose to formulate inks suitable for inkjet printing. Photonic annealing is applied as an alternative to thermal annealing for rapid processing and integration on temperature-sensitive substrates. Characterization of the materials using atomic force microscopy, Raman spectroscopy, thermal gravimetric analysis, and electrical measurements reveals suitable properties for application in printed and flexible electronics. DOI: 10.1002/adma.201502866", + "keywords": [ + "graphene", + "carbon", + "electrode", + "exfoliation", + "nanomaterial", + "inkjet printing", + "printed electronics", + "flexible electronics", + "colloid", + "ink" + ], + "publisher": "Advanced Materials", + "publication_year": 2015, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/adma.201502866", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://onlinelibrary.wiley.com/doi/10.1002/adma.201502866/abstract", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_108_secor_rapid_v1.2", + "mdf_source_name": "pub_108_secor_rapid", + "funding_details": "Office of Naval Research MURI Program (N0014-11-1-0690)", + "all_materials_included": "['graphene', 'carbon']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_822/", + "version": "1.0", + "root_version": 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+ "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Weiss, Emily A.", + "given_name": "Emily A.", + "family_name": "Weiss", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data corresponds to the following publication: 10.1021/acs.jpclett.8b00628.", + "keywords": [ + "2D", + "mos2", + "PTB7", + "ultrafast charge transfer", + "van der Waals heterojunction", + "transient absorption spectroscopy" + ], + "publisher": "The Journal of Physical Chemistry Letters", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.jpclett.8b00628", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.8b00628", + "identifier_type": "URL", + "relation_type": 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Cole", + "given_name": "J. Cole", + "family_name": "Shatto", + "affiliations": [ + "Ulm University" + ] + }, + { + "name": "Uesugi, Kentaro", + "given_name": "Kentaro", + "family_name": "Uesugi", + "affiliations": [ + "Japan Synchrotron Radiation Research Insitute" + ] + }, + { + "name": "Schmidt, S\u00f8ren", + "given_name": "S\u00f8ren", + "family_name": "Schmidt", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Krill III, Carl", + "given_name": "Carl", + "family_name": "Krill III", + "affiliations": [ + "Ulm University" + ] + } + ], + "description": "3D datasets of Al-5 wt% Cu (with crystallographic orientation) recorded over the course of annealing in the semisolid state. The data is saved as compressed (.zip) text files, which contain seven columns. These are (in order): the grain label; row, column & layer voxel indices; followed by a 3 component Rodrigues vector for describing the crystallographic orientation.", + "keywords": [ + "grain rotation", + "interfacial energy", + "3D microstructural evolution", + "experiment", + "multiphase alloys", + "phases", + "sintering", + "microstructures", + "Ostwald ripening", + "Al-Cu", + "metals and alloys", + "annealing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "doi:10.1073/pnas.1602293113", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_51_dake_coarsening_v1.2", + "mdf_source_name": "pub_51_dake_coarsening", + "all_materials_included": "Al-5 wt% Cu", + "funding_details": "['National Science Foundation/Deutsche Forschungsgemeinschaft -- Materials World Network (Project KR 1658/4-1)', 'Danish Agency for Science, Technology and Innovation via DANSCATT for travel support']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_147/", + "version": "1.0", + "root_version": "pub_51_dake_coarsening_v1.2", + "latest": true + } + }, + { + "source_id": "pub_56_de_emsoft_v1.2", + "source_name": "pub_56_de_emsoft", + "version": 1, + "ingest_date": "2019-03-08T16:07:33.819386Z", + "doi": "10.18126/M2D593", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_163/", + "metadata": { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Silicon (Si)", + "authors": [ + { + "name": "De Graef, Marc", + "given_name": "Marc", + "family_name": "De Graef", + "affiliations": [ + "Carnegie Mellon University" + ] + } + ], + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for diamond-Si for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations.", + "keywords": [ + "EBSD", + "Si", + "metals and alloys", + "backscattering", + "master pattern", + "EMSoft", + "simulation", + "diffraction", + "electron backscattered diffraction", + "microscopy", + "dictionary" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/M2SG6G", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.18126/M2NP4J", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.18126/M2J01K", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1017/S1431927613001840", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/doi:10.18126/M2SG6G", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/doi:10.18126/M2NP4J", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/doi:10.18126/M2J01K", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/doi:10.1017/S1431927613001840", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_56_de_emsoft_v1.2", + "mdf_source_name": "pub_56_de_emsoft", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)", + "all_materials_included": "['Si', 'silicon']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_163/", + "version": "1.0", + "root_version": "pub_56_de_emsoft_v1.2", + "latest": true + } + }, + { + "source_id": "pub_55_de_emsoft_v1.2", + "source_name": "pub_55_de_emsoft", + "version": 1, + "ingest_date": "2019-03-08T16:05:52.778247Z", + "doi": "10.18126/M2J01K", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_162/", + "metadata": { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Nickel (Ni)", + "authors": [ + { + "name": "De Graef, Marc", + "given_name": "Marc", + "family_name": "De Graef", + "affiliations": [ + "Carnegie Mellon University" + ] + } + ], + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for fcc-Ni for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations.", + "keywords": [ + "EBSD", + "Ni", + "metals and alloys", + "backscattering", + "master pattern", + "EMSoft", + "simulation", + "diffraction", + "electron backscattered diffraction", + "microscopy", + "dictionary", + "nickel" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/M2SG6G", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.18126/M2NP4J", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1017/S1431927613001840", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://github.com/marcdegraef/EMsoft", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1017/S1431927613001840", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.18126/M2NP4J", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.18126/M2SG6G", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_55_de_emsoft_v1.2", + "mdf_source_name": "pub_55_de_emsoft", + "all_materials_included": "['nickel', 'Ni']", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_162/", + "version": "1.0", + "root_version": "pub_55_de_emsoft_v1.2", + "latest": true + } + }, + { + "source_id": "sirican_tracking_ultrafast_spectroscopy_v1.1", + "source_name": "sirican_tracking_ultrafast_spectroscopy", + "version": 1, + "ingest_date": "2019-07-05T13:57:12.377680Z", + "doi": "10.18126/M28D30", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/sirican_tracking_ultrafast_spectroscopy_v1.1/", + "metadata": { + "title": "Data for \"Tracking ultrafast photocurrents in the Weyl semimetal TaAs using THz emission spectroscopy\"", + "authors": [ + { + "name": "Sirica, N", + "given_name": "N", + "family_name": "Sirica", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Tobey, R.I.", + "given_name": "R.I.", + "family_name": "Tobey", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Zhao, L.X.", + "given_name": "L.X.", + "family_name": "Zhao", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Chen, G.F.", + "given_name": "G.F.", + "family_name": "Chen", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Xu, B.", + "given_name": "B.", + "family_name": "Xu", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Yang, R.", + "given_name": "R.", + "family_name": "Yang", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Shen, B.", + "given_name": "B.", + "family_name": "Shen", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Yarotski, D.A.", + "given_name": "D.A.", + "family_name": "Yarotski", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Bowlan, P.", + "given_name": "P.", + "family_name": "Bowlan", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Trugman, S.A.", + "given_name": "S.A.", + "family_name": "Trugman", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Zhu, J.-X.", + "given_name": "J.-X.", + "family_name": "Zhu", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Dai, Y.M.", + "given_name": "Y.M.", + "family_name": "Dai", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Azad, A.K.", + "given_name": "A.K.", + "family_name": "Azad", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Ni, N.", + "given_name": "N.", + "family_name": "Ni", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Qiu, X.G.", + "given_name": "X.G.", + "family_name": "Qiu", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Taylor, A.G.", + "given_name": "A.G.", + "family_name": "Taylor", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + }, + { + "name": "Prasankumar, R.P.", + "given_name": "R.P.", + "family_name": "Prasankumar", + "affiliations": [ + "Center for Integrated Nanotechnologies, Los Alamos National Laboratory", + "Zernike Insitute for Advanced Materials, University of Groningen", + "Institute of Physics, Chinese Academy of Sciences", + "Department of Physics and Astronomy, University of California, Los Angeles", + "School of Physics, Nanjing University" + ] + } + ], + "description": "Datafiles for figures in manuscript \"Tracking ultrafast photocurrents in the Weyl semimetal TaAs\r\nusing THz emission spectroscopy\"", + "keywords": [ + "spectroscopy", + "THz emission", + "TaAs", + "Weyl semimetal", + "simulation", + "Ultrafast photocurrent" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "sirican_tracking_ultrafast_spectroscopy_v1.1", + "mdf_source_name": "sirican_tracking_ultrafast_spectroscopy", + "funding_details": "['US DOE Office of Science Basic Energy Sciences', 'US Department of Energy', 'G.T. Seaborg Insititute', 'Center for Advancement of Topological Semimetals, an Energy Frontier Research Center funded by the U.S. Department of Energy Office of Science, Office of Basic Energy Sciences, through the Ames Laboratory under its Contract No. DE-AC02-07CH11358', 'CATS EFRC']", + "all_materials_included": "TaAs" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/sirican_tracking_ultrafast_spectroscopy_v1.1/", + "version": "1.0", + "root_version": "sirican_tracking_ultrafast_spectroscopy_v1.1", + "latest": true + } + }, + { + "source_id": "pub_31_abdulgani_new_v1.2", + "source_name": "pub_31_abdulgani_new", + "version": 1, + "ingest_date": "2019-03-08T15:24:24.425929Z", + "doi": "10.18126/M23W7S", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1139/", + "metadata": { + "title": "Dataset for \"New Generation of Effective Core Potentials from Correlated Calculations: 3d Transition Metal Series\"", + "authors": [ + { + "name": "Abdulgani, Annaberdiyev", + "given_name": "Annaberdiyev", + "family_name": "Abdulgani", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ] + }, + { + "name": "Wang, Guangming", + "given_name": "Guangming", + "family_name": "Wang", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ] + }, + { + "name": "Melton, Cody A.", + "given_name": "Cody A.", + "family_name": "Melton", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ] + }, + { + "name": "Shulenburger, Luke", + "given_name": "Luke", + "family_name": "Shulenburger", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ] + } + ], + "description": "Dataset for \"New generation of effective core potentials from correlated calculations: 3d transition metal series\"\r\n\r\nIncluded are various directories including atomic and molecular data for various ECPs. Gaussian and Molpro include input and output files for each code respectively. Each code directory is divided into atoms and molecules, containing individual directories for each species (e.g. Molpro/atoms/Fe or Gaussian/molecules/ZnO). Within each atom or molecule directory, we include data for the all-electron, uncorrelated core, and various ECPs considered in the paper. For the Gaussian molecular calculations, the molecular geometries are separated into individual directories, whereas for Molpro there is only one directory which contains one input file to calculate all geometries.\r\n\r\nAdditionally, there are two separate directories \"spectra\" and \"molecular_csv\" which contain the atomic spectra and molecular binding curves for a given basis set as csv files. Each directory also contains the py", + "keywords": [ + "CPSFM", + "simulation", + "ECP", + "Gaussian", + "MolPro" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://github.com/camelto2/correlated-data", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_31_abdulgani_new_v1.2", + "mdf_source_name": "pub_31_abdulgani_new", + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1139/", + "version": "1.0", + "root_version": "pub_31_abdulgani_new_v1.2", + "latest": true + } + }, + { + "source_id": "vinckeviciutejulija_fundamental_insights_charge_v1.1", + "source_name": "vinckeviciutejulija_fundamental_insights_charge", + "version": 1, + "ingest_date": "2019-07-05T14:00:34.297574Z", + "doi": "10.18126/M2ZW7J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/vinckeviciutejulija_fundamental_insights_charge_v1.1/", + "metadata": { + "title": "Fundamental insights about interlayer cation migration in Li-ion electrodes at high states of charge", + "authors": [ + { + "name": "Vinckeviciute, Julija", + "given_name": "Julija", + "family_name": "Vinckeviciute", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "name": "Radin, Maxwell D.", + "given_name": "Maxwell D.", + "family_name": "Radin", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "name": "Faenza, Nicholas V.", + "given_name": "Nicholas V.", + "family_name": "Faenza", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "name": "Amatucci, Glenn G.", + "given_name": "Glenn G.", + "family_name": "Amatucci", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "name": "Van der Ven, Anton", + "given_name": "Anton", + "family_name": "Van der Ven", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + } + ], + "description": "Calculated dataset to assess effects of composition on interlayer cation migration in common transition-metal oxides used in battery cathodes.", + "keywords": [ + "cathode", + "migration", + "battery", + "electrochemistry", + "energy materials", + "simulation", + "transition-metal oxide", + "layered", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "vinckeviciutejulija_fundamental_insights_charge_v1.1", + "mdf_source_name": "vinckeviciutejulija_fundamental_insights_charge", + "all_materials_included": "['Li', 'Na', 'Mg', 'Ni', 'Co', 'Mn', 'O']", + "funding_details": "['National Science Foundation, Grant DMR-1410242', 'Center for Scientific Computing from the CNSI, MRL, NSF MRSEC (DMR-1121053), and Hewlett-Packard', 'National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231', 'NorthEast Center for Chemical Energy Storage (NECCES), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0012583']" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/vinckeviciutejulija_fundamental_insights_charge_v1.1/", + "version": "1.0", + "root_version": "vinckeviciutejulija_fundamental_insights_charge_v1.1", + "latest": true + } + }, + { + "source_id": "pub_24_antonius_orbital_v1.2", + "source_name": "pub_24_antonius_orbital", + "version": 1, + "ingest_date": "2019-03-08T15:11:11.474716Z", + "doi": "10.18126/M2PS71", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1128/", + "metadata": { + "title": "Orbital Symmetry and the Optical Response of Single-Layer MX Monochalcogenides", + "authors": [ + { + "name": "Antonius, Gabriel", + "given_name": "Gabriel", + "family_name": "Antonius", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Louie, Steven G.", + "given_name": "Steven G.", + "family_name": "Louie", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "Optical absorption calculations in the GW+BSE framework for single-layer GaSe and GaTe in the hexagonal phase.", + "keywords": [ + "GaSe", + "two-dimensional materials, monochalcogenides", + "GaTe", + "simulation", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.7b05286", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://doi.org/10.1021/acs.nanolett.7b05286", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_24_antonius_orbital_v1.2", + "mdf_source_name": "pub_24_antonius_orbital", + "funding_details": "['National Science Foundation under grant DMR-1508412', 'U.S. Department of Energy, Office of Basic Energy Sciences under contract no. DE-AC02- 05CH11231', 'Center for Computa- tional Study of Excited-State Phenomena in Energy Materials (C2SEPEM)']", + "all_materials_included": "['GaSe', 'GaTe']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1128/", + "version": "1.0", + "root_version": "pub_24_antonius_orbital_v1.2", + "latest": true + } + }, + { + "source_id": "pub_29_kramer_third_v1.2", + "source_name": "pub_29_kramer_third", + "version": 1, + "ingest_date": "2019-03-08T15:21:03.869352Z", + "doi": "10.18126/M26D20", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_625/", + "metadata": { + "title": "The Third Sandia Fracture Challenge", + "authors": [ + { + "name": "Kramer, Sharlotte", + "given_name": "Sharlotte", + "family_name": "Kramer", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Boyce, Brad", + "given_name": "Brad", + "family_name": "Boyce", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Jones, Amanda", + "given_name": "Amanda", + "family_name": "Jones", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Gearhart, Jhana", + "given_name": "Jhana", + "family_name": "Gearhart", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Salzbrenner, Brad", + "given_name": "Brad", + "family_name": "Salzbrenner", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "The Third Sandia Fracture Challenge (SFC3) is a collaborative effort for assessment of the state-of-the-art predictive capability in ductile failure. The Challenge is designed such that computational modelers are asked to predict ductile failure in an unfamiliar geometry using provided standard materials data. The SFC3, issued in December 2016, is centered on an additively manufactured 316L stainless steel geometry with through holes and internal cavities that could not be produced by conventional machining. The provided information to enable the predictions includes: base material tensile behavior for different material configurations and loading rates; notched base material tensile behavior; fabrication drawings of the base material specimens and the Challenge (unfamiliar) geometry; electron backscatter diffraction of the Challenge geometry mid-plane; micro-computed tomography data of all Challenge geometry specimens; scanning electron microscope images of the fracture surface of tensile and notched tensile base material specimens; macroscopic images of fractured base material specimens; descriptions of the boundary conditions for the base-material and Challenge-geometry tests; questions to be answered for the prediction (quantities of interest); and an overview presentation of the Challenge.", + "keywords": [ + "EBSD", + "machine learning", + "images", + "challenge", + "experimental", + "microstructure", + "additive manufacturing", + "fracture", + "microscopy", + "SFC3" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_29_kramer_third_v1.2", + "mdf_source_name": "pub_29_kramer_third" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_625/", + "version": "1.0", + "root_version": "pub_29_kramer_third_v1.2", + "latest": true + } + }, + { + "source_id": "pub_28_shapera_databasedriven_v1.2", + "source_name": "pub_28_shapera_databasedriven", + "version": 1, + "ingest_date": "2019-03-08T15:18:10.556123Z", + "doi": "10.18126/M28K8M", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1135/", + "metadata": { + "title": "Database-driven Materials-selection Framework for Semiconductor Heterojunction Design", + "authors": [ + { + "name": "Shapera, Ethan P.", + "given_name": "Ethan P.", + "family_name": "Shapera", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "keywords": [ + "machine learning", + "experimental", + "nanoparticles", + "simulation", + "semiconductor", + "database" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_28_shapera_databasedriven_v1.2", + "mdf_source_name": "pub_28_shapera_databasedriven" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1135/", + "version": "1.0", + "root_version": "pub_28_shapera_databasedriven_v1.2", + "latest": true + } + }, + { + "source_id": "weilingfei_machine_learning_materials_v1.1", + "source_name": "weilingfei_machine_learning_materials", + "version": 1, + "ingest_date": "2019-08-29T17:06:39.897136Z", + "doi": "10.18126/d3gck322", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/weilingfei_machine_learning_materials_v1.1/", + "metadata": { + "title": "Machine learning optimization of p-type transparent conducting materials", + "authors": [ + { + "name": "Wei, Lingfei", + "given_name": "Lingfei", + "family_name": "Wei", + "affiliations": [ + "Lawrence Berkeley National" + ] + }, + { + "name": "Ager, Joel", + "given_name": "Joel", + "family_name": "Ager", + "affiliations": [ + "Lawrence Berkeley National" + ] + } + ], + "description": "P-type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, ultraviolet photodetectors, displays, and flexible sensors. Cu-Zn-S (CZS) thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multi-dimensional parameter space such that optimization for a specific application is challenging and time consuming. Here, we show that strategic design of experiment (DOE) combined with machine learning (ML) allows for efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of 4 experimental CBD factors. The machine learning model is based on support vector regression (SVR) employing a radial basis function (RBF) as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters.", + "keywords": [ + "chemical bath deposition", + "machine learning", + "thin film", + "p-type transparent conductor", + "electronic materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "weilingfei_machine_learning_materials_v1.1", + "mdf_source_name": "weilingfei_machine_learning_materials", + "all_materials_included": "['zinc sulfide', 'copper sulfide']", + "funding_details": "Department of Energy" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/weilingfei_machine_learning_materials_v1.1/", + "version": "1.0", + "root_version": "weilingfei_machine_learning_materials_v1.1", + "latest": true + } + }, + { + "source_id": "pub_96_zhang_spatially_v1.2", + "source_name": "pub_96_zhang_spatially", + "version": 1, + "ingest_date": "2019-03-08T19:33:06.264522Z", + "doi": "10.18126/M2GW5F", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_926/", + "metadata": { + "title": "Spatially Heterogeneous Dynamics in a Metallic Glass Forming Liquid Imaged by Electron Correlation Microscopy", + "authors": [ + { + "name": "Zhang, Pei", + "given_name": "Pei", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Maldonis, Jason", + "given_name": "Jason", + "family_name": "Maldonis", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Liu, Ze", + "given_name": "Ze", + "family_name": "Liu", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Schroers, Jan", + "given_name": "Jan", + "family_name": "Schroers", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Voyles, Paul", + "given_name": "Paul", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + } + ], + "description": "Supercooled liquids exhibit spatial heterogeneity in the dynamics of their fluctuating atomic arrangements. The length and time scales of the heterogeneous dynamics are central to the glass transition and influence nucleation and growth of crystals from the liquid. We report direct experimental visualization of the spatially heterogeneous dynamics as a function of temperature in the supercooled liquid state of a Pt- based metallic glass, using electron correlation microscopy with sub-nanometer resolution. An experimental four point space-time intensity correlation function demonstrates a growing dynamic correlation length, \u03be, upon cooling of the liquid toward the glass transition temperature. \u03be as a function of the relaxation time \u03c4 data are in the good agreement with the Adam-Gibbs, inhomogeneous mode coupling theory and random first order transition theory of the glass transition. The same experiments demonstrate the existence of a nanometer thickness near surface layer with order of magnitude shorter relaxation time than inside the bulk.", + "keywords": [ + "metallic glasses", + "experiment", + "metals and alloys", + "electron correlation microscopy", + "metallic glass", + "glass transition", + "electron microscopy", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1038/s41467-018-03604-2", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_96_zhang_spatially_v1.2", + "mdf_source_name": "pub_96_zhang_spatially", + "all_materials_included": "Pt" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_926/", + "version": "1.0", + "root_version": "pub_96_zhang_spatially_v1.2", + "latest": true + } + }, + { + "source_id": "pub_100_li_detection_v1.2", + "source_name": "pub_100_li_detection", + "version": 1, + "ingest_date": "2019-03-08T19:51:04.097983Z", + "doi": "10.18126/M2692Z", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_997/", + "metadata": { + "title": "Detection of Open Loop Defects in STEM Images of Irradiation-Damaged Alloys \u2013 Source Code for Detection and Image Dataset", + "authors": [ + { + "name": "Li, Wei", + "given_name": "Wei", + "family_name": "Li", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Field, Kevin G.", + "given_name": "Kevin G.", + "family_name": "Field", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "The Dataset consists of the source code for defect detection and analysis, and the STEM images, along with the bounding boxes for open loop defects in the images. The source code is written with Matlab and was run with the Matlab r2017a\u200b version. Essential toolboxes required to run the code are: Image Processing Toolbox, Statistics and Machine Learning Toolbox, Computer Vision Toolbox. The project has been tested on macOS Sierra, Windows 7, Windows 10 and Ubuntu 17.10 platforms. Some parts of the code require GPU that supports CUDA to function correctly. \r\n\r\nThe project has three types of files: functions, scripts and data folders. Functions can be divided into several categories, such as data parser, screening method, etc. Scripts can be divided into several categories, such as image augmentation, train cascade object detector. \r\n\r\nTo run the code, move all the source code to the same folder where all image folders sits. The images folders are located in project folder. Image data folders can be divided into several categories, such as positive images, negative images., eg. \"aug_training_positive_images\u201d.\r\n\r\nFor more information, please refer to the documentation (\u201cREADME.pdf\u201d) in the dataset.", + "keywords": [ + "STEM", + "machine learning", + "experiment", + "model", + "deep learning", + "defects", + "electron microscopy", + "metals and alloys", + "matlab" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/s41524-018-0093-8", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://doi.org/10.1038/s41524-018-0093-8", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_100_li_detection_v1.2", + "mdf_source_name": "pub_100_li_detection" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_997/", + "version": "1.0", + "root_version": "pub_100_li_detection_v1.2", + "latest": true + } + }, + { + "source_id": "pub_70_shin_giwaxs_v1.2", + "source_name": "pub_70_shin_giwaxs", + "version": 1, + "ingest_date": "2019-03-08T16:38:22.386183Z", + "doi": "10.18126/M2VH2X", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1277/", + "metadata": { + "title": "GIWAXS, GISAXS, WAXS and SAXS data for publication: Light-triggered thermal conductivity switching in azobenzene polymers", + "authors": [ + { + "name": "Shin, Jungwoo", + "given_name": "Jungwoo", + "family_name": "Shin", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Sung, Jaeuk", + "given_name": "Jaeuk", + "family_name": "Sung", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Kang, Minjee", + "given_name": "Minjee", + "family_name": "Kang", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Xie, Xu", + "given_name": "Xu", + "family_name": "Xie", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Lee, Byeongdu", + "given_name": "Byeongdu", + "family_name": "Lee", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Lee, Kyung Min", + "given_name": "Kyung Min", + "family_name": "Lee", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "White, Timothy", + "given_name": "Timothy", + "family_name": "White", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Leal, Cecilia", + "given_name": "Cecilia", + "family_name": "Leal", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Sottos, Nancy", + "given_name": "Nancy", + "family_name": "Sottos", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Braun, Paul", + "given_name": "Paul", + "family_name": "Braun", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "name": "Cahill, David", + "given_name": "David", + "family_name": "Cahill", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + } + ], + "description": "Raw 2-D Tif files collected from the APS 12-ID-B and 12-ID-C beamlines.", + "keywords": [ + "GISAXS", + "GIWAXS", + "polymers", + "experiment", + "SAXS", + "energy materials", + "WAXS", + "Polymer", + "Azobenzene", + "thermodynamics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_70_shin_giwaxs_v1.2", + "mdf_source_name": "pub_70_shin_giwaxs", + "funding_details": "['National Science Foundation Engineering Research Center for Power Optimization of Electro-Thermal systems (POETS) with cooperative agreements EEC-1449548', 'Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1277/", + "version": "1.0", + "root_version": "pub_70_shin_giwaxs_v1.2", + "latest": true + } + }, + { + "source_id": "maldonisjasonj_shortrange_order_glass_v1.1", + "source_name": "maldonisjasonj_shortrange_order_glass", + "version": 1, + "ingest_date": "2019-08-29T16:16:28.933803Z", + "doi": "10.18126/1OVYIQOURCMLG", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/maldonisjasonj_shortrange_order_glass_v1.1/", + "metadata": { + "title": "Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr50Cu45Al5 Metallic Glass", + "authors": [ + { + "name": "Maldonis, Jason J.", + "given_name": "Jason J.", + "family_name": "Maldonis", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Banadaki, Arash Dehghan", + "given_name": "Arash Dehghan", + "family_name": "Banadaki", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Patala, Srikanth", + "given_name": "Srikanth", + "family_name": "Patala", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Classical molecular dynamics trajectories for Zr50Cu45Al5 metallic glass as a function of temperature during cooling. Analysis of the atomic positions using motif extraction and point pattern matching. 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A set of strain and orientation pole figures are measured at various locations across the disk using synchrotron high energy X-ray diffraction. This data set contains the strain pole figures and local orientation distribution functions for 105 diffraction volumes interrogated. Data reduction from the raw image files were performed using a suit of Matlab analysis code developed by the Deformation Processes Laboratory at Cornell University. The orientation distribution functions were computed from pole figure data using OdfPf package developed by the Deformation Processes Laboratory at Cornell University.", + "keywords": [ + "shrink-fit", + "titanium alloy", + "lattice strain", + "multi-scale", + "experiment", + "WAXS", + "high energy synchrotron x-ray", + "diffraction", + "residual stress", + "microstructures", + "Ti-8Al-1Mo-1V", + "metals and alloys", + "wide angle x-ray scattering" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "TBD", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_4_park_determination_v1.2", + "mdf_source_name": "pub_4_park_determination", + "funding_details": "['U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences DE-AC02-06CH11357', 'U.S. Air Force Office of Scientific Research Multi-Scale Structural Mechanics Program FA9550-09-1-0642']", + "all_materials_included": "['Ti-8Al-1Mo-1V', 'Ti', 'Mo', 'V']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_37/", + "version": "1.0", + "root_version": "pub_4_park_determination_v1.2", + "latest": true + } + }, + { + "source_id": "pub_80_cheon_mining_v1.2", + "source_name": "pub_80_cheon_mining", + "version": 1, + "ingest_date": "2019-03-08T16:58:04.880043Z", + "doi": "10.18126/M2DD03", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_808/", + "metadata": { + "title": "Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures", + "authors": [ + { + "name": "Cheon, Gowoon", + "given_name": "Gowoon", + "family_name": "Cheon", + "affiliations": [ + "Stanford University" + ] + }, + { + "name": "Duerloo, Karel-Alexander N.", + "given_name": "Karel-Alexander N.", + "family_name": "Duerloo", + "affiliations": [ + "Stanford University" + ] + }, + { + "name": "Sendek, Austin D.", + "given_name": "Austin D.", + "family_name": "Sendek", + "affiliations": [ + "Stanford University" + ] + }, + { + "name": "Porter, Chase", + "given_name": "Chase", + "family_name": "Porter", + "affiliations": [ + "Stanford University" + ] + }, + { + "name": "Chen, Yuan", + "given_name": "Yuan", + "family_name": "Chen", + "affiliations": [ + "Stanford University" + ] + }, + { + "name": "Reed, Evan J.", + "given_name": "Evan J.", + "family_name": "Reed", + "affiliations": [ + "Stanford University" + ] + } + ], + "description": "This is the supporting information for the journal article Cheon, G. et al. Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures. Nano Lett. (2017). doi:10.1021/acs.nanolett.6b05229.\r\nMore information can be found on the journal article or Reed Group's website https://web.stanford.edu/group/evanreed/", + "keywords": [ + "machine learning", + "van der Waals solids", + "two-dimensional materials", + "van der Waals hetereostructures", + "data mining", + "layered materials", + "piezoelectricity" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.6b05229", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1021/acs.nanolett.6b05229", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_80_cheon_mining_v1.2", + "mdf_source_name": "pub_80_cheon_mining", + "funding_details": "This work was partially supported by Army Research Office Grant W911NF-15-1-0570, by Office of Naval Research Grant N00014-15-1-2697, by the U.S. Army Research Laboratory through the Army High Performance Computing Research Center, Cooperative Agreement W911NF-07-0027, by NSF Grant DMR-1455050 and EECS-1436626, and by the Stanford Graduate Fellowship program." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_808/", + "version": "1.0", + "root_version": "pub_80_cheon_mining_v1.2", + "latest": true + } + }, + { + "source_id": "pub_75_sherman_s200_v1.2", + "source_name": "pub_75_sherman_s200", + "version": 1, + "ingest_date": "2019-03-08T16:47:42.320967Z", + "doi": "10.18126/M2135G", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1305/", + "metadata": { + "title": "S200 SiC/SiC Seral Section Data and Fiber Chirality Codes with Tutorial", + "authors": [ + { + "name": "Sherman, Samuel", + "given_name": "Samuel", + "family_name": "Sherman", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + }, + { + "name": "Simmons, Jeff", + "given_name": "Jeff", + "family_name": "Simmons", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + }, + { + "name": "Przybyla, Craig", + "given_name": "Craig", + "family_name": "Przybyla", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + } + ], + "description": "The contents of this publication include a tutorial to approximate the chirality, or twist, of fibers in a fiber reinforced ceramic matrix composite. There is a sample dataset that contains images of a SiC matrix composite with SiC reinforcement fibers. The MATLAB code performs the velocity gradient analysis and visualizes the results of this analysis. In the near future, the authors intend on publishing a paper about the derivation of the applied methods, which will be titled: \"Mesoscale Characterization of Continuous Fiber Reinforced Composites Through Machine Learning: Fiber Chirality\".", + "keywords": [ + "experiment", + "ceramics", + "semiconductors", + "composites", + "SiC", + "fibers" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_75_sherman_s200_v1.2", + "mdf_source_name": "pub_75_sherman_s200", + "all_materials_included": "['SiC ceramic matrix reinforced by SiC fibers', 'SiC']", + "funding_details": "This work was funded in part by the Air Force Office of Scientific ResearchTask # 17RXCOR441 with Jamie Tiley as the program manager." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1305/", + "version": "1.0", + "root_version": "pub_75_sherman_s200_v1.2", + "latest": true + } + }, + { + "source_id": "pub_78_osborn_coherent_v1.2", + "source_name": "pub_78_osborn_coherent", + "version": 1, + "ingest_date": "2019-03-08T16:52:43.546754Z", + "doi": "10.18126/M2C914", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_801/", + "metadata": { + "title": "Coherent Band Excitations in CePd3", + "authors": [ + { + "name": "Osborn, Raymond", + "given_name": "Raymond", + "family_name": "Osborn", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Goremychkin, Eugene", + "given_name": "Eugene", + "family_name": "Goremychkin", + "affiliations": [ + "Joint Institute for Nuclear Research" + ] + }, + { + "name": "Rosenkranz, Stephan", + "given_name": "Stephan", + "family_name": "Rosenkranz", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Park, Hyowon", + "given_name": "Hyowon", + "family_name": "Park", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "These files contain data sets used in the analysis of CePd3, reported in \u201cCoherent Band Excitations in CePd3: A Comparison of Neutron Scattering and ab initio Theory\u201d by Goremychkin et al. See the file \"Readme.txt\" for more details.", + "keywords": [ + "CePd3", + "experiment", + "strongly correlated electron systems", + "superconductors", + "quantum materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_78_osborn_coherent_v1.2", + "mdf_source_name": "pub_78_osborn_coherent", + "all_materials_included": "CePd3", + "funding_details": "Research supported by the Materials Sciences and Engineering Division, Office of Basic Energy Sciences, U.S. Department of Energy." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_801/", + "version": "1.0", + "root_version": "pub_78_osborn_coherent_v1.2", + "latest": true + } + }, + { + "source_id": "pub_45_lee_hotelectron_v1.2", + "source_name": "pub_45_lee_hotelectron", + "version": 1, + "ingest_date": "2019-03-08T15:49:00.666504Z", + "doi": "10.18126/M2C35K", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1161/", + "metadata": { + "title": "Hot-electron mediated ion diffusion in proton-irradiated magnesium oxide", + "authors": [ + { + "name": "Lee, Cheng-Wei", + "given_name": "Cheng-Wei", + "family_name": "Lee", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "keywords": [ + "MgO", + "simulation", + "diffusion" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://arxiv.org/abs/1806.00443", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_45_lee_hotelectron_v1.2", + "mdf_source_name": "pub_45_lee_hotelectron", + "funding_details": "['Financial support from the Sandia National Laboratory-UIUC collaboration is ac- knowledged (SNL grant no. 1736375)', 'An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Exper- iment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.']", + "all_materials_included": "MgO" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1161/", + "version": "1.0", + "root_version": "pub_45_lee_hotelectron_v1.2", + "latest": true + } + }, + { + "source_id": "pub_44_park_set_v1.2", + "source_name": "pub_44_park_set", + "version": 1, + "ingest_date": "2019-03-08T15:47:22.234784Z", + "doi": "10.18126/M2NM0B", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1156/", + "metadata": { + "title": "Data set for \"Non-destructive characterization of subsurface residual stress fields and correlation with microstructural conditions in a shot-peened Inconel component\"", + "authors": [ + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park" + }, + { + "name": "Yildizli, Kemal", + "given_name": "Kemal", + "family_name": "Yildizli" + }, + { + "name": "Demir, Eralp", + "given_name": "Eralp", + "family_name": "Demir" + }, + { + "name": "Dawson, Paul", + "given_name": "Paul", + "family_name": "Dawson" + }, + { + "name": "Matthew, Miller", + "given_name": "Miller", + "family_name": "Matthew" + } + ], + "description": "data set associated with Experimental Mechanics paper \"Non-destructive characterization of subsurface residual stress fields and correlation with microstructural conditions in a shot-peened Inconel component\"", + "keywords": [ + "EBSD", + "nickel-based superalloy", + "high energy x-ray diffraction", + "experiment", + "conical slits", + "HEXD", + "shot peening", + "microscopy", + "residual stress", + "microstructures", + "electron backscatter diffraction", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_44_park_set_v1.2", + "mdf_source_name": "pub_44_park_set", + "funding_details": "['Scientific and Technological Research Council of Turkey (TUBITAK) 2219 Scholar', 'US Air Force Office of Scientific Research FA9550-09-1-0642', 'US DOE Office of Science DE-AC02-06CH11357', 'US National Science Foundation Materials Research Science and Engineering Centers program (DMR-1120296)']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1156/", + "version": "1.0", + "root_version": "pub_44_park_set_v1.2", + "latest": true + } + }, + { + "source_id": "pub_72_heintzkill_appendices_v1.2", + "source_name": "pub_72_heintzkill_appendices", + "version": 1, + "ingest_date": "2019-03-08T16:41:41.741403Z", + "doi": "10.18126/M24S8F", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1294/", + "metadata": { + "title": "Appendices to Silver nanoparticle wetting treatment in sol-gel derived zinc oxide thin films", + "authors": [ + { + "name": "Heintzkill, Reed T.", + "given_name": "Reed T.", + "family_name": "Heintzkill", + "affiliations": [ + "University of Wisconsin - Milwaukee" + ] + } + ], + "description": "transparent conductive oxide; silver-doped zinc oxide; Sol-gel thin films; organic solvent surface wetting; intra-layer surface treatment; PGME-solvent sol-gel; surface morphology", + "keywords": [ + "nanoparticles", + "Ag", + "experiment", + "thin films" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_72_heintzkill_appendices_v1.2", + "mdf_source_name": "pub_72_heintzkill_appendices", + "all_materials_included": "ZnO" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1294/", + "version": "1.0", + "root_version": "pub_72_heintzkill_appendices_v1.2", + "latest": true + } + }, + { + "source_id": "pub_71_heintzkill_appendices_v1.2", + "source_name": "pub_71_heintzkill_appendices", + "version": 1, + "ingest_date": "2019-03-08T16:40:01.115454Z", + "doi": "10.18126/M2K05D", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1280/", + "metadata": { + "title": "Appendices to Morphological effects of organic solvent surface wetting on sol-gel derived ZnO thin films", + "authors": [ + { + "name": "Heintzkill, Reed T.", + "given_name": "Reed T.", + "family_name": "Heintzkill", + "affiliations": [ + "University of Wisconsin - Milwaukee" + ] + } + ], + "keywords": [ + "ZnO", + "experiment", + "thin films" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_71_heintzkill_appendices_v1.2", + "mdf_source_name": "pub_71_heintzkill_appendices", + "all_materials_included": "ZnO" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1280/", + "version": "1.0", + "root_version": "pub_71_heintzkill_appendices_v1.2", + "latest": true + } + }, + { + "source_id": "pub_69_maldonis_motif_v1.2", + "source_name": "pub_69_maldonis_motif", + "version": 1, + "ingest_date": "2019-03-08T16:36:26.293634Z", + "doi": "10.18126/M2FD0D", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1268/", + "metadata": { + "title": "Data for Motif Extraction from Zr50Cu45Al5 Metallic Glass Models", + "authors": [ + { + "name": "Maldonis, Jason J.", + "given_name": "Jason J.", + "family_name": "Maldonis", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Banadaki, Arash Deghan", + "given_name": "Arash Deghan", + "family_name": "Banadaki", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Patala, Srikanth", + "given_name": "Srikanth", + "family_name": "Patala", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "keywords": [ + "metallic glasses", + "machine learning", + "simulation", + "Zr50Cu45Al5", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_69_maldonis_motif_v1.2", + "mdf_source_name": "pub_69_maldonis_motif" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1268/", + "version": "1.0", + "root_version": "pub_69_maldonis_motif_v1.2", + "latest": true + } + }, + { + "source_id": "pub_91_zhang_nonequilibrium_v1.2", + "source_name": "pub_91_zhang_nonequilibrium", + "version": 1, + "ingest_date": "2019-03-08T19:12:43.411855Z", + "doi": "10.18126/M2SD1W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_903/", + "metadata": { + "title": "Dataset for Non-equilibrium BN-ZnO: Optical properties and excitonic effects from first principles", + "authors": [ + { + "name": "Zhang, Xiao", + "given_name": "Xiao", + "family_name": "Zhang", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This is the description file for the published data in the work [doi will be inserted once it\u2019s published]. The calculations are performed in Vienna Ab initio Simulation Package (VASP) thus the inputs and outputs files are in VASP format. Some part of the calculations use our own code that is not published. The data for publication is structured as follows: \r\n\r\n1. Data for structural relaxation: \r\n\r\nThis folder contains the relevant inputs (VASP INCAR, KPOINTS, POSCAR) and outputs (OUTCAR, OSZICAR, etc.) for the structural relaxation of both BN ZnO and WZ ZnO. These includes the INCAR, POSCAR, POTCAR, KPOINTS files used to run calculations for different volumes and OUTCARs and standard output for different volumes. The file names in BN relaxation are chosen such that the second number represents the k-points sampling and the third represents the plane wave cutoff. The file name of the subfolders within each k-point/plane wave cutoff combination denotes the a-lattice constant for that step. The WZ relaxation uses the converged value for these two parameters and has no additional test.\r\n\r\n2. Data for band structure calculations\r\n\r\nThis folder contains the relevant inputs (VASP INCAR, KPOINTS, POSCAR) and outputs (OUTCAR, OSZICAR, band structure, etc.) for the electronic structure calculations for WZ ZnO, BN ZnO in theoretical lattice constant and BN ZnO in experimental lattice constant. The GW result includes 1)G0W0, scGW+GGA, scGW+HSE for WZ ZnO 2)G0W0, scGW+GGA, scGW+HSE for theoretical BN lattice and 3)G0W0+HSE, G0W0+GGA and scGW+HSE for experimental BN lattice. In some cases, G0W0 results are not listed explicitly but the first step in the scGW. \r\n\r\n3. Dielectric constant\r\n\r\nThis folder contains the relevant inputs (VASP KPOINTS, POSCAR, bash script for INCARs) and output spectra files for the dielectric constants calculations, that is required for optical calculations. In each folder, necessary inputs and bash script to generate INCAR files for the optical spectra using different number of bands are provided, as well as the resulting spectra of the calculations. \r\n\r\n4. Optical \r\n\r\nThis folder contains the necessary inputs (VASP KPOINTS, POSCARs, INCARs) for the GGA optical calculations and BSE calculations and the output spectra of the GGA optical calculations and BSE calculations for BN and WZ ZnO. This folder also contains the transmission plot we generated using Maxwell\u2019s modeling. We use our own code for BSE calculations and Maxwell\u2019s modeling thus whoever want to reproduce the BSE calculations may need to contact the authors. This folder also contains the partial charge density files to generate the wave function plots in our paper.\r\n\r\n5. Exciton binding energy\r\n\r\nThis folder contains the necessary inputs (KPOINTS, INCARs, POSCARs) for calculating the exciton binding energy for different k-points sampling and BSE energy cutoff and the corresponding BSE output and exciton eigenvalues files for BN and WZ ZnO. The test of convergence of BSE energy cutoff is only done for BN ZnO. Again, to reproduce the BSE calculations, one may need to contact the authors.", + "keywords": [ + "ZnO", + "non-toxic", + "thin film", + "bandgap", + "wurtzite", + "simulation", + "semiconductor", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_91_zhang_nonequilibrium_v1.2", + "mdf_source_name": "pub_91_zhang_nonequilibrium", + "all_materials_included": "['BN-ZnO', 'boron nitride (BN) phase of zinc oxide (ZnO)']", + "funding_details": "This material is based upon work supported by the National Science Foundation un- der Grant No. DMR-1555153. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_903/", + "version": "1.0", + "root_version": "pub_91_zhang_nonequilibrium_v1.2", + "latest": true + } + }, + { + "source_id": "pub_95_paranjape_grain_v1.2", + "source_name": "pub_95_paranjape_grain", + "version": 1, + "ingest_date": "2019-03-08T19:31:25.882617Z", + "doi": "10.18126/M2NK5W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_414/", + "metadata": { + "title": "Grain Structure, Grain-averaged Lattice Strains, and Macro-scale Strain Data for Superelastic Nickel-Titanium Shape Memory Alloy Polycrystal Loaded in Tension", + "authors": [ + { + "name": "Paranjape, Harshad M.", + "given_name": "Harshad M.", + "family_name": "Paranjape", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Paul, Partha P.", + "given_name": "Partha P.", + "family_name": "Paul", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Sharma, Hemant", + "given_name": "Hemant", + "family_name": "Sharma", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Duerig, T.W.", + "given_name": "T.W.", + "family_name": "Duerig", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Brinson, L. Catherine", + "given_name": "L. Catherine", + "family_name": "Brinson", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + }, + { + "name": "Stebner, Aaron P.", + "given_name": "Aaron P.", + "family_name": "Stebner", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ] + } + ], + "description": "A cylindrical dog bone specimen from NiTi shape memory alloy is loaded in tension at room temperature for eleven cycles. Maximum stress in each cycle is approximately 220 MPa. The experiment is performed in-situ at Sector 1, beam line 1ID-E of Advanced Photon Source, Argonne National Laboratory. Using far-field high-energy diffraction microscopy technique, grain centroids, grain-averaged lattice strain tensor, grain volume, and grain-averaged crystal orientation are obtained at the start of the first cycle and at nine stress values of the eleventh cycle.", + "keywords": [ + "electron backscattering diffraction", + "EBSD", + "lattice strain", + "metals and alloys", + "HEDM", + "shape memory alloy", + "microstructure", + "diffraction", + "microstructures", + "Nickel-Titanium", + "SMA", + "synchrotron diffraction", + "microscopy", + "NiTi", + "high energy diffraction microscopy", + "In situ X-ray diffraction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.jmps.2017.02.007", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.jmps.2017.02.007", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://arxiv.org/pdf/1610.08429.pdf", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_95_paranjape_grain_v1.2", + "mdf_source_name": "pub_95_paranjape_grain", + "funding_details": "HMP, PPP, and LCB acknowledge the financial support from Department of Energy, Basic Energy Sciences (grant no. de-sc0010594). APS acknowledges funding from NSF-Career award no. 1454668. Electron microscopy work reported in this article was performed at NUANCE and OMM Facilities (funded by NSF DMR-1121262) at Northwestern University. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Fa- cility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The ff-HEDM experiments were performed at 1-ID-E beamline at the Advanced Photon Source. This work used the Extreme Science and Engineering Discovery Environ- ment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. Technical support from Prof. B. Kappes (Colorado School of Mines) is acknowledged.", + "all_materials_included": "['NiTi', 'Nickel-Titanium', 'Ni = 50.9 atomic %. Ti = balance']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_414/", + "version": "1.0", + "root_version": "pub_95_paranjape_grain_v1.2", + "latest": true + } + }, + { + "source_id": "pub_98_brajuskovic_realspace_v1.2", + "source_name": "pub_98_brajuskovic_realspace", + "version": 1, + "ingest_date": "2019-03-08T19:39:27.799439Z", + "doi": "10.18126/M22P48", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_432/", + "metadata": { + "title": "Real-space Observation of Magnetic Excitations and Avalanche Behavior in Artificial Quasicrystal Lattices", + "authors": [ + { + "name": "Brajuskovic, V.", + "given_name": "V.", + "family_name": "Brajuskovic", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Barrows, F.", + "given_name": "F.", + "family_name": "Barrows", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Phatak, C.D.", + "given_name": "C.D.", + "family_name": "Phatak", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Petford-Long, A.K.", + "given_name": "A.K.", + "family_name": "Petford-Long", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "description": "Thin films of Permalloy were patterned into quasicrystal artificial spin ice lattices. Lorentz TEM was used to image the magnetic induction in the lattices in the as-deposited state, after a demagnetization protocol, and after thermal annealing in a sputter deposition chamber.", + "keywords": [ + "FIB", + "LTEM", + "magnetic materials", + "artificial spin ice lattice", + "focused ion beam milling", + "sputter deposition", + "quasicrystal", + "Lorentz microscopy", + "Permalloy", + "thermal annealing", + "magnetron sputtering", + "transport of intensity equation", + "microsopy", + "quantum materials", + "TEM", + "microscopy", + "thin films" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/srep34384", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1016/j.ultramic.2014.01.005", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1038/srep34384", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://doi.org/10.1016/j.ultramic.2014.01.005", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_98_brajuskovic_realspace_v1.2", + "mdf_source_name": "pub_98_brajuskovic_realspace", + "all_materials_included": "['Permalloy (Ni=80%,Fe=20%)', 'Ni', 'Fe', 'nickel', 'iron']", + "funding_details": "['United States Department of Energy, Office of Science, Basic Energy Sciences, Materials Science Division', 'Use of the Center for Nanoscale Materials was supported by the United Sates Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_432/", + "version": "1.0", + "root_version": "pub_98_brajuskovic_realspace_v1.2", + "latest": true + } + }, + { + "source_id": "pub_94_yager_synthetic_v1.2", + "source_name": "pub_94_yager_synthetic", + "version": 1, + "ingest_date": "2019-03-08T19:29:45.416714Z", + "doi": "10.18126/M2Z30Z", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_406/", + "metadata": { + "title": "Dataset of Synthetic X-ray Scattering Images for Classification Using Deep Learning", + "authors": [ + { + "name": "Yager, Kevin G.", + "given_name": "Kevin G.", + "family_name": "Yager", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "name": "Lhermitte, Julien", + "given_name": "Julien", + "family_name": "Lhermitte", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "name": "Yu, Dantong", + "given_name": "Dantong", + "family_name": "Yu", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "name": "Wang, Boyu", + "given_name": "Boyu", + "family_name": "Wang", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "name": "Guan, Ziqiao", + "given_name": "Ziqiao", + "family_name": "Guan", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "name": "Liu, Jiliang", + "given_name": "Jiliang", + "family_name": "Liu", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + } + ], + "description": "This dataset contains a large number of example x-ray scattering images; each image is tagged with a variety of attributes describing the data features appearing in the image ('rings', 'anisotropic', etc.) or describing the underlying material ('BCC', 'FCC', etc.). The main purpose of this dataset is as a training set for machine-learning methods. The images were generated synthetically, using a combination of ad hoc methods (e.g. superimposing features such as rings and halos) and simple simulations (e.g. generating realspace arrangements of nanoparticles, and then computing the far-field scattering pattern). The presented code iterates across a wide variety of input conditions, such that the output images cover a wide range of expected x-ray scattering image types. Experimentally-realistic artifacts, including masks, parasitic streaks, and Poisson noise, are also included.", + "keywords": [ + "x-ray", + "xray", + "machine learning", + "CNN", + "convolutional neural network", + "simulation", + "wide-angle x-ray scattering", + "waxs", + "saxs", + "convolutional autoencoders", + "small-angle x-ray scattering" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1109/WACV.2014.6836004", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1109/NYSDS.2016.7747813", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://arxiv.org/abs/1611.03313", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_94_yager_synthetic_v1.2", + "mdf_source_name": "pub_94_yager_synthetic", + "all_materials_included": "synthetic", + "funding_details": "This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_406/", + "version": "1.0", + "root_version": "pub_94_yager_synthetic_v1.2", + "latest": true + } + }, + { + "source_id": "pub_87_senabulya_on_v1.2", + "source_name": "pub_87_senabulya_on", + "version": 1, + "ingest_date": "2019-03-08T17:18:07.020522Z", + "doi": "10.18126/M2J053", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_857/", + "metadata": { + "title": "On the kinetic and equilibrium shapes of icosahedral Al71Pd19Mn10 quasicrystals", + "authors": [ + { + "name": "Senabulya, Nancy", + "given_name": "Nancy", + "family_name": "Senabulya", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + }, + { + "name": "Han, Insung", + "given_name": "Insung", + "family_name": "Han", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + }, + { + "name": "Shahani, Ashwin J.", + "given_name": "Ashwin J.", + "family_name": "Shahani", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + } + ], + "description": "This data was collected to study the growth and relaxation dynamics of an icosahedral quasicrystal from a liquid. The composition of the imaged QC is Al-19at%Pd-10at%Mn, and the growth and relaxation dynamics of an icosahedral quasicrystal were observed under slow cooling conditions. \r\nThe quasicrystalline phase shows weaker projection intensity because of more X-ray absorption resulting from its heavy elemental (Pd and Mn) composition. \r\nThis contrast difference allowed us to segment the quasicrystal from the liquid phase and visualize the 3D volume of the icosahedral quasicrystal. \r\nDuring the growth, a pentagonal dodecahedral shape with 6 five-fold facets was clearly visible growing from the oxide skin (Al2O3) into the liquid. Facet velocities are noticeably anisotropic with facets perpendicular to the gravitational field growing faster than facets further away from gravity. \r\nGrowth is clearly bulk transport limited. Near equilibrium, we observe a morphological transformation to a truncated dodecahedral shape characterized by the truncation of five-fold vertices in the growth shape (pentagonal dodecahedron) into triangular facets. \r\nThis signifies a relaxation mechanism to a lower energy equilibrium shape in this icosahedral quasicrystal thus the growth and equilibrium shapes in icosahedral QCs are not identical. \r\nFor details on the resulting 3D reconstruction, visualization, and kinetic analyses, the readers are encouraged to look up the following publication.", + "keywords": [ + "synchrotron", + "gravity-driven convection", + "Al-Pd-Mn alloys", + "4D x-ray tomography", + "icosahedral quasicrystal", + "metals and alloys", + "anisotropic velocity", + "kinetic shape", + "experiment", + "supersaturation", + "mass transport", + "growth", + "continuous cooling", + "microscopy", + "microstructures", + "in situ", + "equilibrium shape" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_87_senabulya_on_v1.2", + "mdf_source_name": "pub_87_senabulya_on", + "all_materials_included": "['Al-Pd-Mn', 'Al', 'Pd']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_857/", + "version": "1.0", + "root_version": "pub_87_senabulya_on_v1.2", + "latest": true + } + }, + { + "source_id": "pub_12_narayan_characterizing_v2.1", + "source_name": "pub_12_narayan_characterizing", + "version": 1, + "ingest_date": "2019-03-07T22:40:08.473449Z", + "doi": "10.18126/M2905X", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_590/", + "metadata": { + "title": "Characterizing the Unifying Thread in High Temperature Superconductors Using Realistic Simulations", + "authors": [ + { + "name": "Narayan, Awadhesh", + "given_name": "Awadhesh", + "family_name": "Narayan", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Busemeyer, Brian", + "given_name": "Brian", + "family_name": "Busemeyer", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Wagner, Lucas K.", + "given_name": "Lucas K.", + "family_name": "Wagner", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This is a dataset collecting high accuracy quantum Monte Carlo (QMC) results for pnictide and cuprate compounds, some of which are high temperature superconductors. Our trial wavefunction consists of a Slater determinant augmented by a Jastrow factor. We construct the Slater determinant using orbitals from density functional theory (DFT). Our DFT calculations are done using the CRYSTAL package, with the PBE0 functional. We use Burkatzki-Filippi-Dolg pseudopotentials to replace the core electrons. We then use the open-source QWalk package to optimize the trial wavefunction and perform diffusion Monte Carlo calculations. We control finite size errors by constructing 2x2x1 supercells and averaging over 8 twist boundary conditions.", + "keywords": [ + "high temperature superconductivity", + "QMC", + "machine learning", + "iron based superconductors", + "spin charge coupling", + "QWalk", + "simulation", + "cuprates", + "superconductors", + "PBE0", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_12_narayan_characterizing_v2.1", + "mdf_source_name": "pub_12_narayan_characterizing", + "funding_details": "Center for Emergent Superconductivity, United States Department of Energy, Office of Science, Basic Energy Sciences under award number DEAC0298CH1088", + "all_materials_included": "['BaCr2As2', 'BaMn2As2', 'BaFe2As2', 'BaCo2As2', 'BaNi2As2', 'BaCo2As2', 'FeSe', 'FeTe', 'Ca2CuCl2O2']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_590/", + "version": "1.0", + "root_version": "pub_12_narayan_characterizing_v2.1", + "latest": true + } + }, + { + "source_id": "pub_66_kim_mechanical_v1.2", + "source_name": "pub_66_kim_mechanical", + "version": 1, + "ingest_date": "2019-03-08T16:30:24.781504Z", + "doi": "10.18126/M24S3J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_232/", + "metadata": { + "title": "Mechanical Properties and Phase Stability of Monoborides using Density Functional Theory Calculations", + "authors": [ + { + "name": "Kim, Hyojung", + "given_name": "Hyojung", + "family_name": "Kim", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This data demonstrates the Ti-monoborides with improved polycrystalline elastic properties such as Young's modulus and Pugh's ratio, and stacking fault energies. The lattice parameters, total energies and elastic constants of monoborides are computed using density functional theory", + "keywords": [ + "solubility limit", + "Bf structure", + "machine learning", + "monoboride", + "B27 structure", + "phases", + "polycrystalline mechanical properties", + "ab-initio", + "simulation", + "Vegard's law", + "stacking fault energy", + "special quasirandom structure", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_66_kim_mechanical_v1.2", + "mdf_source_name": "pub_66_kim_mechanical", + "all_materials_included": "['XB (X=Ti/Fe/Mo/Nb/V)', '(X^1_{0.5}X^2_{0.5})B', '(X^1_{0.25}X^2_{0.75})B', '(X^1_{0.75}X^2_{0.25})B', '(X^1_{1/24}X^2_{23/24})B', '(X^1_{23/24}X^2_{1/24})B']", + "funding_details": "['National Science Foundation (NSF/DMREF Grant 1435545)', 'Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number (ACI-1053575)']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_232/", + "version": "1.0", + "root_version": "pub_66_kim_mechanical_v1.2", + "latest": true + } + }, + { + "source_id": "pub_90_obrien_silicon_v1.2", + "source_name": "pub_90_obrien_silicon", + "version": 1, + "ingest_date": "2019-03-08T17:32:36.759425Z", + "doi": "10.18126/M2C88F", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_374/", + "metadata": { + "title": "Silicon and Dielectric Dry-Etch Recipes", + "authors": [ + { + "name": "O'Brien Jr., Thomas Robert", + "given_name": "Thomas Robert", + "family_name": "O'Brien Jr.", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Chow, Edmond", + "given_name": "Edmond", + "family_name": "Chow", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Nahrstedt, Klara", + "given_name": "Klara", + "family_name": "Nahrstedt", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Lewis, Bruce", + "given_name": "Bruce", + "family_name": "Lewis", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Dallesasse, John Michael", + "given_name": "John Michael", + "family_name": "Dallesasse", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This work contains a series of SEM images and descriptions describing various etches used commonly for dielectrics (SiNx, SiO2) and Si. Some are failures, some are successes. Each was taken on the S4800 Hitachi SEM in the Micro and Nano Technology Lab at the University of Illinois at Urbana-Champaign during the RET taking place the summer of 2016.", + "keywords": [ + "Si", + "dielectric etch", + "dry-etching", + "semiconductors", + "plasma-etching", + "Si(111)", + "silicon etch", + "microscopy", + "Scanning Electron Microscopy (SEM)" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_90_obrien_silicon_v1.2", + "mdf_source_name": "pub_90_obrien_silicon", + "funding_details": "National Science Foundation NSF ACI 1443013" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_374/", + "version": "1.0", + "root_version": "pub_90_obrien_silicon_v1.2", + "latest": true + } + }, + { + "source_id": "pub_74_kylanpaa_compton_v2.1", + "source_name": "pub_74_kylanpaa_compton", + "version": 1, + "ingest_date": "2019-03-08T16:46:01.888408Z", + "doi": "10.18126/M28K90", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1302/", + "metadata": { + "title": "Dataset for Compton profile of VO2 across the metal-insulator transition: evidence of a non-Fermi liquid metal", + "authors": [ + { + "name": "Kylanpaa, Ilkka", + "given_name": "Ilkka", + "family_name": "Kylanpaa", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Luo, Ye", + "given_name": "Ye", + "family_name": "Luo", + "affiliations": [ + "Computational Science Division, Argonne National Laboratory, Lemont, IL" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory, Lemont, IL" + ] + }, + { + "name": "Kent, Paul", + "given_name": "Paul", + "family_name": "Kent", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Krogel, Jaron", + "given_name": "Jaron", + "family_name": "Krogel", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + } + ], + "description": "Dataset for \"Compton profile of VO2 across the metal-insulator transition: evidence of a non-Fermi\r\nliquid metal\", Ilkka Kylanpaa, Ye Luo , Olle Heinonen, Paul R. C. Kent, and Jaron T. Krogel (2019).", + "keywords": [ + "QMC", + "DMC", + "metals and alloys", + "QMCPACK", + "simulation", + "Momentum distribution", + "Diffusion Monte Carlo", + "Quantum Monte Carlo", + "Compton profile", + "Vanadium dioxide" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_74_kylanpaa_compton_v2.1", + "mdf_source_name": "pub_74_kylanpaa_compton", + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.", + "all_materials_included": "['VO2', 'Compton profile']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1302/", + "version": "1.0", + "root_version": "pub_74_kylanpaa_compton_v2.1", + "latest": true + } + }, + { + "source_id": "pub_88_ren_atom_v1.2", + "source_name": "pub_88_ren_atom", + "version": 1, + "ingest_date": "2019-03-08T17:25:52.413645Z", + "doi": "10.18126/M2MS3W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_371/", + "metadata": { + "title": "Atom Probe Tomography Analysis of Ag Doping in 2D Layered Material (PbSe)5(Bi2Se3)3", + "authors": [ + { + "name": "Ren, Xiaochen", + "given_name": "Xiaochen", + "family_name": "Ren", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Singh, Arunima K.", + "given_name": "Arunima K.", + "family_name": "Singh", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Fang, Lei", + "given_name": "Lei", + "family_name": "Fang", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Kanatzidis, Mercouri G.", + "given_name": "Mercouri G.", + "family_name": "Kanatzidis", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Tavazza, Francesca", + "given_name": "Francesca", + "family_name": "Tavazza", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Davydov, Albert V.", + "given_name": "Albert V.", + "family_name": "Davydov", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ] + } + ], + "description": "This dataset contains the original APT data and analysis for the published paper in Nano Lett., 2016, 16 (10), pp 6064\u20136069.", + "keywords": [ + "density functional theory", + "APT", + "materials genome initiative", + "experiment", + "atom probe tomography", + "doping", + "2D materials", + "MGI", + "simulation", + "CHiMaD", + "NIST", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.6b02104", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/full/10.1021/acs.nanolett.6b02104", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_88_ren_atom_v1.2", + "mdf_source_name": "pub_88_ren_atom", + "funding_details": "['This work was performed under the following financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD).', 'F.T. and A.V.D. are funded by the Materials Genome Initiative funding allocated to National Institute of Standards and Technology (NIST).', 'A.K.S. is funded by the Professional Research Experience Postdoctoral Fellowship under Award No. 70NANB11H012.']", + "all_materials_included": "['Ag doped (PbSe)5(Bi2Se3)3', '(PbSe)5(Bi2Se3)3']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_371/", + "version": "1.0", + "root_version": "pub_88_ren_atom_v1.2", + "latest": true + } + }, + { + "source_id": "pub_102_liu_point_v1.2", + "source_name": "pub_102_liu_point", + "version": 1, + "ingest_date": "2019-03-08T19:56:31.715892Z", + "doi": "10.18126/M29G64", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_143/", + "metadata": { + "title": "Point Defects and Grain Boundaries in Rotationally Commensurate MoS2 on Epitaxial Graphene", + "authors": [ + { + "name": "Liu, Xiaolong", + "given_name": "Xiaolong", + "family_name": "Liu", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "The data corresponds to the characterization of various point defects and grain boundaries in rotationally commensurate MoS2 on epitaxial graphene. The MoS2 was grown on the epitaxial graphene on silicon carbide using chemical vapor deposition. The structural and electronic nature of the defects are investigated using scanning tunneling microscopy and spectroscopy. This work gives insight into common defects in MoS2 in efforts to guide defect engineering for tailoring the performance of MoS2/graphene heterostructure. DOI: 10.1021/acs.jpcc.6b02073", + "keywords": [ + "silicon carbide", + "STM", + "graphene", + "experiment", + "MoS2", + "chemical vapor deposition", + "molybdenum disulfide", + "scanning tunneling microscopy", + "CVD", + "microstructures", + "SiC", + "defects", + "van der Waals heterostructure", + "2D", + "2D Materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.jpcc.6b02073", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/abs/10.1021/acs.jpcc.6b02073", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_102_liu_point_v1.2", + "mdf_source_name": "pub_102_liu_point", + "funding_details": "['National Institute of Standards and Technology (NIST CHiMaD 70NANB14H012)', 'U.S. Department of Energy SISGR program (DOE DE- FG02-09ER16109)', 'Office of Naval Research (ONR N00014- 14-1-0669)', 'Argonne\u2212Northwestern Solar Energy Research (ANSER) Energy Frontier Research Center (DOE DE- SC0001059)']", + "all_materials_included": "['graphene', 'epitaxial graphene', 'MoS2', 'molybdenum disulfude', 'SiC', 'silicon carbide']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_143/", + "version": "1.0", + "root_version": "pub_102_liu_point_v1.2", + "latest": true + } + }, + { + "source_id": "pub_42_sharma_nanoscale_v1.2", + "source_name": "pub_42_sharma_nanoscale", + "version": 1, + "ingest_date": "2019-03-08T15:44:00.192915Z", + "doi": "10.18126/M2TD16", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1150/", + "metadata": { + "title": "Dataset for Nanoscale Control of Oxygen Defects and Metal\u2212Insulator Transition in Epitaxial Vanadium Dioxides", + "authors": [ + { + "name": "Sharma, Yogesh", + "given_name": "Yogesh", + "family_name": "Sharma", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Balachandran, Janakiraman", + "given_name": "Janakiraman", + "family_name": "Balachandran", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Sohn, Changhee", + "given_name": "Changhee", + "family_name": "Sohn", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Collins, Liam", + "given_name": "Liam", + "family_name": "Collins", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Ievlev, Anton V.", + "given_name": "Anton V.", + "family_name": "Ievlev", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Li, Qian", + "given_name": "Qian", + "family_name": "Li", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Gao, Xiang", + "given_name": "Xiang", + "family_name": "Gao", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Balke, Nina", + "given_name": "Nina", + "family_name": "Balke", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Ovchinnikova, Olga S.", + "given_name": "Olga S.", + "family_name": "Ovchinnikova", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Kalinin, Sergei V.", + "given_name": "Sergei V.", + "family_name": "Kalinin", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory, Lemont, IL" + ] + }, + { + "name": "Lee, Ho Nyung", + "given_name": "Ho Nyung", + "family_name": "Lee", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + } + ], + "description": "Dataset for \"Nanoscale Control of Oxygen Defects and Metal\u2212Insulator Transition in Epitaxial Vanadium Dioxides\" Yogesh Sharma, Janakiraman Balachandran, Changhee Sohn, Jaron T. Krogel, Panchapakesan Ganesh, Liam Collins, Anton V. Ievlev, Qian Li, Xiang Gao, Nina Balke, Olga S. Ovchinnikova, Sergei V. Kalinin, Olle Heinonen, and Ho Nyung Lee (2018).", + "keywords": [ + "density functional theory", + "DMC", + "VASP", + "Quantum Espresso", + "experiment", + "metals and alloys", + "oxygen vacancy", + "metal-insulator transition", + "scanning probe microscopy", + "vanadium dioxide", + "QMCPACK", + "diffusion Monte Carlo", + "simulation", + "quantum Monte Carlo", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "CPSFM", + "extensions": { + "mdf_source_id": "pub_42_sharma_nanoscale_v1.2", + "mdf_source_name": "pub_42_sharma_nanoscale", + "all_materials_included": "VO2", + "funding_details": "This work was supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (synthesis) and as part of the Computational Materials Sciences Program (characterization and theory). Scanning probe microscopy and scanning transmission electron microscopy studies were performed as user projects at the Center for Nanophase Materials Sciences, which is sponsored at Oak Ridge National Laboratory (ORNL) by the Scientific User Facilities Division, BES, DOE. This research used resources of the Oak Ridge Leadership Computing Facility at ORNL, which is supported by the Office of Science of the DOE under Contract DE-AC05- 00OR22725. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of DOE under Contract DE-AC02-05CH11231." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1150/", + "version": "1.0", + "root_version": "pub_42_sharma_nanoscale_v1.2", + "latest": true + } + }, + { + "source_id": "pub_27_jang_anisotropic_v1.2", + "source_name": "pub_27_jang_anisotropic", + "version": 1, + "ingest_date": "2019-03-08T15:16:11.862633Z", + "doi": "10.18126/M2BC7S", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_111/", + "metadata": { + "title": "Anisotropic Thermal Conductivity of Exfoliated Black Phosphorus", + "authors": [ + { + "name": "Jang, Hyejin", + "given_name": "Hyejin", + "family_name": "Jang", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "name": "Wood, Joshua D.", + "given_name": "Joshua D.", + "family_name": "Wood", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "name": "Ryder, Christopher R.", + "given_name": "Christopher R.", + "family_name": "Ryder", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "name": "Cahill, David G.", + "given_name": "David G.", + "family_name": "Cahill", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + } + ], + "description": "These are the raw data of Time-domain Thermoreflectance (TDTR) measurements on exfoliated black phosphorus (BP) flakes. The flakes are mechanically exfoliated onto Si substrates and have the thicknesses ranging from 138 to 552 nm. They are encapsulated with ALD-grown ~3 nm alumina layer to prevent possible sample degradation. For TDTR measurements, the samples are further coated with ~70 nm NbV transducer. The through-plane thermal conductivity of the BP flakes along with their two interfactial thermal conductances (at the top and bottom of the flakes) are determined by using conventional TDTR method. We use two different modulation frequencies to adjust the laser thermal penetration depth.", + "keywords": [ + "time-domain thermoreflectance", + "speed of sound", + "experiment", + "TDTR", + "2D materials", + "black phosphorus", + "thermal conductivity", + "elastic constants" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/adma.201503466", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1002/adma.201503466", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_27_jang_anisotropic_v1.2", + "mdf_source_name": "pub_27_jang_anisotropic", + "all_materials_included": "phosphorus", + "funding_details": "['NSF EFRI-1433467', 'NSF DMR-1121262', 'ONR N00014-14-1-066', 'ANSER DOE DE-SC0001059']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_111/", + "version": "1.0", + "root_version": "pub_27_jang_anisotropic_v1.2", + "latest": true + } + }, + { + "source_id": "pub_16_dolejsi_gisaxs_v1.2", + "source_name": "pub_16_dolejsi_gisaxs", + "version": 1, + "ingest_date": "2019-03-07T22:47:02.471210Z", + "doi": "10.18126/M25630", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1112/", + "metadata": { + "title": "GISAXS Data for Polymer Thin Films and Gratings", + "authors": [ + { + "name": "Dolejsi, Moshe", + "given_name": "Moshe", + "family_name": "Dolejsi", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Bowen, Alec", + "given_name": "Alec", + "family_name": "Bowen", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "de Pablo, Juan", + "given_name": "Juan", + "family_name": "de Pablo", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Nealey, Paul", + "given_name": "Paul", + "family_name": "Nealey", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "Data set contains the raw GISAXS profiles for a number of polymer grating samples and DSA samples. Data is organized by the sample name and the date taken.", + "keywords": [ + "GISAXS", + "polymers", + "experiment", + "grazing-incidence small-angle X-ray scattering", + "polymer gratings", + "DSA", + "scattering" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1107/S1600576716004453", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://doi.org/10.1107/S1600576716004453", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_16_dolejsi_gisaxs_v1.2", + "mdf_source_name": "pub_16_dolejsi_gisaxs", + "funding_details": "The submitted manuscript was created by UChicago Argonne, LLC, the Operator of Argonne National Laboratory (`Argonne'). Argonne, a US Department of Energy (DOE) Office of Science Laboratory, is operated under contract No. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up nonexclusive irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Goverment. This research used resources of the Advanced Photon Source and the Center for Nanoscale Materials, a US DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under contract No. DE-AC02-06CH11357." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1112/", + "version": "1.0", + "root_version": "pub_16_dolejsi_gisaxs_v1.2", + "latest": true + } + }, + { + "source_id": "pub_93_shin_nature_v1.2", + "source_name": "pub_93_shin_nature", + "version": 1, + "ingest_date": "2019-03-08T19:16:03.540054Z", + "doi": "10.18126/M2NK9D", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_914/", + "metadata": { + "title": "Dataset for Nature of Interlayer Binding and Stacking of sp\u2013sp2 Hybridized Carbon Layers: A Quantum Monte Carlo Study", + "authors": [ + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, United States" + ] + }, + { + "name": "Kim, Jeongnim", + "given_name": "Jeongnim", + "family_name": "Kim", + "affiliations": [ + "Intel Corporation, Hillsboro, Oregon 97124, United States" + ] + }, + { + "name": "Lee, Hoonkyung", + "given_name": "Hoonkyung", + "family_name": "Lee", + "affiliations": [ + "Department of Physics, Konkuk University, Seoul 05029, Korea" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Material Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, United States" + ] + }, + { + "name": "Kwon, Yongkyung", + "given_name": "Yongkyung", + "family_name": "Kwon", + "affiliations": [ + "Department of Physics, Konkuk University, Seoul 05029, Korea" + ] + } + ], + "description": "Dataset for \"Nature of Interlayer Binding and Stacking of sp\u2013sp2 Hybridized Carbon Layers: A Quantum Monte Carlo Study\" Hyeondeok Shin, Jeongnim Kim, Hoonkyung Lee, Olle Heinonen, Anouar Benali, and Yongkyung Kwon", + "keywords": [ + "QMC", + "DMC", + "Graphyne", + "2D materials", + "Monte Carlo", + "QMCPACK", + "simulation", + "Quantum Monte Carlo", + "Graphene", + "Diffusion Monte Carlo" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.jctc.7b00747", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/abs/10.1021/acs.jctc.7b00747", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CPSFM", + "extensions": { + "mdf_source_id": "pub_93_shin_nature_v1.2", + "mdf_source_name": "pub_93_shin_nature", + "all_materials_included": "C", + "funding_details": "['This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials.', 'This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology.']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_914/", + "version": "1.0", + "root_version": "pub_93_shin_nature_v1.2", + "latest": true + } + }, + { + "source_id": "pub_79_shea_size_v1.2", + "source_name": "pub_79_shea_size", + "version": 1, + "ingest_date": "2019-03-08T16:54:25.294851Z", + "doi": "10.18126/M2J63D", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_807/", + "metadata": { + "title": "Size consistent excited states via algorithmic transformations between variational principles", + "authors": [ + { + "name": "Shea, Jacqueline A. R.", + "given_name": "Jacqueline A. R.", + "family_name": "Shea", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Neuscamman, Eric", + "given_name": "Eric", + "family_name": "Neuscamman", + "affiliations": [ + "University of California, Berkeley" + ] + } + ], + "description": "We demonstrate that a broad class of excited state variational principles is not size consistent. In light of this difficulty, we develop and test an approach to excited state optimization that transforms between variational principles in order to achieve state selectivity, size consistency, and compatibility with quantum Monte Carlo. 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Shifler. N.M.L. was supported by a Holbrook Foundation Graduate Fellowship through the UCSB IEE; a UCSB Chancellor's Graduate Fellowship; a Doctoral Fellowship from the Advanced Light Source (ALS), a Division of Lawrence Berkeley National Laboratory (LBNL), with funding provided by the U.S. Department of Energy; and a National Science Foundation (NSF) Graduate Research Fellowship under Grant No. 1144085. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The study was also supported by Beamline 8.3.2 at the ALS, a Division of LBNL, and the Molecular Foundry at LBNL. The ALS and the Molecular Foundry are supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. DOE under Contract No. DE-AC02-05CH11231. The MRL Shared Experimental Facilities at UCSB are supported by the MRSEC Program of the NSF under Award No. DMR 1121053, a member of the NSF-funded Materials Research Facilities Network. The SiC fibers used in this study were kindly provided by Pratt and Whitney. Finally, the authors gratefully acknowledge John Williams for fruitful discussions, Dr. John Shaw for help running the XCT experiment, Dr. Ilir Beta for running the TGMS experiments, as well as Drs. Dula Parkinson and Alastair MacDowell (ALS) for assistance in planning and executing the XCT experiments." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1319/", + "version": "1.0", + "root_version": "larsonnataliem_xct_insitu_conversion_v1.1", + "latest": true + } + }, + { + "source_id": "larsonnataliem_exsitu_xct_beds_v1.1", + "source_name": "larsonnataliem_exsitu_xct_beds", + "version": 1, + "ingest_date": "2019-07-06T03:04:53.181793Z", + "doi": "10.18126/M2QM0Z", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1322/", + "metadata": { + "title": "Ex-situ XCT dataset for \"X-ray computed tomography of microstructure evolution during matrix impregnation and curing in unidirectional fiber beds\"", + "authors": [ + { + "name": "Larson, Natalie M.", + "given_name": "Natalie M.", + "family_name": "Larson", + "affiliations": [ + "University of California, Santa Barbara" + ] + }, + { + "name": "Zok, Frank W.", + 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Urbana Champaign" + ] + }, + { + "name": "Tawfick, Sameh", + "given_name": "Sameh", + "family_name": "Tawfick", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://petreldata.net/mdf/detail/nanohub_gresq_id_52_v1.1", + "identifier_type": "URL", + "relation_type": "IsDerivedFrom" + } + ], + "organization": "NanoMFG", + "extensions": { + "mdf_source_id": "raman_analysis_nanohub_gresq_id_52_v1.1", + "mdf_source_name": "raman_analysis_nanohub_gresq_id_52" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/raman_analysis_nanohub_gresq_id_52_v1.1/", + "version": "1.0", + "root_version": "raman_analysis_nanohub_gresq_id_52_v1.1", + "latest": true + } + }, + { + "source_id": "raman_analysis_nanohub_gresq_id_50_v1.1", + "source_name": "raman_analysis_nanohub_gresq_id_50", + "version": 1, + 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"affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Zhang, Kaihao", + "given_name": "Kaihao", + "family_name": "Zhang", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Surana, Mitisha", + "given_name": "Mitisha", + "family_name": "Surana", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Seol, Chae", + "given_name": "Chae", + "family_name": "Seol", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Adams, Darren", + "given_name": "Darren", + "family_name": "Adams", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Ertekin, Elif", + "given_name": "Elif", + "family_name": "Ertekin", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Tawfick, Sameh", + "given_name": "Sameh", + "family_name": "Tawfick", + "affiliations": [ + "University of Illinois at 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Synthesis on Palladium leaf University of Illinois at Urbana-Champaign", + "authors": [ + { + "name": "Zhang, Kaihao", + "given_name": "Kaihao", + "family_name": "Zhang", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "organization": "NanoMFG", + "extensions": { + "mdf_source_id": "nanohub_gresq_id_50_v1.1", + "mdf_source_name": "nanohub_gresq_id_50", + "nanomfg": { + "max_temperature": 1000.0, + "carbon_source": "CH4", + "base_pressure": 0.0147, + "catalyst": "Palladium leaf" + } + }, + "download_url": "https://data.materialsdatafacility.org/legacy/nanohub_gresq_id_50_v1.1/", + "version": "1.0", + "root_version": "nanohub_gresq_id_50_v1.1", + "latest": true + } + }, + { + "source_id": "nanohub_gresq_id_46_v1.1", + "source_name": "nanohub_gresq_id_46", + "version": 1, + "ingest_date": "2019-11-25T16:00:26.195539Z", + "doi": null, + "endpoint_path": 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"nanohub_gresq_id_51_v1.1", + "latest": true + } + }, + { + "source_id": "nanohub_gresq_id_53_v1.1", + "source_name": "nanohub_gresq_id_53", + "version": 1, + "ingest_date": "2019-11-25T16:01:06.909155Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/nanohub_gresq_id_53_v1.1/", + "metadata": { + "title": "Graphene Synthesis on Palladium leaf University of Illinois at Urbana-Champaign", + "authors": [ + { + "name": "Zhang, Kaihao", + "given_name": "Kaihao", + "family_name": "Zhang", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "NanoMFG", + "extensions": { + "mdf_source_id": "nanohub_gresq_id_53_v1.1", + "mdf_source_name": "nanohub_gresq_id_53", + "nanomfg": { + "max_temperature": 1100.0, + "carbon_source": "CH4", + "base_pressure": 0.0149, + "catalyst": "Palladium leaf" + } + }, + "download_url": "https://data.materialsdatafacility.org/legacy/nanohub_gresq_id_53_v1.1/", + "version": "1.0", + "root_version": "nanohub_gresq_id_53_v1.1", + "latest": true + } + }, + { + "source_id": "leveilleejoshua_freeelectron_effects_principles_v1.1", + "source_name": "leveilleejoshua_freeelectron_effects_principles", + "version": 1, + "ingest_date": "2019-07-05T13:56:32.626182Z", + "doi": "10.18126/M2D64J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/leveilleejoshua_freeelectron_effects_principles_v1.1/", + "metadata": { + "title": "Dataset for \"Free-electron effects on optical absorption of hybrid perovskite CH3NH3PbI3 from first principles\"", + "authors": [ + { + "name": "Leveillee, Joshua", + "given_name": "Joshua", + "family_name": "Leveillee", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + } + ], + "description": "The calculations are performed in Vienna Ab initio Simulation Package (VASP) thus the inputs and outputs files are in VASP format. Parts of the calculations concerning the Bethe-Salpeter equation use our own code that is not published. The data for publication is structured as follows: \r\n\r\nThere are two main directories: (1) 1_Data_Sets_In_Paper contains the figures and grace files (which contain explicit data sets). The grace files can be opened using the xmgrace software, and arrays of data sets can be exported to txt files using xmgrace export functionality. (2) 2_VASP_and_BSE_IO contains the inputs for the VASP calculations performed in this work. Details will be discussed in the following section.\r\n\r\nDISCLAIMER: For the energy ranges discussed in the paper and presented in figures, optical spectra have been carefully calculated and converged. Outside of these energy ranges, however, there is no guarantee of the physicality of the results. Please be mindful of this when downloading data from the xmgrace files, and consult with the figures in the paper for the relevant energy range where results are physically meaningful. For further questions, please contact the authors. \r\n\r\nVASP_and_BSE_IO: Contains the explicit files related to VASP and the BSE code used in this study. POSCAR is the atomic structure, INCAR are setting for electronic structure calculations, and KPOINTS define the kmesh use to integrate over the Brillouin zone. Transmatrix files contain the transition dipole matrix elements between bands as a function of k-point. Details on file format can be found in the VASP manual at: https://cms f.mpi.univie.ac.at/vasp/vasp/vasp.html.\r\n\r\n(1): 1_Atomic_Structure contains, for each phase, the calculated ground state electronic structure needed to reproduce the results in this study. The DATA_X files (X indicates the phase) contain the energy v.s. volume curves used to confirm the stable ground state volume.\r\n\r\n(2): 2_Band_Structures contains the relevant files (VASP POSCAR, KPOINTS, INCAR) and band data to reproduce the band structures for each phase in this study. \r\n\r\n(3): 3_Density_of_States contains relevant files (VASP POSCAR, KPOINTS, INCAR) to reproduce the DOS for each phase in this study.\r\n\r\n(4): 4_GW0 contains the relevant files (VASP POSCAR, KPOINTS, INCAR) and QP eigenvalues to reproduce the prediction of the GW0 QP band gap for each phase found in this study.\r\n\r\n(5): 5_Optical_Spectra_and_BSE: This directory contains all relevant files to reproduce the optical response for each phase in this study. The two sub directories are detailed below. For others interested in reproducing the BSE results found herin, you will need to contact the authors for details in the code and implementation.\r\n\r\n(5a): shifted_kmesh: Coarse k-point meshes with random shifts are used to calculate optical spectra over a wide energy range. This directory contains three sub directories (C_SPEC_IO, T_SPEC_IO, and O_SPEC_IO, where C=cubic, T=tetragonal, and O=orthorhombic). The T and O directories only contains the SOC spectra IO, without BSE. The C_SPEC_IO contains SOC, EL, and FC directories. The SOC directory contains files to calculate the PBE+dGW0+SOC spectra without exciton effects on the shifted kmesh. The EL directory contains the IO for BSE+dGW0+SOC containing exciton effects and with interband electronic screening of the electron-hole Coulomb interaction only. The SPEC_OPP and SPEC_PAR files contain the spin conserving and spin-flip transitions spectra under SOC, respectively. The BSE folders contain IO for the BSE code used in this study. The FC directory contains the IO for BSE+dGW0+SOC containing exciton effects and with interband electronic screening PLUS Thomas-Fermi screening by additional free-electrons in the CB of the electron-hole Coulomb interaction. Similarly, it contains BSE run files (in the Wpar, Wopp, Vpar, Vopp, and CSC directories), and spectra output in the SPEC directories. Free electron concentrations are indicated by the presence of an FCX string in the file name, where X gives the free electron concentration in the CBM in cm^-3.\r\n\r\n(5b): hybrid_kmesh contains calculations using the hybrid k-point mesh technique to carefully sample the Brillouin zone around the band extrema. From hybrid mesh calculations we predict the converged exciton binding energies and the carefully sampled optical spectra around optical onset. Burstein-Moss shifts and band gap renormalization are also calculated on these grids. Similar to 5a, it contains C, T, and O subdirectories, where the O and T directories only include PBE+dGW0+SOC calculations. The C_IO directory contains both PBE+dGW0+SOC and BSE+dGW0+SOC calculations, and are arranged in the same structure as discussed in 5a.", + "keywords": [ + "spectroscopy", + "MAPbI3", + "experiment", + "electrochemistry", + "semiconductors" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "leveilleejoshua_freeelectron_effects_principles_v1.1", + "mdf_source_name": "leveilleejoshua_freeelectron_effects_principles" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/leveilleejoshua_freeelectron_effects_principles_v1.1/", + "version": "1.0", + "root_version": "leveilleejoshua_freeelectron_effects_principles_v1.1", + "latest": true + } + }, + { + "source_id": "pub_25_agarwal_solute_v1.2", + "source_name": "pub_25_agarwal_solute", + "version": 1, + "ingest_date": "2019-03-08T15:12:50.706594Z", + "doi": "10.18126/M20G83", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_620/", + "metadata": { + "title": "Solute transport database in Mg using ab initio and exact diffusion theory", + "authors": [ + { + "name": "Agarwal, Ravi", + "given_name": "Ravi", + "family_name": "Agarwal", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This dataset demonstrate the improved solute transport in hcp Mg through exact diffusion theory:Green function approach. We report the database of 61 solutes consisting of solute diffusivity and solute drag ratio. We also report the results from previously used approximate models of diffusion namely 8-frequency and 13-frequency models. The parameters needed for the diffusion models i.e. binding energy, migration barrier and attempt frequency are obtained through density functional theory calculations", + "keywords": [ + "solute diffusivity", + "diffusion", + "binding energy", + "metals and alloys", + "Arrhenius parameters", + "13-frequency", + "migration barrier", + "database", + "Green function", + "simulation", + "drag ratio", + "8-frequency", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.5281/zenodo.494987", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://doi.org/10.5281/zenodo.494987", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://github.com/DallasTrinkle/Onsager", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pypi.python.org/pypi/onsager", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dallastrinkle.github.io/Onsager", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_25_agarwal_solute_v1.2", + "mdf_source_name": "pub_25_agarwal_solute", + "funding_details": "['National Science Foundation (NSF grant 1411106)', 'Office of Naval Research (ONR grant N000141210752)']", + "all_materials_included": "['Mg-X', 'Mg']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_620/", + "version": "1.0", + "root_version": "pub_25_agarwal_solute_v1.2", + "latest": true + } + }, + { + "source_id": "pub_53_de_emsoft_v1.2", + "source_name": "pub_53_de_emsoft", + "version": 1, + "ingest_date": "2019-03-08T16:02:33.629273Z", + "doi": "10.18126/M2SG6G", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_160/", + "metadata": { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Iron (Fe)", + "authors": [ + { + "name": "De Graef, Marc", + "given_name": "Marc", + "family_name": "De Graef", + "affiliations": [ + "Carnegie Mellon University" + ] + } + ], + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for bcc-Fe for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations.", + "keywords": [ + "EBSD", + "Fe", + "pattern dictionary", + "machine learning", + "metals and alloys", + "backscattering", + "master pattern", + "EMSoft", + "simulation", + "diffraction", + "electron backscattered diffraction", + "microscopy", + "dictionary" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1017/S1431927613001840", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://github.com/marcdegraef/EMsoft", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1017/S1431927613001840", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_53_de_emsoft_v1.2", + "mdf_source_name": "pub_53_de_emsoft", + "all_materials_included": "['iron', 'Fe']", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_160/", + "version": "1.0", + "root_version": "pub_53_de_emsoft_v1.2", + "latest": true + } + }, + { + "source_id": "pub_57_wu_highthroughput_v1.2", + "source_name": "pub_57_wu_highthroughput", + "version": 1, + "ingest_date": "2019-03-08T16:09:20.557396Z", + "doi": "10.18126/M2X59R", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_164/", + "metadata": { + "title": "Dataset for High-throughput Ab-initio Dilute Solute Diffusion Database", + "authors": [ + { + "name": "Wu, Henry", + "given_name": "Henry", + "family_name": "Wu", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Mayeshiba, Tam", + "given_name": "Tam", + "family_name": "Mayeshiba", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity.", + "keywords": [ + "iridium", + "calcium", + "DFT", + "high-throughput", + "aluminum", + "lead", + "copper", + "metals and alloys", + "nickel", + "platinum", + "experiment", + "magnesium", + "simulation", + "solute", + "gold", + "molybdenum", + "palladium", + "diffusion", + "dilute", + "database", + "tungsten" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/sdata.2016.54", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1038/sdata.2016.54", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.6084/m9.figshare.1546772", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_57_wu_highthroughput_v1.2", + "mdf_source_name": "pub_57_wu_highthroughput", + "all_materials_included": "['Al', 'Au', 'Ca', 'Cu', 'Fe', 'Ir', 'Mg', 'Mo', 'Ni', 'Pb', 'Pt', 'W']", + "funding_details": "['NSF SI2-SSI 1148011', 'NSF DGE-0718123', 'UW-Madison GERS']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_164/", + "version": "1.0", + "root_version": "pub_57_wu_highthroughput_v1.2", + "latest": true + } + }, + { + "source_id": "pub_52_zhang_characterization_v2.1", + "source_name": "pub_52_zhang_characterization", + "version": 1, + "ingest_date": "2019-03-08T16:00:52.258198Z", + "doi": "10.18126/M22017", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_156/", + "metadata": { + "title": "Dataset for \"Characterization of Neutron-irradiated HT-UPS Steel by High-energy Xray Diffraction Microscopy\"", + "authors": [ + { + "name": "Zhang, Xuan", + "given_name": "Xuan", + "family_name": "Zhang" + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park" + }, + { + "name": "Almer, Jonathan", + "given_name": "Jonathan", + "family_name": "Almer" + }, + { + "name": "Li, Meimei", + "given_name": "Meimei", + "family_name": "Li" + } + ], + "description": "Measurement of neutron-irradiated microstructure using far-field high energy\r\nX-ray diffraction microscopy (FF-HEDM) in a high-temperature ultrafine-precipitate-strengthened\r\n(HT-UPS) austenitic stainless steel. Grain center of mass, grain size distribution, crystallographic\r\norientation (texture), diffraction spot broadening and lattice constant distributions of individual grains\r\nwere obtained for samples in three different conditions: non-irradiated, neutron-irradiated (3 dpa/\r\n500 deg C), and irradiated + annealed (3 dpa / 500 deg C + 600 deg C/ 1 h)", + "keywords": [ + "mesoscale", + "spectroscopy", + "FF-HEDM", + "Nb", + "irradiation effect", + "experiment", + "metals and alloys", + "High-energy X-ray Diffraction Microscopy", + "HEDM", + "Ti", + "microstructures", + "neutron", + "austenitic steel", + "microscopy", + "XRD", + "annealing", + "steel" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1016/j.jnucmat.2015.11.063", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.jnucmat.2015.11.063", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_52_zhang_characterization_v2.1", + "mdf_source_name": "pub_52_zhang_characterization", + "funding_details": "U.S. Department of Energy DE-AC02-06CH11357", + "reduction_method": "MIDAS", + "all_materials_included": "austenitic stainless steel" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_156/", + "version": "1.0", + "root_version": "pub_52_zhang_characterization_v2.1", + "latest": true + } + }, + { + "source_id": "pub_58_agne_heat_v1.2", + "source_name": "pub_58_agne_heat", + "version": 1, + "ingest_date": "2019-03-08T16:10:59.295858Z", + "doi": "10.18126/M22H1D", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1187/", + "metadata": { + "title": "Heat capacity of Mg3Sb2, Mg3Bi2 and their alloys", + "authors": [ + { + "name": "Agne, Matthias", + "given_name": "Matthias", + "family_name": "Agne", + "affiliations": [ + "Dept. Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Imasato, Kazuki", + "given_name": "Kazuki", + "family_name": "Imasato", + "affiliations": [ + "Dept. Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Anand, Shashwat", + "given_name": "Shashwat", + "family_name": "Anand", + "affiliations": [ + "Dept. Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Lee, Kathleen", + "given_name": "Kathleen", + "family_name": "Lee", + "affiliations": [ + "Thermal Energy Conversion Research and Advancement Group, Jet Propulsion Laboratory/California Institute of Technology" + ] + }, + { + "name": "Bux, Sabah", + "given_name": "Sabah", + "family_name": "Bux", + "affiliations": [ + "Thermal Energy Conversion Research and Advancement Group, Jet Propulsion Laboratory/California Institute of Technology" + ] + }, + { + "name": "Zevalkink, Alex", + "given_name": "Alex", + "family_name": "Zevalkink", + "affiliations": [ + "Dept. Materials Science and Engineering, Michigan State University" + ] + }, + { + "name": "Rettie, Alexander", + "given_name": "Alexander", + "family_name": "Rettie", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory" + ] + }, + { + "name": "Chung, Duck", + "given_name": "Duck", + "family_name": "Chung", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory" + ] + }, + { + "name": "Kanatzidis, Mercouri", + "given_name": "Mercouri", + "family_name": "Kanatzidis", + "affiliations": [ + "Dept. Chemistry, Northwestern University" + ] + }, + { + "name": "Snyder, G. Jeffrey", + "given_name": "G. Jeffrey", + "family_name": "Snyder", + "affiliations": [ + "Dept. Materials Science and Engineering, Northwestern University" + ] + } + ], + "description": "Heat capacity values (J/mol/K) from from 1.8 to 220 K for end members and alloys in the Mg3Sb2-Mg3Bi2 system measured using a Quantum Design Dynacool PPMS.", + "keywords": [ + "Mg3Bi2", + "Alloy", + "Mg3Sb2", + "experiment", + "Thermoelectric", + "Heat capacity", + "Low temperature", + "PPMS", + "thermodynamics", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_58_agne_heat_v1.2", + "mdf_source_name": "pub_58_agne_heat", + "all_materials_included": "['Mg3Sb2', 'Mg3Bi2']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1187/", + "version": "1.0", + "root_version": "pub_58_agne_heat_v1.2", + "latest": true + } + }, + { + "source_id": "raman_analysis_nanohub_gresq_id_22_v2.1", + "source_name": "raman_analysis_nanohub_gresq_id_22", + "version": 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"affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Zhang, Kaihao", + "given_name": "Kaihao", + "family_name": "Zhang", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Surana, Mitisha", + "given_name": "Mitisha", + "family_name": "Surana", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Seol, Chae", + "given_name": "Chae", + "family_name": "Seol", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Adams, Darren", + "given_name": "Darren", + "family_name": "Adams", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Ertekin, Elif", + "given_name": "Elif", + "family_name": "Ertekin", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + }, + { + "name": "Tawfick, Sameh", + "given_name": "Sameh", + "family_name": "Tawfick", + "affiliations": [ + "University of Illinois at Urbana Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://petreldata.net/mdf/detail/nanohub_gresq_id_22_v2.1", + "identifier_type": "URL", + "relation_type": "IsDerivedFrom" + } + ], + "organization": "NanoMFG", + "extensions": { + "mdf_source_id": "raman_analysis_nanohub_gresq_id_22_v2.1", + "mdf_source_name": "raman_analysis_nanohub_gresq_id_22" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/raman_analysis_nanohub_gresq_id_22_v2.1/", + "version": "2.0", + "root_version": "raman_analysis_nanohub_gresq_id_22", + "latest": true + } + }, + { + "source_id": "sstein_stein_bandgap_2019_v2.1", + "source_name": "sstein_stein_bandgap_2019", + "version": 2, + "ingest_date": "2019-06-18T16:41:42.004304Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/sstein_stein_bandgap_2019_v2.1/", + "metadata": { + "title": "Machine learning of optical properties of materials \u2013 predicting spectra from images and images from spectra", + "authors": [ + { + "name": "Stein, Helge S.", + "given_name": "Helge S.", + "family_name": "Stein", + "affiliations": [ + "Joint Center for Artificial Photosynthesis, California Institute of Technology" + ] + }, + { + "name": "Guevarra, Dan", + "given_name": "Dan", + "family_name": "Guevarra", + "affiliations": [ + "Joint Center for Artificial Photosynthesis, California Institute of Technology" + ] + }, + { + "name": "Newhouse, Paul F.", + "given_name": "Paul F.", + "family_name": "Newhouse", + "affiliations": [ + "Joint Center for Artificial Photosynthesis, California Institute of Technology" + ] + }, + { + "name": "Soedarmadjia, Edwin", + "given_name": "Edwin", + "family_name": "Soedarmadjia", + "affiliations": [ + "Joint Center for Artificial Photosynthesis, California Institute of Technology" + ] + }, + { + "name": "Gregoire, John M.", + "given_name": "John M.", + "family_name": "Gregoire", + "affiliations": [ + "Joint Center for Artificial Photosynthesis, California Institute of Technology" + ] + } + ], + "keywords": [ + "spectroscopy", + "machine learning", + "high-throughput", + "experiment", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1039/C8SC03077D", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "extensions": { + "mdf_source_id": "sstein_stein_bandgap_2019_v2.1", + "mdf_source_name": "sstein_stein_bandgap_2019" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/sstein_stein_bandgap_2019_v2.1/", + "version": "2.0", + "root_version": "sstein_stein_bandgap_2019", + "latest": true + } + }, + { + "source_id": "electronic_descriptors_metals_v2.1", + "source_name": "electronic_descriptors_metals", + "version": 2, + "ingest_date": "2019-10-03T20:04:07.503531Z", + "doi": 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"affiliations": [ + "Pennsylvania State University" + ] + }, + { + "name": "Qian, X.", + "given_name": "X.", + "family_name": "Qian", + "affiliations": [ + "Texas A&M University" + ] + }, + { + "name": "Qi, L.", + "given_name": "L.", + "family_name": "Qi", + "affiliations": [ + "University of Michigan" + ] + } + ], + "keywords": [ + "materials commons", + "defects", + "metals", + "simulation" + ], + "publisher": "Materials Commons", + "publication_year": 2019, + "resource_type": "Dataset", + "organization": "Materials Commons", + "extensions": { + "mdf_source_id": "electronic_descriptors_metals_v2.1", + "mdf_source_name": "electronic_descriptors_metals" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/electronic_descriptors_metals_v2.1/", + "version": "2.0", + "root_version": "electronic_descriptors_metals", + "latest": true + } + }, + { + "source_id": "na_hyun_jo_eucd2as2_prbr_2020_v1.3", + "source_name": "na_hyun_jo_eucd2as2_prbr_2020", + "version": 1, + 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"eld, Paul C. Can", + "given_name": "Paul C. Can", + "family_name": "eld", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "McGill University", + "University of Waterloo" + ] + } + ], + "description": "Dataset for \"Manipulating magnetism in the topological semimetal EuCd2As2\" published in Physical Review B (R) 2020\nThe data is provided for each figure panel as indicated by file name. \nThe data is in following forms:\n1) Figure 1 data are xy format in Excel file. \n2) ARPES color plot: 2D matrix data in delimited format\n3) Energy distribution plots: first column is binding energy, rest are the intensities for each energy point.\n3) Momentum distribution plots: first column is momentum, rest are the intensities for each energy point.", + "keywords": [ + "experiment", + "DFT", + "topological semimetal", + "magnetic Weyl semimetal" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "na_hyun_jo_eucd2as2_prbr_2020_v1.3", + "mdf_source_name": "na_hyun_jo_eucd2as2_prbr_2020" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/na_hyun_jo_eucd2as2_prbr_2020_v1.3/", + "version": "1.0", + "root_version": "na_hyun_jo_eucd2as2_prbr_2020_v1.3", + "latest": true + } + }, + { + "source_id": "chatterjee_surface_dynamics_v1.2", + "source_name": "chatterjee_surface_dynamics", + "version": 1, + "ingest_date": "2020-10-31T19:51:42.649636Z", + "doi": "10.18126/cvrk-0hdg", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/chatterjee_surface_dynamics_v1.2/", + "metadata": { + "title": "Data for Fast Surface Dynamics on a Metallic Glass Nanowire", + "authors": [ + { + "name": "Chatterjee, Debaditya", + "given_name": "Debaditya", + "family_name": "Chatterjee", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + }, + { + "name": "Annamareddy, Ajay", + "given_name": "Ajay", + "family_name": "Annamareddy", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + }, + { + "name": "Ketkaew, Jittisa", + "given_name": "Jittisa", + "family_name": "Ketkaew", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + }, + { + "name": "Schroers, Jan", + "given_name": "Jan", + "family_name": "Schroers", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "Yale University", + "University of Wisconsin-Madison" + ] + } + ], + "description": "Data for \"Fast Surface Dynamics on a Metallic Glass Nanowire\" by Debaditya Chatterjee, Ajay Annamareddy, Jittisa Ketkaew, Jan Schroers, Dane Morgan, Paul M. Voyles. Tilted dark-field electron correlation microscopy data and molecular dynamics simulations related to the near-surface layer on a metallic glass with fast dynamics.", + "keywords": [ + "simulation", + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy", + "surface", + "electron correlation microscopy", + "molecular dynamics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chatterjee_surface_dynamics_v1.2", + "mdf_source_name": "chatterjee_surface_dynamics" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chatterjee_surface_dynamics_v1.2/", + "version": "1.0", + "root_version": "chatterjee_surface_dynamics_v1.2", + "latest": true + } + }, + { + "source_id": "jiwonyeom_pfimages_learning_v1.1", + "source_name": "jiwonyeom_pfimages_learning", + "version": 1, + "ingest_date": "2020-06-23T01:30:51.688841Z", + "doi": "10.18126/fj1u-cvn9", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/jiwonyeom_pfimages_learning_v1.1/", + "metadata": { + "title": "Synthetic Training Images for Semantic Segmentation of Dendrites via Machine Learning", + "authors": [ + { + "name": "Jiwon, Yeom", + "given_name": "Yeom", + "family_name": "Jiwon", + "affiliations": [ + "Korea Advanced Institute of Science and Technology", + "Northwestern University" + ] + }, + { + "name": "Stan, Tiberiu", + "given_name": "Tiberiu", + "family_name": "Stan", + "affiliations": [ + "Korea Advanced Institute of Science and Technology", + "Northwestern University" + ] + }, + { + "name": "Seungbum, Hong", + "given_name": "Hong", + "family_name": "Seungbum", + "affiliations": [ + "Korea Advanced Institute of Science and Technology", + "Northwestern University" + ] + }, + { + "name": "Peter, Voorhees", + "given_name": "Voorhees", + "family_name": "Peter", + "affiliations": [ + "Korea Advanced Institute of Science and Technology", + "Northwestern University" + ] + } + ], + "description": "Synthetically generated phase-field images for training and validation sets used to train dendrite neural nets. \nTest image is coming from X-ray tomography for measuring the performance of neural nets.", + "keywords": [ + "simulation", + "machine learning", + "metals and alloys", + "convolutional neural network", + " semantic segmentation", + " solidification", + "serial sectioning", + " Phase-field model", + "microscopy", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "jiwonyeom_pfimages_learning_v1.1", + "mdf_source_name": "jiwonyeom_pfimages_learning" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/jiwonyeom_pfimages_learning_v1.1/", + "version": "1.0", + "root_version": "jiwonyeom_pfimages_learning_v1.1", + "latest": true + } + }, + { + "source_id": "silica_structure_v1.1", + "source_name": "silica_structure", + "version": 1, + "ingest_date": "2020-10-28T14:10:45.703966Z", + "doi": "10.18126/uffm-o07g", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/silica_structure_v1.1/", + "metadata": { + "title": "Dataset and BKS silica structures for machine learning studies on the structure-thermodynamic stability relationship in silica", + "authors": [ + { + "name": "Yu, Zheng", + "given_name": "Zheng", + "family_name": "Yu", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Liu, Qitong", + "given_name": "Qitong", + "family_name": "Liu", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Szlufarska, Izabela", + "given_name": "Izabela", + "family_name": "Szlufarska", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Wang*, Bu", + "given_name": "Bu", + "family_name": "Wang*", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "The dataset contains 24,157 silica inherent structures with a wide range of thermodynamic stabilities. \nThe structures are generated by the replica exchange molecular dynamics and the melt-quenching molecular dynamics simulations with the BKS potential. \nThe enthalpies and the structural features (covering density, coordination defects, pair distribution functions, S(q), rings, Si-O-Si bond angles, and torsion angles) of these structures are provided. ", + "keywords": [ + "simulation", + "machine learning", + "high-throughput" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "silica_structure_v1.1", + "mdf_source_name": "silica_structure" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/silica_structure_v1.1/", + "version": "1.0", + "root_version": "silica_structure_v1.1", + "latest": true + } + }, + { + "source_id": "lee_malone_morales_symmetry_v1.1", + "source_name": "lee_malone_morales_symmetry", + "version": 1, + "ingest_date": "2020-04-16T22:22:47.947963Z", + "doi": "10.18126/6vto-9dd0", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lee_malone_morales_symmetry_v1.1/", + "metadata": { + "title": "Dataset for \"Utilizing Essential Symmetry Breaking in Auxiliary-Field Quantum Monte Carlo: Application to the Spin Gaps of the C36 Fullerene and an Iron Porphyrin Model Complex\"", + "authors": [ + { + "name": "Lee, Joonho", + "given_name": "Joonho", + "family_name": "Lee", + "affiliations": [ + "Columbia University", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Malone, Fionn D", + "given_name": "Fionn D", + "family_name": "Malone", + "affiliations": [ + "Columbia University", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Morales, Miguel A", + "given_name": "Miguel A", + "family_name": "Morales", + "affiliations": [ + "Columbia University", + "Lawrence Livermore National Laboratory" + ] + } + ], + "description": "Dataset for \"Utilizing Essential Symmetry Breaking in Auxiliary-Field Quantum Monte Carlo: Application to the Spin Gaps of the C36 Fullerene and an Iron Porphyrin Model Complex\". Included raw AFQMC output files, input files and wavefunctions.", + "keywords": [ + "simulation", + "QMC", + "QMCPACK" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "lee_malone_morales_symmetry_v1.1", + "mdf_source_name": "lee_malone_morales_symmetry" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/lee_malone_morales_symmetry_v1.1/", + "version": "1.0", + "root_version": "lee_malone_morales_symmetry_v1.1", + "latest": true + } + }, + { + "source_id": "cd3as2_magnetoresistance_stemmer_v1.1", + "source_name": "cd3as2_magnetoresistance_stemmer", + "version": 1, + "ingest_date": "2020-10-20T20:35:50.605242Z", + "doi": "10.18126/le1j-8vzd", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cd3as2_magnetoresistance_stemmer_v1.1/", + "metadata": { + "title": "Magnetoresistance effects in cadmium arsenide thin films", + "authors": [ + { + "name": "Goyal, Manik", + "given_name": "Manik", + "family_name": "Goyal", + "affiliations": [ + "University of California, Santa Barbara" + ] + }, + { + "name": "Kealhofer, David A.", + "given_name": "David A.", + "family_name": "Kealhofer", + "affiliations": [ + "University of California, Santa Barbara" + ] + }, + { + "name": "Schumann, Timo", + "given_name": "Timo", + "family_name": "Schumann", + "affiliations": [ + "University of California, Santa Barbara" + ] + }, + { + "name": "Stemmer, Susanne", + "given_name": "Susanne", + "family_name": "Stemmer", + "affiliations": [ + "University of California, Santa Barbara" + ] + } + ], + "description": "Data sets for figures in article \"Magnetoresistance effects in cadmium arsenide thin films\" by Manik Goyal, David A. Kealhofer, Timo Schumann, and Susanne Stemmer (Applied Physics Letters, 2020)", + "keywords": [ + "experiment", + "Dirac semimetal", + "magnetoresistance", + "topological material" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cd3as2_magnetoresistance_stemmer_v1.1", + "mdf_source_name": "cd3as2_magnetoresistance_stemmer" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cd3as2_magnetoresistance_stemmer_v1.1/", + "version": "1.0", + "root_version": "cd3as2_magnetoresistance_stemmer_v1.1", + "latest": true + } + }, + { + "source_id": "zhang_atomic_resolution_networks_v1.1", + "source_name": "zhang_atomic_resolution_networks", + "version": 1, + "ingest_date": "2019-09-20T17:10:56.984156Z", + "doi": "10.18126/4nm2-0g70", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhang_atomic_resolution_networks_v1.1/", + "metadata": { + "title": "Data for Atomic Resolution Convergent Beam Electron Diffraction Analysis Using Convolutional Neural Networks", + "authors": [ + { + "name": "Zhang, Chenyu", + "given_name": "Chenyu", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Feng, Jie", + "given_name": "Jie", + "family_name": "Feng", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Rangel DaCosta, Luis", + "given_name": "Luis", + "family_name": "Rangel DaCosta", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Simulated and experimental 4D STEM data sets acquired from SrTiO3 [100] as a function of thickness. Convolutional neural networks trained to determine the sample thickness from 4D STEM data.", + "keywords": [ + "simulation", + "experiment", + "machine learning", + "microscopy", + "4D STEM", + "CBED", + "PACBED", + "CNN", + "neural network" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zhang_atomic_resolution_networks_v1.1", + "mdf_source_name": "zhang_atomic_resolution_networks" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zhang_atomic_resolution_networks_v1.1/", + "version": "1.0", + "root_version": "zhang_atomic_resolution_networks_v1.1", + "latest": true + } + }, + { + "source_id": "ganesh_doping_bad_metal_v1.1", + "source_name": "ganesh_doping_bad_metal", + "version": 1, + "ingest_date": "2020-04-13T14:30:28.472204Z", + "doi": "10.18126/nrpj-u7bp", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ganesh_doping_bad_metal_v1.1/", + "metadata": { + "title": "Dataset for Doping a bad metal: Origin of suppression of the metal-insulator transition in nonstoichiometric VO2", + "authors": [ + { + "name": "Ganesh, P.", + "given_name": "P.", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + }, + { + "name": "Lechermann, Frank", + "given_name": "Frank", + "family_name": "Lechermann", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + }, + { + "name": "Kylanpaa, Ilkka", + "given_name": "Ilkka", + "family_name": "Kylanpaa", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + }, + { + "name": "Kent, Paul R. C.", + "given_name": "Paul R. C.", + "family_name": "Kent", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Oak Ridge National Laboratory", + "Universitat Hamburg", + "Tampere University", + "Argonne National Laboratory" + ] + } + ], + "keywords": [ + "simulation", + "QMCPACK", + "QMC", + "DMC", + "Quantum Monte Carlo", + "vanadium dioxide" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ganesh_doping_bad_metal_v1.1", + "mdf_source_name": "ganesh_doping_bad_metal" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ganesh_doping_bad_metal_v1.1/", + "version": "1.0", + "root_version": "ganesh_doping_bad_metal_v1.1", + "latest": true + } + }, + { + "source_id": "otis_hybrid_qmc_v1.2", + "source_name": "otis_hybrid_qmc", + "version": 1, + "ingest_date": "2020-10-28T21:49:10.263029Z", + "doi": "10.18126/kduf-y6cp", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/otis_hybrid_qmc_v1.2/", + "metadata": { + "title": "Dataset for A hybrid approach to excited-state-specific variational Monte Carlo and doubly excited states", + "authors": [ + { + "name": "Otis, Leon", + "given_name": "Leon", + "family_name": "Otis", + "affiliations": [ + "CPSFM" + ] + }, + { + "name": "Craig, Isabel", + "given_name": "Isabel", + "family_name": "Craig", + "affiliations": [ + "University of California Berkeley" + ] + }, + { + "name": "Neuscamman, Eric", + "given_name": "Eric", + "family_name": "Neuscamman", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ] + } + ], + "keywords": [ + "QMC", + "QMCPack", + "optimization", + "doubly excited states" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "otis_hybrid_qmc_v1.2", + "mdf_source_name": "otis_hybrid_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/otis_hybrid_qmc_v1.2/", + "version": "1.0", + "root_version": "otis_hybrid_qmc_v1.2", + "latest": true + } + }, + { + "source_id": "ferguson_peptide_v1.1", + "source_name": "ferguson_peptide", + "version": 1, + "ingest_date": "2019-12-05T03:25:16.130221Z", + "doi": "10.18126/xqiz-hzc2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ferguson_peptide_v1.1/", + "metadata": { + "title": "Dataset for Discovery of Self-Assembling \u03c0-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation", + "authors": [ + { + "name": "Shmilovich, Kirill", + "given_name": "Kirill", + "family_name": "Shmilovich", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Mansbach, Rachael A.", + "given_name": "Rachael A.", + "family_name": "Mansbach", + "affiliations": [ + "Los Alamos National Laboratory" + ] + }, + { + "name": "Sidky, Hythem", + "given_name": "Hythem", + "family_name": "Sidky" + }, + { + "name": "Dunne, Olivia", + "given_name": "Olivia", + "family_name": "Dunne" + }, + { + "name": "Panda, Sayak Subhra", + "given_name": "Sayak Subhra", + "family_name": "Panda" + }, + { + "name": "Tovar, John D.", + "given_name": "John D.", + "family_name": "Tovar" + }, + { + "name": "Ferguson, Andrew L.", + "given_name": "Andrew L.", + "family_name": "Ferguson", + "affiliations": [ + "University of Chicago", + "Los Alamos National Laboratory" + ] + } + ], + "description": "This dataset contains 186 molecular dynamics simulation trajectories of coarse-grained pi-conjugated peptides in the Asp-X-X-X-(oligophenylenevinylene)3-X-X-X-Asp (DXXX-OPV3-XXXD) family, where X denotes one of the 20 natural amino acids. The oligopeptide wings are constrained to be mirror-symmetric both in the identity of the amino acids and the N-to-C directionality such that each molecule possesses two C-termini. These trajectories were generated during an active learning search for the peptide sequences that yield structurally optimal self-assembled pseudo-1D nanoaggregates with good stacking between the pi-cores. Directories containing the trajectories are named \"dxxx\" where each \"x\" corresponds to an amino acid in the DXXX-OPV3-XXXD sequence (e.g. 'dfag' corresponds to the structure DFAG-OPV3-GAFD). Each trajectory itself is a 3,000 nanosecond simulation of 96 identical pi-conjugated peptides in water (/solvent), and the same trajectories where the water has been removed (/no-solvent).", + "keywords": [ + "simulation", + "biomaterials", + "molecular dynamics", + "peptides" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ferguson_peptide_v1.1", + "mdf_source_name": "ferguson_peptide" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ferguson_peptide_v1.1/", + "version": "1.0", + "root_version": "ferguson_peptide_v1.1", + "latest": true + } + }, + { + "source_id": "hanus_heat_capacity_pbte_v1.1", + "source_name": "hanus_heat_capacity_pbte", + "version": 1, + "ingest_date": "2020-01-24T18:20:29.633345Z", + "doi": "10.18126/1obf-bje9", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hanus_heat_capacity_pbte_v1.1/", + "metadata": { + "title": "Heat capacity of undoped PbTe", + "authors": [ + { + "name": "Hanus, Riley", + "given_name": "Riley", + "family_name": "Hanus", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Agne, Matthias T.", + "given_name": "Matthias T.", + "family_name": "Agne", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Rettie, Alexander J. E.", + "given_name": "Alexander J. E.", + "family_name": "Rettie", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Chung, Duck Young", + "given_name": "Duck Young", + "family_name": "Chung", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Kanatzidis, Mercouri G.", + "given_name": "Mercouri G.", + "family_name": "Kanatzidis", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Snyder, G. Jeffrey", + "given_name": "G. Jeffrey", + "family_name": "Snyder", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "description": "Heat capacity measurements from ~2 - 200 K of pure undoped PbTe dense polycrystalline samples with various amounts of internal strain induced by high energy ball milling. Sample preparation and characterization can be found in the associated publication.", + "keywords": [ + "experiment", + "semiconductors", + "energy materials", + "heat capacity", + "thermal properties", + "lead telluride" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": " https://doi.org/10.1002/adma.201900108", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": " https://doi.org/10.1002/adma.201900108", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hanus_heat_capacity_pbte_v1.1", + "mdf_source_name": "hanus_heat_capacity_pbte" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hanus_heat_capacity_pbte_v1.1/", + "version": "1.0", + "root_version": "hanus_heat_capacity_pbte_v1.1", + "latest": 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"globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shin_diffusion_monte_graphene_v1.2", + "metadata": { + "title": "Dataset for \"Diffusion Monte Carlo Study of the O2 Adsorption on a Single Layer Graphene\"", + "authors": [ + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Argonne National Laboratory", + "Konkuk University" + ] + }, + { + "name": "Luo, Ye", + "given_name": "Ye", + "family_name": "Luo", + "affiliations": [ + "Argonne National Laboratory", + "Konkuk University" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "Konkuk University" + ] + }, + { + "name": "Kwon, Yongkyung", + "given_name": "Yongkyung", + "family_name": "Kwon", + "affiliations": [ + "Argonne National Laboratory", + "Konkuk University" + ] + } + ], + "description": "Dataset for \"Diffusion Monte Carlo Study of the O2 Adsorption on a Single Layer 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for cells and biofilms on titanium measured by laser spallation. Original fringe data and substrate stress pulses are shared for the two calibration configurations.", + "keywords": [ + "experiment", + "adhesion", + "biofilm", + "laser spallation", + "stress waves", + "adhesion index" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "biofilm_cell_adhesion_index_v1.2", + "mdf_source_name": "biofilm_cell_adhesion_index" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/biofilm_cell_adhesion_index_v1.2/", + "version": "1.0", + "root_version": "biofilm_cell_adhesion_index_v1.2", + "latest": true + } + }, + { + "source_id": "chargespin_susceptibility_v1.1", + "source_name": "chargespin_susceptibility", + "version": 1, + "ingest_date": "2019-12-04T23:16:55.609683Z", + "doi": "10.18126/6oby-l2lp", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/chargespin_susceptibility_v1.1/", + "metadata": { + "title": "Data sets for \"Charge-spin susceptibility as a useful probe of electronic correlations in quantum materials\"", + "authors": [ + { + "name": "Rodrigues, Joao N. B.", + "given_name": "Joao N. B.", + "family_name": "Rodrigues", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Wagner, Lucas K.", + "given_name": "Lucas K.", + "family_name": "Wagner", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "Data files and plotting scripts used to calculate the charge-spin susceptibility of a set of layered materials (with 2D arrays of transition metal atoms).", + "keywords": [ + "simulation", + "DFT", + "QMC", + "strongly correlated" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chargespin_susceptibility_v1.1", + "mdf_source_name": "chargespin_susceptibility" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chargespin_susceptibility_v1.1/", + "version": "1.0", + "root_version": "chargespin_susceptibility_v1.1", + "latest": true + } + }, + { + "source_id": "thurston_selfassembled_peptide_spectra_v1.1", + "source_name": "thurston_selfassembled_peptide_spectra", + "version": 1, + "ingest_date": "2019-08-07T18:00:39.956795Z", + "doi": "10.18126/t28a-uwgw", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/thurston_selfassembled_peptide_spectra_v1.1", + "metadata": { + "title": "Self-Assembled Peptide Absorption Spectra", + "authors": [ + { + "name": "Thurston, B.A.", + "given_name": "B.A.", + "family_name": "Thurston", + "affiliations": [ + "University of Chicago", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Shapera, E.P.", + "given_name": "E.P.", + "family_name": "Shapera", + "affiliations": [ + "University of Chicago", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Tovar, J.D.", + "given_name": "J.D.", + "family_name": "Tovar", + "affiliations": [ + "University of Chicago", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, A.", + "given_name": "A.", + "family_name": "Schleife", + "affiliations": [ + "University of Chicago", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Ferguson, A.L.", + "given_name": "A.L.", + "family_name": "Ferguson", + "affiliations": [ + "University of Chicago", + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "Dataset for ``Revealing the sequence-structure-electronic property relation of self-assembling $\\pi$-conjugated oligopeptides by integrated molecular and quantum mechanical modeling''.", + "keywords": [ + "organic compounds", + "molecule", + "spectroscopy", + "peptide", + "high-throughput", + "molecular dynamics", + "self-assembled", + "simulation", + "organic molecule" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "thurston_selfassembled_peptide_spectra_v1.1", + "mdf_source_name": "thurston_selfassembled_peptide_spectra" + }, + "download_url": "https://data.materialsdatafacility.org/thurston_selfassembled_peptide_spectra_v1.1", + "version": "1.0", + "root_version": "thurston_selfassembled_peptide_spectra_v1.1", + "latest": true + } + }, + { + "source_id": "maldonis_local_structure_glass_v1.1", + "source_name": "maldonis_local_structure_glass", + "version": 1, + "ingest_date": "2019-09-03T18:02:26.418914Z", + "doi": "10.18126/1cko-gow0", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/maldonis_local_structure_glass_v1.1", + "metadata": { + "title": "Local Structure Controlling the Glass Transition in a Prototype Metal-Metalloid Glass", + "authors": [ + { + "name": "Maldonis, Jason J.", + "given_name": "Jason J.", + "family_name": "Maldonis", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Molecular dynamics trajectories on cooling of Pd82Si18 computed with the Sheng EAM potential. Motif extraction applied to those trajectories. Atomic models of the resulting motifs.\n\nFunding: NSF DMR-1728933", + "keywords": [ + "machine learning", + "Si", + "Pd82Si18", + "MD", + "metals", + "glass", + "metallic glass", + "molecular dynamics", + "simulation", + "trajectory", + "Pd" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "maldonis_local_structure_glass_v1.1", + "mdf_source_name": "maldonis_local_structure_glass" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/maldonis_local_structure_glass_v1.1", + "version": "1.0", + "root_version": "maldonis_local_structure_glass_v1.1", + "latest": true + } + }, + { + "source_id": "melton_lasco3_qmc_v1.2", + "source_name": "melton_lasco3_qmc", + "version": 1, + "ingest_date": "2020-03-04T22:19:23.743884Z", + "doi": "10.18126/p35v-tgst", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/melton_lasco3_qmc_v1.2/", + "metadata": { + "title": "Dataset for \"Many - body Electronic Structure of LaScO3 by Real Space Quantum Monte Carlo Methods\"", + "authors": [ + { + "name": "Melton, Cody A.", + "given_name": "Cody A.", + "family_name": "Melton", + "affiliations": [ + "Sandia National Laboratories" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University" + ] + } + ], + "keywords": [ + "simulation", + "QMC", + "QMCPack", + "Monte Carlo", + "CPSFM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "melton_lasco3_qmc_v1.2", + "mdf_source_name": "melton_lasco3_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/melton_lasco3_qmc_v1.2/", + "version": "1.0", + "root_version": "melton_lasco3_qmc_v1.2", + "latest": true + } + }, + { + "source_id": "ti64_ir_xray_lpbf_v1.1", + "source_name": "ti64_ir_xray_lpbf", + "version": 1, + "ingest_date": "2020-05-04T17:32:43.314970Z", + "doi": "10.18126/yjyd-kayg", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ti64_ir_xray_lpbf_v1.1/", + "metadata": { + "title": "X-ray and infrared imaging of Ti-6Al-4V in laser powder bed fusion", + "authors": [ + { + "name": "Paulson, Noah H.", + "given_name": "Noah H.", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Gould, Benjamin", + "given_name": "Benjamin", + "family_name": "Gould", + "affiliations": [ + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Wolff, Sarah J.", + "given_name": "Sarah J.", + "family_name": "Wolff", + "affiliations": [ + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Stan, Marius", + "given_name": "Marius", + "family_name": "Stan", + "affiliations": [ + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Greco, Aaron C.", + "given_name": "Aaron C.", + "family_name": "Greco", + "affiliations": [ + "Argonne National Laboratory", + "Texas A&M University" + ] + } + ], + "description": "###### Description\nThis data set consists of simultaneous X-ray and infrared (IR) imaging of laser powder bed fusion of Ti-6Al-4V powder. Fifteen experimental runs were performed at different scan speeds, power levels, and numbers of passes to investigate porosity formation in real time and correlate it to the IR signature of the top surface. Details of the experiment are provided in the accompanying publication by the same authors [1]. The raw IR data is not provided for runs 12 and 15 because it is being used in an ongoing study.\n\n###### Reference\n[1] Paulson, N.H., Gould, B., Wolff, S.J., Stan, M., Greco, A.C., Correlations between thermal history and keyhole porosity in laser powder bed fusion, Additive Manufacturing (2020), https://doi.org/10.1016/j.addma.2020.101213\n\n###### Acknowledgements\nThis work is supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The authors also acknowledge the staff at beamline 32-ID of the Advanced Photon Source for their help in data collection, particularly Tao Sun, Niranjan Parab, Cang Zhao, and Kamel Fezzaa.", + "keywords": [ + "metals and alloys", + "experiment", + "additive manufacturing", + "Ti-6Al-4V", + "titanium", + "L-PBF", + "SLM", + "X-ray", + "Infrared" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.addma.2020.101213", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ti64_ir_xray_lpbf_v1.1", + "mdf_source_name": "ti64_ir_xray_lpbf" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ti64_ir_xray_lpbf_v1.1/", + "version": "1.0", + "root_version": "ti64_ir_xray_lpbf_v1.1", + "latest": true + } + }, + { + "source_id": "ting_comparing_zwitterionic_micelles_v1.1", + "source_name": "ting_comparing_zwitterionic_micelles", + "version": 1, + "ingest_date": "2020-05-26T15:24:03.712619Z", + "doi": "10.18126/n4un-6usf", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ting_comparing_zwitterionic_micelles_v1.1/", + "metadata": { + "title": "Dataset for Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles", + "authors": [ + { + "name": "Ting, Jeffrey", + "given_name": "Jeffrey", + "family_name": "Ting", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Marras, Alexander", + "given_name": "Alexander", + "family_name": "Marras", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Mitchell, Joseph", + "given_name": "Joseph", + "family_name": "Mitchell", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Campagna, Trinity", + "given_name": "Trinity", + "family_name": "Campagna", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Tirrell, Matthew", + "given_name": "Matthew", + "family_name": "Tirrell", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + } + ], + "description": "(1) background subtracted small angle X-ray scattering (SAXS) data from the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357, and the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. Data is organized by polyelectrolyte complex micelles at investigated chain lengths.\n\n(2) autocorrelation functions from the dynamic light scattering (DLS) data in Figures 5 and S-9. Data is organized by scattering angle (Figure 5) or salt condition (Figure S-9) for each polyelectrolyte complex micelle system.", + "keywords": [ + "CHiMaD", + "experiment", + "polymers", + "polyelectrolyte", + "SAXS", + "DLS", + "dynamic light scattering" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.26434/chemrxiv.12237020.v1", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ting_comparing_zwitterionic_micelles_v1.1", + "mdf_source_name": "ting_comparing_zwitterionic_micelles" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ting_comparing_zwitterionic_micelles_v1.1/", + "version": "1.0", + "root_version": "ting_comparing_zwitterionic_micelles_v1.1", + "latest": true + } + }, + { + "source_id": "polar_racemates_nw_hc_fordham_v1.2", + "source_name": "polar_racemates_nw_hc_fordham", + "version": 1, + "ingest_date": "2020-03-24T18:30:45.950106Z", + "doi": "10.18126/vnhj-jaz5", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/polar_racemates_nw_hc_fordham_v1.2/", + "metadata": { + "title": "Machine-learning-assisted Synthesis of Polar Racemates", + "authors": [ + { + "name": "Nisbet, Matthew L.", + "given_name": "Matthew L.", + "family_name": "Nisbet", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Pendleton, Ian M.", + "given_name": "Ian M.", + "family_name": "Pendleton", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Nolis, Gene M.", + "given_name": "Gene M.", + "family_name": "Nolis", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Griffith, Kent J.", + "given_name": "Kent J.", + "family_name": "Griffith", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Schrier, Joshua", + "given_name": "Joshua", + "family_name": "Schrier", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Cabana, Jordi", + "given_name": "Jordi", + "family_name": "Cabana", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Norquist, Alexander J.", + "given_name": "Alexander J.", + "family_name": "Norquist", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "name": "Poeppelmeier, Kenneth R.", + "given_name": "Kenneth R.", + "family_name": "Poeppelmeier", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + } + ], + "description": "The dataset used here consists of 51 experiments transcribed from laboratory notebook records, using the ESCALATE \"entity, materials, actions, observations\" ontology. The composition spaces of the (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) systems were explored by varying the amounts of bpy and HF(aq) used in each reaction, while the amounts of CuO and MO2 (M = Ti, Zr, Hf) were held constant. In addition to this raw experimental data, additional calculated stoichiometric properties and computed electronic structure properties were added. Stoichiometric features, such as molar amounts and molar ratios, were calculated directly from the experimental observations. Electronic structure calculations were performed on the [TiF6]2\u2212, [ZrF6]2\u2212, and [HfF6]2\u2212 anionic building units to provide data on geometry, energetics, and charges using Gaussian 09, The B3LYP/LANL2DZ model chemistry was used as it provides good estimations (+-10 pm) for bond lengths of transition metal oxides and halides. Atomic charges were assessed for the optimized geometries using Mulliken, Hirshfeld, CM5, Natural Bond Orbital (NBO), and electrostatic potential fitting (Merz\u2013Singh\u2013Kollman using UFF radii, MKUFF) methods. ", + "keywords": [ + "experiment", + "machine learning", + "ferroelectric", + "piezoelectric", + "nonlinear optics", + "composition space" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "polar_racemates_nw_hc_fordham_v1.2", + "mdf_source_name": "polar_racemates_nw_hc_fordham" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/polar_racemates_nw_hc_fordham_v1.2/", + "version": "1.0", + "root_version": "polar_racemates_nw_hc_fordham_v1.2", + "latest": true + } + }, + { + "source_id": "mobility_5d_v1.1", + "source_name": "mobility_5d", + "version": 1, + "ingest_date": "2020-06-08T22:34:14.062425Z", + "doi": "10.18126/qh91-00tw", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mobility_5d_v1.1/", + "metadata": { + "title": "Reduced grain boundary mobilities in pure iron", + "authors": [ + { + "name": "Zhang, Jin", + "given_name": "Jin", + "family_name": "Zhang", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Ludwig, Wolfgang", + "given_name": "Wolfgang", + "family_name": "Ludwig", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Zhang, Yubin", + "given_name": "Yubin", + "family_name": "Zhang", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "S\u00f8rensen, Hans Henrik B.", + "given_name": "Hans Henrik B.", + "family_name": "S\u00f8rensen", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Rowenhorst, David J.", + "given_name": "David J.", + "family_name": "Rowenhorst", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Yamanaka, Akinori", + "given_name": "Akinori", + "family_name": "Yamanaka", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "name": "Poulsen, Henning F.", + "given_name": "Henning F.", + "family_name": "Poulsen", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + } + ], + "description": "Reduced grain boundary mobilities of 344 boundaries in pure iron measured by a fitting method described in *Grain boundary mobilities in polycrystals*, *Acta Materialia* 191 (2020) 211-220. The first three columns are components of the Rodrigues vector (in the fundamental zone of the 3D Rodrigues space) of the misorientation of the boundary. Columns 4 to 6 are components of the inclination (grain boundary normal in crystal coordinates) in the crystal coordinates of the first grain. Columns 7 to 9 are components of the inclination in the crystal coordinates of the second grain. Column 10 is the reduced grain boundary mobility in the unit of \u03bcm2/s. This is the data used to plot Fig. 4 in the publication.", + "keywords": [ + "experiment", + "metals and alloys", + "grain boundary mobilities" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2020.03.044", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mobility_5d_v1.1", + "mdf_source_name": "mobility_5d" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/mobility_5d_v1.1/", + "version": "1.0", + "root_version": "mobility_5d_v1.1", + "latest": true + } + }, + { + "source_id": "cdx_impurity_levels_dft_v1.1", + "source_name": "cdx_impurity_levels_dft", + "version": 1, + "ingest_date": "2020-06-03T22:35:39.505834Z", + "doi": "10.18126/oq93-9asv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cdx_impurity_levels_dft_v1.1/", + "metadata": { + "title": "Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides", + "authors": [ + { + "name": "Mannodi-Kanakkithodi, Arun", + "given_name": "Arun", + "family_name": "Mannodi-Kanakkithodi", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Toriyama, Michael Y.", + "given_name": "Michael Y.", + "family_name": "Toriyama", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Sen, Fatih G.", + "given_name": "Fatih G.", + "family_name": "Sen", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Davis, Michael J.", + "given_name": "Michael J.", + "family_name": "Davis", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Klie, Robert F.", + "given_name": "Robert F.", + "family_name": "Klie", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Chan, Maria K. Y.", + "given_name": "Maria K. Y.", + "family_name": "Chan", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + } + ], + "description": "This dataset contains density functional theory (DFT) computed formation energies and charge transition levels of substitutional and interstitial atomic impurities in Cd-chalcogenide semiconductors, published in this paper: https://www.nature.com/articles/s41524-020-0296-7.\n\nPBE_data.csv contains impurity data computed at the PBE level of theory.\n\nHSE_data.csv contains impurity data computed at the HSE06 level of theory.\n", + "keywords": [ + "simulation", + "machine learning", + "semiconductors", + "defects", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1038/s41524-020-0296-7", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cdx_impurity_levels_dft_v1.1", + "mdf_source_name": "cdx_impurity_levels_dft" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cdx_impurity_levels_dft_v1.1/", + "version": "1.0", + "root_version": "cdx_impurity_levels_dft_v1.1", + "latest": true + } + }, + { + "source_id": "hf_thermodynamics_v1.1", + "source_name": "hf_thermodynamics", + "version": 1, + "ingest_date": "2020-01-31T19:07:10.061427Z", + "doi": "10.18126/8anf-37a6", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hf_thermodynamics_v1.1/", + "metadata": { + "title": "Measurements of the Enthalpy and Specific Heat of Hf", + "authors": [ + { + "name": "Paulson, Noah", + "given_name": "Noah", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Jennings, Elise", + "given_name": "Elise", + "family_name": "Jennings", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Stan, Marius", + "given_name": "Marius", + "family_name": "Stan", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "This is a collection of experimental measurements of the enthalpy and specific heat of Hf for the alpha, beta and liquid phases. Measurements were extracted from the table and figures of the original publications. Corrections have been made for historical temperature scales and Zr contamination where necessary. Reported errors have been converted to 1-sigma normal errors according to the GUM standard and estimated when not available.", + "keywords": [ + "Hafnium", + "metals and alloys", + "specific heat", + "enthalpy", + "thermodynamic property" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.ijengsci.2019.05.011", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hf_thermodynamics_v1.1", + "mdf_source_name": "hf_thermodynamics" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hf_thermodynamics_v1.1/", + "version": "1.0", + "root_version": "hf_thermodynamics_v1.1", + "latest": true + } + }, + { + "source_id": "kim_ml_qh_v1.1", + "source_name": "kim_ml_qh", + "version": 1, + "ingest_date": "2020-03-23T21:33:33.033083Z", + "doi": "10.18126/rovl-ingj", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kim_ml_qh_v1.1/", + "metadata": { + "title": "Dataset for Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds", + "authors": [ + { + "name": "Kim, Kyoungdoc", + "given_name": "Kyoungdoc", + "family_name": "Kim", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "He, Jiangang", + "given_name": "Jiangang", + "family_name": "He", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Krishna, Amar", + "given_name": "Amar", + "family_name": "Krishna", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Agrawal, Ankit", + "given_name": "Ankit", + "family_name": "Agrawal", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Wolverton, C.", + "given_name": "C.", + "family_name": "Wolverton", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Full dataset and software associated with \"Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds.\" ", + "keywords": [ + "machine learning", + "simulation", + "semiconductors", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1103/PhysRevMaterials.2.123801", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kim_ml_qh_v1.1", + "mdf_source_name": "kim_ml_qh" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kim_ml_qh_v1.1/", + "version": "1.0", + "root_version": "kim_ml_qh_v1.1", + "latest": true + } + }, + { + "source_id": "local_structure_v1.1", + "source_name": "local_structure", + "version": 1, + "ingest_date": "2019-11-05T17:55:24.827596Z", + "doi": "10.18126/sujk-r6co", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/local_structure_v1.1/", + "metadata": { + "title": "Dataset for the paper \"Local structure of potassium doped nickel oxide: a combined experimental-theoretical study\"", + "authors": [ + { + "name": "Wrobel, Friederike", + "given_name": "Friederike", + "family_name": "Wrobel", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Sterbinsky, George E.", + "given_name": "George E.", + "family_name": "Sterbinsky", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Hsiao, Haw-Wen", + "given_name": "Haw-Wen", + "family_name": "Hsiao", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Zuo, Jian-Min", + "given_name": "Jian-Min", + "family_name": "Zuo", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Kent, Paul R.C.", + "given_name": "Paul R.C.", + "family_name": "Kent", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bhattacharya, Anand", + "given_name": "Anand", + "family_name": "Bhattacharya", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Dataset for the paper \"Local structure of potassium doped nickel oxide: a combined experimental-theoretical study\", Friederike Wrobel, Hyeondeok Shin, George E. Sterbinsky, Haw-Wen Hsiao, Jian-Min Zuo, Panchapakesan Ganesh, Jaron T. Krogel, Anouar Benali, Paul R.C. Kent, Olle Heinonen, and Anand Bhattacharya, Physical Review M (2019).", + "keywords": [ + "simulation", + "experiment", + "oxides", + "QMCPack", + "QMC", + "Monte Carlo", + "XAFS", + "Gaussian process" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "local_structure_v1.1", + "mdf_source_name": "local_structure" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/local_structure_v1.1/", + "version": "1.0", + "root_version": "local_structure_v1.1", + "latest": true + } + }, + { + "source_id": "charge_ice_prappl_2020_v1.1", + "source_name": "charge_ice_prappl_2020", + "version": 1, + "ingest_date": "2020-03-26T15:23:18.595763Z", + "doi": "10.18126/6ndo-w7yk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/charge_ice_prappl_2020_v1.1/", + "metadata": { + "title": "Data for \u201cTailoring spin wave channels in a reconfigurable artificial spin ice\u201d ", + "authors": [ + { + "name": "Iacocca, Ezio", + "given_name": "Ezio", + "family_name": "Iacocca", + "affiliations": [ + "Northumbria University" + ] + }, + { + "name": "Gliga, Sebastian", + "given_name": "Sebastian", + "family_name": "Gliga", + "affiliations": [ + "Paul Scherrer Institute" + ] + }, + { + "name": "Heinonen, Olle G.", + "given_name": "Olle G.", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "Data for \u201cTailoring spin wave channels in a reconfigurable artificial spin ice\u201d, by Ezio Iacocca, Sebastian Gliga, and Olle G. Heinonen, to be published in Physical Review Applied in 2020, also at URL: https://arxiv.org/abs/1911.05354. Data content and organization are described in top level README file\n ", + "keywords": [ + "simulation", + "artificial spin ice", + "magnonic lattices" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "charge_ice_prappl_2020_v1.1", + "mdf_source_name": "charge_ice_prappl_2020" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/charge_ice_prappl_2020_v1.1/", + "version": "1.0", + "root_version": "charge_ice_prappl_2020_v1.1", + "latest": true + } + }, + { + "source_id": "guo_gapml_model_hexrd_v1.1", + "source_name": "guo_gapml_model_hexrd", + "version": 1, + "ingest_date": "2022-06-27T15:08:46.229482Z", + "doi": "10.18126/66pj-gpnr", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/guo_gapml_model_hexrd_v1.1/", + "metadata": { + "title": "GAP-ML model, training and MD dataset for \"A Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by HEXRD\"", + "authors": [ + { + "name": "Guo, Jicheng", + "given_name": "Jicheng", + "family_name": "Guo", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Babuji, Yadu", + "given_name": "Yadu", + "family_name": "Babuji", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Hoyt, Nathaniel", + "given_name": "Nathaniel", + "family_name": "Hoyt", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Williamson, Mark", + "given_name": "Mark", + "family_name": "Williamson", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Jackson, Nicholas", + "given_name": "Nicholas", + "family_name": "Jackson", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Benmore, Chris", + "given_name": "Chris", + "family_name": "Benmore", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sivaraman, Ganesh", + "given_name": "Ganesh", + "family_name": "Sivaraman", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "## Usage Notes \n\n### ML training dataset and potential \n\n1) \"POT/\"\n\n### MD\n1. The full MD trajectory for the thermal conducitivity calculations can be found in \"MD_WAVE_METHOD/\"\n\n2. All the MD trajectories used for structure factor / CN estimation at multiple composition / temperatures can be foudn in \"MD/\". The \".tar.gz\" file with '.extxyz' trajectory is according to the LiCl-KCl molar fraction followed by the temperature in K units.\n\n3. The \"MD/\" folder also the log files for two additional compositions (i.e. 30-70, & 80-20) and can be used to verify the density from volume relaxation. \n\n\n### Misc. Info\n\nMolten LiCl as a system on its own is reported in https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.1c00901 \n\nThe potential provided in this data deposit does not include any pure LiCl melt training data. Hence should be used with caution at ultra low concentrations of LiCl.", + "keywords": [ + "simulation", + "machine learning", + "DFT", + "energy materials", + "SCAN", + "HEXRD", + "MOLTEN SALTS", + "GAP", + "forcefield" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://chemrxiv.org/engage/chemrxiv/article-details/61fb07df32e10e139bd27770", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.26434/chemrxiv-2022-8w9ft", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "guo_gapml_model_hexrd_v1.1", + "mdf_source_name": "guo_gapml_model_hexrd" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/guo_gapml_model_hexrd_v1.1/", + "version": "1.0", + "root_version": "guo_gapml_model_hexrd_v1.1", + "latest": true + } + }, + { + "source_id": "krogel_surrogate_hessian_relax_v1.1", + "source_name": "krogel_surrogate_hessian_relax", + "version": 1, + "ingest_date": "2022-01-10T18:30:00.559946Z", + "doi": "10.18126/082j-dtbv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/krogel_surrogate_hessian_relax_v1.1/", + "metadata": { + "title": "Surrogate Hessian Accelerated Structural Optimization for Stochastic Electronic Structure Theories", + "authors": [ + { + "name": "Tiihonen, Juha", + "given_name": "Juha", + "family_name": "Tiihonen", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Kent, Paul R. C.", + "given_name": "Paul R. C.", + "family_name": "Kent", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + } + ], + "description": "The data in this repository comprise the original runs, including input and output data, for the diffusion Monte Carlo structural relaxation examples given in the publication for the benzene, coronene, and ovalene molecules.\n\nThis repository also includes the original code implementation of the Surrogate Hessian Accelerated Structural Optimization Method that was used to relax these molecules.\n", + "keywords": [ + "simulation", + "molecular structures", + "DFT", + "QMCPack", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1063/5.0079046", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "krogel_surrogate_hessian_relax_v1.1", + "mdf_source_name": "krogel_surrogate_hessian_relax" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/krogel_surrogate_hessian_relax_v1.1/", + "version": "1.0", + "root_version": "krogel_surrogate_hessian_relax_v1.1", + "latest": true + } + }, + { + "source_id": "cencer_cyclic_alkenes_fromp_v1.1", + "source_name": "cencer_cyclic_alkenes_fromp", + "version": 1, + "ingest_date": "2021-12-16T20:43:15.181748Z", + "doi": "10.18126/bpmg-0a78", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cencer_cyclic_alkenes_fromp_v1.1/", + "metadata": { + "title": "Cyclic Alkenes as Candidates for FROMP", + "authors": [ + { + "name": "Cencer, Morgan M.", + "given_name": "Morgan M.", + "family_name": "Cencer", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Moore, Jeffrey S.", + "given_name": "Jeffrey S.", + "family_name": "Moore", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "This dataset consists of data about known FROMP monomers and a variety of candidate monomers, which are all cyclic alkenes. While this data was focused on FROMP monomers, we believe that the list of candidate cyclic alkenes (>11 million SMILES strings) and the ~1800 high theory level (M06/def2tzvp) DFT files may prove useful for other projects.\n\nThe data and code in this collection was created by Morgan Cencer (except where otherwise noted) during doctoral research with Prof. Jeff Moore. More information can be found in chapter 5 of Cencer, M. M. (2022) Computational Tools for Materials Design: Applications in Dynamic \nCovalent Chemistry, Polymers, and Electrochemical Systems (Ph.D. Dissertation) University of Illinois, Urbana-Champaign.", + "keywords": [ + "molecular structures", + "DFT", + "polymers", + "cyclic alkenes", + "FROMP" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cencer_cyclic_alkenes_fromp_v1.1", + "mdf_source_name": "cencer_cyclic_alkenes_fromp" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cencer_cyclic_alkenes_fromp_v1.1/", + "version": "1.0", + "root_version": "cencer_cyclic_alkenes_fromp_v1.1", + "latest": true + } + }, + { + "source_id": "abx3_perovs_alloys_v1.1", + "source_name": "abx3_perovs_alloys", + "version": 1, + "ingest_date": "2022-01-26T23:34:35.637009Z", + "doi": "10.18126/3jn1-o5nk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/abx3_perovs_alloys_v1.1/", + "metadata": { + "title": "High-Throughput DFT Dataset of Halide Perovskite Alloys", + "authors": [ + { + "name": "Mannodi-Kanakkithodi, Arun", + "given_name": "Arun", + "family_name": "Mannodi-Kanakkithodi", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "name": "Chan, Maria K.Y.", + "given_name": "Maria K.Y.", + "family_name": "Chan", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "name": "Yang, Jiaqi", + "given_name": "Jiaqi", + "family_name": "Yang", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "name": "Manganaris, Panayotis", + "given_name": "Panayotis", + "family_name": "Manganaris", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + } + ], + "description": "This dataset contains DFT computations on 550 halide perovskite compounds, 90 of which are pure ABX3 compositions and the remaining involve mixing/alloying at the A, B or X sites. Computation folders include geometry optimization using GGA-PBE and HSE06, optical absorption calculations, vacancy defect calculations, and dielectric constant calculations. Machine learning models were trained using this data to accelerate the prediction of stability and optoelectronic properties of a combinatorial perovskite dataset. One publication is currently under review: https://arxiv.org/abs/2109.10798, and two others are in preparation.", + "keywords": [ + "simulation", + "machine learning", + "semiconductors", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "abx3_perovs_alloys_v1.1", + "mdf_source_name": "abx3_perovs_alloys" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/abx3_perovs_alloys_v1.1/", + "version": "1.0", + "root_version": "abx3_perovs_alloys_v1.1", + "latest": true + } + }, + { + "source_id": "kirill_marcus_screening2_v1.1", + "source_name": "kirill_marcus_screening2", + "version": 1, + "ingest_date": "2022-01-18T19:00:59.453286Z", + "doi": "10.18126/ef9q-qzod", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kirill_marcus_screening2_v1.1/", + "metadata": { + "title": "Supplemental data for \"Computational discovery of high charge mobility self-assembling \u03c0-conjugated peptides\"", + "authors": [ + { + "name": "Shmilovich, Kirill", + "given_name": "Kirill", + "family_name": "Shmilovich", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Yao, Yifan", + "given_name": "Yifan", + "family_name": "Yao", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Tovar, John D.", + "given_name": "John D.", + "family_name": "Tovar", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Katz, Howard E.", + "given_name": "Howard E.", + "family_name": "Katz", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Ferguson, Andrew L.", + "given_name": "Andrew L.", + "family_name": "Ferguson", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "This dataset contains supplemental data related to the molecular dynamics simulations and electronic structure calculations in \"Computational discovery of high charge mobility self-assembling \u03c0-conjugated peptides\" by Kirill Shmilovich, Yifan Yao, John D. Tovar, Howard E. Katz, Andr\u00e9 Schleife, and Andrew L. Ferguson.", + "keywords": [ + "simulation", + "DFT", + "semiconductors", + "high-throughput" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kirill_marcus_screening2_v1.1", + "mdf_source_name": "kirill_marcus_screening2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kirill_marcus_screening2_v1.1/", + "version": "1.0", + "root_version": "kirill_marcus_screening2_v1.1", + "latest": true + } + }, + { + "source_id": "staros_cri3_v1.1", + "source_name": "staros_cri3", + "version": 1, + "ingest_date": "2021-12-20T23:23:23.114793Z", + "doi": "10.18126/51af-op9h", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/staros_cri3_v1.1/", + "metadata": { + "title": "A Combined First Principles Study of the Structural, Magnetic, and Phonon Properties of Monolayer CrI3", + "authors": [ + { + "name": "Staros, Daniel", + "given_name": "Daniel", + "family_name": "Staros", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Hu, Guoxiang", + "given_name": "Guoxiang", + "family_name": "Hu", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Tiihonen, Juha", + "given_name": "Juha", + "family_name": "Tiihonen", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Nanguneri, Ravindra", + "given_name": "Ravindra", + "family_name": "Nanguneri", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Bennett, M. Chandler", + "given_name": "M. Chandler", + "family_name": "Bennett", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Heinonen, Olle G.", + "given_name": "Olle G.", + "family_name": "Heinonen", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Rubenstein, Brenda", + "given_name": "Brenda", + "family_name": "Rubenstein", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + } + ], + "keywords": [ + "2D materials", + "magnetism", + "spin-phonon coupling", + "Diffusion Monte Carlo", + "Density Functional Theory", + "Center for Predictive Simulation of Functional Materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1063/5.0074848", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "staros_cri3_v1.1", + "mdf_source_name": "staros_cri3" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/staros_cri3_v1.1/", + "version": "1.0", + "root_version": "staros_cri3_v1.1", + "latest": true + } + }, + { + "source_id": "sinha_correlative_microscopy_steel_v1.1", + "source_name": "sinha_correlative_microscopy_steel", + "version": 1, + "ingest_date": "2019-08-07T14:43:17.599920Z", + "doi": "10.18126/iv89-3293", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/sinha_correlative_microscopy_steel_v1.1", + "metadata": { + "title": "Correlative Microscopy Data for Quantification of Prior Austenite Grain Size in AF9628 Steel", + "authors": [ + { + "name": "Sinha, V.", + "given_name": "V.", + "family_name": "Sinha", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ] + }, + { + "name": "Gonzales, M.", + "given_name": "M.", + "family_name": "Gonzales", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ] + }, + { + "name": "Payton, E.J.", + "given_name": "E.J.", + "family_name": "Payton", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ] + } + ], + "description": "The files in this dataset are analyzed, discussed, and referenced in these two publications: \n\n1. V. Sinha, M. Gonzales, R.A. Abrahams, B.S. Song, and E.J. Payton, \u201cCorrelative microscopy for quantification of prior austenite grain size in AF9628 steel\u201d, Materials Characterization (Accepted).\n\n2. V. Sinha, M. Gonzales, and E.J. Payton, \u201cDatasets acquired with correlative microscopy method for delineation of prior austenite grain boundaries and characterization of prior austenite grain size in a low-alloy high-performance steel\u201d, Data in Brief (Submitted).\n", + "keywords": [ + "EBSD", + "experiment", + "metals and alloys", + "metals", + "microstructures", + "composites", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sinha_correlative_microscopy_steel_v1.1", + "mdf_source_name": "sinha_correlative_microscopy_steel" + }, + "download_url": "https://data.materialsdatafacility.org/sinha_correlative_microscopy_steel_v1.1", + "version": "1.0", + "root_version": "sinha_correlative_microscopy_steel_v1.1", + "latest": true + } + }, + { + "source_id": "myh20220118snse_v1.1", + "source_name": "myh20220118snse", + "version": 1, + "ingest_date": "2022-01-18T15:09:51.256722Z", + "doi": "10.18126/s0qs-fkgf", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/myh20220118snse_v1.1/", + "metadata": { + "title": "SnSe NRIXS part 1", + "authors": [ + { + "name": "Hu, Michael Y.", + "given_name": "Michael Y.", + "family_name": "Hu", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "NRIXS data and analysis of SnSe lattice dynamics at various temperatures", + "keywords": [ + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1103/PhysRevB.104.184303", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "myh20220118snse_v1.1", + "mdf_source_name": "myh20220118snse" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/myh20220118snse_v1.1/", + "version": "1.0", + "root_version": "myh20220118snse_v1.1", + "latest": true + } + }, + { + "source_id": "mapbx3_defect_v1.1", + "source_name": "mapbx3_defect", + "version": 1, + "ingest_date": "2022-01-26T20:09:23.958525Z", + "doi": "10.18126/vncu-w2so", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mapbx3_defect_v1.1/", + "metadata": { + "title": "High-Throughput Density Functional Theory Dataset of Pb-site Impurities in Hybrid Perovskites", + "authors": [ + { + "name": "Mannodi-Kanakkithodi, Arun", + "given_name": "Arun", + "family_name": "Mannodi-Kanakkithodi", + "affiliations": [ + "Purdue University" + ] + }, + { + "name": "Chan, Maria K.Y.", + "given_name": "Maria K.Y.", + "family_name": "Chan", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "keywords": [ + "simulation", + "machine learning", + "semiconductors", + "high-throughput", + "DFT", + "defects" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1021/acs.chemmater.8b04017", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": " https://doi.org/10.1021/acs.jpcc.0c02486", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mapbx3_defect_v1.1", + "mdf_source_name": "mapbx3_defect" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/mapbx3_defect_v1.1/", + "version": "1.0", + "root_version": "mapbx3_defect_v1.1", + "latest": true + } + }, + { + "source_id": "ravi_braggnn_training_v1.2", + "source_name": "ravi_braggnn_training", + "version": 1, + "ingest_date": "2022-06-25T02:06:38.483041Z", + "doi": "10.18126/iftp-twz1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ravi_braggnn_training_v1.2/", + "metadata": { + "title": "BraggNN: Training Dataset", + "authors": [ + { + "name": "Ravi, Nikil", + "given_name": "Nikil", + "family_name": "Ravi", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Liu, Zhengchun", + "given_name": "Zhengchun", + "family_name": "Liu", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sharma, Hemant", + "given_name": "Hemant", + "family_name": "Sharma", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Chaturvedi, Pranshu", + "given_name": "Pranshu", + "family_name": "Chaturvedi", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Huerta, E.A.", + "given_name": "E.A.", + "family_name": "Huerta", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Scourtas, Aristana", + "given_name": "Aristana", + "family_name": "Scourtas", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "KJ, Schmidt", + "given_name": "Schmidt", + "family_name": "KJ", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Chard, Ryan", + "given_name": "Ryan", + "family_name": "Chard", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "# BraggNN Training Dataset\n\n## Data\nThere are two HDF5 files in the dataset \n\n* The `frames-exp4train.hdf5` contains diffraction frames, stored as a 3D array (dataset name must be \"frames\"). The first dimension is the frame ID starting with 0, i.e., the series of frames at different scanning angle. The second and third dimensions are the height and width of the area detector.\n\n* The file `peaks-exp4train-psz11.hdf5` contains the peak position information, generated using conventional methods (e.g., using MIDAS: https://github.com/marinerhemant/MIDAS). In our work, we used the peak position that we got using 2D psuedo Voigt fitting. This file stores three 1D array with each record / index represent different information of a peak. The first 1D array, must be named as `peak_fidx` represents the index of the frame (in the frames.h5) that the peak sits on; the second array, `peak_row` is the vertical distance, in pixel and can be floating point number, from the peak center to the top edge of the frame. Similarly, the `peak_col` denotes horizental distance, in pixel and can be floating point number, from peak center to left edge of the frame. \n\n* By default, this implementation will use 80% of the samples for training, the rest 20% for online model validation.\n\n## Code\n** Important ** \nTo run the DLHub versions of these models using GPU resources, users must first request access to the following Globus group: https://app.globus.org/groups/d0b13474-c265-11ec-9444-51db4d10f5bd/about\n\nNotebooks are provided in the `code-examples` folder to showcase how to load the datasets and run the PyTorch, TRT, and SambaNova models.", + "keywords": [ + "machine learning", + "microstructures", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1107/S2052252521011258", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ravi_braggnn_training_v1.2", + "mdf_source_name": "ravi_braggnn_training" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ravi_braggnn_training_v1.2/", + "version": "1.0", + "root_version": "ravi_braggnn_training_v1.2", + "latest": true + } + }, + { + "source_id": "ravi_braggnn_validation_v1.1", + "source_name": "ravi_braggnn_validation", + "version": 1, + "ingest_date": "2022-06-24T21:08:28.417449Z", + "doi": "10.18126/hvny-s7ax", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ravi_braggnn_validation_v1.1/", + "metadata": { + "title": "BraggNN: Validation Dataset", + "authors": [ + { + "name": "Ravi, Nikil", + "given_name": "Nikil", + "family_name": "Ravi", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Liu, Zhengchun", + "given_name": "Zhengchun", + "family_name": "Liu", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sharma, Hemant", + "given_name": "Hemant", + "family_name": "Sharma", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Chaturvedi, Pranshu", + "given_name": "Pranshu", + "family_name": "Chaturvedi", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Huerta, E.A.", + "given_name": "E.A.", + "family_name": "Huerta", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Scourtas, Aristana", + "given_name": "Aristana", + "family_name": "Scourtas", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "KJ, Schmidt", + "given_name": "Schmidt", + "family_name": "KJ", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Chard, Ryan", + "given_name": "Ryan", + "family_name": "Chard", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "# BraggNN Validation Dataset\n\nThis dataset contains `13799` samples from the training dataset,\nand is used to evaluate, and provide metrics for, the models\nthat were trained on the larger training dataset. \n\n## Data\nThe data are located in the `/data` folder, and store the collected peaks and frames.\n\nThere are two datasets in the hdf5 file:\n- `Patch`: input to the BraggNN model. It is a 3D array where its first dimension is the index of samples, i.e., 13799. The second and third dimension are the height and width of the patch/peak respectively.\n- `ploc`: the peak location for each of the 13799 peaks, i.e., label of the dataset.\n\n## Code\n\n** Important ** \nTo run the DLHub versions of these models using GPU resources, users must first request access to the following Globus group: https://app.globus.org/groups/d0b13474-c265-11ec-9444-51db4d10f5bd/about\n\nNotebooks are provided in the `code-examples` folder to showcase how to load the datasets and run the PyTorch, TRT, and SambaNova models.", + "keywords": [ + "machine learning", + "microstructures", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1107/S2052252521011258", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ravi_braggnn_validation_v1.1", + "mdf_source_name": "ravi_braggnn_validation" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ravi_braggnn_validation_v1.1/", + "version": "1.0", + "root_version": "ravi_braggnn_validation_v1.1", + "latest": true + } + }, + { + "source_id": "butera_sem_images_v1.1", + "source_name": "butera_sem_images", + "version": 1, + "ingest_date": "2022-08-24T18:58:42.781529Z", + "doi": "10.18126/d1bg-nwtg", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/butera_sem_images_v1.1/", + "metadata": { + "title": "Data: Sucrose-mediated formation and adhesion strength of Streptococcus mutans biofilms on titanium", + "authors": [ + { + "name": "Butera, Tony", + "given_name": "Tony", + "family_name": "Butera", + "affiliations": [ + "University of Kentucky" + ] + }, + { + "name": "Waldman, Laura J.", + "given_name": "Laura J.", + "family_name": "Waldman", + "affiliations": [ + "University of Kentucky" + ] + }, + { + "name": "Grady, Martha E.", + "given_name": "Martha E.", + "family_name": "Grady", + "affiliations": [ + "University of Kentucky" + ] + } + ], + "description": "Bacterial biofilms associated with implants remain a significant source of infections in dental, implant, and other healthcare industries due to challenges in biofilm removal. Biofilms consist of bacterial cells surrounded by a matrix of extracellular polymeric substance (EPS), which protects the colony from many countermeasures, including antibiotic treatments. Biofilm EPS composition is also affected by environmental factors. In the oral cavity, the presence of sucrose affects the growth of Streptococcus mutans that produce acids, eroding enamel and forming dental caries. Biofilm formation on dental implants commonly leads to severe infections and failure of the implant. This work determines the effect of sucrose concentration on biofilm EPS formation and adhesion of Streptococcus mutans, a common oral colonizer. Bacterial biofilms are grown with varying concentrations of sucrose on titanium substrates simulating dental implant material. Strategies for measuring adhesion for films such as peel tests are inadequate for biofilms, which have low cohesive strength and will fall apart when tensile loading is applied directly. The laser spallation technique is used to apply a stress wave loading to the biofilm, causing the biofilm to delaminate at a critical tensile stress threshold. Biofilm formation and EPS structures are visualized at high magnification with scanning electron microscopy (SEM). Sucrose enhanced the EPS production of S. mutans biofilms and increased the adhesion strength to titanium, the most prevalent dental implant material. However, there exists a wide range of sucrose concentrations that are conducive for robust formation and adhesion of S. mutans biofilms on implant surfaces.", + "keywords": [ + "biomaterials", + "biofilms", + "laser spallation", + "scanning electron microscopy", + "adhesion", + "dental implants", + "streptococcus mutans", + "sucrose", + "titanium", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "butera_sem_images_v1.1", + "mdf_source_name": "butera_sem_images" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/butera_sem_images_v1.1/", + "version": "1.0", + "root_version": "butera_sem_images_v1.1", + "latest": true + } + }, + { + "source_id": "tekawade_realtime_porosity_tomography_v1.1", + "source_name": "tekawade_realtime_porosity_tomography", + "version": 1, + "ingest_date": "2022-08-30T19:57:46.270231Z", + "doi": "10.18126/c54t-4ceh", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tekawade_realtime_porosity_tomography_v1.1/", + "metadata": { + "title": "Real-time Porosity Mapping and Visualization for Synchrotron Tomography", + "authors": [ + { + "name": "Tekawade, Aniket", + "given_name": "Aniket", + "family_name": "Tekawade", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Nikitin, Viktor", + "given_name": "Viktor", + "family_name": "Nikitin", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Satapathy, Yashas", + "given_name": "Yashas", + "family_name": "Satapathy", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Liu, Zhengchun", + "given_name": "Zhengchun", + "family_name": "Liu", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Zhang, Xuan", + "given_name": "Xuan", + "family_name": "Zhang", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "De Carlo, Francesco", + "given_name": "Francesco", + "family_name": "De Carlo", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Kettimuthu, Rajkumar", + "given_name": "Rajkumar", + "family_name": "Kettimuthu", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + } + ], + "description": "This data is of a 3d-printed steel cylindrical specimen (nominal diameter 6-millimeter) scanned under monochromatic hard X-ray. The mosaic comprised of 3 horizontal and 6 vertical positions (total 18 scans). The field-of-view for each scan was approximately 2.2 mm wide and 1.4 mm tall with 1.17 micrometer voxel size. With an exposure time of 0.13 milliseconds for 3000 projections, scanning this mosaic took ~2 hours not counting instrument-specific overheads in moving motors to reposition the sample between scans. This resulted in 3000 projections over 180 degrees sample rotation with raw image size 4096 x 4096 (100 gigabytes of raw data at 16-bit precision) that would reconstruct to a 3D volume with 4096^3 voxels. Two versions of the raw-data are shared. The original data (referred to as {4k} volume) with 1.17 micrometer pixel size (3000 projections) and a binned version ({2k} volume) with 2.34 micrometer pixel size (1500 projections).", + "keywords": [ + "experiment", + "machine learning", + "microstructures", + "microscopy", + "tomography", + "CT", + "convolutional neural networks", + "real-time analysis" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "tekawade_realtime_porosity_tomography_v1.1", + "mdf_source_name": "tekawade_realtime_porosity_tomography" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/tekawade_realtime_porosity_tomography_v1.1/", + "version": "1.0", + "root_version": "tekawade_realtime_porosity_tomography_v1.1", + "latest": true + } + }, + { + "source_id": "kononov_firstprinciples_simulation_graphene_v1.1", + "source_name": "kononov_firstprinciples_simulation_graphene", + "version": 1, + "ingest_date": "2022-08-23T18:45:59.776918Z", + "doi": "10.18126/kkid-wa9u", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kononov_firstprinciples_simulation_graphene_v1.1/", + "metadata": { + "title": "First-principles simulation of light-ion microscopy of graphene", + "authors": [ + { + "name": "Kononov, Alina", + "given_name": "Alina", + "family_name": "Kononov", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Olmstead, Alexandra", + "given_name": "Alexandra", + "family_name": "Olmstead", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Baczewski, Andrew D.", + "given_name": "Andrew D.", + "family_name": "Baczewski", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "TDDFT simulations of light-ion irradiated graphene performed with Qbox/Qb@ll (https://github.com/LLNL/qball) ", + "keywords": [ + "simulation", + "microscopy", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/xnxt-wn6i", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kononov_firstprinciples_simulation_graphene_v1.1", + "mdf_source_name": "kononov_firstprinciples_simulation_graphene" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kononov_firstprinciples_simulation_graphene_v1.1/", + "version": "1.0", + "root_version": "kononov_firstprinciples_simulation_graphene_v1.1", + "latest": true + } + }, + { + "source_id": "gladier_v1.1", + "source_name": "gladier", + "version": 1, + "ingest_date": "2022-09-02T15:57:54.653159Z", + "doi": "10.18126/17yw-eeht", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gladier_v1.1/", + "metadata": { + "title": "Gladier - Linking Scientific Instruments and Computation - Example Data", + "authors": [ + { + "name": "Vescovi, Rafael", + "given_name": "Rafael", + "family_name": "Vescovi", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Chard, Ryan", + "given_name": "Ryan", + "family_name": "Chard", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Saint, Nickolaus", + "given_name": "Nickolaus", + "family_name": "Saint", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Pruyne, Jim", + "given_name": "Jim", + "family_name": "Pruyne", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Bicer, Tekin", + "given_name": "Tekin", + "family_name": "Bicer", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Lavens, Alex", + "given_name": "Alex", + "family_name": "Lavens", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Liu, Zhengchun", + "given_name": "Zhengchun", + "family_name": "Liu", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Papka, Michael E.", + "given_name": "Michael E.", + "family_name": "Papka", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Narayanan, Suresh", + "given_name": "Suresh", + "family_name": "Narayanan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Schwarz, Nicholas", + "given_name": "Nicholas", + "family_name": "Schwarz", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Chard, Kyle", + "given_name": "Kyle", + "family_name": "Chard", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ] + } + ], + "description": "This dataset includes test data to reproduce and build upon the results in the paper \"Linking Scientific Instruments and Computation:\nPatterns, Technologies, Experiences\" to appear. The dataset includes example datasets from four use cases High Energy Diffraction Microscopy (HEDM), BraggNN, Ptychography, and Serial X-ray Crystallography. ", + "keywords": [ + "experiment", + "machine learning", + "high-throughput", + "automation", + "beamline", + "facility" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "gladier_v1.1", + "mdf_source_name": "gladier" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/gladier_v1.1/", + "version": "1.0", + "root_version": "gladier_v1.1", + "latest": true + } + }, + { + "source_id": "shark_v1.1", + "source_name": "shark", + "version": 1, + "ingest_date": "2022-09-02T19:18:55.416738Z", + "doi": "10.18126/snb5-r5pf", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shark_v1.1/", + "metadata": { + "title": "Dataset for \"Bioapatite in shark centra studied by wide angle and by small angle x-ray scattering\"", + "authors": [ + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "Almer, Jonathan", + "given_name": "Jonathan", + "family_name": "Almer", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "James, Kelsey C.", + "given_name": "Kelsey C.", + "family_name": "James", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "Natanson, Lisa J.", + "given_name": "Lisa J.", + "family_name": "Natanson", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "Stock, Stuart", + "given_name": "Stuart", + "family_name": "Stock", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + } + ], + "description": "This is the raw WAXS and SAXS patterns from the Advanced Photon Source 1-ID beamline for the paper titled \"Bioapatite in shark centra studied by wide angle and by small angle x-ray scattering\" published in Journal of the Royal Society Interface. The WAXS patterns were acquired using the HYDRA array with only GE2 panel active. The SAXS patterns were acquired using the Pixirad detector. The instrument parameters are available as GSAS-2 imctrl file. Also a set of Matlab m-files used for data processing is included.", + "keywords": [ + "biomaterials", + "microstructures", + "SAXS", + "WAXS", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "shark_v1.1", + "mdf_source_name": "shark" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/shark_v1.1/", + "version": "1.0", + "root_version": "shark_v1.1", + "latest": true + } + }, + { + "source_id": "yager_pathway_priming_sem_v1.2", + "source_name": "yager_pathway_priming_sem", + "version": 1, + "ingest_date": "2022-08-26T17:45:57.671893Z", + "doi": "10.18126/hx77-iub1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/yager_pathway_priming_sem_v1.2/", + "metadata": { + "title": "Scanning Electron Microscopy (SEM) images of Pathway-primed Layered Block Copolymer Thin Films", + "authors": [ + { + "name": "Yager, Kevin G.", + "given_name": "Kevin G.", + "family_name": "Yager", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Russell, Sebastian T.", + "given_name": "Sebastian T.", + "family_name": "Russell", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "description": "Scanning electron microscopy (SEM) images (top-view and perspective view) of a variety of block copolymer (BCP) thin films. The films were formed by layering different BCP types and thermally annealing (at various temperatures and annealing times). The SEM images provide information about the nanostructure that forms as a result of the particular preparation/annealing history.", + "keywords": [ + "experiment", + "polymers", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "yager_pathway_priming_sem_v1.2", + "mdf_source_name": "yager_pathway_priming_sem" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/yager_pathway_priming_sem_v1.2/", + "version": "1.0", + "root_version": "yager_pathway_priming_sem_v1.2", + "latest": true + } + }, + { + "source_id": "qmc_ml_v1.1", + "source_name": "qmc_ml", + "version": 1, + "ingest_date": "2022-07-28T22:59:33.901784Z", + "doi": "10.18126/hxlp-v732", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/qmc_ml_v1.1/", + "metadata": { + "title": "Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning", + "authors": [ + { + "name": "Huang, Bing", + "given_name": "Bing", + "family_name": "Huang", + "affiliations": [ + "Argonne National Laboratory", + "University of Toronto", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "von Lilienfeld, O. Anatole", + "given_name": "O. Anatole", + "family_name": "von Lilienfeld", + "affiliations": [ + "Argonne National Laboratory", + "University of Toronto", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Argonne National Laboratory", + "University of Toronto", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "University of Toronto", + "Oak Ridge National Laboratory" + ] + } + ], + "keywords": [ + "simulation", + "machine learning", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "qmc_ml_v1.1", + "mdf_source_name": "qmc_ml" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/qmc_ml_v1.1/", + "version": "1.0", + "root_version": "qmc_ml_v1.1", + "latest": true + } + }, + { + "source_id": "semiconductor_defectlevels_v1.1", + "source_name": "semiconductor_defectlevels", + "version": 1, + "ingest_date": "2022-07-19T19:48:15.925895Z", + "doi": "10.18126/ite0-3iah", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/semiconductor_defectlevels_v1.1/", + "metadata": { + "title": "Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets", + "authors": [ + { + "name": "Polak, Maciej P.", + "given_name": "Maciej P.", + "family_name": "Polak", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Jacobs, Ryan", + "given_name": "Ryan", + "family_name": "Jacobs", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Mannodi-Kanakkithodi, Arun", + "given_name": "Arun", + "family_name": "Mannodi-Kanakkithodi", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Chan, Maria K. Y.", + "given_name": "Maria K. Y.", + "family_name": "Chan", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Dataset containing DFT-calculated defect charge state transition levels of 2910 semiconductor-impurity pairs", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "semiconductor_defectlevels_v1.1", + "mdf_source_name": "semiconductor_defectlevels" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_semiconductor_defectlevels", + "n_items": 2910, + "splits": [ + { + "type": "train", + "path": "Dataset_semiconductor_defectlevels_v1.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ 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"10.18126/7yg1-osf2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/voyles_mdf_dmref_glasses_v2.2/", + "metadata": { + "title": "Metallic Glasses and their Properties", + "authors": [ + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles" + }, + { + "name": "Schultz, Lane E.", + "given_name": "Lane E.", + "family_name": "Schultz" + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan" + }, + { + "name": "Francis, Carter", + "given_name": "Carter", + "family_name": "Francis" + }, + { + "name": "Afflerbach, Benjamin", + "given_name": "Benjamin", + "family_name": "Afflerbach" + }, + { + "name": "Hakeem, Abdulrhman", + "given_name": "Abdulrhman", + "family_name": "Hakeem" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + 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In atomic percentage" + }, + { + "name": "DOI", + "role": "input", + "units": "None", + "description": "The DOI source which reported the glass. " + }, + { + "name": "Tg_[K]", + "role": "input", + "units": "K", + "description": "The reported glass transition in for the glass. " + }, + { + "name": "Tx_[K]", + "role": "input", + "units": "K", + "description": "The reported crystallization temperature for the glass. If multiple values are reported the lowest value is reported" + }, + { + "name": "Tl_[K]", + "role": "input", + "units": "K", + "description": "The Liquidus temperature for the glass." + }, + { + "name": "Dmax_[mm]", + "role": "input", + "units": "mm", + "description": "The maximum rod size formed which is fully amorphous using a suction casting apparatus" + }, + { + "name": "Zmax_[mm]", + "role": "input", + "units": "mm", + "description": "The maximum thickness which is fully amorphous using a wedge casting apparatus" + }, + { + "name": "E_[GPA]", + "role": "input", + "units": "GPA", + "description": "The Youngs Modulus" + }, + { + "name": "Tm_[K]", + "role": "input", + "units": "K", + "description": "The melting temperature in degrees kelvin" + }, + { + "name": "Sigma_[Mpa]", + "role": "input", + "units": "Mpa", + "description": "The yield strength for the glass" + }, + { + "name": "Possion_Ratio", + "role": "input", + "units": "Ratio", + "description": "Possions Ratio for the glass" 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"input", + "units": "Measure", + "description": "What was measured for the sample [ Thickness, width x thickness etc]" + }, + { + "name": "Size1", + "role": "input", + "units": "None", + "description": "The first size measured" + }, + { + "name": "Size2", + "role": "input", + "units": "None", + "description": "The second size measured (if applicable)" + }, + { + "name": "Environment", + "role": "input", + "units": "None", + "description": "What the gas enviroment was when casting" + }, + { + "name": "Characterization", + "role": "input", + "units": "None", + "description": "What characterization was applied to the glass" + }, + { + "name": "Melting", + "role": "input", + "units": "None", + "description": "How the glass was melted" + }, + { + "name": "density_[g/cm^3]", + "role": "input", + "units": "g/cm^3", + "description": "The density of the glass" + }, + { + "name": "Acoustic_Velocity_Long_[km/s]", + "role": "input", + "units": "None", + "description": "The longitudinal velocity" + }, + { + "name": "Acoustic_Velocity_Trans_[km/s]", + "role": "input", + "units": "None", + "description": "The transverse velocity" + }, + { + "name": "K_[GPa]", + "role": "input", + "units": "GPa", + "description": "Bulk Modulus" + }, + { + "name": "E/density_[GPacm^3/g]", + "role": "input", + "units": "GPacm^3/g", + "description": "The specific Young\u2019s Modulus" + }, + { + "name": "Debye_Temp_[K]", + "role": "input", + "units": "None", + "description": "The debye temperature " + }, + { + "name": "Yield_Strength_[Gpa]", + "role": "input", + "units": "None", + "description": "The yield strength for the glass" + }, + { + "name": "Kc_[Mpa m^05]", + "role": "input", + "units": "Mpa m^05", + "description": "The fracture toughness for the glass" + }, + { + "name": "Gbas_[Gpa]", + "role": "input", + "units": "Gpa", + "description": "shear modulus (For a bulk sample)" + }, + { + "name": "Kbas_[Gpa]", + "role": "input", + "units": "Gpa", + "description": "Bulk modulus (bulk sample)" + }, + { + "name": "Vbas", + "role": "input", + "units": "None", + "description": "Possion Ratio (bulk sample)" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/voyles_mdf_dmref_glasses_v2.2/", + "version": "2.0", + "root_version": "voyles_mdf_dmref_glasses", + "latest": true + } + }, + { + "source_id": "steel_strength_v1.1", + "source_name": "steel_strength", + "version": 1, + "ingest_date": "2022-07-18T19:04:21.825566Z", + "doi": "10.18126/524z-vd6m", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/steel_strength_v1.1/", + "metadata": { + "title": "Mechanical properties of some steels", + "authors": [ + { + "name": "Conduit, Gareth", + "given_name": "Gareth", + "family_name": "Conduit", + "affiliations": [ + "Cambridge University and Intellegens" + ] + } + ], + "description": "Dataset containing compositions and mechanical properties (yield strength, tensile strength, elongation) of 312 steel alloys", + "keywords": [ + "machine 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"units": "MPa", + "description": "yield strength in MPa" + }, + { + "name": "tensile strength", + "role": "target", + "units": "MPa", + "description": "tensile strength in MPa" + }, + { + "name": "elongation", + "role": "target", + "units": "%", + "description": "elongation at fracture" + }, + { + "name": "cr_amount", + "role": "input", + "units": "", + "description": "categorization of Cr amount" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/steel_strength_v1.1/", + "version": "1.0", + "root_version": "steel_strength_v1.1", + "latest": true + } + }, + { + "source_id": "elwood_md_v1.2", + "source_name": "elwood_md", + "version": 1, + "ingest_date": "2022-08-03T20:42:31.089986Z", + "doi": "10.18126/8p6m-e135", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/elwood_md_v1.2/", + "metadata": { + "title": "Project Elwood: MD Simulated Monomer Properties", + "authors": [ + { + "name": "Schneider, L", + "given_name": "L", + "family_name": "Schneider" + }, + { + "name": "Schwarting, M", + "given_name": "M", + "family_name": "Schwarting" + }, + { + "name": "Mysona, J", + "given_name": "J", + "family_name": "Mysona" + }, + { + "name": "Liang, H", + "given_name": "H", + "family_name": "Liang" + }, + { + "name": "Han, M", + "given_name": "M", + "family_name": "Han" + }, + { + "name": "Rauscher, P", + "given_name": "P", + "family_name": "Rauscher" + }, + { + "name": "Ting, J", + "given_name": "J", + "family_name": "Ting" + }, + { + "name": "Venkatram, S", + "given_name": "S", + "family_name": "Venkatram" + }, + { + "name": "Ross, R", + "given_name": "R", + "family_name": "Ross" + }, + { + "name": "Schmidt, K", + "given_name": "K", + "family_name": "Schmidt" + }, + { + "name": "Blaiszik, B", + "given_name": "B", + "family_name": "Blaiszik" + }, + { + "name": "Foster, I", + "given_name": "I", + "family_name": "Foster" + }, + { + "name": "de Pablo, J", + "given_name": "J", + "family_name": "de Pablo" + } + ], + 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"Angstrom^2", + "description": "Simulated squared radius of gyration (in Angstroms^2)" + }, + { + "name": "Densities (kg/m^3)", + "role": "target", + "units": "kg/m^3", + "description": "Simulated density (in kg/m^3)" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/elwood_md_v1.2/", + "version": "1.0", + "root_version": "elwood_md_v1.2", + "latest": true + } + }, + { + "source_id": "foundry_moses_v1.1", + "source_name": "foundry_moses", + "version": 1, + "ingest_date": "2022-06-30T19:45:20.965384Z", + "doi": "10.18126/rp13-3k3h", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/foundry_moses_v1.1/", + "metadata": { + "title": "Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models", + "authors": [ + { + "name": "Polykovskiy, Daniil", + "given_name": "Daniil", + "family_name": "Polykovskiy" + }, + { + "name": "Zhebrak, Alexander", + "given_name": "Alexander", + "family_name": "Zhebrak" + }, + { + "name": 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"family_name": "Chen" + }, + { + "name": "Nikolenko, Sergey", + "given_name": "Sergey", + "family_name": "Nikolenko" + }, + { + "name": "Aspuru-Guzik, Alan", + "given_name": "Alan", + "family_name": "Aspuru-Guzik" + }, + { + "name": "Zhavoronkov, Alex", + "given_name": "Alex", + "family_name": "Zhavoronkov" + } + ], + "keywords": [ + "machine learning", + "foundry", + "molecules", + "materials", + "moses" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "foundry_moses_v1.1", + "mdf_source_name": "foundry_moses" + }, + "ml": { + "data_format": "tabular", + "short_name": "moses", + "n_items": 1936962, + "splits": [ + { + "type": "train", + "path": "MOS-search.tsv", + "label": "data", + "n_items": null + } + ], + "keys": [ + { + "name": "inchi", + "role": "input", + "units": null, + "description": "International 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containing experimentally measured thermal conductivities for 872 materials", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "citrine_thermalconductivity_v1.2", + "mdf_source_name": "citrine_thermalconductivity" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_citrine_thermalconductivity", + "keys": [ + { + "name": "formula", + "role": "input", + "units": "", + "description": "Material composition" + }, + { + "name": "k_expt", + "role": "target", + "units": "W/m-K", + "description": "Experimental thermal conductivity" + }, + { + "name": "log(k)", + "role": "target", + "units": "W/m-K", + "description": "Log10 value of k_expt" + }, + { + "name": "k-units", + "role": "input", + "units": "", + "description": "Unit information for k_expt" + }, + { + "name": "k_condition", + "role": "input", + "units": "K", + "description": "Temperature information for k_expt measurements" + }, + { + "name": "k_condition_units", + "role": "input", + "units": "", + "description": "Unit information for k_condition" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/citrine_thermalconductivity_v1.2/", + "version": "1.0", + "root_version": "citrine_thermalconductivity_v1.2", + "latest": true + } + }, + { + "source_id": "training_locating_atoms_stem_images_v1.2", + "source_name": "training_locating_atoms_stem_images", + "version": 1, + "ingest_date": "2022-05-17T17:50:12.983239Z", + "doi": "10.18126/qsdl-aj6x", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/training_locating_atoms_stem_images_v1.2/", + "metadata": { + "title": "Training Dataset for Locating Atoms in STEM images ", + "authors": [ + { + "name": "Wei, Jingrui", + "given_name": "Jingrui", + "family_name": "Wei" + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik" + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan" + }, + { + "name": "Voyles, Paul M", + "given_name": "Paul M", + "family_name": "Voyles" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "training_locating_atoms_stem_images_v1.2", + "mdf_source_name": "training_locating_atoms_stem_images" + }, + "ml": { + "data_format": "hdf5", + "short_name": "wei_atom_locating_train", + "n_items": 1495, + "splits": [ + { + "type": "train", + "path": "AtomaiTraining.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "imgs", + "role": "input", + "units": null, + "description": "input, unlabeled images, covering 5 different crystal lattices (SrTiO3[100], SrTiO3[110], WS2[0001], Si[110], and DyScO3[110]) and various image quality" + }, + { + "name": "labels", + "role": "target", + "units": null, + "description": "label, binary mask at atomic column positions" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/training_locating_atoms_stem_images_v1.2/", + "version": "1.0", + "root_version": "training_locating_atoms_stem_images_v1.2", + "latest": true + } + }, + { + "source_id": "anharmonicity_cipse_sivadas2021_v1.2", + "source_name": "anharmonicity_cipse_sivadas2021", + "version": 1, + "ingest_date": "2021-12-22T02:15:42.717496Z", + "doi": "10.18126/scyh-sden", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/anharmonicity_cipse_sivadas2021_v1.2/", + "metadata": { + "title": "Anharmonic stabilization of ferrielectricity in CuInP2Se6", + "authors": [ + { + "name": "Sivadas, Nikhil", + "given_name": "Nikhil", + "family_name": "Sivadas", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Doak, Peter", + "given_name": "Peter", + "family_name": "Doak", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ganesh, P.", + "given_name": "P.", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + } + ], + "description": "The folder contains the input files (both for VASP and the structure files to study mode-decomposed distortions) as well as some sample post-processing files to generate the total-energy profile for anharmonic systems using CuInP2Se6 as an example. More details about our work can be found in https://arxiv.org/abs/2106.08783.", + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "anharmonicity_cipse_sivadas2021_v1.2", + "mdf_source_name": "anharmonicity_cipse_sivadas2021" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/anharmonicity_cipse_sivadas2021_v1.2/", + "version": "1.0", + "root_version": "anharmonicity_cipse_sivadas2021_v1.2", + "latest": true + } + }, + { + "source_id": "ge_nanoparticles_v1.1", + "source_name": "ge_nanoparticles", + "version": 1, + "ingest_date": "2018-11-06T17:25:23.949096Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ge_nanoparticles_v1/", + "metadata": { + "title": "Pressure-induced amorphisation and a new high density amorphous metallic phase in matrix-free Ge nanoparticles: simulation data", + "authors": [ + { + "name": "Corsini, Niccolo", + "given_name": "Niccolo", + "family_name": "Corsini", + "affiliations": [ + "Imperial College London" + ] + } + ], + "description": "Over the last two decades, it has been demonstrated that size effects have significant consequences for the atomic arrangements and phase behavior of matter under extreme pressure. Furthermore, it has been shown that an understanding of how size affects critical pressure\u2013temperature conditions provides vital guidance in the search for materials with novel properties. Here, we report on the remarkable behavior of small (under \u223c5 nm) matrix-free Ge nanoparticles under hydrostatic compression that is drastically different from both larger nanoparticles and bulk Ge. 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point-based data fusion of non-synchronized in-situ and ex-situ data in laser powder bed fusion", + "authors": [ + { + "name": "Kozjek, Dominik", + "given_name": "Dominik", + "family_name": "Kozjek", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Porter, Conor", + "given_name": "Conor", + "family_name": "Porter", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Carter, Fred M.", + "given_name": "Fred M.", + "family_name": "Carter", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Bhattad, Pradeep", + "given_name": "Pradeep", + "family_name": "Bhattad", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Brackman, Paul", + "given_name": "Paul", + "family_name": "Brackman", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Lisovich, Aleksandr", + "given_name": "Aleksandr", + "family_name": "Lisovich", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Mogonye, Jon-Erik", + "given_name": "Jon-Erik", + "family_name": "Mogonye", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Cao, Jian", + "given_name": "Jian", + "family_name": "Cao", + "affiliations": [ + "Northwestern University", + "DMG MORI Advanced Solutions Inc.", + "Carl Zeiss Industrial Metrology", + "US Army DEVCOM Army Research Laboratory" + ] + } + ], + "description": "A description of the dataset can be found in the following paper:\nKozjek, D., Porter, C., Carter, F. M. III, Bhattad P., Brackman, P., Lisovich, A., Mogonye, J.-E., Iterative closest point-based data fusion of non-synchronized in-situ and ex-situ data in laser powder bed fusion, Journal of Manufacturing Systems, Volume 66, 2023, Pages 179-199.\nLink: https://www.sciencedirect.com/science/article/pii/S0278612522002266", + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.jmsy.2022.12.007", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kozjek_iterative_closest_fusion_v1.1", + "mdf_source_name": "kozjek_iterative_closest_fusion" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kozjek_iterative_closest_fusion_v1.1/", + "version": "1.0", + "root_version": "kozjek_iterative_closest_fusion_v1.1", + "latest": true + } + }, + { + "source_id": "ocelotml_2d_v1.2", + "source_name": "ocelotml_2d", + "version": 1, + "ingest_date": "2022-12-15T21:05:27.410102Z", + "doi": "10.18126/3ilu-3f69", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ocelotml_2d_v1.2/", + "metadata": { + "title": "Electronic, redox, and optical property prediction of organic \u03c0-conjugated molecules through a hierarchy of machine learning approaches", + "authors": [ + { + "name": "Bhat, Vinayak", + "given_name": "Vinayak", + "family_name": "Bhat", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + }, + { + "name": "Sornberger, Parker", + "given_name": "Parker", + "family_name": "Sornberger", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + }, + { + "name": "Pokuri, Balaji Sesha Sarath", + "given_name": "Balaji Sesha Sarath", + "family_name": "Pokuri", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + }, + { + "name": "Duke, Rebekah", + "given_name": "Rebekah", + "family_name": "Duke", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + }, + { + "name": "Ganapathysubramanian, Baskar", + "given_name": "Baskar", + "family_name": "Ganapathysubramanian", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + }, + { + "name": "Risko, Chad", + "given_name": "Chad", + "family_name": "Risko", + "affiliations": [ + "University of Kentucky", + "Iowa State University" + ] + } + ], + "description": "Trained machine learning models on the [ocelot_chromophore_v1 dataset](https://acdc.alcf.anl.gov/mdf/detail/ocelot_chromophore_v1_v1.1/).", + "keywords": [ + "machine learning", + "molecular structures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1039/D2SC04676H", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.18126/iqex-p27w", + "identifier_type": "DOI", + 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"given_name": "Arash", + "family_name": "Rabbani", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + }, + { + "name": "Russell, Kaitlyn P.", + "given_name": "Kaitlyn P.", + "family_name": "Russell", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + }, + { + "name": "Zydney, Andrew L.", + "given_name": "Andrew L.", + "family_name": "Zydney", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + }, + { + "name": "Gomez, Enrique D.", + "given_name": "Enrique D.", + "family_name": "Gomez", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + }, + { + "name": "Hattrick-Simpers, Jason", + "given_name": "Jason", + "family_name": "Hattrick-Simpers", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + }, + { + "name": "Werber, Jay R.", + "given_name": "Jay R.", + "family_name": "Werber", + "affiliations": [ + "University of Toronto", + "University of Leeds", + "Pennsylvania State University" + ] + } + ], + "description": "FIB-SEM data set of the Viresolve\u00ae Pro ", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chamani_fibsem_set_reconstruction_v1.2", + "mdf_source_name": "chamani_fibsem_set_reconstruction" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chamani_fibsem_set_reconstruction_v1.2/", + "version": "1.0", + "root_version": "chamani_fibsem_set_reconstruction_v1.2", + "latest": true + } + }, + { + "source_id": "shi_neutron_diffraction_glasses_v1.3", + "source_name": "shi_neutron_diffraction_glasses", + "version": 1, + "ingest_date": "2022-12-17T15:34:39.586464Z", + "doi": "10.18126/2ntz-4tqe", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shi_neutron_diffraction_glasses_v1.3/", + "metadata": { + "title": "Neutron diffraction structure factors of silicate glasses", + "authors": [ + { + "name": "Shi, Ying", + "given_name": "Ying", + "family_name": "Shi", + "affiliations": [ + "Corning Inc." + ] + }, + { + "name": "Neuefeind, J\u00f6rg", + "given_name": "J\u00f6rg", + "family_name": "Neuefeind", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bauchy, Mathieu", + "given_name": "Mathieu", + "family_name": "Bauchy", + "affiliations": [ + "University of California, Los Angeles" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "shi_neutron_diffraction_glasses_v1.3", + "mdf_source_name": "shi_neutron_diffraction_glasses" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/shi_neutron_diffraction_glasses_v1.3/", + "version": "1.0", + "root_version": "shi_neutron_diffraction_glasses_v1.3", + "latest": true + } + }, + { + "source_id": "foundry_g4mp2_solvation_v1.2", + "source_name": "foundry_g4mp2_solvation", + "version": 1, + "ingest_date": "2022-01-24T17:46:06.787200Z", + "doi": "10.18126/jos5-wj65", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/foundry_g4mp2_solvation_v1.2/", + "metadata": { + "title": "DFT Estimates of Solvation Energy in Multiple Solvents", + "authors": [ + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Dandu, Naveen", + "given_name": "Naveen", + "family_name": "Dandu", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Narayanan, Badri", + "given_name": "Badri", + "family_name": "Narayanan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Assary, Rajeev S.", + "given_name": "Rajeev S.", + "family_name": "Assary", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Redfern, Paul C.", + "given_name": "Paul C.", + "family_name": "Redfern", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "name": "Curtiss, Larry A.", + "given_name": "Larry A.", + "family_name": "Curtiss", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "foundry_g4mp2_solvation_v1.2", + "mdf_source_name": "foundry_g4mp2_solvation" + }, + "ml": { + "data_format": "tabular", + "short_name": "g4mp2_solvation", + "n_items": 130258, + "splits": [ + { + "type": "train", + "path": "g4mp2_data.json", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "smiles_0", + "role": "input", + "units": "", + "description": "Input SMILES string" + }, + { + "name": "smiles_1", + "role": "input", + "units": "", + "description": "SMILES string 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"units": "Ha", + "description": "B3LYP Energy of HOMO" + }, + { + "name": "lumo", + "role": "input", + "units": "Ha", + "description": "B3LYP Energy of LUMO" + }, + { + "name": "zpe", + "role": "input", + "units": "Ha", + "description": "B3LYP Zero point vibrational energy" + }, + { + "name": "u0", + "role": "input", + "units": "Ha", + "description": "B3LYP Internal energy at 0K" + }, + { + "name": "u", + "role": "input", + "units": "Ha", + "description": "B3LYP Internal energy at 298.15K" + }, + { + "name": "h", + "role": "input", + "units": "Ha", + "description": "B3LYP Enthalpy at 298.15K" + }, + { + "name": "u0_atom", + "role": "input", + "units": "Ha", + "description": "B3LYP atomization energy at 0K" + }, + { + "name": "g", + "role": "input", + "units": "Ha", + "description": "B3LYP Free energy at 298.15K" + }, + { + "name": "g4mp2_0k", + "role": "target", + "units": "Ha", + "description": "G4MP2 Internal energy at 0K" + }, + { + "name": "g4mp2_energy", + "role": "target", + "units": "Ha", + "description": "G4MP2 Internal energy at 298.15K" + }, + { + "name": "g4mp2_enthalpy", + "role": "target", + "units": "Ha", + "description": "G4MP2 Enthalpy at 298.15K" + }, + { + "name": "g4mp2_free", + "role": "target", + "units": "Ha", + "description": "G4MP2 Free eergy at 0K" + }, + { + "name": "g4mp2_atom", + "role": "target", + "units": "Ha", + "description": "G4MP2 atomization energy at 0K" + }, + { + "name": "sol_acetone", + "role": "target", + "units": "kcal/mol", + "description": "Solvation energy, acetone" + }, + { + "name": "sol_acn", + "role": "target", + "units": "kcal/mol", + "description": "Solvation energy, acetonitrile" + }, + { + "name": "sol_dmso", + "role": "target", + "units": "kcal/mol", + "description": "Solvation energy, dimethyl sulfoxide" + }, + { + "name": "sol_ethanol", + "role": "target", + "units": "kcal/mol", + "description": "Solvation energy, ethanol" + }, + { + "name": "sol_water", + "role": "target", + "units": "kcal/mol", + "description": "Solvation energy, water" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/foundry_g4mp2_solvation_v1.2/", + "version": "1.0", + "root_version": "foundry_g4mp2_solvation_v1.2", + "latest": true + } + }, + { + "source_id": "tb166_sr_v1.1", + "source_name": "tb166_sr", + "version": 1, + "ingest_date": "2023-01-19T21:49:28.568146Z", + "doi": "10.18126/vwae-pa0m", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tb166_sr_v1.1/", + "metadata": { + "title": "Orbital character of the spin-reorientation transition in TbMn6Sn6", + "authors": [ + { + "name": "McQueeney, Robert J", + "given_name": "Robert J", + "family_name": "McQueeney", + "affiliations": [ + "Ames Laboratory", + "Iowa State University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "tb166_sr_v1.1", + "mdf_source_name": "tb166_sr" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/tb166_sr_v1.1/", + "version": "1.0", + "root_version": "tb166_sr_v1.1", + "latest": true + } + }, + { + "source_id": "dense_flow_v1.1", + "source_name": "dense_flow", + "version": 1, + "ingest_date": "2023-02-08T15:18:14.647502Z", + "doi": "10.18126/xpcw-kgpv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/dense_flow_v1.1/", + "metadata": { + "title": "Dataset for Minimally Rigid Clusters in Dense Suspension Flow", + "authors": [ + { + "name": "van der Naald, Michael", + "given_name": "Michael", + "family_name": "van der Naald", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + }, + { + "name": "Singh, Abhi", + "given_name": "Abhi", + "family_name": "Singh", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + }, + { + "name": "Eid, Toka", + "given_name": "Toka", + "family_name": "Eid", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + }, + { + "name": "Tang, Kenan", + "given_name": "Kenan", + "family_name": "Tang", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + }, + { + "name": "de Pablo, Juan J.", + "given_name": "Juan J.", + "family_name": "de Pablo", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + }, + { + "name": "Jaeger, Heinrich M.", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ] + } + ], + "description": "# Rigid Clusters in Dense Suspension Materials Database\n\nThe database includes simulated rheology for two dimensional shear thickening suspensions with various sliding friction values and number of particles, initially generated and analyzed for the paper \u201cMinimally Rigid Clusters in Dense Suspension Flow\u201d (https://doi.org/10.21203/rs.3.rs-2468688/v1). All data was generated using LF_DEM (https://github.com/ryseto/LF_DEM), which is a state of the art tool for simulating dense suspension flow in both two and three dimensions. All analysis was done using in-house code that can be found here https://github.com/mikevandernaald/rigidCluster_LFDEM. For this paper we focused on suspensions in two dimensions with hydrodynamic forces, normal repulsive force, and a frictional contact force. This repository contains all of the data needed to reproduce all figures from the paper. In addition to this \u201cREADME\u201d file there is a \u201csampleCode.py\u201d that shows how some of the data can be read and manipulated.\n", + "keywords": [ + "simulation", + "suspension", + "flow", + "rheology" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.21203/rs.3.rs-2468688/v1", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "dense_flow_v1.1", + "mdf_source_name": "dense_flow" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/dense_flow_v1.1/", + "version": "1.0", + "root_version": "dense_flow_v1.1", + "latest": true + } + }, + { + "source_id": "bannigan_machine_learning_injectables_v1.1", + "source_name": "bannigan_machine_learning_injectables", + "version": 1, + "ingest_date": "2023-04-25T14:25:37.175587Z", + "doi": "10.18126/4hfb-s3y1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bannigan_machine_learning_injectables_v1.1/", + "metadata": { + "title": "Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables", + "authors": [ + { + "name": "Bannigan, Pauric", + "given_name": "Pauric", + "family_name": "Bannigan" + }, + { + "name": "Bao, Zeqing", + "given_name": "Zeqing", + "family_name": "Bao" + }, + { + "name": "Hickman, Riley John", + "given_name": "Riley John", + "family_name": "Hickman" + }, + { + "name": "Aldeghi, Matteo", + "given_name": "Matteo", + "family_name": "Aldeghi" + }, + { + "name": "H\u00e4se, Florian", + "given_name": "Florian", + "family_name": "H\u00e4se" + }, + { + "name": "Aspuru-Guzik, Al\u00e1n", + "given_name": "Al\u00e1n", + "family_name": "Aspuru-Guzik" + }, + { + "name": "Allen, Christine", + "given_name": "Christine", + "family_name": "Allen" + } + ], + "description": "\nLong-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. Our study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles using this dataset.\n\nThe dataset was constructed from previously published studies by our research group and other research groups.The studies performed by our group include spherical and cylinder shaped polymeric LAIs. Data from external sources was identified using the Web of Science search engine and the keyword combination \u201cpolymeric microparticle\u201d and \u201cdrug delivery\u201d. Information related to the preparation, final composition, and release kinetics of drug from LAIs was collected. The latter was primarily extracted from figures of in vitro drug release profiles using the \u201cGetData Graph Digitizer\u201d application. The final dataset contained 181 drug release profiles for 43 unique drug-polymer combinations. In total this comprised 3783 individual fractional release measurements. The initially collected dataset was composed of a table of drug and polymer names, as well as physicochemical properties of the formulation, and fractional drug release values at various timepoints. In order to use this data to construct and train ML models it is necessary to describe various elements using machine-readable descriptors which were generated using RDkit. The polymers and LAI formulations were described exclusively using information reported in the relevant published articles, these included; polymer molecular weight (Polymer_MW), lactide-to-glycolide ratio (LA/GA; for non-PLGA systems this was set as zero), molecular crosslinking ratio of polymers (CL_Ratio; for non-cross-linked systems this was set as zero), initial drug-to-polymer ratio (Initial D/M ratio), drug loading capacity (DLC), surface area-to-volume (SA-V) ratio for the LAI system, fractional drug release at 6 h (T=0.25), fractional drug release at 12 h (T=0.5), fractional drug release at 24 h (T=1.0), and the precent of surfactant present in the release media (SE; where no surfactant was present in the release media, this was set as zero). With the exception of SA-V, T=0.25, T=0.5, and T=1.0, the 17 input features were either extracted from original publications or calculated using the RDkit package. SA-V was constructed and implemented for this study as it confers information that is related to the size and shape of the LAI system. This enables the inclusion of both spherical and cylindrical shaped LAIs in one model. For initial fractional drug release timepoints (i.e., T=0.25, T=0.5, and T=1.0), where these values were not available from the previously published studies, they were imputed using best fit polynomial curves that range from T = 0 to T = 2 days.\n\nThe code and results that support the findings of our study are available at the Aspuru-Guzik Group\u2019s GitHub page (https://github.com/aspuru-guzik-group/long-acting-injectables) and in the preprint of the related manuscript available on ChemRxiv (https://doi.org/10.26434/chemrxiv-2021-mxrxw-v2).\n", + "keywords": [ + "chemistry", + "materials science", + "therapeutic", + "polymer", + "LAI", + "long acting injectable", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7309021", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bannigan_machine_learning_injectables_v1.1", + "mdf_source_name": "bannigan_machine_learning_injectables" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bannigan_machine_learning_injectables_v1.1/", + "version": "1.0", + "root_version": "bannigan_machine_learning_injectables_v1.1", + "latest": true + } + }, + { + "source_id": "kotaro_figure_reproduction_estimation_v1.2", + "source_name": "kotaro_figure_reproduction_estimation", + "version": 1, + "ingest_date": "2023-04-25T15:07:45.481524Z", + "doi": "10.18126/j8hi-6987", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kotaro_figure_reproduction_estimation_v1.2/", + "metadata": { + "title": "Figure reproduction for 'Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation'", + "authors": [ + { + "name": "Kotaro, Saito", + "given_name": "Saito", + "family_name": "Kotaro" + } + ], + "description": "\nThese datasets and a Jupyter notebook reproduce figures in a publication by Saito et al in Scientific Reports. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.\n\nThis work is supported by the Elements Strategy Initiative Center for Magnetic Materials (ESICMM) under the outsourcing project of the Ministry of Education, Culture, Sports, Science, Technology (MEXT) and the Magnetic Materials for High- Efficient Motors (MagHEM) project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). K.S. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 701647. H.H. is partly supported by JST CREST grant number JPMJCR1761.\n", + "keywords": [ + "small angle neutron scattering", + "small angle x-ray scattering", + "materials science", + "high throughput measurement" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.2547757", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kotaro_figure_reproduction_estimation_v1.2", + "mdf_source_name": "kotaro_figure_reproduction_estimation" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kotaro_figure_reproduction_estimation_v1.2/", + "version": "1.0", + "root_version": "kotaro_figure_reproduction_estimation_v1.2", + "latest": true + } + }, + { + "source_id": "brinkhaus_decimer_handdrawn_images_v1.1", + "source_name": "brinkhaus_decimer_handdrawn_images", + "version": 1, + "ingest_date": "2023-04-24T21:33:44.171527Z", + "doi": "10.18126/k0pm-4alm", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/brinkhaus_decimer_handdrawn_images_v1.1/", + "metadata": { + "title": "DECIMER - Hand-drawn molecule images dataset", + "authors": [ + { + "name": "Brinkhaus, Henning Otto", + "given_name": "Henning Otto", + "family_name": "Brinkhaus" + }, + { + "name": "Zielesny, Achim", + "given_name": "Achim", + "family_name": "Zielesny" + }, + { + "name": "Steinbeck, Christoph", + "given_name": "Christoph", + "family_name": "Steinbeck" + }, + { + "name": "Rajan, Kohulan", + "given_name": "Kohulan", + "family_name": "Rajan" + } + ], + "description": "\nThe translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available.\n\nHere we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.\n", + "keywords": [ + "chemistry", + "image dataset", + "hand drawn", + "OCR", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7617107", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "brinkhaus_decimer_handdrawn_images_v1.1", + "mdf_source_name": "brinkhaus_decimer_handdrawn_images" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/brinkhaus_decimer_handdrawn_images_v1.1/", + "version": "1.0", + "root_version": "brinkhaus_decimer_handdrawn_images_v1.1", + "latest": true + } + }, + { + "source_id": "cannelli_atomiclevel_description_publication_v1.2", + "source_name": "cannelli_atomiclevel_description_publication", + "version": 1, + "ingest_date": "2023-04-25T14:58:20.281075Z", + "doi": "10.18126/aa9d-7ngv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cannelli_atomiclevel_description_publication_v1.2/", + "metadata": { + "title": "Dataset of 'Atomic-level description of thermal fluctuations in inorganic lead halide perovskites' publication", + "authors": [ + { + "name": "Cannelli, Oliviero", + "given_name": "Oliviero", + "family_name": "Cannelli" + }, + { + "name": "Wiktor, Julia", + "given_name": "Julia", + "family_name": "Wiktor" + }, + { + "name": "Colonna, Nicola", + "given_name": "Nicola", + "family_name": "Colonna" + }, + { + "name": "Leroy, Ludmila M. D.", + "given_name": "Ludmila M. D.", + "family_name": "Leroy" + }, + { + "name": "Puppin, Michele", + "given_name": "Michele", + "family_name": "Puppin" + }, + { + "name": "Bacellar, Camila", + "given_name": "Camila", + "family_name": "Bacellar" + }, + { + "name": "Sadykov, Ilia", + "given_name": "Ilia", + "family_name": "Sadykov" + }, + { + "name": "Krieg, Franziska", + "given_name": "Franziska", + "family_name": "Krieg" + }, + { + "name": "Smolentsev, Grigory", + "given_name": "Grigory", + "family_name": "Smolentsev" + }, + { + "name": "Kovalenko, Maksym V.", + "given_name": "Maksym V.", + "family_name": "Kovalenko" + }, + { + "name": "Pasquarello, Alfredo", + "given_name": "Alfredo", + "family_name": "Pasquarello" + }, + { + "name": "Chergui, Majed", + "given_name": "Majed", + "family_name": "Chergui" + }, + { + "name": "Mancini, Giulia F.", + "given_name": "Giulia F.", + "family_name": "Mancini" + } + ], + "description": "\nThe \"Zenodo_22-02-2022.zip\" file contains a \"files\" folder and a jupyter notebook to plot the figures reported in the publication. The \"files\" folder contains several subfolders with the txt files required to plot the figures.\n\nThe \"XAS_simulations.zip\" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.\n\n", + "keywords": [ + "lead halide perovskites", + "CsPbBr3", + "X-ray absorption spectroscopy", + "X-ray diffraction", + "thermal disorder", + "phonon anharmonicity", + "molecular dynamics", + "soft mode model", + "displacive phase transition" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.6394065", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cannelli_atomiclevel_description_publication_v1.2", + "mdf_source_name": "cannelli_atomiclevel_description_publication" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cannelli_atomiclevel_description_publication_v1.2/", + "version": "1.0", + "root_version": "cannelli_atomiclevel_description_publication_v1.2", + "latest": true + } + }, + { + "source_id": "manojlovi\u0107_titanium_alloys_applications_v1.1", + "source_name": "manojlovi\u0107_titanium_alloys_applications", + "version": 1, + "ingest_date": "2023-04-25T01:57:23.775925Z", + "doi": "10.18126/rwuh-osea", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manojlovi\u0107_titanium_alloys_applications_v1.1/", + "metadata": { + "title": "Titanium Alloys Database for Medical Applications", + "authors": [ + { + "name": "Manojlovi\u0107, Vaso D", + "given_name": "Vaso D", + "family_name": "Manojlovi\u0107" + }, + { + "name": "Markovi\u0107, Gordana", + "given_name": "Gordana", + "family_name": "Markovi\u0107" + } + ], + "description": "\nIn this database, 238 titanium alloys were collected, almost entirely of biocompatible alloying elements. The primary motivation behind creating such a database is to establish a foundation for designing new alloys using machine learning methods. The database can assist researchers, engineers, and biomedical professionals in developing titanium alloys for various medical applications, thereby improving health outcomes and driving advancements in biomaterials and biomedical engineering.\n\nFor more information read the paper at: https://doi.org/10.30544/MMD5\n\nNOTE: To avoid misunderstandings, please cite both the database and the published article when citing this database.\n\nWe invite other authors to contribute to the updating of this database (send at least 20 new alloys to appear as co-author)\n", + "keywords": [ + "materials", + "titanium", + "medical", + "biocompatibility", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7802694", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "manojlovi\u0107_titanium_alloys_applications_v1.1", + "mdf_source_name": "manojlovi\u0107_titanium_alloys_applications" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/manojlovi\u0107_titanium_alloys_applications_v1.1/", + "version": "1.0", + "root_version": "manojlovi\u0107_titanium_alloys_applications_v1.1", + "latest": true + } + }, + { + "source_id": "avramova_retrotransformdb_transforms_reactions_v1.1", + "source_name": "avramova_retrotransformdb_transforms_reactions", + "version": 1, + "ingest_date": "2023-04-24T21:42:18.120892Z", + "doi": "10.18126/kqww-lkdy", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/avramova_retrotransformdb_transforms_reactions_v1.1/", + "metadata": { + "title": "RetroTransformDB - a dataset of transforms (retrosynthetic reactions)", + "authors": [ + { + "name": "Avramova, Svetlana", + "given_name": "Svetlana", + "family_name": "Avramova" + }, + { + "name": "Kochev, Nikolay", + "given_name": "Nikolay", + "family_name": "Kochev" + }, + { + "name": "Angelov, Plamen", + "given_name": "Plamen", + "family_name": "Angelov" + } + ], + "description": "\nHere we present a dataset of transforms compiled and coded in SMIRKS line notation by us. The collection is comprised of more than 100 records each including id, reactions name, SMIRKS linear notation, target functional group and transform type. All SMIRKS transforms were tested syntactically, semantically and from chemical point of view in different software platforms. SMIRKS notations are written with explicit H atoms, therefore it is expected that the used software will apply the SMIRKS transforms against molecules with explicit H atoms.\n", + "keywords": [ + "chemistry", + "synthesis", + "retrosynthesis", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.1209313", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "avramova_retrotransformdb_transforms_reactions_v1.1", + "mdf_source_name": "avramova_retrotransformdb_transforms_reactions" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/avramova_retrotransformdb_transforms_reactions_v1.1/", + "version": "1.0", + "root_version": "avramova_retrotransformdb_transforms_reactions_v1.1", + "latest": true + } + }, + { + "source_id": "kellerrudek_mpimainz_uvvis_molecules_v1.1", + "source_name": "kellerrudek_mpimainz_uvvis_molecules", + "version": 1, + "ingest_date": "2023-04-24T21:55:44.760265Z", + "doi": "10.18126/x2vw-dqx6", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kellerrudek_mpimainz_uvvis_molecules_v1.1/", + "metadata": { + "title": "The MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules", + "authors": [ + { + "name": "Keller-Rudek, Hannelore", + "given_name": "Hannelore", + "family_name": "Keller-Rudek" + }, + { + "name": "Moortgat, Geert K.", + "given_name": "Geert K.", + "family_name": "Moortgat" + }, + { + "name": "Sander, Rolf", + "given_name": "Rolf", + "family_name": "Sander" + }, + { + "name": "S\u00f6rensen, R\u00fcdiger", + "given_name": "R\u00fcdiger", + "family_name": "S\u00f6rensen" + } + ], + "description": "\nThis archive contains a frozen snapshot of all cross section and quantum yield data files from the MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules. To view the data, open the files cross_sections.html and quantum_yields.html in your browser. The up-to-date version of the Spectral Atlas is available at: http://www.uv-vis-spectral-atlas-mainz.org\n", + "keywords": [ + "chemistry", + "gaseous", + "molecules", + "quantum yield", + "uv-vis", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.6951", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kellerrudek_mpimainz_uvvis_molecules_v1.1", + "mdf_source_name": "kellerrudek_mpimainz_uvvis_molecules" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kellerrudek_mpimainz_uvvis_molecules_v1.1/", + "version": "1.0", + "root_version": "kellerrudek_mpimainz_uvvis_molecules_v1.1", + "latest": true + } + }, + { + "source_id": "li_reproducible_attributable_workflows_v1.1", + "source_name": "li_reproducible_attributable_workflows", + "version": 1, + "ingest_date": "2023-04-25T15:10:48.645623Z", + "doi": "10.18126/s783-fa4g", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/li_reproducible_attributable_workflows_v1.1/", + "metadata": { + "title": "Reproducible and Attributable Materials Science Workflows", + "authors": [ + { + "name": "Li, Ye", + "given_name": "Ye", + "family_name": "Li" + }, + { + "name": "Wilson, Sara", + "given_name": "Sara", + "family_name": "Wilson" + }, + { + "name": "Altman, Micah", + "given_name": "Micah", + "family_name": "Altman" + } + ], + "description": "\nThis set includes the deidentified data, reproducible analysis and research report of the project on Reproducible and Attributable Materials Science Workflows.\nSupplement to\nhttps://github.com/YeLibrarian/MatSciWorkflow/tree/v01_pre_PeerReview\n\n", + "keywords": [ + "materials science", + "reproduciblity", + "research workflow", + "data management", + "data sharing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7158715", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "li_reproducible_attributable_workflows_v1.1", + "mdf_source_name": "li_reproducible_attributable_workflows" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/li_reproducible_attributable_workflows_v1.1/", + "version": "1.0", + "root_version": "li_reproducible_attributable_workflows_v1.1", + "latest": true + } + }, + { + "source_id": "hoja_qm7x_comprehensive_molecules_v1.1", + "source_name": "hoja_qm7x_comprehensive_molecules", + "version": 1, + "ingest_date": "2023-04-25T15:54:27.591037Z", + "doi": "10.18126/5y39-v72p", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hoja_qm7x_comprehensive_molecules_v1.1/", + "metadata": { + "title": "QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules", + "authors": [ + { + "name": "Hoja, Johannes", + "given_name": "Johannes", + "family_name": "Hoja" + }, + { + "name": "Medrano Sandonas, Leonardo", + "given_name": "Leonardo", + "family_name": "Medrano Sandonas" + }, + { + "name": "Ernst, Brian", + "given_name": "Brian", + "family_name": "Ernst" + }, + { + "name": "Vazquez-Mayagoitia, Alvaro", + "given_name": "Alvaro", + "family_name": "Vazquez-Mayagoitia" + }, + { + "name": "DiStasio Jr., Robert A.", + "given_name": "Robert A.", + "family_name": "DiStasio Jr." + }, + { + "name": "Tkatchenko, Alexandre", + "given_name": "Alexandre", + "family_name": "Tkatchenko" + } + ], + "description": "\nHere, we introduce QM7-X, a comprehensive dataset of > 40 physicochemical properties for ~4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures---comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-trans-and conformational isomers)---as well as 100 non-equilibrium structural variations thereof to reach a total of ~4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly converged dataset of quantum-mechanically computed physical and chemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.\n", + "keywords": [ + "chemistry", + "qm7", + "molecular property", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.4288677", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hoja_qm7x_comprehensive_molecules_v1.1", + "mdf_source_name": "hoja_qm7x_comprehensive_molecules" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hoja_qm7x_comprehensive_molecules_v1.1/", + "version": "1.0", + "root_version": "hoja_qm7x_comprehensive_molecules_v1.1", + "latest": true + } + }, + { + "source_id": "metallic_glass_database_v1.2", + "source_name": "metallic_glass_database", + "version": 1, + "ingest_date": "2023-05-10T20:20:27.532913Z", + "doi": "10.18126/5svl-7ruk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/metallic_glass_database_v1.2/", + "metadata": { + "title": "Quantifying the Origin of Metallic Glass Formation", + "authors": [ + { + "name": "W.L. Johnson, J.H. Na & M.D. Demetriou1", + "given_name": "J.H. Na & M.D. Demetriou1", + "family_name": "W.L. Johnson" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "metallic_glass_database_v1.2", + "mdf_source_name": "metallic_glass_database" + }, + "ml": { + "data_format": "tabular", + "short_name": "metallic glass", + "splits": [ + { + "type": "train", + "path": "train.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "Tg_[K]", + "role": "input", + "units": "K", + "description": "The rheological glass transition temperature, Tg (K) defined as the temperature at which the equilibrium liquid viscosity is 10^12 Pa-s" + }, + { + "name": "Tl_[K]", + "role": "input", + "units": "K", + "description": "The alloy liquidus temperature, TL (K) defined as the temperature above which the equilibrium alloy is fully liquid (contains no solid phases)" + }, + { + "name": "m", + "role": "input", + "units": "", + "description": "The dimensionless Angell Fragility Parameter" + }, + { + "name": "log10(Dmax_sq)", + "role": "target", + "units": "mm", + "description": "Log of the square of the experimentally observed glass forming ability of the alloy expressed in terms of the maximum reported diameter of a rod, dmax, (in mm) for which the liquid alloy can be quenched to a glass with no detectable crystallinity" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/metallic_glass_database_v1.2/", + "version": "1.0", + "root_version": "metallic_glass_database_v1.2", + "latest": true + } + }, + { + "source_id": "casting_thickness_database_v1.4", + "source_name": "casting_thickness_database", + "version": 1, + "ingest_date": "2023-05-10T22:09:38.105587Z", + "doi": "10.18126/uxu4-kvnt", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/casting_thickness_database_v1.4/", + "metadata": { + "title": "Metallic Glass Critical Thicknesses for Atomistic Feature Model", + "authors": [ + { + "name": "Zhang, Shixin", + "given_name": "Shixin", + "family_name": "Zhang" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "casting_thickness_database_v1.4", + "mdf_source_name": "casting_thickness_database" + }, + "ml": { + "data_format": "tabular", + "short_name": "casting thickness", + "splits": [ + { + "type": "train", + "path": "Lmax.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "Composition", + "role": "input", + "units": "", + "description": "The reported composition for the glass. In atomic percentage" + }, + { + "name": "Lmax_[mm]", + "role": "target", + "units": "mm", + "description": "The combination of Dmax and Zmax" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/casting_thickness_database_v1.4/", + "version": "1.0", + "root_version": "casting_thickness_database_v1.4", + "latest": true + } + }, + { + "source_id": "uml_dependency_chain_v1.1", + "source_name": "uml_dependency_chain", + "version": 1, + "ingest_date": "2023-04-26T15:40:00.256716Z", + "doi": "10.18126/fu80-s9bt", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/uml_dependency_chain_v1.1/", + "metadata": { + "title": "Transient torque, pressure, and temperature data for material extrusion additive manufacturing of acrylonitrile butadiene styrene", + "authors": [ + { + "name": "Colon, Austin R.", + "given_name": "Austin R.", + "family_name": "Colon", + "affiliations": [ + "University of Massachusetts Lowell" + ] + }, + { + "name": "Kazmer, David O.", + "given_name": "David O.", + "family_name": "Kazmer", + "affiliations": [ + "University of Massachusetts Lowell" + ] + }, + { + "name": "Peterson, Amy M.", + "given_name": "Amy M.", + "family_name": "Peterson", + "affiliations": [ + "University of Massachusetts Lowell" + ] + } + ], + "description": "Dataset includes transient torque, infeed pressure, melt pressure, and melt temperature that were acquired by an instrumented hot end for material extrusion additive manufacturing of acrylonitrile butadiene styrene (ABS). Data were collected according to a design of experiments wherein the volumetric flow rate, temperature setpoint, and the nozzle orifice diameter were varied one factor at a time.", + "keywords": [ + "experiment", + "polymers" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "uml_dependency_chain_v1.1", + "mdf_source_name": "uml_dependency_chain" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/uml_dependency_chain_v1.1/", + "version": "1.0", + "root_version": "uml_dependency_chain_v1.1", + "latest": true + } + }, + { + "source_id": "george_corresponding_publication_rules_v1.1", + "source_name": "george_corresponding_publication_rules", + "version": 1, + "ingest_date": "2023-04-25T16:47:47.399912Z", + "doi": "10.18126/9yh2-ywlf", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/george_corresponding_publication_rules_v1.1/", + "metadata": { + "title": "Data corresponding to the publication 'The limited predictive power of the Pauling Rules'", + "authors": [ + { + "name": "George, Janine", + "given_name": "Janine", + "family_name": "George" + }, + { + "name": "Waroquiers, David", + "given_name": "David", + "family_name": "Waroquiers" + }, + { + "name": "Di Stefano, Davide", + "given_name": "Davide", + "family_name": "Di Stefano" + }, + { + "name": "Petretto, Guido", + "given_name": "Guido", + "family_name": "Petretto" + }, + { + "name": "Rignanese, Gian-Marco", + "given_name": "Gian-Marco", + "family_name": "Rignanese" + }, + { + "name": "Hautier, Geoffroy", + "given_name": "Geoffroy", + "family_name": "Hautier" + } + ], + "description": "\nThis is the data set corrresponding to the publication \"The limited predictive power of the Pauling rules\" (see https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202000829). This data can be reproduced with the following code: https://doi.org/10.5281/zenodo.3654428.\n", + "keywords": [ + "materials science", + "oxides", + "Pauling rules", + "coordination environments" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.3654989", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "george_corresponding_publication_rules_v1.1", + "mdf_source_name": "george_corresponding_publication_rules" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/george_corresponding_publication_rules_v1.1/", + "version": "1.0", + "root_version": "george_corresponding_publication_rules_v1.1", + "latest": true + } + }, + { + "source_id": "stevens_effect_charged_cores_v1.2", + "source_name": "stevens_effect_charged_cores", + "version": 1, + "ingest_date": "2023-05-09T21:22:10.366808Z", + "doi": "10.18126/m15g-20yj", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/stevens_effect_charged_cores_v1.2/", + "metadata": { + "title": "Data for Effect of Charged Block Length Mismatch on Double Diblock Polyelectrolyte Complex Micelle Cores", + "authors": [ + { + "name": "Stevens, Kaden C.", + "given_name": "Kaden C.", + "family_name": "Stevens", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Marras, Alexander E.", + "given_name": "Alexander E.", + "family_name": "Marras", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Campagna, Trinity R.", + "given_name": "Trinity R.", + "family_name": "Campagna", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Ting, Jeffrey M.", + "given_name": "Jeffrey M.", + "family_name": "Ting", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Tirrell, Matthew V.", + "given_name": "Matthew V.", + "family_name": "Tirrell", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "Raw SAXS data.", + "keywords": [ + "polymers", + "polyelectrolytes", + "micelles", + "SAXS" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "stevens_effect_charged_cores_v1.2", + "mdf_source_name": "stevens_effect_charged_cores" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/stevens_effect_charged_cores_v1.2/", + "version": "1.0", + "root_version": "stevens_effect_charged_cores_v1.2", + "latest": true + } + }, + { + "source_id": "pacbedcnn_thickness_mistilt_training_v1.1", + "source_name": "pacbedcnn_thickness_mistilt_training", + "version": 1, + "ingest_date": "2023-06-07T20:16:05.784118Z", + "doi": "10.18126/2q0n-ce0d", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/pacbedcnn_thickness_mistilt_training_v1.1/", + "metadata": { + "title": "Training Dataset of PACBED-CNN for Infering Specimen Thickness and Mistilt", + "authors": [ + { + "name": "Oberaigner, Michael", + "given_name": "Michael", + "family_name": "Oberaigner" + }, + { + "name": "Clausen, Alexander", + "given_name": "Alexander", + "family_name": "Clausen" + }, + { + "name": "Weber, Dieter", + "given_name": "Dieter", + "family_name": "Weber" + }, + { + "name": "Kothleitner, Gerald", + "given_name": "Gerald", + "family_name": "Kothleitner" + }, + { + "name": "E Dunin-Borkowski, Rafal", + "given_name": "Rafal", + "family_name": "E Dunin-Borkowski" + }, + { + "name": "Knez, Daniel", + "given_name": "Daniel", + "family_name": "Knez" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "pacbedcnn_thickness_mistilt_training_v1.1", + "mdf_source_name": "pacbedcnn_thickness_mistilt_training" + }, + "ml": { + "data_format": "hdf5", + "short_name": "PACBEDCNN_thickness_mistilt_training", + "n_items": 384030, + "splits": [ + { + "type": "train", + "path": "data.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "pacbed", + "role": "input", + "units": null, + "description": "all simulated pacbed images from three different material, different conv angle, and different electron energy" + }, + { + "name": "thickness_mistilt_plus_metadata", + "role": "target", + "units": null, + "description": "groundtruth thickness and mistilt for each pacbed together with other metadata" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/pacbedcnn_thickness_mistilt_training_v1.1/", + "version": "1.0", + "root_version": "pacbedcnn_thickness_mistilt_training_v1.1", + "latest": true + } + }, + { + "source_id": "melting_acoustics_v1.1", + "source_name": "melting_acoustics", + "version": 1, + "ingest_date": "2023-06-28T16:43:08.188468Z", + "doi": "10.18126/fjhl-cy15", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/melting_acoustics_v1.1/", + "metadata": { + "title": "Dataset for Hydrodynamic Coupling Melts Acoustically Levitated Crystalline Rafts", + "authors": [ + { + "name": "Wu, Brady", + "given_name": "Brady", + "family_name": "Wu", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "VanSaders, Bryan", + "given_name": "Bryan", + "family_name": "VanSaders", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Lim, Melody X.", + "given_name": "Melody X.", + "family_name": "Lim", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Jaeger, Heinrich M.", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "The database includes the raw data file for acoustically levitated granular raft with particle diameter 30-60 micrometers and various acoustic pressure, conducted and analyzed for the paper \u201cHydrodynamic Coupling Melts Acoustically Levitated Crystalline Rafts\u201d (https://arxiv.org/abs/2211.02750). This repository contains the necessary data needed to reproduce all figures from the paper. The \u201cREADME\u201d file describes the structure of the data and any relevant experimental conditions and simulation parameters. ", + "keywords": [ + "simulation", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://arxiv.org/abs/2211.02750", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "melting_acoustics_v1.1", + "mdf_source_name": "melting_acoustics" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/melting_acoustics_v1.1/", + "version": "1.0", + "root_version": "melting_acoustics_v1.1", + "latest": true + } + }, + { + "source_id": "akeel_soft_liners_v1.1", + "source_name": "akeel_soft_liners", + "version": 1, + "ingest_date": "2023-04-30T01:31:31.885858Z", + "doi": "10.18126/1tjo-vurp", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/akeel_soft_liners_v1.1/", + "metadata": { + "title": "Impact of CeO2 on Mechanical Properties of Resilient Liner", + "authors": [ + { + "name": "Akeel, Mustafa", + "given_name": "Mustafa", + "family_name": "Akeel", + "affiliations": [ + "Baghdad University" + ] + }, + { + "name": "Sadiq, Wasmaa", + "given_name": "Wasmaa", + "family_name": "Sadiq", + "affiliations": [ + "Baghdad University" + ] + } + ], + "description": "When a patient's gums, tongue, or cheeks are sensitive to pressure from their dentures, the dentist may recommend a softer lining material. By incorporating a pliable lining material, dentures can better conform to the gum tissue beneath them.\n\nThe soft, resilient denture lining material acts as a shock absorber, spreading the force out over a larger area and protecting the underlying tissue from damage. Over time, soft liners have become the standard for easing denture discomfort. Soft liners are used commonly by individuals who are unable to wear a traditional denture base. So mechanical properties of lining materials need to be improve by many methods, one of them adding nanomaterials.", + "keywords": [ + "polymers", + "dental materials", + "materials science", + "experiment", + "composites" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "akeel_soft_liners_v1.1", + "mdf_source_name": "akeel_soft_liners" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/akeel_soft_liners_v1.1/", + "version": "1.0", + "root_version": "akeel_soft_liners_v1.1", + "latest": true + } + }, + { + "source_id": "bubbles_v1.1", + "source_name": "bubbles", + "version": 1, + "ingest_date": "2023-06-06T13:41:37.000056Z", + "doi": "10.18126/9pj2-hpef", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bubbles_v1.1/", + "metadata": { + "title": "Labeled Dataset of Hydrogen and Oxygen Bubbles Evolving on Electrodes", + "authors": [ + { + "name": "Lake, Jack", + "given_name": "Jack", + "family_name": "Lake", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Rufer, Simon", + "given_name": "Simon", + "family_name": "Rufer", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "James, Jim", + "given_name": "Jim", + "family_name": "James", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Pruyne, Nathan", + "given_name": "Nathan", + "family_name": "Pruyne", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Scourtas, Aristana", + "given_name": "Aristana", + "family_name": "Scourtas", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Schwarting, Marcus", + "given_name": "Marcus", + "family_name": "Schwarting", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Ambadkar, Aadit", + "given_name": "Aadit", + "family_name": "Ambadkar", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Varanasi, Kripa K.", + "given_name": "Kripa K.", + "family_name": "Varanasi", + "affiliations": [ + "Massachusetts Institute of Technology", + "Argonne National Laboratory", + "University of Chicago" + ] + } + ], + "description": "Microscopy images of hydrogen or oxygen bubbles evolving on 3mm diameter electrodes. Data is labeled for training of segmentation algorithms to provide high-throughput and low-effort data collection. ", + "keywords": [ + "machine learning", + "high-throughput", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bubbles_v1.1", + "mdf_source_name": "bubbles" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bubbles_v1.1/", + "version": "1.0", + "root_version": "bubbles_v1.1", + "latest": true + } + }, + { + "source_id": "manukyan_multiscale_analysis_money_v1.1", + "source_name": "manukyan_multiscale_analysis_money", + "version": 1, + "ingest_date": "2023-06-22T14:17:46.551301Z", + "doi": "10.18126/knhr-sr7f", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manukyan_multiscale_analysis_money_v1.1/", + "metadata": { + "title": "Data for Multiscale analysis of Benjamin Franklin\u2019s innovations in American paper money", + "authors": [ + { + "name": "Manukyan, Khachatur", + "given_name": "Khachatur", + "family_name": "Manukyan", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Yeghishyan, Armenuhi", + "given_name": "Armenuhi", + "family_name": "Yeghishyan", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Aprahamian, Ani", + "given_name": "Ani", + "family_name": "Aprahamian", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Jordan, Louis", + "given_name": "Louis", + "family_name": "Jordan", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Kurkowski, Michael", + "given_name": "Michael", + "family_name": "Kurkowski", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Raddell, Mark", + "given_name": "Mark", + "family_name": "Raddell", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Le, Laura Richter", + "given_name": "Laura Richter", + "family_name": "Le", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Schultz, Zachary D.", + "given_name": "Zachary D.", + "family_name": "Schultz", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Spillane, Liam", + "given_name": "Liam", + "family_name": "Spillane", + "affiliations": [ + "University of Notre Dame" + ] + }, + { + "name": "Wiescher, Michael", + "given_name": "Michael", + "family_name": "Wiescher", + "affiliations": [ + "University of Notre Dame" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "manukyan_multiscale_analysis_money_v1.1", + "mdf_source_name": "manukyan_multiscale_analysis_money" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/manukyan_multiscale_analysis_money_v1.1/", + "version": "1.0", + "root_version": "manukyan_multiscale_analysis_money_v1.1", + "latest": true + } + }, + { + "source_id": "mead_detection_be_superlattices_v1.3", + "source_name": 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}, + { + "name": "Fiordaliso, Elisabetta Maria", + "given_name": "Elisabetta Maria", + "family_name": "Fiordaliso", + "affiliations": [ + "Northwestern University", + "McMaster University", + "Technical University of Denmark" + ] + }, + { + "name": "LaPierre, Ray R.", + "given_name": "Ray R.", + "family_name": "LaPierre", + "affiliations": [ + "Northwestern University", + "McMaster University", + "Technical University of Denmark" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Northwestern University", + "McMaster University", + "Technical University of Denmark" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mead_detection_be_superlattices_v1.3", + "mdf_source_name": "mead_detection_be_superlattices" + }, + "download_url": 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"Latypov, Marat", + "given_name": "Marat", + "family_name": "Latypov", + "affiliations": [ + "University of Arizona" + ] + } + ], + "keywords": [ + "simulation", + "machine learning", + "metals and alloys", + "high-throughput" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cs_v1.1", + "mdf_source_name": "cs" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cs_v1.1/", + "version": "1.0", + "root_version": "cs_v1.1", + "latest": true + } + }, + { + "source_id": "lpbf_process_inputoutput_spring2022_v1.4", + "source_name": "lpbf_process_inputoutput_spring2022", + "version": 1, + "ingest_date": "2023-07-10T21:45:23.414335Z", + "doi": "10.18126/jv0r-cyu9", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lpbf_process_inputoutput_spring2022_v1.4/", + "metadata": { + "title": "Laser powder bed fusion process and structure data set, spring 2022", + "authors": [ + { + "name": "Wood, Nathaniel", + "given_name": "Nathaniel", + "family_name": "Wood", + "affiliations": [ + "Ohio State University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Schwalbach, Edwin", + "given_name": "Edwin", + "family_name": "Schwalbach", + "affiliations": [ + "Ohio State University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Gillman, Andrew", + "given_name": "Andrew", + "family_name": "Gillman", + "affiliations": [ + "Ohio State University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Hoelzle, David J.", + "given_name": "David J.", + "family_name": "Hoelzle", + "affiliations": [ + "Ohio State University", + "Air Force Research Laboratory" + ] + } + ], + "description": "This dataset is a series of synchronized process input and output measurements, and structural data, for laser powder bed fusion. The material is IN718. The input and output data are available in both raw and processed form, in both .csv format and MATLAB .mat format. The measured inputs are real-time measurements of the laser power and position commands, real-time trigger signals for synchronizing the measurement devices, and the set point of the beam radius. There are two classes of process outputs: 1) thermographic videos from infrared cameras that are mounted in the build chamber and coaxially with the beam path, respectively, and 2) temperature measurements from thermocouples that are embedded within all test Samples. The structural data are micrographs of the the as-built Sample surfaces. This dataset contains measurements from 8 Samples: four Samples have a rectangular cross-section, and four Samples have an I-beam-shaped cross section. Data are collected in three regimes of testing: 1) at a low beam power, such that the dominant mode of heat transfer in the Samples is conduction, 2) in conditions that provoke melting in the exposed Samples surfaces, and 3) building five new layers of material atop all Samples. \n\nThe data descriptor article for this dataset is still under peer review and this description will be updated with the relevant DOI once the article is published.", + "keywords": [ + "metals and alloys", + "experiment", + "Powder Bed Fusion", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "lpbf_process_inputoutput_spring2022_v1.4", + "mdf_source_name": "lpbf_process_inputoutput_spring2022" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/lpbf_process_inputoutput_spring2022_v1.4/", + "version": "1.0", + "root_version": "lpbf_process_inputoutput_spring2022_v1.4", + "latest": true + } + }, + { + "source_id": "hrtem_nanoparticles_generalization_v1.1", + "source_name": "hrtem_nanoparticles_generalization", + "version": 1, + "ingest_date": "2023-07-05T21:42:26.610366Z", + "doi": "10.18126/z4mr-xwk5", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/hrtem_nanoparticles_generalization_v1.1/", + "metadata": { + "title": "HRTEM images for generalization across experimental parameters of nanoparticle segmentation model", + "authors": [ + { + "name": "Sytwu, Katherine", + "given_name": "Katherine", + "family_name": "Sytwu" + }, + { + "name": "DaCosta, Luis Rangel", + "given_name": "Luis Rangel", + "family_name": "DaCosta" + }, + { + "name": "Scott, Mary C.", + "given_name": "Mary C.", + "family_name": "Scott" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "hrtem_nanoparticles_generalization_v1.1", + "mdf_source_name": "hrtem_nanoparticles_generalization" + }, + "ml": { + "data_format": "hdf5", + "short_name": "HRTEM_nanoparticles_generalization", + "n_items": 407, + "splits": [ + { + "type": "train", + "path": "Au_on_UTC.h5", + "label": "Au_on_UTC", + "n_items": null + }, + { + "type": "train", + "path": "Ag_on_UTC.h5", + "label": "Ag_on_UTC", + "n_items": null + }, + { + "type": "train", + "path": "Au_on_SiN.h5", + "label": "Au_on_SiN", + "n_items": null + }, + { + "type": "train", + "path": "CdSe_on_UTC.h5", + "label": "CdSe_on_UTC", + "n_items": null + } + ], + "keys": [ + { + "name": "image", + "role": "input", + "units": null, + "description": "input, experimental 4Kx4K HRTEM images" + }, + { + "name": "label", + "role": "target", + "units": null, + "description": "binary mask at nanoparticles" + }, + { + "name": "metadata", + "role": "input", + "units": null, + "description": "tabular information describing the experimental conditions and materials for each image" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/hrtem_nanoparticles_generalization_v1.1/", + "version": "1.0", + "root_version": "hrtem_nanoparticles_generalization_v1.1", + "latest": true + } + }, + { + "source_id": "silica_water_v1.1", + "source_name": "silica_water", + "version": 1, + "ingest_date": "2023-07-14T17:44:38.140753Z", + "doi": "10.18126/pzjr-x7pv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/silica_water_v1.1/", + "metadata": { + "title": "silica-water dataset with potentials", + "authors": [ + { + "name": "Roy, Swagata", + "given_name": "Swagata", + "family_name": "Roy", + "affiliations": [ + "Evonik Operations GmbH", + "Massachusetts Institute of Technology", + "IBM Research" + ] + }, + { + "name": "D\u00fcrholt, Johannes P.", + "given_name": "Johannes P.", + "family_name": "D\u00fcrholt", + "affiliations": [ + "Evonik Operations GmbH", + "Massachusetts Institute of Technology", + "IBM Research" + ] + }, + { + "name": "Asche, Thomas S.", + "given_name": "Thomas S.", + "family_name": "Asche", + "affiliations": [ + "Evonik Operations GmbH", + "Massachusetts Institute of Technology", + "IBM Research" + ] + }, + { + "name": "Zipoli, Federico", + "given_name": "Federico", + "family_name": "Zipoli", + "affiliations": [ + "Evonik Operations GmbH", + "Massachusetts Institute of Technology", + "IBM Research" + ] + }, + { + "name": "G\u00f3mez-Bombarelli, Rafael", + "given_name": "Rafael", + "family_name": "G\u00f3mez-Bombarelli", + "affiliations": [ + "Evonik Operations GmbH", + "Massachusetts Institute of Technology", + "IBM Research" + ] + } + ], + "description": "The data set of 220K molecule geometries of silica-water-sodium stoichiometries with their energies and forces. The Neural network potentials trained on this data set are also available. If you use these please cite https://arxiv.org/abs/2307.01705", + "keywords": [ + "machine learning", + "simulation", + "DFT", + "Reactions" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://arxiv.org/abs/2307.01705", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "silica_water_v1.1", + "mdf_source_name": "silica_water" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/silica_water_v1.1/", + "version": "1.0", + "root_version": "silica_water_v1.1", + "latest": true + } + }, + { + "source_id": "hou_exchangedriven_intermixing_bi2se3_v1.1", + "source_name": "hou_exchangedriven_intermixing_bi2se3", + "version": 1, + "ingest_date": "2023-07-17T15:52:03.429618Z", + "doi": "10.18126/uxu0-5vsq", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hou_exchangedriven_intermixing_bi2se3_v1.1/", + "metadata": { + "title": "Exchange-Driven Intermixing of Bulk and Topological Surface States by Chiral Excitons in Bi2Se3", + "authors": [ + { + "name": "Hou, Bowen", + "given_name": "Bowen", + "family_name": "Hou", + "affiliations": [ + "Yale University", + "University of California, Merced" + ] + }, + { + "name": "Wang, Dan", + "given_name": "Dan", + "family_name": "Wang", + "affiliations": [ + "Yale University", + "University of California, Merced" + ] + }, + { + "name": "Barker, Bradford A.", + "given_name": "Bradford A.", + "family_name": "Barker", + "affiliations": [ + "Yale University", + "University of California, Merced" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Yale University", + "University of California, Merced" + ] + } + ], + "description": "This dataset includes key inputs files for QE and BGW.", + "keywords": [ + "DFT", + "GW", + "GW+BSE" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1103/PhysRevLett.130.216402", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hou_exchangedriven_intermixing_bi2se3_v1.1", + "mdf_source_name": "hou_exchangedriven_intermixing_bi2se3" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hou_exchangedriven_intermixing_bi2se3_v1.1/", + "version": "1.0", + "root_version": "hou_exchangedriven_intermixing_bi2se3_v1.1", + "latest": true + } + }, + { + "source_id": "organic_crystal_prediction_v1.4", + "source_name": "organic_crystal_prediction", + "version": 1, + "ingest_date": "2023-07-03T16:08:38.653290Z", + "doi": "10.18126/ac06-tft6", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/organic_crystal_prediction_v1.4/", + "metadata": { + "title": "Accelerating Crystal Structure Prediction of Organic Salts via Machine Learning", + "authors": [ + { + "name": "Shapera, Ethan P.", + "given_name": "Ethan P.", + "family_name": "Shapera", + "affiliations": [ + "Graz University of Technology", + "Intellectual Ventures", + "University College London" + ] + }, + { + "name": "Bucar, Dejan-Kresimir", + "given_name": "Dejan-Kresimir", + "family_name": "Bucar", + "affiliations": [ + "Graz University of Technology", + "Intellectual Ventures", + "University College London" + ] + }, + { + "name": "Prasankumar, Rohit P.", + "given_name": "Rohit P.", + "family_name": "Prasankumar", + "affiliations": [ + "Graz University of Technology", + "Intellectual Ventures", + "University College London" + ] + }, + { + "name": "Heil, Christoph", + "given_name": "Christoph", + "family_name": "Heil", + "affiliations": [ + "Graz University of Technology", + "Intellectual Ventures", + "University College London" + ] + } + ], + "description": "Dataset for \"Accelerating Crystal Structure Prediction of Organic Salts via Machine Learning\"", + "keywords": [ + "machine learning", + "simulation", + "high-throughput", + "DFT", + "organic crystal" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "organic_crystal_prediction_v1.4", + "mdf_source_name": "organic_crystal_prediction" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/organic_crystal_prediction_v1.4/", + "version": "1.0", + "root_version": "organic_crystal_prediction_v1.4", + "latest": true + } + }, + { + "source_id": "pub_9_gibbs_liquidsolid_v1.2", + "source_name": "pub_9_gibbs_liquidsolid", + "version": 1, + "ingest_date": "2019-03-07T22:35:07.295071Z", + "doi": "10.18126/M23W2W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_46/", + "metadata": { + "title": "Liquid-solid Metallic Mixture Coarsening Data - 80% solid", + "authors": [ + { + "name": "Gibbs, John W.", + "given_name": "John W.", + "family_name": "Gibbs", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Fife, Julie L.", + "given_name": "Julie L.", + "family_name": "Fife", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\nIn theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\nThe data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6", + "keywords": [ + "registration", + "4D data", + "coarsening", + "experimentation", + "computed tomography", + "in situ", + "Al-Cu", + "metals and alloys", + "tomography", + "liquid-solid mixture", + "segmentation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1186/2193-9772-3-6", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1016/j.actamat.2014.01.024", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.gibbsium.org/thesis", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_9_gibbs_liquidsolid_v1.2", + "mdf_source_name": "pub_9_gibbs_liquidsolid", + "experiment_nominal_alloy_composition": "['Al (85 wt%); Cu (15 wt%)', 'Al:85 wt%', 'Cu:15 wt%']", + "experiment_pixel_size": "['1.44 \\\\u00b5m', '1.44 um']", + "experiment_time_between_scans": "140 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "Piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "Al-Cu", + "experiment_total_duration": "670 min", + "experiment_holding_temperature": "['558 C', '5 C above the eutectic temperature']", + "funding_details": "['DOE Grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "20 keV" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_46/", + "version": "1.0", + "root_version": "pub_9_gibbs_liquidsolid_v1.2", + "latest": true + } + }, + { + "source_id": "pub_8_gibbs_liquidsolid_v1.2", + "source_name": "pub_8_gibbs_liquidsolid", + "version": 1, + "ingest_date": "2019-03-07T22:33:28.295735Z", + "doi": "10.18126/M27P4T", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_45/", + "metadata": { + "title": "Liquid-solid Metallic Mixture Coarsening Data - 28% Solid", + "authors": [ + { + "name": "Gibbs, John W.", + "given_name": "John W.", + "family_name": "Gibbs", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Fife, Julie L.", + "given_name": "Julie L.", + "family_name": "Fife", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\n In theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\n The data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6", + "keywords": [ + "registration", + "4D data", + "coarsening", + "experimentation", + "computed tomography", + "in situ", + "Al-Cu", + "metals and alloys", + "tomography", + "liquid-solid mixture", + "segmentation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1186/2193-9772-3-6", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1016/j.actamat.2014.01.024", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.gibbsium.org/thesis", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_8_gibbs_liquidsolid_v1.2", + "mdf_source_name": "pub_8_gibbs_liquidsolid", + "experiment_nominal_alloy_composition": "['Al (74 wt%); Cu (26 wt%)', 'Al:74 wt%', 'Cu:26 wt%']", + "experiment_pixel_size": "['1.44 \\\\u00b5m', '1.44 um']", + "experiment_time_between_scans": "340 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "Piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "Al-Cu", + "experiment_total_duration": "864 min", + "funding_details": "['DOE Grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "30 keV" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_45/", + "version": "1.0", + "root_version": "pub_8_gibbs_liquidsolid_v1.2", + "latest": true + } + }, + { + "source_id": "pub_32_shahani_ostwald_v2.1", + "source_name": "pub_32_shahani_ostwald", + "version": 1, + "ingest_date": "2019-03-08T15:26:09.095488Z", + "doi": "10.18126/M2Z592", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_115/", + "metadata": { + "title": "Ostwald Ripening of Faceted Si Particles in an Al-Si-Cu Melt", + "authors": [ + { + "name": "Shahani, Ashwin J.", + "given_name": "Ashwin J.", + "family_name": "Shahani", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ] + }, + { + "name": "Skinner, Kwan", + "given_name": "Kwan", + "family_name": "Skinner", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ] + }, + { + "name": "Peters, Matthew", + "given_name": "Matthew", + "family_name": "Peters", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ] + } + ], + "description": "This data was collected to study the isothermal coarsening of faceted Si particles in an Al-Si-Cu liquid. The as-cast, hyper-eutectic sample consisted of primary Si particles in a eutectic matrix. Upon heating to above the eutectic temperature (here, 650 C), the eutectic constituents melted and the Si particles were in contact with a featureless liquid. Tracking the evolution of the solid-liquid interfaces under isothermal conditions was the focus of this work. The raw data were obtained in the following sequence: (i) for the first 60 time-steps (i.e., 3D reconstructions), projections were collected continuously using 1000 projections per 180 degree revolution and a rotation rate of 9 degrees per s; (ii) for the next 20 time-steps, scans were spaced 1 min apart, using 3000 projections and a rotation rate of 3 degrees per s; (iii) for the following 20 time-steps, scans were spaced 5 min apart using the same imaging parameters; (iv) for the last 5 time-steps, the interval between scans increased to 11 minutes, using the same imaging parameters as in (ii)-(iii). Thus, a total of 105 reconstructions were collected over the course of 5 hours, where each reconstruction captured the average microstructure in a 20 s window. The motivation for collecting the X-ray projections in this manner was that the system-average length-scale increased logarithmically with time during coarsening. Therefore, this data collection scheme adequately captured the interfacial dynamics. The data here includes the raw projections. Note that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the data processing and quantitative analysis, the reader is pointed to the following publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu).", + "keywords": [ + "synchrotron", + "anisotropy", + "experiment", + "coarsening", + "4D materials science", + "X-ray computed tomography", + "molecular dynamics", + "simulation", + "microstructures", + "Al-Si-Cu alloys", + "facets", + "in situ", + "tomography", + "metals and alloys", + "ostwald ripening" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_32_shahani_ostwald_v2.1", + "mdf_source_name": "pub_32_shahani_ostwald", + "experiment_nominal_alloy_composition": "['Al (53 wt%); Si (32 wt%), Cu (15 wt%)', 'Al 53 wt%', 'Si 32 wt%', 'Cu 15 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "Gridrec algorithm", + "processing_segmentation_method": "anisotropic diffusion filter followed by thresholding", + "all_materials_included": "Al-Si-Cu", + "experiment_holding_temperature": "['isothermal at 650 C', '650 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)']", + "experiment_xray_energy": "pink beam" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_115/", + "version": "1.0", + "root_version": "pub_32_shahani_ostwald_v2.1", + "latest": true + } + }, + { + "source_id": "pub_37_shahani_mechanism_v1.2", + "source_name": "pub_37_shahani_mechanism", + "version": 1, + "ingest_date": "2019-03-08T15:34:38.547988Z", + "doi": "10.18126/M26P4H", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_112/", + "metadata": { + "title": "The Mechanism of Eutectic Growth in Highly Anisotropic Materials", + "authors": [ + { + "name": "Shahani, Ashwin J.", + "given_name": "Ashwin J.", + "family_name": "Shahani", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "description": "This data was collected to study the growth process of an irregular eutectic from a featureless liquid. An irregular eutectic (such as Al-Ge in this work) consists of a faceted constituent (e.g., Si or Ge) and a nonfaceted one (e.g., Al or Ag) which may grow in a coupled manner. The growth process of an irregular eutectic alloy remains poorly understood due to the lack of 4D (i.e., 3D space plus time) resolved data. Thus, the collected data aims to fill-in-the-gaps in our understanding of how irregular eutectics solidify. To achieve the necessary temporal and spatial resolution necessary to resolve the eutectic growth process we use the TIMBIR reconstruction algorithm, described elsewhere. In this technique, we use the following acquisition parameters: 4 sub-frames and 2700 projections per frame. Due to the small penetration depth through the heavy element Ge, the large number of projections and the high exposure time guaranteed high quality images. Included is the raw data collected in the form of the projections. The TIMBIR algorithm is open-source and can be found here: Note also that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the resulting 3D reconstruction visualization, the reader is pointed to the associated journal publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu).", + "keywords": [ + "defects", + "irregular eutectic", + "anisotropy", + "patterns", + "synchrotron", + "experiment", + "eutectic", + "solidification", + "4D materials science", + "X-ray computed tomography", + "growth", + "facets", + "Al-Ge alloys", + "in situ", + "twinning", + "metals and alloys", + "twins" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_37_shahani_mechanism_v1.2", + "mdf_source_name": "pub_37_shahani_mechanism", + "experiment_nominal_alloy_composition": "['Al (51.6 wt%); Ge (48.4 wt%)', 'Al 51.6 wt%', 'Ge 48.4 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "TIMBIR Algorithm", + "processing_segmentation_method": "median filtering followed by Otsu thresholding", + "all_materials_included": "['Al-Ge', 'Al', 'Ge']", + "experiment_holding_temperature": "['Sample was allowed to equilibrate immediately above the eutectic temperature (we use 420 C); then, the sample temperature was lowered to 417 C, which is in the eutectic regime. The sample was held at this temperature isothermally while X-ray projections were recorded continuously.', '417 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)', 'DOE (grant DE-FG02-99ER45782)']", + "experiment_xray_energy": "pink beam" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_112/", + "version": "1.0", + "root_version": "pub_37_shahani_mechanism_v1.2", + "latest": true + } + }, + { + "source_id": "pub_85_cool_cslm4_v1.2", + "source_name": "pub_85_cool_cslm4", + "version": 1, + "ingest_date": "2019-03-08T17:14:46.770017Z", + "doi": "10.18126/M2JW52", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_842/", + "metadata": { + "title": "CSLM-4 48h Coarsened PbSn (30 volume percent dendrites)", + "authors": [ + { + "name": "Cool, Thomas", + "given_name": "Thomas", + "family_name": "Cool", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter", + "given_name": "Peter", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + } + ], + "keywords": [ + "serial sectioning", + "experiment", + "dendrites", + "coarsening", + "dendrite fragmentation", + "isothermal coarsening", + "reconstruction", + "dendritic coarsening", + "metals and alloys", + "tomography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2017.01.029", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2017.01.029", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_85_cool_cslm4_v1.2", + "mdf_source_name": "pub_85_cool_cslm4", + "all_materials_included": "Pb-Sn (30 volume percent dendrites)", + "funding_details": "NASA contract NNX14AB73G" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_842/", + "version": "1.0", + "root_version": "pub_85_cool_cslm4_v1.2", + "latest": true + } + }, + { + "source_id": "pub_84_cool_cslm4_v1.2", + "source_name": "pub_84_cool_cslm4", + "version": 1, + "ingest_date": "2019-03-08T17:12:06.046711Z", + "doi": "10.18126/M2PK9Q", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_841/", + "metadata": { + "title": "CSLM-4 27h Coarsened PbSn (30 volume percent dendrites)", + "authors": [ + { + "name": "Cool, Thomas", + "given_name": "Thomas", + "family_name": "Cool", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter", + "given_name": "Peter", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + } + ], + "keywords": [ + "serial sectioning", + "experiment", + "dendrites", + "coarsening", + "dendrite fragmentation", + "isothermal coarsening", + "reconstruction", + "dendritic coarsening", + "metals and alloys", + "tomography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2017.01.029", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2017.01.029", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_84_cool_cslm4_v1.2", + "mdf_source_name": "pub_84_cool_cslm4", + "funding_details": "NASA contract NNX14AB73G", + "all_materials_included": "Pb-Sn (30 volume percent dendrites)" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_841/", + "version": "1.0", + "root_version": "pub_84_cool_cslm4_v1.2", + "latest": true + } + }, + { + "source_id": "pub_23_song_qmc_v1.2", + "source_name": "pub_23_song_qmc", + "version": 1, + "ingest_date": "2019-03-08T15:09:31.644435Z", + "doi": "10.18126/M20923", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1127/", + "metadata": { + "title": "QMC Data sets for Benchmarks and Reliable DFT Results for Spin Gaps of Small Ligand Fe(II) Complexes", + "authors": [ + { + "name": "Song, Suhwan", + "given_name": "Suhwan", + "family_name": "Song", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + }, + { + "name": "Kim, Min-Cheol", + "given_name": "Min-Cheol", + "family_name": "Kim", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + }, + { + "name": "Sim, Eunji", + "given_name": "Eunji", + "family_name": "Sim", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + }, + { + "name": "Burke, Kieron", + "given_name": "Kieron", + "family_name": "Burke", + "affiliations": [ + "Argonne National Laboratory", + "University of California, Irvine" + ] + } + ], + "keywords": [ + "QMCPACK", + "simulation", + "QMC", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "CPSFM", + "extensions": { + "mdf_source_id": "pub_23_song_qmc_v1.2", + "mdf_source_name": "pub_23_song_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1127/", + "version": "1.0", + "root_version": "pub_23_song_qmc_v1.2", + "latest": true + } + }, + { + "source_id": "pub_22_jokisaari_phase_v1.2", + "source_name": "pub_22_jokisaari_phase", + "version": 1, + "ingest_date": "2019-03-08T15:07:49.942464Z", + "doi": "10.18126/M2QS6Z", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1126/", + "metadata": { + "title": "Phase Field Benchmark II Dataset", + "authors": [ + { + "name": "Jokisaari, Andrea", + "given_name": "Andrea", + "family_name": "Jokisaari", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Voorhees, Peter", + "given_name": "Peter", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Guyer, Jonathan", + "given_name": "Jonathan", + "family_name": "Guyer", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "name": "Warren, James", + "given_name": "James", + "family_name": "Warren", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "keywords": [ + "simulation", + "metals and alloys", + "phases" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_22_jokisaari_phase_v1.2", + "mdf_source_name": "pub_22_jokisaari_phase" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1126/", + "version": "1.0", + "root_version": "pub_22_jokisaari_phase_v1.2", + "latest": true + } + }, + { + "source_id": "pub_19_zhao_variational_v1.2", + "source_name": "pub_19_zhao_variational", + "version": 1, + "ingest_date": "2019-03-07T22:52:03.862529Z", + "doi": "10.18126/M2K63Q", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1121/", + "metadata": { + "title": "Dataset for A Variational Approach to Optical Band Gaps", + "authors": [ + { + "name": "Zhao, Luning", + "given_name": "Luning", + "family_name": "Zhao", + "affiliations": [ + "University of California, Berkeley" + ] + }, + { + "name": "Neuscamman, Eric", + "given_name": "Eric", + "family_name": "Neuscamman", + "affiliations": [ + "University of California, Berkeley and Lawrence Berkeley National Laboratory" + ] + } + ], + "keywords": [ + "VMC", + "QMC", + "Optical Gaps", + "QUANTUM ESPRESSO", + "Monte Carlo", + "QMCPACK", + "simulation", + "variational Monte Carlo", + "bandgap" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "organization": "CPSFM", + "extensions": { + "mdf_source_id": "pub_19_zhao_variational_v1.2", + "mdf_source_name": "pub_19_zhao_variational" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1121/", + "version": "1.0", + "root_version": "pub_19_zhao_variational_v1.2", + "latest": true + } + }, + { + "source_id": "pub_11_gibbs_liquidsolid_v1.2", + "source_name": "pub_11_gibbs_liquidsolid", + "version": 1, + "ingest_date": "2019-03-07T22:38:29.512052Z", + "doi": "10.18126/M2VC7F", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_48/", + "metadata": { + "title": "Liquid-solid Metallic Mixture Coarsening Data - 55% solid", + "authors": [ + { + "name": "Gibbs, John W.", + "given_name": "John W.", + "family_name": "Gibbs", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Fife, Julie L.", + "given_name": "Julie L.", + "family_name": "Fife", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\nIn theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\nThe data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6", + "keywords": [ + "metals and alloys", + "experiment", + "tomography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1186/2193-9772-3-6", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1016/j.actamat.2014.01.024", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.gibbsium.org/thesis", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_11_gibbs_liquidsolid_v1.2", + "mdf_source_name": "pub_11_gibbs_liquidsolid", + "experiment_nominal_alloy_composition": "['Al (81 wt%); Cu (19 wt%)', 'Al:81 wt%', 'Cu:19 wt%']", + "experiment_pixel_size": "['1.79 um', '1.79 \\\\u00b5m']", + "experiment_time_between_scans": "44 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "Al-Cu", + "experiment_total_duration": "79 min", + "experiment_holding_temperature": "['558 C (5 C above the eutectic temperature)', '558 C', '5 C above the eutectic temperature']", + "funding_details": "['DOE Grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (Grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "polychromatic" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_48/", + "version": "1.0", + "root_version": "pub_11_gibbs_liquidsolid_v1.2", + "latest": true + } + }, + { + "source_id": "pub_7_gibbs_segmentation_v1.2", + "source_name": "pub_7_gibbs_segmentation", + "version": 1, + "ingest_date": "2019-03-07T22:31:46.475989Z", + "doi": "10.18126/M2CC73", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_41/", + "metadata": { + "title": "Dataset for Segmentation of Four-dimensional, X-ray Computed Tomography Data", + "authors": [ + { + "name": "Gibbs, John W.", + "given_name": "John W.", + "family_name": "Gibbs", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Fife, Julie L.", + "given_name": "Julie L.", + "family_name": "Fife", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \r\nIn theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \r\nThe data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \r\n 1: J.W. Gibbs, P.W. Voorhees, \"Segmentation of four-dimensional, X-ray computed tomography data\" IMMI (2014). doi: 10.1186/2193-9772-3-6", + "keywords": [ + "metals and alloys", + "experimentation", + "tomography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.gibbsium.org/thesis", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_7_gibbs_segmentation_v1.2", + "mdf_source_name": "pub_7_gibbs_segmentation", + "experiment_nominal_alloy_composition": "20 wt% Cu", + "experiment_pixel_size": "['1.44 \\\\u00b5m', '1.44 um']", + "experiment_time_between_scans": "231 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "['Al', 'Cu', 'Al-Cu', 'hypo-eutectic Al-Cu alloys']", + "experiment_total_duration": "362 min", + "experiment_holding_temperature": "558 C 5 C above the eutectic temperature", + "funding_details": "['DOE grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "25 keV" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_41/", + "version": "1.0", + "root_version": "pub_7_gibbs_segmentation_v1.2", + "latest": true + } + }, + { + "source_id": "pub_64_moody_atomic_v1.2", + "source_name": "pub_64_moody_atomic", + "version": 1, + "ingest_date": "2019-03-08T16:23:00.801411Z", + "doi": "10.18126/M26351", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1218/", + "metadata": { + "title": "Dataset for Atomic Layer Deposition of Molybdenum Oxides with Tunable Stoichiometry Enables Controllable Doping of MoS2", + "authors": [ + { + "name": "Moody, Michael J.", + "given_name": "Michael J.", + "family_name": "Moody", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Henning, Alex", + "given_name": "Alex", + "family_name": "Henning", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Jurca, Titel", + "given_name": "Titel", + "family_name": "Jurca", + "affiliations": [ + "Department of Chemistry, Northwestern University" + ] + }, + { + "name": "Shang, Ju Ying", + "given_name": "Ju Ying", + "family_name": "Shang", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Olding, Jack N.", + "given_name": "Jack N.", + "family_name": "Olding", + "affiliations": [ + "Graduate Program in Applied Physics, Northwestern University" + ] + }, + { + "name": "Weiss, Emily A.", + "given_name": "Emily A.", + "family_name": "Weiss", + "affiliations": [ + "Department of Chemistry and Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Department of Materials Science and Engineering, Department of Chemistry, Department of Medicine, Department of Electrical Engineering and Computer Science, Northwestern University" + ] + }, + { + "name": "Lohr, Tracy L.", + "given_name": "Tracy L.", + "family_name": "Lohr", + "affiliations": [ + "Department of Chemistry, Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Department of Chemistry and Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + } + ], + "keywords": [ + "experiment", + "MoS2", + "2D materials", + "semiconductors", + "ALD" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.chemmater.8b01171", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://pubs.acs.org/doi/10.1021/acs.chemmater.8b01171", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_64_moody_atomic_v1.2", + "mdf_source_name": "pub_64_moody_atomic", + "all_materials_included": "['MoO2', 'MoO3', 'MoS2']", + "funding_details": "['This work was supported by the NSF via EFRI-1433510 (M.J.M., T.J.) and DMR-1720139 (A.H., J.N.O.) and by U.S. Department of Commerce, National Institute of Standards and Technology under financial assistance award number 70NANB14H012 (J.Y.S, H.B, I.B.).', 'It made use of the J. B. Cohen X-ray Diffraction Facility and the Keck-II, EPIC, and SPID facilities of the NUANCE Center at Northwestern University, which have received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF NNCI-1542205); the MRSEC program (NSF DMR-1121262) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the Keck Foundation; and the State of Illinois, through the IIN.', 'M.J.M. gratefully acknowledges support from the Ryan Fellowship and the Northwestern University International Institute for Nanotechnology.', 'A.H. acknowledges the support of a Research Fellowship from the Deutsche Forschungsgemeinschaft (Grant HE 7999/1-1).', 'H.B. acknowledges support from the NSERC Postgraduate Scholarship-Doctoral Program and National Science Foundation Graduate Research Fellowship.']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1218/", + "version": "1.0", + "root_version": "pub_64_moody_atomic_v1.2", + "latest": true + } + }, + { + "source_id": "parate_histaminesensing6_v1.1", + "source_name": "parate_histaminesensing6", + "version": 1, + "ingest_date": "2020-09-26T01:03:48.763987Z", + "doi": "10.18126/htxv-4ioz", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam_protected/parate_histaminesensing6_v1.1/", + "metadata": { + "title": "Aerosol-jet-printed graphene electrochemical histamine sensors for food safety monitoring", + "authors": [ + { + "name": "Parate, Kshama", + "given_name": "Kshama", + "family_name": "Parate", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Pola, C\u00edcero C", + "given_name": "C\u00edcero C", + "family_name": "Pola", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Rangnekar, Sonal V", + "given_name": "Sonal V", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Mendivelso-Perez, Deyny L", + "given_name": "Deyny L", + "family_name": "Mendivelso-Perez", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Smith, Emily A", + "given_name": "Emily A", + "family_name": "Smith", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Hersam, Mark C", + "given_name": "Mark C", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Gomes, Carmen L", + "given_name": "Carmen L", + "family_name": "Gomes", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Claussen, Jonathan C", + "given_name": "Jonathan C", + "family_name": "Claussen", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + } + ], + "description": "Carbon nanomaterials such as graphene exhibit unique material properties including high electrical conductivity, surface area, and biocompatibility that have the potential to significantly improve the performance of electrochemical sensors. Since in-field electrochemical sensors are typically disposable, they require materials that are amenable to low-cost, high-throughput, and scalable manufacturing. Conventional graphene devices based on low-yield chemical vapor deposition techniques are too expensive for such applications, while low-cost alternatives such as screen and inkjet printing do not possess sufficient control over electrode geometry to achieve favorable electrochemical sensor performance. In this work, aerosol jet printing (AJP) is used to create high-resolution (~40 \u03bcm line width) interdigitated electrodes (IDEs) on flexible substrates, which are then converted into histamine sensors by covalently linking monoclonal antibodies to oxygen moieties created on the graphene surface through a CO2 thermal annealing process. The resulting electrochemical sensors exhibit a wide histamine sensing range of 6.25\u2013200 ppm (56.25 \u03bcM\u20131.8 mM) and a low detection limit of 3.41 ppm (30.7 \u03bcM) within actual tuna broth samples. These sensor metrics are significant since histamine levels over 50 ppm in fish induce adverse health effects including severe allergic reactions (e.g. Scombroid food poisoning). Beyond the histamine case study presented here, the AJP and functionalization process can likely be generalized to a diverse range of sensing applications including environmental toxin detection, foodborne pathogen detection, wearable health monitoring, and health diagnostics.", + "keywords": [ + "graphene", + "biosensors", + "experiment", + "histamine", + "aerosol jet printing", + "ink", + "electrochemical sensing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1088/2053-1583/ab8919", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "Hersam Group", + "extensions": { + "mdf_source_id": "parate_histaminesensing6_v1.1", + "mdf_source_name": "parate_histaminesensing6" + }, + "download_url": "https://data.materialsdatafacility.org/hersam_protected/parate_histaminesensing6_v1.1/", + "version": "1.0", + "root_version": "parate_histaminesensing6_v1.1", + "latest": true + } + }, + { + "source_id": "moraes_chargetransportgrec2_v1.1", + "source_name": "moraes_chargetransportgrec2", + "version": 1, + "ingest_date": "2020-09-26T01:04:28.818425Z", + "doi": "10.18126/wxl1-ih5d", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam_protected/moraes_chargetransportgrec2_v1.1/", + "metadata": { + "title": "Elucidating charge transport mechanisms in cellulose-stabilized graphene inks", + "authors": [ + { + "name": "de Moraes, Ana C. M.", + "given_name": "Ana C. M.", + "family_name": "de Moraes", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Obrzut, Jan", + "given_name": "Jan", + "family_name": "Obrzut", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Downing, Julia R.", + "given_name": "Julia R.", + "family_name": "Downing", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Chaney, Lindsay E.", + "given_name": "Lindsay E.", + "family_name": "Chaney", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Patel, Dinesh K.", + "given_name": "Dinesh K.", + "family_name": "Patel", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Elmquist, Randolph E.", + "given_name": "Randolph E.", + "family_name": "Elmquist", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ] + } + ], + "description": "Solution-processed graphene inks that use ethyl cellulose as a polymer stabilizer are blade-coated into large-area thin films. Following blade-coating, the graphene thin films are cured to pyrolyze the cellulosic polymer, leaving behind an sp2-rich amorphous carbon residue that serves as a binder in addition to facilitating charge transport between graphene flakes. Systematic charge transport measurements, including temperature-dependent Hall effect and non-contact microwave resonant cavity characterization, reveal that the resulting electrically percolating graphene thin films possess high mobility (\u2248160 cm2 V\u22121 s\u22121), low energy gap, and thermally activated charge transport, which develop weak localization behavior at cryogenic temperatures.", + "keywords": [ + "experiment", + "graphene", + "semiconductors", + "2D materials", + "inks" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1039/D0TC03309J", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "Hersam Group", + "extensions": { + "mdf_source_id": "moraes_chargetransportgrec2_v1.1", + "mdf_source_name": "moraes_chargetransportgrec2" + }, + "download_url": "https://data.materialsdatafacility.org/hersam_protected/moraes_chargetransportgrec2_v1.1/", + "version": "1.0", + "root_version": "moraes_chargetransportgrec2_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_545_v1.1", + "source_name": "mdr_item_545", + "version": 1, + "ingest_date": "2018-11-15T18:18:47.431977Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_545_v1/", + "metadata": { + "title": "Al\u2013Pt First-principles calculations and thermodynamic modeling", + "authors": [ + { + "name": "Kim, D.E.", + "given_name": "D.E.", + "family_name": "Kim", + "affiliations": [ + "Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA USA" + ] + }, + { + "name": "Liu, Z.-K.", + "given_name": "Z.-K.", + "family_name": "Liu", + "affiliations": [ + "Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA USA" + ] + }, + { + "name": "Manga, V.R.", + "given_name": "V.R.", + "family_name": "Manga", + "affiliations": [ + "Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA USA" + ] + }, + { + "name": "Prins, S.N.", + "given_name": "S.N.", + "family_name": "Prins", + "affiliations": [ + "Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA USA" + ] + } + ], + "keywords": [ + "Al\u2013Pt" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "license": { + "name": "Attribution-ShareAlike 3.0 United States", + "url": "http://creativecommons.org/licenses/by-sa/3.0/us/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_545_v1.1", + "mdf_source_name": "mdr_item_545" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_545_v1/", + "version": "1.0", + "root_version": "mdr_item_545_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_141_v1.1", + "source_name": "mdr_item_141", + "version": 1, + "ingest_date": "2018-11-15T16:58:42.905971Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_141_v1/", + "metadata": { + "title": "NaNbO3\u2013KNbO3: Can spinodal decomposition generate relaxor ferroelectricity?", + "authors": [ + { + "name": "Burton, Benjamin P.", + "given_name": "Benjamin P.", + "family_name": "Burton", + "affiliations": [ + "Materials Science and Engineering Laboratory, Ceramics Division National Institute of Standards and Technology, Gaithersburg, MD USA" + ] + } + ], + "keywords": [ + "NaNbO3\u2013KNbO3" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "license": { + "name": "Attribution-NonCommercial-ShareAlike 3.0 United States", + "url": "http://creativecommons.org/licenses/by-nc-sa/3.0/us/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_141_v1.1", + "mdf_source_name": "mdr_item_141" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_141_v1/", + "version": "1.0", + "root_version": "mdr_item_141_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_146_v1.1", + "source_name": "mdr_item_146", + "version": 1, + "ingest_date": "2018-11-15T17:04:10.315060Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_146_v1/", + "metadata": { + "title": "Au - HCP - Migration energy", + "authors": [ + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "keywords": [ + "Computational File Repository Categories::METHODS::First Principles" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2013, + "resource_type": "Dataset", + "license": { + "name": "Attribution-NonCommercial-NoDerivs 3.0 United States", + "url": "http://creativecommons.org/licenses/by-nc-nd/3.0/us/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_146_v1.1", + "mdf_source_name": "mdr_item_146" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_146_v1/", + "version": "1.0", + "root_version": "mdr_item_146_v1.1", + "latest": true + } + }, + { + "source_id": "mdr_item_1045_v1.1", + "source_name": "mdr_item_1045", + "version": 1, + "ingest_date": "2018-11-15T20:03:33.438586Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_1045_v1/", + "metadata": { + "title": "Al-Mg COST 507 Thermochemical database for light metal alloys", + "authors": [ + { + "name": "Ansara, I", + "given_name": "I", + "family_name": "Ansara" + }, + { + "name": "Dinsdale, AT", + "given_name": "AT", + "family_name": "Dinsdale" + }, + { + "name": "Rand, MH", + "given_name": "MH", + "family_name": "Rand" + } + ], + "keywords": [ + "Light metal alloys" + ], + "publisher": "NIST Materials Data Repository", + "publication_year": 2016, + "resource_type": "Dataset", + "license": { + "name": "CC0 2.0 Attribution", + "url": "https://creativecommons.org/licenses/by/2.0/" + }, + "organization": "National Institute of Standards and Technology", + "extensions": { + "mdf_source_id": "mdr_item_1045_v1.1", + "mdf_source_name": "mdr_item_1045" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/mdr_item_1045_v1/", + "version": "1.0", + "root_version": "mdr_item_1045_v1.1", + "latest": true + } + }, + { + "source_id": "pub_18_xie_high_v1.2", + "source_name": "pub_18_xie_high", + "version": 1, + "ingest_date": "2019-03-07T22:50:24.149106Z", + "doi": "10.18126/M28S79", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1119/", + "metadata": { + "title": "High and low thermal conductivity of amorphous macromolecules", + "authors": [ + { + "name": "Xie, Xu", + "given_name": "Xu", + "family_name": "Xie", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Yang, Kexin", + "given_name": "Kexin", + "family_name": "Yang", + "affiliations": [ + "Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Li, Dongyao", + "given_name": "Dongyao", + "family_name": "Li", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Tsai, Tsung-Han", + "given_name": "Tsung-Han", + "family_name": "Tsai", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Shin, Jungwoo", + "given_name": "Jungwoo", + "family_name": "Shin", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Braun, Paul V.", + "given_name": "Paul V.", + "family_name": "Braun", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Cahill, David G.", + "given_name": "David G.", + "family_name": "Cahill", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + } + ], + "description": "Prior to the TDTR measurement, a thin layer of Al optical transducer (\u224890 nm) was deposited (via magnetron sputtering) onto the sample. The water contents in the sample after Al coating were expected to be negligible, due to the long-time pumping in vacuum before deposition (90 \u00b0C baking for 2 h at 1\u00d7 10^-4 Torr, followed by pumping at <10^\u22127 Torr overnight). The Al coating prevents the water vapor from diffusing into the sample. To check these assumptions, AFM was used to verify the thicknesses of several hydrophilic samples (PANa, PALi, PVPA, PMCP, PDDA). A portion of the polymers was scratched off the substrate by a razor blade before Al deposition to create a step edge that facilitates the AFM measurement.\r\nThe height of the step edges matched with the thicknesses measured by ellipsometry at 90 \u00b0C to within the combined experimental uncertainties of <8 nm. In a TDTR measurement, the ratio of in-phase\r\n(Vin) and out-of-phase (Vout) signals of the reflected probe beam recorded by an radio frequency (RF) lock-in amplifier with the reference frequency set at f is measured. Changing the modulation\r\nfrequency f between 1 and 9 MHz allowed the modification of the sensitivity of Vin/Vout with respect to thermal conductivity and heat capacity for thin films with thicknesses of \u2248100 \u223c 200 nm. We typically\r\nacquire data for Vin/Vout at three modulation frequencies, 1.1, 5.1, and 9.1 MHz, as a function of pump-probe delay time from \u221225 to 3600 ps. To extract the thermal conductivity and heat capacity from the TDTR data, we used a heat diffusion model to fit the three Vin/Vout curves simultaneously. The model consists of three layers: the Al transducer, the thin film for investigation, and the Si substrate. All\r\nthe geometric parameters and thermal properties of the Al transducer and Si substrate are measured separately or adopted from literature values; the only fitting variables are and C of\r\nthe thin film. The effects of the interfacial thermal conductance for the Al/polymer and polymer/Si interfaces are small since the Kapitza lengths of the interfaces are on the order of a\r\nfew nanometers. Therefore both of the interfacial conductances is set to a nominal value of 100 MW m^\u22122 K^\u22121 in the modeling. The TDTR measurements is validated by testing a reference\r\nsample of PMMA. The extracted and C are 0.19 \u00b1 0.02 W m^\u22121 K^\u22121 and 1.60 \u00b1 0.15 J cm^\u22123 K^\u22121, respectively, which is consistent with prior studies.", + "keywords": [ + "experiment", + "thermal conductivity", + "macromolecules", + "polymers" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1103/PhysRevB.95.035406", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://dx.doi.org/10.1103/PhysRevB.95.035406", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_18_xie_high_v1.2", + "mdf_source_name": "pub_18_xie_high", + "funding_details": "Air Force Office of Scientific Research (AFOSR); Multidisciplinary Research Program of the University Research Initiative (MURI) (Grant No. FA9550-12-1-0002);", + "all_materials_included": "['PALi: Poly(acrylic acid lithium salt)', 'PANa: Poly(acrylic acid sodium salt)', 'PACa: Poly(acrylic acid calcium salt)', 'PAFe: Poly(acrylic acid iron salt)', 'PACu: Poly(acrylic acid copper salt)', 'PVPA: Poly(vinylphosphonic acid)', 'PVPLi: Poly(vinylphosphonic acid lithium salt)', 'PVPMg: Poly(vinylphosphonic acid magnesium salt)', 'PVPCa: Poly(vinylphosphonic acid calcium salt)', 'PVSNa: Poly(vinylsulfonic acid sodium salt)', 'PDDA: Poly(diallyldimethylammonium chloride)', 'PAH: Poly(allylamine hydrochloride)', 'PMPC: Poly (2-methacryloyloxyethyl phosphorylcholine)', 'PS: Polystyrene', \"ADP: 4,4' -(1,3-adamantanediyl)diphenol\", 'DSQ: Dodecaphenyl silsesquioxanes', 'GHSQ: (3-glycidyl)propoxyheptaisobutyl silsesquioxanes', 'PC71BM: [6,6]-phenyl C71 butyric acid methyl ester']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1119/", + "version": "1.0", + "root_version": "pub_18_xie_high_v1.2", + "latest": true + } + }, + { + "source_id": "pub_67_marras_polyelectrolyte_v1.2", + "source_name": "pub_67_marras_polyelectrolyte", + "version": 1, + "ingest_date": "2019-03-08T16:32:04.022671Z", + "doi": "10.18126/M2QW8R", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1264/", + "metadata": { + "title": "Dataset for Polyelectrolyte Complexation of Oligonucleotides by Charged Hydrophobic \u2013 Neutral Hydrophilic Block Polymers", + "authors": [ + { + "name": "Marras, Alexander E.", + "given_name": "Alexander E.", + "family_name": "Marras", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Vieregg, Jeffrey R.", + "given_name": "Jeffrey R.", + "family_name": "Vieregg", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Ting, Jeffrey M.", + "given_name": "Jeffrey M.", + "family_name": "Ting", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Rubien, Jack D.", + "given_name": "Jack D.", + "family_name": "Rubien", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Tirrell, Matthew V.", + "given_name": "Matthew V.", + "family_name": "Tirrell", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "\u201cPolyelectrolyte Complexation of Oligonucleotides by Charged Hydrophobic \u2013 Neutral Hydrophilic Block Polymers\u201c\r\n\r\nAlexander E. Marras, Jeffrey R. Vieregg, Jeffrey M. Ting, Jack D. Rubien, Matthew V. Tirrell\r\n\r\nInstitute for Molecular Engineering, University of Chicago, Chicago, IL 60637\r\n\r\ncontact via twitter @AEMarras, @J_Ting1 or jvieregg@uchicago.edu\r\n\r\npreprint DOI: 10.26434/chemrxiv.7456322.v1\r\n\r\nFiles include background subtracted small angle X-ray scattering (SAXS) data from the Advanced Photon Source at Argonne National Laboratory. Data is organized by cation type (poly-l-lysine-PEG vs. poly-vinylbenzyltrimethyl-ammonium) and nucleic acid hybridization (single-stranded vs. double-stranded).", + "keywords": [ + "spectroscopy", + "experiment", + "electrochemistry", + "polyelectrolytes", + "phase separation", + "phases", + "oligonucleotides", + "nanoparticles", + "complex coacervation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.26434/chemrxiv.7456322.v1", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.dx.doi.org/10.26434/chemrxiv.7456322.v1", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_67_marras_polyelectrolyte_v1.2", + "mdf_source_name": "pub_67_marras_polyelectrolyte", + "funding_details": "This work was supported by the U.S. Department of Energy Office of Science, Program in Basic Energy Sciences, Materials Sciences and Engineering Division and used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. J.M.T. acknowledges support from the NIST-CHiMaD Postdoctoral Fellowship, supported by the U.S. Department of Commerce, National Institute of Standards and Technology (NIST) through the Center for Hierarchical Materials Design (CHiMaD) under financial assistance award 70NANB14H012.", + "all_materials_included": "['poly-l-lysine-PEG', 'poly-vinylbenzyltrimethyl-ammonium', 'nucleic acid']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1264/", + "version": "1.0", + "root_version": "pub_67_marras_polyelectrolyte_v1.2", + "latest": true + } + }, + { + "source_id": "pub_17_jain_vacancy_v1.2", + "source_name": "pub_17_jain_vacancy", + "version": 1, + "ingest_date": "2019-03-07T22:48:44.109038Z", + "doi": "10.18126/M2R04K", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1113/", + "metadata": { + "title": "Vacancy Mediated and Interstitial Solute Transport in Zr from Density Functional Theory Calculations", + "authors": [ + { + "name": "Jain, Abhinav C. P.", + "given_name": "Abhinav C. P.", + "family_name": "Jain", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + }, + { + "name": "Burr, Patrick A.", + "given_name": "Patrick A.", + "family_name": "Burr", + "affiliations": [ + "The University of New South Wales" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "Imperial College London" + ] + } + ], + "description": "The Interstitial folder contains the DFT dataset for the interstitial site and migration energies of all solutes, scripts required to extract the data and compute diffusivity. The Vacancy_Mediated folder contains the DFT dataset for the solute-vacancy binding and vacancy migration energies, scripts required to extract the data and compute diffusivity. The scripts/ directory in site Interstitial and Vacancy_Mediated folders contains the python and bash scripts, which extracts the input data for diffusion model from DFT dataset. The extraction of data can be done by running ExtractDFT.sh scripts for Interstitial or Vacancy_Mediated data for each solute located inside the /scripts directory of either data set. The vacancy mediated extracted data for each solute contains solute-vacancy binding energies and migration barriers in one file called solute_elist.yaml, and frequencies for sites and transitions in another file called solute_flist.yaml. Similarly, the interstitial data is separated into two yaml files for each solute. For the convenience, these files are already generated. The Diffusion folders inside Vacancy_Mediated and Interstitial directories each have subdirectories for the solutes. These solute sub-directories have scripts that read the extracted data and compute diffusivity. The scripts/ directories of each data set also consist of a master script \"TransportCalculation.sh\" which computes the diffusion coefficients of each solute instead of executing them manually. The results of diffusion calculations and arrhenius fits are saved under corresponding solute folders within the Diffusion directory. The \"POTCARS.txt\" file details the POTCAR used for Zr and solutes. Note: Before running \"ExtractDFT.sh\", make sure the pythonpath is correctly set in FrequencyCalculation.sh and AttemptFrequency.sh which are present in scripts/ directory. The python 3.4 and above with modules yaml, numpy, argparse, os, scipy, h5py and Onsager is required. The Onsager module can be obtained from [ONSAGER,http://dallastrinkle.github.io/Onsager].", + "keywords": [ + "DFT", + "simulation", + "diffusion" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_17_jain_vacancy_v1.2", + "mdf_source_name": "pub_17_jain_vacancy", + "all_materials_included": "Zr" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1113/", + "version": "1.0", + "root_version": "pub_17_jain_vacancy_v1.2", + "latest": true + } + }, + { + "source_id": "ab_initio_solute_database_v1.2", + "source_name": "ab_initio_solute_database", + "version": 1, + "ingest_date": "2018-11-24T08:12:11.852893Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ab_initio_solute_database_v1-2/", + "metadata": { + "title": "High-throughput Ab-initio Dilute Solute Diffusion Database", + "authors": [ + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Mayeshiba, Tam", + "given_name": "Tam", + "family_name": "Mayeshiba", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Henry, Wu", + "given_name": "Wu", + "family_name": "Henry", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. We apply a correction method for solute diffusion in alloys using experimental and simulated values of host self-diffusivity.", + "keywords": [ + "dilute", + "solute", + "DFT", + "diffusion", + "dataset", + "database", + "high-throughput" + ], + "publisher": "MDF (placeholder)", + "publication_year": 2016, + "resource_type": "JSON", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1038/sdata.2016.54", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "extensions": { + "mdf_source_id": "ab_initio_solute_database_v1.2", + "mdf_source_name": "ab_initio_solute_database" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/ab_initio_solute_database_v1-2/", + "version": "1.0", + "root_version": "ab_initio_solute_database_v1.2", + "latest": true + } + }, + { + "source_id": "oqmd_v13.13", + "source_name": "oqmd", + "version": 13, + "ingest_date": "2018-11-09T19:44:43.687681Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/oqmd_v13/", + "metadata": { + "title": "The Open Quantum Materials Database", + "authors": [ + { + "name": "Wolverton, Chris", + "given_name": "Chris", + "family_name": "Wolverton", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kirklin, Scott", + "given_name": "Scott", + "family_name": "Kirklin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hegde, Vinay", + "given_name": "Vinay", + "family_name": "Hegde", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "The OQMD is a database of DFT-calculated thermodynamic and structural properties.", + "keywords": [ + "simulation", + "DFT", + "high-throughput", + "database", + "energy storage", + "metals", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2013, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1007/s11837-013-0755-4", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "http://dx.doi.org/10.1038/npjcompumats.2015.10", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "extensions": { + "mdf_source_id": "oqmd_v13.13", + "mdf_source_name": "oqmd" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/oqmd_v13/", + "version": "13.0", + "root_version": "oqmd", + "latest": true + } + }, + { + "source_id": "shang_situ_transport_deposition_v1.1", + "source_name": "shang_situ_transport_deposition", + "version": 1, + "ingest_date": "2020-12-04T19:36:57.201106Z", + "doi": "10.18126/ypnx-y3of", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/lauhon_protected/shang_situ_transport_deposition_v1.1/", + "metadata": { + "title": "In Situ Transport Measurements Reveal Source of Mobility Enhancement of MoS2 and MoTe2 during Dielectric Deposition", + "authors": [ + { + "name": "Shang, Ju Ying", + "given_name": "Ju Ying", + "family_name": "Shang", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Moody, Michael J.", + "given_name": "Michael J.", + "family_name": "Moody", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Chen, Jiazhen", + "given_name": "Jiazhen", + "family_name": "Chen", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Krylyuk, Sergiy", + "given_name": "Sergiy", + "family_name": "Krylyuk", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Davydov, Albert V.", + "given_name": "Albert V.", + "family_name": "Davydov", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Northwestern University" + ] + } + ], + "keywords": [ + "semiconductors", + "2D materials", + "experiment", + "atomic layer deposition", + "ALD", + "transition metal dichalcogenide" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsaelm.0c00085", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "Lauhon Group", + "extensions": { + "mdf_source_id": "shang_situ_transport_deposition_v1.1", + "mdf_source_name": "shang_situ_transport_deposition" + }, + "download_url": "https://data.materialsdatafacility.org/lauhon_protected/shang_situ_transport_deposition_v1.1/", + "version": "1.0", + "root_version": "shang_situ_transport_deposition_v1.1", + "latest": true + } + }, + { + "source_id": "pub_3_reyes_microstructures_v1.1", + "source_name": "pub_3_reyes_microstructures", + "version": 1, + "ingest_date": "2019-03-08T20:42:25.113437Z", + "doi": "10.18126/M2N920", + "endpoint_path": "globus://61177e74-e1ce-11e7-8031-0a208f818180/published/publication_1060/", + "metadata": { + "title": "\u03b3+\u03b3\u2019 Microstructures in the Co-Ta-V Ternary System", + "authors": [ + { + "name": "Tirado, Fernando Reyes", + "given_name": "Fernando Reyes", + "family_name": "Tirado", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Toinin, Jacques Perrin", + "given_name": "Jacques Perrin", + "family_name": "Toinin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Dunand, David", + "given_name": "David", + "family_name": "Dunand", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "The Co-Ta-V ternary systems are investigated in a search for L12-ordered \u03b3\u2019 precipitation. Alloys are arc-melted, homogenized at 1250 \u00b0C, and aged at 900 \u00b0C for 2 h. 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M. Jokisaari, S. S. Naghavi, C. Wolverton, P. W. Voorhees, O. G. Heinonen, Predicting the morphologies of gamma prime precipitates in cobalt-based superalloys, submitted as a preprint to arXiv and accepted to Acta Materialia.\r\n\r\nPlease contact O. G. Heinonen (heinonen@anl.gov) or A. M. Jokisaari (andrea.jokisaari@northwestern.edu) with questions, etc.\r\n\r\nThe mesh data may be visualized with Paraview.\r\n\r\nThere are three main directories: \r\n1) phase field code, which contains the .h and .C files for the MOOSE-based phase field simulations.\r\n2) phase field data, which contains the outputs of the phase field simulations. \r\n3) DFT data, which contains the data used for the DFT portion of the work.\r\n\r\nEach phase field data set contains the solutions (meshes) in .e files, postprocessor data in a .csv file, and the input in a .i file.\r\n\r\nThe phase field data was generated by a MOOSE-based application, including the phase_field module (see http://mooseframework.org). To run this code, you will need to generate your own MOOSE-based application, add the code to the appropriate directories and register the new code in the src/base/MyApp.C file (see http://mooseframework.com/wiki/MooseSystems/Kernels/ for an example). Because this code is in the Materials Data Facility, it is not being kept up-to-date - you may need to update it for API changes within MOOSE or the phase_field module.", + "keywords": [ + "superalloy", + "Co", + "Al", + "experimental", + "cobalt", + "W", + "microstructure", + "simulation", + "microstructures", + "CHiMaD", + "metals and alloys", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_34_jokisaari_simulated_v1.2", + "mdf_source_name": "pub_34_jokisaari_simulated", + "funding_details": "Financial assistance award: 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design", + "all_materials_included": "['Co-Al-W', 'Co', 'Al', 'W']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_630/", + "version": "1.0", + "root_version": "pub_34_jokisaari_simulated_v1.2", + "latest": true + } + }, + { + "source_id": "pub_59_miao_loadpartitioning_v1.2", + "source_name": "pub_59_miao_loadpartitioning", + "version": 1, + "ingest_date": "2019-03-08T16:12:39.374730Z", + "doi": "10.18126/M28G6T", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_174/", + "metadata": { + "title": "Dataset for \"Load-partitioning in an Oxide Dispersion-strengthened 310 Steel at Elevated Temperatures\"", + "authors": [ + { + "name": "Miao, Yinbin", + "given_name": "Yinbin", + "family_name": "Miao" + }, + { + "name": "Mo, Kun", + "given_name": "Kun", + "family_name": "Mo" + }, + { + "name": "Zhou, Zhangjian", + "given_name": "Zhangjian", + "family_name": "Zhou" + }, + { + "name": "Liu, Xiang", + "given_name": "Xiang", + "family_name": "Liu" + }, + { + "name": "Lan, Kuan-Che", + "given_name": "Kuan-Che", + "family_name": "Lan" + }, + { + "name": "Zhang, Guangming", + "given_name": "Guangming", + "family_name": "Zhang" + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park" + }, + { + "name": "Almer, Jonathan", + "given_name": "Jonathan", + "family_name": "Almer" + }, + { + "name": "Stubbins, James", + "given_name": "James", + "family_name": "Stubbins" + } + ], + "description": "Here the high temperature tensile performance of an oxide dispersion-strengthened (ODS) 310 steel is reported upon. The microstructure of the steel was examined through both transmission electron microscopy (TEM) and synchrotron scattering. In situ synchrotron X-ray tensile investigation was performed at a variety of temperatures, from room temperature up to 800 \u25e6C. Pyrochlore structure yttrium titanate and sodium chloride structure titanium nitride phases were identified in the steel along with an austenite matrix and marginal residual a -martensite. The inclusion phases strengthen the steel by taking extra load through particle-dislocation interaction during plastic deformation or dislocation creep procedures. As temperature rises, lattice strain measurement implies that the load partitioning effect of conventional precipitate phases starts to diminish, whereas those ultra-fine oxygen-enriched nanoparticles continue to maintain a considerable amount of extra lattice strain. Introduction of oxygen-enriched nanoparticles in austenitic steel is shown to improve the high temperature performance, making austenitic ODS steels promising for advanced nuclear applications.", + "keywords": [ + "oxides", + "experiment", + "HEDM", + "microscopy", + "microstructures", + "metals and alloys", + "steel" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1016/j.matdes.2016.09.015", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.sciencedirect.com/science/article/pii/S0264127516311728", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_59_miao_loadpartitioning_v1.2", + "mdf_source_name": "pub_59_miao_loadpartitioning", + "funding_details": "This work was supported by 973 DOE INL120293. The TEM experiments were carried out in part at the Frederick Seitz Materials Research Laboratory Central Facilities, University of Illinois, which is partially supported by the U.S. Department of Energy (DOE) under Grants DEFG02-07ER46453 and DE-FG02-07ER46471. The author gratefully acknowledge the support of the International Institute for Carbon Neutral Energy Research (WPI-I2CNER), sponsored by the World Premier International Research Center Initiative (WPI), Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The efforts involving Argonne National Laboratory were sponsored under Contract no. DE-AC02-06CH11357 between UChicago Argonne, LLC and the U.S. Department of Energy.", + "all_materials_included": "Fe-24.0Cr-18.3Ni-1,9Mo-0.3Ti-0.35Y2O3" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_174/", + "version": "1.0", + "root_version": "pub_59_miao_loadpartitioning_v1.2", + "latest": true + } + }, + { + "source_id": "pub_109_sangwan_selfaligned_v1.2", + "source_name": "pub_109_sangwan_selfaligned", + "version": 1, + "ingest_date": "2019-03-08T20:20:25.627162Z", + "doi": "10.18126/M2FK9J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_1109/", + "metadata": { + "title": "Self-Aligned van der Waals Heterojunction Diodes and Transistors", + "authors": [ + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Beck, Megan E.", + "given_name": "Megan E.", + "family_name": "Beck", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Henning, Alex", + "given_name": "Alex", + "family_name": "Henning", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Luo, Jiajia", + "given_name": "Jiajia", + "family_name": "Luo", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kang, Junmo", + "given_name": "Junmo", + "family_name": "Kang", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Inbar, Hadass", + "given_name": "Hadass", + "family_name": "Inbar", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Data corresponds to the demonstration of a novel self-aligned method to fabricate transistors and van der Waals heterojunctions based on 2D materials. Monolayer MoS2 is grown by chemical vapor deposition, and few-layer black phosphorus is mechanically exfoliated and transferred on top of MoS2. For self-aligned transistors, a dielectric extension is obtained by using natural undercut in photoresist and e-beam resist profiles. The channel length of ~150 nm is obtained, and resulting devices showed better current saturation than the literature reports on conventional short-channel transistors. The observed behavior is due to a source-gating effect, as verified by commercial device simulator Sentaurus TCAD. Similarly, BP-MoS2 heterojunction diode showed significantly better electrostatic control than lateral or vertical heterojunction between these two semiconductors. This behavior, as verified from device simulator, also originates from the novel self-aligned structure. The approach is generalized to mixed dimensional heterojunction between pentacene-MoS2.", + "keywords": [ + "van der Waals heterojunction", + "self-aligned", + "short-channel", + "source-gated", + "current saturation", + "p-n diode", + "anti-ambipolar", + "mixed dimensional", + "MoS2", + "black phosophorus", + "drift-diffusion model", + "screening", + "mobility" + ], + "publisher": "Nano Letters", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.7b05177", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://pubs.acs.org/doi/abs/10.1021/acs.nanolett.7b05177", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_109_sangwan_selfaligned_v1.2", + "mdf_source_name": "pub_109_sangwan_selfaligned", + "funding_details": "['NSF DMR-1720139', 'NSF EFRI-1433510', 'NIST CHiMaD 70NANB14H012', 'ONR N00014-16-1-3179']", + "all_materials_included": "['MoS2', 'black phosphorus']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_1109/", + "version": "1.0", + "root_version": "pub_109_sangwan_selfaligned_v1.2", + "latest": true + } + }, + { + "source_id": "pub_110_sangwan_multiterminal_v1.2", + "source_name": "pub_110_sangwan_multiterminal", + "version": 1, + "ingest_date": "2019-03-08T20:22:05.021577Z", + "doi": "10.18126/M29W68", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_1110/", + "metadata": { + "title": "Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide", + "authors": [ + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Lee, Hong-Sub", + "given_name": "Hong-Sub", + "family_name": "Lee", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Beck, Megan", + "given_name": "Megan", + "family_name": "Beck", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Chen, Kan-Sheng", + "given_name": "Kan-Sheng", + "family_name": "Chen", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Data corresponds to the demonstration of the first memtransistor on monolayer MoS2. Polycrystalline monolayer MoS2 was grown by chemical vapor deposition with grain sizes of 3-5 microns. Memtransistor devices were fabricated on Si substrates coated with 300 nm thermal oxide by following custom-made photolithography and reactive ion etching recipes. Characterization of the devices using atomic force microscopy, electrostatic force microscopy, and cryogenic measurement revealed switching mechanism governed by a dynamically tunable Schottky barrier at contact. Schottky barrier tuning, as modeled by a device physics model, arises from the migration of defects near metal edge possibly assisted by grain boundaries. The devices show large resistive switching ratios, large gate-tunability of resistive switching ratio, retention of distinct states for at least 24 hours, the endurance of >400 cycles, and some device to device variability. The device is compatible with multi-terminal architecture thus mimicking biological neurons.", + "keywords": [ + "MoS2", + "memristor", + "memtransistor", + "gate-tunable", + "long-term potentiation", + "spike-timing-dependenct plasticity", + "heterosynaptic plasticity", + "defects" + ], + "publisher": "Nature", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/nature25747", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://www.nature.com/articles/nature25747", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_110_sangwan_multiterminal_v1.2", + "mdf_source_name": "pub_110_sangwan_multiterminal", + "funding_details": "['NSF DMR-1720139', 'NSF EFRI-1433510', 'NIST CHiMaD 70NANB14H012', 'ONR N00014-16-1-3179', 'NSF ECCS-1542205']", + "all_materials_included": "MoS2" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_1110/", + "version": "1.0", + "root_version": "pub_110_sangwan_multiterminal_v1.2", + "latest": true + } + }, + { + "source_id": "pub_104_homan_ultrafast_v1.2", + "source_name": "pub_104_homan_ultrafast", + "version": 1, + "ingest_date": "2019-03-08T20:02:51.312747Z", + "doi": "10.18126/M2405C", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_810/", + "metadata": { + "title": "Ultrafast Exciton Dissociation and Long-Lived Charge Separation in a Photovoltaic Pentacene\u2212MoS2 van der Waals Heterojunction", + "authors": [ + { + "name": "Homan, Stephanie B.", + "given_name": "Stephanie B.", + "family_name": "Homan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Weiss, Emily A.", + "given_name": "Emily A.", + "family_name": "Weiss", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data corresponds to the study of the dynamics of charge transfer in an ultrathin p-n heterojunction of the organic molecule of pentacene and monolayer MoS2. The transient absorption spectroscopy measurements show that exciton dissociation occurs on an ultrafast timescale and that the charge separation is long-lived with respect to recombination and trapping mechanisms. DOI: 10.1021/acs.nanolett.6b03704", + "keywords": [ + "organic", + "transition metal dichalcogenide", + "transient absorption spectroscopy", + "ultrafast", + "charge transfer" + ], + "publisher": "Nano Letters", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.6b03704", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/abs/10.1021/acs.nanolett.6b03704", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_104_homan_ultrafast_v1.2", + "mdf_source_name": "pub_104_homan_ultrafast", + "all_materials_included": "['MoS2', 'pentacene', 'quartz']", + "funding_details": "['Materials Research Science and Engineering Center (MRSEC) of Northwestern University (NSF DMR-1121262)', '2-DARE program (NSF EFRI- 1433510)', 'National Institute of Standards and Technology (NIST CHiMaD 70NANB14H012)', 'Argonne\u2212Northwestern Solar Energy Research (ANSER) Energy Frontier Research Center (DOE DE-SC0001059)']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_810/", + "version": "1.0", + "root_version": "pub_104_homan_ultrafast_v1.2", + "latest": true + } + }, + { + "source_id": "pub_103_shastry_mutual_v1.2", + "source_name": "pub_103_shastry_mutual", + "version": 1, + "ingest_date": "2019-03-08T19:59:11.318551Z", + "doi": "10.18126/M27P7Z", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_809/", + "metadata": { + "title": "Mutual Photoluminescence Quenching and Photovoltaic Effect in Large-Area Single-Layer MoS2\u2013Polymer Heterojunctions", + "authors": [ + { + "name": "Shastry, Tejas A.", + "given_name": "Tejas A.", + "family_name": "Shastry", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Balla, Itamar", + "given_name": "Itamar", + "family_name": "Balla", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Bergeron, Hadallia", + "given_name": "Hadallia", + "family_name": "Bergeron", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Amsterdam, Samuel H.", + "given_name": "Samuel H.", + "family_name": "Amsterdam", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data corresponds to the demonstration of the photovoltaic effect in an ultrathin heterojunction of monolayer MoS2 and poly[[4,8-bis[(2- ethylhexyl)oxy]benzo[1,2-b:4,5-b\u2032]dithiophene-2,6-diyl][3-flu-oro-2[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]] (PTB7). Tuning of the PTB7 thickness enables complete quenching of the MoS2 photoluminescence. Solar cells made from the heterojunction on transparent indium tin oxide demonstrate exceptional current density per absorbing thickness. DOI: 10.1021/acsnano.6b06592", + "keywords": [ + "two-dimensional materials", + "transition metal dichalcogenides", + "photovoltaic effect", + "heterojunction solar cell", + "photoluminescence" + ], + "publisher": "ACS Nano", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsnano.6b06592", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://pubs.acs.org/doi/pdf/10.1021/acsnano.6b06592", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_103_shastry_mutual_v1.2", + "mdf_source_name": "pub_103_shastry_mutual", + "funding_details": "['Argonne-Northwestern Solar Energy Research (ANSER) Center (DE-SC0001059)', 'National Institute of Standards and Technology (NIST CHiMaD 70NANB14H012)', 'n']", + "all_materials_included": "['MoS2', 'PTB7', 'ITO']" + }, + "download_url": "https://data.materialsdatafacility.org/hersam/published/publication_809/", + "version": "1.0", + "root_version": "pub_103_shastry_mutual_v1.2", + "latest": true + } + }, + { + "source_id": "wanglinlin_single_pair_semimetal_v1.1", + "source_name": "wanglinlin_single_pair_semimetal", + "version": 1, + "ingest_date": "2019-07-05T13:56:32.554896Z", + "doi": "10.18126/I0UOPQF3J3SW", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/wanglinlin_single_pair_semimetal_v1.1/", + "metadata": { + "title": "A Single Pair of Weyl Fermions in Half-metallic EuCd 2 As 2 Semimetal", + "authors": [ + { + "name": "Wang, Lin-lin", + "given_name": "Lin-lin", + "family_name": "Wang", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "Jo, Na Hyun", + "given_name": "Na Hyun", + "family_name": "Jo", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "Kuthanazhi, Brinda", + "given_name": "Brinda", + "family_name": "Kuthanazhi", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "Wu, Yun", + "given_name": "Yun", + "family_name": "Wu", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "McQueeney, Robert J.", + "given_name": "Robert J.", + "family_name": "McQueeney", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "Kaminski, Adam", + "given_name": "Adam", + "family_name": "Kaminski", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "name": "Canfield, Paul C.", + "given_name": "Paul C.", + "family_name": "Canfield", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + } + ], + "description": "arXiv:1901.08234\r\n\r\nTitle: A Single Pair of Weyl Fermions in Half-metallic EuCd2As2 Semimetal\r\nAuthors: Lin-Lin Wang, Na Hyun Jo, Brinda Kuthanazhi, Yun Wu, Robert J. McQueeney, Adam Kaminski and Paul C. Canfield\r\n\r\nllw@ameslab.gov\r\n\r\nBand structures in Fig.1, 3 and 5 have been calculated in VASP.5.2.12 with PBE+U+SOC and other settings for different magnetic configurations.\r\n\r\nCrystal structure in Fig.1b has been plotted in Vesta-3.4.0.\r\n\r\nBerrycurvature in Fig.1f and Fermi arcs in Fig.2 have been calculated in WannierTools-2.0 using tight-binding model constructed with maximally localized Wannier functions in Wannier90-2.1.0.\r\n\r\nBand structures in Fig.4 have been plotted for the 4-band low-energy Hamiltionian with parameters listed in the main text.", + "keywords": [ + "Weyl semimetal", + "simulation", + "metals and alloys", + "EuCd2As2" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "wanglinlin_single_pair_semimetal_v1.1", + "mdf_source_name": "wanglinlin_single_pair_semimetal", + "all_materials_included": "EuCd2As2", + "funding_details": "['Center for the Advancement of Topological Semimetals , an Energy Frontier Research Center funded by the U.S. Department of Energy Office of Science, Office of Basic Energy Sciences through the Ames Laboratory under its Contract No. DE - AC02 - 07CH11358', 'CATS EFRC']" + }, + "download_url": "https://data.materialsdatafacility.org/legacy/wanglinlin_single_pair_semimetal_v1.1/", + "version": "1.0", + "root_version": "wanglinlin_single_pair_semimetal_v1.1", + "latest": true + } + }, + { + "source_id": "pub_86_kylanpaa_accuracy_v1.2", + "source_name": "pub_86_kylanpaa_accuracy", + "version": 1, + "ingest_date": "2019-03-08T17:16:28.490701Z", + "doi": "10.18126/M2NS7Q", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_851/", + "metadata": { + "title": "Dataset for Accuracy of ab initio electron correlation and electron densities in vanadium dioxide", + "authors": [ + { + "name": "Kylanpaa, Ilkka", + "given_name": "Ilkka", + "family_name": "Kylanpaa", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Balachandran, Janakiraman", + "given_name": "Janakiraman", + "family_name": "Balachandran", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory, Argonne, IL" + ] + }, + { + "name": "Kent, Paul R. C.", + "given_name": "Paul R. C.", + "family_name": "Kent", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + } + ], + "description": "Dataset for \"Accuracy of ab initio electron correlation and electron densities in vanadium dioxide\" Ilkka Kylanpaa , Janakiraman Balachandran, Panchapakesan Ganesh , Olle Heinonen, Paul R. C. Kent, and Jaron T. Krogel (2017).", + "keywords": [ + "QMCPACK", + "Monte Carlo", + "Diffusion Monte Carlo", + "DMC", + "Variational Monte Carlo", + "VMC", + "Quantum Monte Carlo", + "Vanadium dioxide", + "Electron density" + ], + "publisher": "Oak Ridge National Laboratory, Oak Ridge, TN", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_86_kylanpaa_accuracy_v1.2", + "mdf_source_name": "pub_86_kylanpaa_accuracy", + "all_materials_included": "VO2", + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_851/", + "version": "1.0", + "root_version": "pub_86_kylanpaa_accuracy_v1.2", + "latest": true + } + }, + { + "source_id": "pub_97_da_berkeleygw_v1.2", + "source_name": "pub_97_da_berkeleygw", + "version": 1, + "ingest_date": "2019-03-08T19:34:45.960368Z", + "doi": "10.18126/M2X06W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_937/", + "metadata": { + "title": "BerkeleyGW 2018 Workshop Examples", + "authors": [ + { + "name": "da Jornada, Felipe H.", + "given_name": "Felipe H.", + "family_name": "da Jornada", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Deslippe, Jack", + "given_name": "Jack", + "family_name": "Deslippe", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Strubbe, David A.", + "given_name": "David A.", + "family_name": "Strubbe", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Vigil-Fowler, Derek", + "given_name": "Derek", + "family_name": "Vigil-Fowler", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Antonius, Gabriel", + "given_name": "Gabriel", + "family_name": "Antonius", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "del Ben, Mauro", + "given_name": "Mauro", + "family_name": "del Ben", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Louie, Steven G.", + "given_name": "Steven G.", + "family_name": "Louie", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "Tutorial files for the BerkeleyGW 2018 Workshop. See: sites.google.com/site/berkeleygw2018/", + "keywords": [ + "BerkeleyGW", + "workshop", + "tutorial", + "examples", + "2018" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "sites.google.com/site/berkeleygw2018/", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_97_da_berkeleygw_v1.2", + "mdf_source_name": "pub_97_da_berkeleygw", + "funding_details": "DE-AC02-05CH11231" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_937/", + "version": "1.0", + "root_version": "pub_97_da_berkeleygw_v1.2", + "latest": true + } + }, + { + "source_id": "pub_10_gibbs_liquidsolid_v1.2", + "source_name": "pub_10_gibbs_liquidsolid", + "version": 1, + "ingest_date": "2019-03-07T22:36:52.451268Z", + "doi": "10.18126/M2059C", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_47/", + "metadata": { + "title": "Liquid-solid Metallic Mixture Coarsening Data - 35% Solid", + "authors": [ + { + "name": "Gibbs, John W.", + "given_name": "John W.", + "family_name": "Gibbs", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Fife, Julie L.", + "given_name": "Julie L.", + "family_name": "Fife", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\nIn theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\nThe data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6\"", + "keywords": [ + "registration", + "4D data", + "coarsening", + "experimentation", + "computed tomography", + "in situ", + "Al-Cu", + "metals and alloys", + "tomography", + "liquid-solid mixture", + "segmentation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1186/2193-9772-3-6", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1016/j.actamat.2014.01.024", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://www.gibbsium.org/thesis", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_10_gibbs_liquidsolid_v1.2", + "mdf_source_name": "pub_10_gibbs_liquidsolid", + "experiment_nominal_alloy_composition": "['Al (73 wt%); Cu (27 wt%)', 'Al:73 wt%', 'Cu:27 wt%']", + "experiment_pixel_size": "['1.79 \\\\u00b5m', '1.79 um']", + "experiment_time_between_scans": "44 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "Al-Cu", + "experiment_total_duration": "75 min", + "experiment_holding_temperature": "['558 C', '5 C above the eutectic', '558 C (5 C above the eutectic)']", + "funding_details": "['DOE Grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "polychromatic" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_47/", + "version": "1.0", + "root_version": "pub_10_gibbs_liquidsolid_v1.2", + "latest": true + } + }, + { + "source_id": "pub_36_xie_thermal_v1.2", + "source_name": "pub_36_xie_thermal", + "version": 1, + "ingest_date": "2019-03-08T15:32:58.589979Z", + "doi": "10.18126/M2GP9W", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1144/", + "metadata": { + "title": "Thermal Conductivity, Heat Capacity, and Elastic Constants of Water-Soluble Polymers and Polymer Blends", + "authors": [ + { + "name": "Xie, Xu", + "given_name": "Xu", + "family_name": "Xie", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Li, Dongyao", + "given_name": "Dongyao", + "family_name": "Li", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA; International Institute for Carbon Neutral Energy Research, Kyushu University, Fukuoka 819-0395, Japan" + ] + }, + { + "name": "Tsai, Tsung-Han", + "given_name": "Tsung-Han", + "family_name": "Tsai", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Liu, Jun", + "given_name": "Jun", + "family_name": "Liu", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Braun, Paul V.", + "given_name": "Paul V.", + "family_name": "Braun", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "name": "Cahill, David G.", + "given_name": "David G.", + "family_name": "Cahill", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA ; International Institute for Carbon Neutral Energy Research, Kyushu University, Fukuoka 819-0395, Japan" + ] + } + ], + "description": "In the present work a laser-based pump\u2212probe technique, time-domain thermoreflectance (TDTR), is applied to measure the thermal conductivity and heat capacity of several common water-soluble polymers and one of their blends.", + "keywords": [ + "polymers", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.macromol.5b02477", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1021/acs.macromol.5b02477", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_36_xie_thermal_v1.2", + "mdf_source_name": "pub_36_xie_thermal", + "all_materials_included": "['poly(vinyl alcohol) (PVA)', 'poly(acrylic acid) (PAA)', 'polyacrylamide (PAM)', 'poly(vinylpyrrolidone) (PVP)', 'methyl cellulose (MC)', 'poly(4-styrenesulfonic acid) (PSS)', 'poly(N-acryloylpiperidine) (PAP)', 'poly(methyl methacrylate) (PMMA)', 'polymer blend of PVA/PAA']", + "funding_details": "['Air Force Office of Scientific Research (AFOSR) Multidisciplinary Research Program of the University Research Initiative (MURI) (Grant No. FA9550-12-1-0002)', 'Frederick Seitz Materials Research Laboratory Central Research Facilities, University of Illinois', 'International Institute for Carbon Neutral Energy Research (WPI-I2CNER, sponsored by the World Premier International Research Center Initiative (WPI), MEXT, Japan)']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1144/", + "version": "1.0", + "root_version": "pub_36_xie_thermal_v1.2", + "latest": true + } + }, + { + "source_id": "pub_35_hanson_crystallographic_v1.2", + "source_name": "pub_35_hanson_crystallographic", + "version": 1, + "ingest_date": "2019-03-08T15:31:19.513218Z", + "doi": "10.18126/M2063F", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1142/", + "metadata": { + "title": "Crystallographic character of grain boundaries resistant to hydrogen-assisted fracture in Ni-base alloy 725", + "authors": [ + { + "name": "Hanson, John P.", + "given_name": "John P.", + "family_name": "Hanson", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Bagri, Akbar", + "given_name": "Akbar", + "family_name": "Bagri", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Lind, Jonathan", + "given_name": "Jonathan", + "family_name": "Lind", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Suter, Robert M.", + "given_name": "Robert M.", + "family_name": "Suter", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Grade\u010dak, Silvija", + "given_name": "Silvija", + "family_name": "Grade\u010dak", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + }, + { + "name": "Demkowicz, Michael J.", + "given_name": "Michael J.", + "family_name": "Demkowicz", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ] + } + ], + "description": "Hydrogen embrittlement (HE) causes sudden, costly failures of metal components across a wide range of industries. Yet, despite over a century of research, the physical mechanisms of HE are too poorly understood to predict HE-induced failures with confidence. We use non-destructive, synchrotron-based techniques to investigate the relationship between the crystallographic character of grain boundaries and their susceptibility to hydrogen-assisted fracture in a nickel superalloy. Our data leads us to identify a class of grain boundaries with striking resistance to hydrogen-assisted crack propagation: boundaries with low-index planes (BLIPs). BLIPs are boundaries where at least one of the neighboring grains has a low Miller index facet\u2014{001}, {011}, or {111}\u2014along the grain boundary plane. These boundaries deflect propagating cracks, toughening the material and improving its HE-resistance. Our finding paves the way to improved predictions of HE based on the density and distribution of BLIPs in metal microstructures.\r\n\r\nSee the associated publications:\r\n\r\nJohn P. Hanson, Akbar Bagri, Jonathan Lind, Peter Kenesei, Robert M. Suter, Silvija Gradecak, Michael J. Demkowicz, \"Crystallographic character of grain boundaries resistant to hydrogen-assisted fracture in Ni-base alloy 725\", Nature Communications 9:338 (2018). DOI: 10.1038/s41467-018-05549-y\r\n\r\nAkbar Bagri, John P. Hanson, Jonathan Lind, Peter Kenesei, Robert M. Suter, Silvija Gradecak, Michael J. Demkowicz, \"Measuring grain boundary character distributions in Ni-base alloy 725 using high-energy diffraction microscopy\", Metallurgical and Materials Transactions A 48, 354-361 (2017). DOI: 10.1007/s11661-016-3831-x", + "keywords": [ + "superalloy", + "grain boundaries", + "experiment", + "metals and alloys", + "microstructure", + "hydrogen embrittlement", + "fracture", + "microstructures", + "failure" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1007/s11661-016-3831-x", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.1038/s41467-018-05549-y", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_35_hanson_crystallographic_v1.2", + "mdf_source_name": "pub_35_hanson_crystallographic", + "all_materials_included": "Ni" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1142/", + "version": "1.0", + "root_version": "pub_35_hanson_crystallographic_v1.2", + "latest": true + } + }, + { + "source_id": "pub_40_han_probing_v1.2", + "source_name": "pub_40_han_probing", + "version": 1, + "ingest_date": "2019-03-08T15:39:39.656944Z", + "doi": "10.18126/M2K910", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_634/", + "metadata": { + "title": "Probing the growth and melting pathways of a decagonal quasicrystal in real-time", + "authors": [ + { + "name": "Han, Insung", + "given_name": "Insung", + "family_name": "Han", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + }, + { + "name": "Shahani, Ashwin J.", + "given_name": "Ashwin J.", + "family_name": "Shahani", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ] + } + ], + "description": "This data was collected to study the principles of the growth and melting of a decagonal quasicrystal from a liquid. The composition of imaged sample is Al-9.55at%Ni-9.55at%Co, and the growth and melting of a decagonal quasicrystal were observed during the continuous slow cooling. The quasicrystalline phase shows weaker projection intensity because of more X-ray absorption resulting from its heavy elemental (Ni and Co) composition. This contrast difference allowed us to segment the quasicrystal from the liquid phase and visualize 3D volume of a decagonal quasicrystal. During the growth, the ten facets of the quasicrystal were clearly seen. On the other hand, marked interface curvature was observed during the melting, such that the quasicrystal eventually lost its ten habit planes. In addition, the growth rate is nearly constant for every facet; however, the melting rate is shown to depend on the physical orientation. Therefore, the growth and melting processes do not have time-reversal symmetry and different mechanisms are applied to explain this behavior. To determine the underlying mechanisms of the microstructural evolution, we analyzed the projection intensity and correlated it to the phase diagram. The 3D visualization and projection intensity analysis proved that growth is governed by first-order kinetics due to interfacial attachments and the melting is affected more strongly by gravity-driven convection. For details on the resulting 3D reconstruction, visualization, and kinetic analyses, the readers are encouraged to look up the following publication.", + "keywords": [ + "density", + "interface kinetics", + "melting", + "anisotropy", + "synchrotron", + "gravity-driven convection", + "isotropy", + "4D x-ray tomography", + "experiment", + "tomography", + "supersaturation", + "growth", + "decagonal quasicrystal", + "continuous cooling", + "composition analysis", + "in situ", + "undercooling", + "metals and alloys", + "Al-Ni-Co alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "extensions": { + "mdf_source_id": "pub_40_han_probing_v1.2", + "mdf_source_name": "pub_40_han_probing", + "all_materials_included": "['Al-Ni-Co', 'Al', 'Ni', 'Co']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_634/", + "version": "1.0", + "root_version": "pub_40_han_probing_v1.2", + "latest": true + } + }, + { + "source_id": "pub_73_gabriel_temperatureinduced_v1.2", + "source_name": "pub_73_gabriel_temperatureinduced", + "version": 1, + "ingest_date": "2019-03-08T16:44:23.238092Z", + "doi": "10.18126/M2TP77", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_786/", + "metadata": { + "title": "Temperature-Induced Topological Phase Transitions", + "authors": [ + { + "name": "Gabriel, Antonius", + "given_name": "Antonius", + "family_name": "Gabriel", + "affiliations": [ + "University of California, Berkeley" + ] + }, + { + "name": "Steven, Louie", + "given_name": "Louie", + "family_name": "Steven", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "Relaxed structures and pseudopotentials.", + "keywords": [ + "chalcogenides", + "first-principles calculations", + "transition temperature", + "electron-phonon coupling", + "topological insulators", + "phases", + "superconductors", + "phonons", + "quantum materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2017, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1103/PhysRevLett.117.246401", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://link.aps.org/doi/10.1103/PhysRevLett.117.246401", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_73_gabriel_temperatureinduced_v1.2", + "mdf_source_name": "pub_73_gabriel_temperatureinduced", + "all_materials_included": "['BiThS2', 'BiThSe2']", + "funding_details": "['National Science Foundation under Grant No. DMR- 1508412', 'Center for Computational Study of Excited- State Phenomena in Energy Materials, funded by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-AC02-05CH11231', 'National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE- AC02-05CH11231', 'Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. 787 ACI-1053575']" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_786/", + "version": "1.0", + "root_version": "pub_73_gabriel_temperatureinduced_v1.2", + "latest": true + } + }, + { + "source_id": "pub_68_kaufman_naxcoo2_v1.2", + "source_name": "pub_68_kaufman_naxcoo2", + "version": 1, + "ingest_date": "2019-03-08T16:34:44.486805Z", + "doi": "10.18126/M2505P", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1265/", + "metadata": { + "title": "NaxCoO2 phase stability and hierarchical orderings in the O3/P3 structure family", + "authors": [ + { + "name": "Kaufman, Jonas L.", + "given_name": "Jonas L.", + "family_name": "Kaufman", + "affiliations": [ + "Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States" + ] + }, + { + "name": "Van der Ven, Anton", + "given_name": "Anton", + "family_name": "Van der Ven", + "affiliations": [ + "Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States" + ] + } + ], + "description": "The dataset contains VASP calculations for Na-vacancy orderings in various host structures of NaxCoO2 enumerated using the CASM code (https://github.com/prisms-center/CASMcode).", + "keywords": [ + "density functional theory", + "oxides", + "VASP", + "layered intercalation compounds", + "CASM", + "Na-ion batteries", + "simulation", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://github.com/prisms-center/CASMcode", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_68_kaufman_naxcoo2_v1.2", + "mdf_source_name": "pub_68_kaufman_naxcoo2", + "funding_details": "['United States Department of Energy through the Computational Science Graduate Fellowship (DOE CSGF) under grant number: DE-FG02-97ER25308', 'The Center for Synthetic Control Across Length-scales for Advancing Rechargeables (SCALAR), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award\\xa0# DE-SC0019381', 'Center for Scientific Computing from the CNSI, MRL: an NSF MRSEC (DMR-1720256)', 'National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231']", + "all_materials_included": "NaxCoO2" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1265/", + "version": "1.0", + "root_version": "pub_68_kaufman_naxcoo2_v1.2", + "latest": true + } + }, + { + "source_id": "pub_54_de_emsoft_v1.2", + "source_name": "pub_54_de_emsoft", + "version": 1, + "ingest_date": "2019-03-08T16:04:13.987402Z", + "doi": "10.18126/M2NP4J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_161/", + "metadata": { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Magnesium (Mg)", + "authors": [ + { + "name": "De Graef, Marc", + "given_name": "Marc", + "family_name": "De Graef", + "affiliations": [ + "Caregie Mellon University" + ] + } + ], + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for hexagonal Mg for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations.", + "keywords": [ + "EBSD", + "machine learning", + "magnesium", + "backscattering", + "master pattern", + "EMSoft", + "simulation", + "Mg", + "diffraction", + "electron backscattered diffraction", + "microscopy", + "dictionary" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1017/S1431927613001840", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "10.18126/M2SG6G", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "https://github.com/marcdegraef/EMsoft", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1017/S1431927613001840", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_54_de_emsoft_v1.2", + "mdf_source_name": "pub_54_de_emsoft", + "all_materials_included": "['magnesium', 'Mg']", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_161/", + "version": "1.0", + "root_version": "pub_54_de_emsoft_v1.2", + "latest": true + } + }, + { + "source_id": "pub_26_qiu_environmental_v1.2", + "source_name": "pub_26_qiu_environmental", + "version": 1, + "ingest_date": "2019-03-08T15:14:32.884894Z", + "doi": "10.18126/M2DD1G", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1134/", + "metadata": { + "title": "Environmental Screening Effects in 2D Materials: Renormalization of the Bandgap, Electronic Structure, and Optical Spectra of Few-Layer Black Phosphorus", + "authors": [ + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ] + }, + { + "name": "da Jornada, Felipe H.", + "given_name": "Felipe H.", + "family_name": "da Jornada", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ] + }, + { + "name": "Louie, Steven G.", + "given_name": "Steven G.", + "family_name": "Louie", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ] + } + ], + "description": "Quantum Espresso input and output file and pseudopotentials associated with calculations published in \"Environmental Screening Effects in 2D Materials: Renormalization of the Bandgap, Electronic Structure, and Optical Spectra of Few-Layer Black Phosphorus,\" Nano Lett. 17, 4706-4712 (2017).", + "keywords": [ + "2D materials", + "semiconductors", + "black phosphorus", + "layered materials", + "black phosphorus, layered materials, substrates, encapsulation", + "bandgap" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.7b01365", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + }, + { + "identifier": "http://dx.doi.org/10.1021/acs.nanolett.7b01365", + "identifier_type": "URL", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_26_qiu_environmental_v1.2", + "mdf_source_name": "pub_26_qiu_environmental", + "funding_details": "This work was supported by the Center for Computational Study of Excited-State Phenomena in Energy Materials at the Lawrence Berkeley National Laboratory, which is funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05CH11231, as part of the Computational Materials Sciences Program. This research used resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575.", + "all_materials_included": "phosphorus" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1134/", + "version": "1.0", + "root_version": "pub_26_qiu_environmental_v1.2", + "latest": true + } + }, + { + "source_id": "pub_20_wang_far_v1.2", + "source_name": "pub_20_wang_far", + "version": 1, + "ingest_date": "2019-03-08T15:03:30.306935Z", + "doi": "10.18126/M2Z05T", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1122/", + "metadata": { + "title": "Far field high energy diffraction data for \"Study of slip activity in a Mg-Y alloy by in situ high energy X-ray diffraction microscopy and elastic viscoplastic self-consistent modeling\"", + "authors": [ + { + "name": "Wang, Leyun", + "given_name": "Leyun", + "family_name": "Wang" + }, + { + "name": "Huang, Zhonghe", + "given_name": "Zhonghe", + "family_name": "Huang" + }, + { + "name": "Wang, Huamiao", + "given_name": "Huamiao", + "family_name": "Wang" + }, + { + "name": "Maldar, Alireza", + "given_name": "Alireza", + "family_name": "Maldar" + }, + { + "name": "Yi, Sangbong", + "given_name": "Sangbong", + "family_name": "Yi" + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park" + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei" + }, + { + "name": "Lilleodden, Erica", + "given_name": "Erica", + "family_name": "Lilleodden" + }, + { + "name": "Zeng, Xiaoqin", + "given_name": "Xiaoqin", + "family_name": "Zeng" + } + ], + "description": "FF-HEDM data set (including the raw data) and reduced grain-by-grain information for several points along the stress-strain curve. These are GE image files.", + "keywords": [ + "Mg alloy", + "experiment", + "metals and alloys", + "HEDM", + "Synchrotron radiation", + "microstructures", + "EVPSC", + "microscopy", + "Solute strengthening" + ], + "publisher": "Materials Data Facility", + "publication_year": 2018, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.actamat.2018.05.065", + "identifier_type": "DOI", + "relation_type": "IsSupplementTo" + } + ], + "extensions": { + "mdf_source_id": "pub_20_wang_far_v1.2", + "mdf_source_name": "pub_20_wang_far", + "all_materials_included": "['Mg', 'Y']", + "funding_details": "['National Natural Science Foundation of China (Nos. 51671127 and 51631006)', 'Alexander von Humboldt Foundation and Shanghai Pujiang Program (No 16PJ1404600)', 'United States Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357']", + "reduction_method": "MIDAS at APS" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_1122/", + "version": "1.0", + "root_version": "pub_20_wang_far_v1.2", + "latest": true + } + }, + { + "source_id": "pub_30_shahani_twinmediated_v1.2", + "source_name": "pub_30_shahani_twinmediated", + "version": 1, + "ingest_date": "2019-03-08T15:22:43.272879Z", + "doi": "10.18126/M2301J", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_113/", + "metadata": { + "title": "Twin-mediated Crystal Growth: an Enigma Resolved", + "authors": [ + { + "name": "Shahani, Ashwin", + "given_name": "Ashwin", + "family_name": "Shahani", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Gulsoy, E. Begum", + "given_name": "E. Begum", + "family_name": "Gulsoy", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Poulsen, Stefan O.", + "given_name": "Stefan O.", + "family_name": "Poulsen", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "description": "This data was collected to study the growth behavior of faceted Si particles in an Al-Si-Cu liquid, upon continuous cooling. The as-cast, hyper-eutectic sample consisted of primary Si particles in a eutectic matrix. Upon heating to above the liquidus temperature for Si (here, we heat to 910 C), the Si particles melted, leaving behind a featureless liquid. Then, the sample temperature was lowered at a rate of 1 C per minute while projections were recorded. After a brief incubation period, the Si particles grew from the oxide skin of the sample into the liquid. The weight fraction of Si particles at temperature was consistent with predictions from equilibrium, indicating that the growth of solid Si can keep up with the quench rate. Nevertheless, the morphologies of the Si particles were not given by the equilibrium shape of Si due to the prevalence of defects during the growth process. Identifying the crystallography and dynamics of these defects (including twin boundaries that intersect the solid-liquid interfaces) was central to this work. The raw data were obtained in the following sequence: for the first 20 min, projections were collected continuously, providing a temporal resolution of 30 s between subsequent 3D reconstructions; for the next 120 minutes, 40 more tomographic scans with the same parameters were spaced 150 s apart. Thus, 80 scans were collected over the course of over 2 hours. The motivation for collecting the X-ray projections in this manner was that the system-average length-scale increased logarithmically with time during bulk diffusion-limited growth. Therefore, this data collection scheme adequately captured the interfacial dynamics. The data here includes the raw projections. Note that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the data processing and quantitative analysis, the reader is pointed to the following publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu).", + "keywords": [ + "synchrotron", + "anisotropy", + "solidification", + "4D materials science", + "X-ray computed tomography", + "growth", + "microscopy", + "microstructures", + "Al-Si-Cu alloys", + "facets", + "defects", + "in situ", + "twinning", + "metals and alloys", + "twins" + ], + "publisher": "Materials Data Facility", + "publication_year": 2016, + "resource_type": "Dataset", + "organization": "CHiMaD", + "extensions": { + "mdf_source_id": "pub_30_shahani_twinmediated_v1.2", + "mdf_source_name": "pub_30_shahani_twinmediated", + "experiment_nominal_alloy_composition": "['Al (53 wt%); Si (32 wt%), Cu (15 wt%)', 'Al 53 wt%', 'Si 32 wt%', 'Cu 15 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "Gridrec algorithm", + "processing_segmentation_method": "median filtering and morphological operations", + "all_materials_included": "Al-Si-Cu", + "experiment_holding_temperature": "['Sample was held at 910 C prior to data collection. This is above the liquidus temperature for Si for the alloy composition of interest. Then, the sample temperature was lowered at a rate of 1 C per minute while tomographic projections were recorded. Note that the temperature corresponding to a given 3D reconstruction can be calculated using this information and the time-stamp of each HDF file.', '910 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)', 'DOE (grant DE-FG02-99ER45782)']", + "experiment_xray_energy": "pink beam" + }, + "download_url": "https://data.materialsdatafacility.org/published/publication_113/", + "version": "1.0", + "root_version": "pub_30_shahani_twinmediated_v1.2", + "latest": true + } + }, + { + "source_id": "pub_89_ondrejcek_fluctuation_v1.2", + "source_name": "pub_89_ondrejcek_fluctuation", + "version": 1, + "ingest_date": "2019-03-08T17:29:55.636526Z", + "doi": "10.18126/M2H018", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_372/", + "metadata": { + "title": "Fluctuation spectroscopy of step edges on Pt(111); Crossover from bulk to surface diffusion", + "authors": [ + { + "name": "Ondrejcek, Michal", + "given_name": "Michal", + "family_name": "Ondrejcek", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Swiech, Wacek", + "given_name": "Wacek", + "family_name": "Swiech", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Rajappan, Mahesh", + "given_name": "Mahesh", + "family_name": "Rajappan", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Yang, G.", + "given_name": "G.", + "family_name": "Yang", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "name": "Flynn, C. Peter", + "given_name": "C. Peter", + "family_name": "Flynn", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + } + ], + "description": "By step fluctuation spectroscopy, using low-energy electron microscopy LEEM, we investigate step energies and relaxation on clean Pt(111) surface at temperatures above half the melting temperature Tm (range 1190K109, 103\u2013114 (2016). DOI: 10.1016/j.actamat.2016.02.006. Includes variable-resolution fluctuation electron microscopy raw data and analysis and hybrid reverse Monte Carlo derived atomic structural models and related analyses.", + "keywords": [ + "simulation", + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy", + "fluctuation electron microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2016.02.006", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zhang_actamat2016_zrcual_v1.1", + "mdf_source_name": "zhang_actamat2016_zrcual" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zhang_actamat2016_zrcual_v1.1/", + "version": "1.0", + "root_version": "zhang_actamat2016_zrcual_v1.1", + "latest": true + } + }, + { + "source_id": "he_mm2015_ecm_v1.1", + "source_name": "he_mm2015_ecm", + "version": 1, + "ingest_date": "2020-06-30T22:19:35.465516Z", + "doi": "10.18126/oph0-nrge", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/he_mm2015_ecm_v1.1/", + "metadata": { + "title": "Data for Electron Correlation Microscopy: A New Technique for Studying Local Atom Dynamics Applied to a Supercooled Liquid", + "authors": [ + { + "name": "He, Li", + "given_name": "Li", + "family_name": "He", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "name": "Zhang, Pei", + "given_name": "Pei", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "name": "Besser, Matthew F.", + "given_name": "Matthew F.", + "family_name": "Besser", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "name": "Kramer, Matthew Joseph", + "given_name": "Matthew Joseph", + "family_name": "Kramer", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + } + ], + "description": "Data for He, L., Zhang, P., Besser, M. F., Kramer, M. J. & Voyles, P. M. Electron Correlation Microscopy: A New Technique for Studying Local Atom Dynamics Applied to a Supercooled Liquid. Microsc. Microanal. 21, 1026\u20131033 (2015). DOI: 10.1017/S1431927615000641. Electron correlation microscopy experiments on a Pd40Ni20P20 metallic glass in the supercooled liquid state. Contains raw data and analysis.", + "keywords": [ + "electron correlation microscopy", + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1017/S1431927615000641", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "he_mm2015_ecm_v1.1", + "mdf_source_name": "he_mm2015_ecm" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/he_mm2015_ecm_v1.1/", + "version": "1.0", + "root_version": "he_mm2015_ecm_v1.1", + "latest": true + } + }, + { + "source_id": "tsopanidis_wha_amaps_v1.1", + "source_name": "tsopanidis_wha_amaps", + "version": 1, + "ingest_date": "2020-05-04T16:07:51.238909Z", + "doi": "10.18126/aph0-olbz", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tsopanidis_wha_amaps_v1.1/", + "metadata": { + "title": "WHA SEM Fracture Images Dataset and Activation Maps", + "authors": [ + { + "name": "Tsopanidis, Stylianos", + "given_name": "Stylianos", + "family_name": "Tsopanidis", + "affiliations": [ + "University Carlos III of Madrid" + ] + }, + { + "name": "Osovski, Shmuel", + "given_name": "Shmuel", + "family_name": "Osovski", + "affiliations": [ + "Technion - Israel Institute of Technology" + ] + } + ], + "description": "The WHA Dataset is a composed by SEM images of the fracture surface of WHA samples with different tungsten composition. This dataset is used to evaluate the efficiency of two Unsupervised Machine Learning algorithms that aim to cluster and classify the fracture images according to the tungsten composition. Additionally, the Activation Maps of the last convolution layer of the neural network (VGG16) that is used in an intermediate stage of the algorithms are computed, according to their weights in the next stages of the clustering algorithm.", + "keywords": [ + "machine learning", + "metals and alloys", + "microscopy", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "tsopanidis_wha_amaps_v1.1", + "mdf_source_name": "tsopanidis_wha_amaps" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/tsopanidis_wha_amaps_v1.1/", + "version": "1.0", + "root_version": "tsopanidis_wha_amaps_v1.1", + "latest": true + } + }, + { + "source_id": "zhang_tensorsvd_4dstem_v1.2", + "source_name": "zhang_tensorsvd_4dstem", + "version": 1, + "ingest_date": "2020-07-02T17:19:26.047803Z", + "doi": "10.18126/vh9q-i1l6", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhang_tensorsvd_4dstem_v1.2/", + "metadata": { + "title": "Data for \"Denoising Atomic Resolution 4D Scanning Transmission Electron Microscopy Data with Tensor Singular Value Decomposition\"", + "authors": [ + { + "name": "Zhang, Chenyu", + "given_name": "Chenyu", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Han, Rungang", + "given_name": "Rungang", + "family_name": "Han", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Zhang, Anru", + "given_name": "Anru", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Data for \"Denoising Atomic Resolution 4D Scanning Transmission Electron Microscopy Data with Tensor Singular Value Decomposition\" by\nChenyu Zhang, Rungang Han, Anru R. Zhang, Paul. M. Voyles. Consists of experimental and simulated 4D STEM data both as-created and after denoising with tensor singular value decomposition. Simulated data sets are from SrTiO3 [100] and an edge dislocation in Si [110]. Experimental data sets are from SrTiO3 [100] and an epitaxial LiZnSb thin film on GaSb substrate. MATLAB code for tensor SVD denoising is also included.\n", + "keywords": [ + "simulation", + "experiment", + "machine learning", + "semiconductors", + "oxides", + "microscopy", + "4D STEM", + "denoising", + "component analysis", + "tensor SVD" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zhang_tensorsvd_4dstem_v1.2", + "mdf_source_name": "zhang_tensorsvd_4dstem" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zhang_tensorsvd_4dstem_v1.2/", + "version": "1.0", + "root_version": "zhang_tensorsvd_4dstem_v1.2", + "latest": true + } + }, + { + "source_id": "ophus_mbed_01_v1.1", + "source_name": "ophus_mbed_01", + "version": 1, + "ingest_date": "2020-12-03T17:05:48.657563Z", + "doi": "10.18126/7eav-q9vl", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ophus_mbed_01_v1.1/", + "metadata": { + "title": "Multibeam Electron Diffraction - experimental datasets", + "authors": [ + { + "name": "Hong, Xuhao", + "given_name": "Xuhao", + "family_name": "Hong", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Zeltmann, Steven E", + "given_name": "Steven E", + "family_name": "Zeltmann", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Savitzky, Benjamin H", + "given_name": "Benjamin H", + "family_name": "Savitzky", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Dacosta, Luis Rangel", + "given_name": "Luis Rangel", + "family_name": "Dacosta", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Muller, Alexander", + "given_name": "Alexander", + "family_name": "Muller", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Bustillo, Karen C", + "given_name": "Karen C", + "family_name": "Bustillo", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Minor, Andrew M", + "given_name": "Andrew M", + "family_name": "Minor", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "name": "Ophus, Colin", + "given_name": "Colin", + "family_name": "Ophus", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + } + ], + "description": "Experimental datasets for multibeam electron diffraction. Includes diffraction patterns from various 4D-STEM scans of Au nanoparticles, recorded using a multibeam aperture containing 7 outer beams spaced around a ring with radius 60 mrads, around a central beam.", + "keywords": [ + "Scanning Transmission Electron Microscopy", + "Experiment", + "Multibeam Electron Diffraction", + "4D-STEM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ophus_mbed_01_v1.1", + "mdf_source_name": "ophus_mbed_01" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ophus_mbed_01_v1.1/", + "version": "1.0", + "root_version": "ophus_mbed_01_v1.1", + "latest": true + } + }, + { + "source_id": "nahyunjo_prb2020_v1.1", + "source_name": "nahyunjo_prb2020", + "version": 1, + "ingest_date": "2020-07-03T14:54:25.938237Z", + "doi": "10.18126/285a-7ael", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nahyunjo_prb2020_v1.1/", + "metadata": { + "title": "Intrinsic axion insulating behavior in antiferromagnetic MnBi6Te10", + "authors": [ + { + "name": "Jo, Na Hyun", + "given_name": "Na Hyun", + "family_name": "Jo", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Wang, Lin-Lin", + "given_name": "Lin-Lin", + "family_name": "Wang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Slager, Robert-Jan", + "given_name": "Robert-Jan", + "family_name": "Slager", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Yan, Jiaqiang", + "given_name": "Jiaqiang", + "family_name": "Yan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Wu, Yun", + "given_name": "Yun", + "family_name": "Wu", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Lee, Kyungchan", + "given_name": "Kyungchan", + "family_name": "Lee", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Schrunk, Benjamin", + "given_name": "Benjamin", + "family_name": "Schrunk", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Vishwanath, Ashvin", + "given_name": "Ashvin", + "family_name": "Vishwanath", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Kaminski, Adam", + "given_name": "Adam", + "family_name": "Kaminski", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Data set for publication \"Intrinsic axion insulating behavior in antiferromagnetic MnBi6Te10\" in Phys Rev B 2020", + "keywords": [ + "experiment", + "simulation", + "Topological matter", + "Axion insulator" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "nahyunjo_prb2020_v1.1", + "mdf_source_name": "nahyunjo_prb2020" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/nahyunjo_prb2020_v1.1/", + "version": "1.0", + "root_version": "nahyunjo_prb2020_v1.1", + "latest": true + } + }, + { + "source_id": "muley_mgfilmstability_v1.1", + "source_name": "muley_mgfilmstability", + "version": 1, + "ingest_date": "2020-07-03T19:42:10.298643Z", + "doi": "10.18126/xa3c-69j6", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/muley_mgfilmstability_v1.1/", + "metadata": { + "title": "Data for Varying Kinetic Stability, Icosahedral Ordering, and Mechanical Properties of a Model Zr-Cu-Al Metallic Glass by Sputtering", + "authors": [ + { + "name": "Muley, Sachin V.", + "given_name": "Sachin V.", + "family_name": "Muley", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Cao, Chengrong", + "given_name": "Chengrong", + "family_name": "Cao", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Chatterjee, Debaditya", + "given_name": "Debaditya", + "family_name": "Chatterjee", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Francis, Carter", + "given_name": "Carter", + "family_name": "Francis", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Lu, Felix P.", + "given_name": "Felix P.", + "family_name": "Lu", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Ediger, M. D.", + "given_name": "M. D.", + "family_name": "Ediger", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Perpezko, John H.", + "given_name": "John H.", + "family_name": "Perpezko", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Data for \"Varying Kinetic Stability, Icosahedral Ordering, and Mechanical Properties of a Model Zr-Cu-Al Metallic Glass by Sputtering\" by\nSachin V. Muley, Chengrong Cao, Debaditya Chatterjee, Carter Francis, Felix P. Lu, M. D. Ediger, John H. Perepezko, and Paul M. Voyles. This paper shows that the kinetic and thermodynamic stability of Zr65Cu27.5Al7.5 metallic glass thin films may be manipulated by changing the film deposition rate during sputtering. Changing stability increases the ordering of the glass structure, as shown by fluctuation electron microscopy and angular correlations in nanodiffraction, and increases the film's elastic modulus and hardness. The data include x-ray diffraction, calorimetry, nanoindentation, SEM, AFM, and electron nanodiffraction on the films and on a bulk ribbon glass with the same composition quenched from the melt.\n", + "keywords": [ + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy", + "thin films", + "stability", + "medium-range order", + "fluctuation electron microscopy", + "electron nanodiffraction", + "nanoindentation", + "calorimetry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "muley_mgfilmstability_v1.1", + "mdf_source_name": "muley_mgfilmstability" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/muley_mgfilmstability_v1.1/", + "version": "1.0", + "root_version": "muley_mgfilmstability_v1.1", + "latest": true + } + }, + { + "source_id": "fsp_silica_bayesopt_v1.1", + "source_name": "fsp_silica_bayesopt", + "version": 1, + "ingest_date": "2020-07-27T13:32:01.678880Z", + "doi": "10.18126/w809-nuxk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/fsp_silica_bayesopt_v1.1/", + "metadata": { + "title": "Flame spray pyrolysis for silica nanomaterial synthesis", + "authors": [ + { + "name": "Paulson, Noah", + "given_name": "Noah", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Libera, Joe", + "given_name": "Joe", + "family_name": "Libera", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Stan, Marius", + "given_name": "Marius", + "family_name": "Stan", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "This dataset describes 17 experimental runs for the production of silica nanomaterials via flame spray pyrolysis (FSP) from tetraethyl orthosilicate (TEOS, pure, Merck KGaA, Germany) in reagent alcohol (C2H5OH + CH3OH 94.0-96.0%, C3H8O 4.0-6.0%, VWR Chemicals BDH, USA). The dataset principally contains the various gas and liquid flowrates and the scanning mobility particle sizer outputs including particle counts for different diameter bins. Finally, the dataset contains the calculated statistics of the measured particle size distribution for each experimental run. See the following article for further details: https://doi.org/10.1016/j.matdes.2020.108972.", + "keywords": [ + "flame spray pyrolysis", + "silica", + "SiO2", + "nanomaterials", + "particle size distribution" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.matdes.2020.108972", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "fsp_silica_bayesopt_v1.1", + "mdf_source_name": "fsp_silica_bayesopt" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/fsp_silica_bayesopt_v1.1/", + "version": "1.0", + "root_version": "fsp_silica_bayesopt_v1.1", + "latest": true + } + }, + { + "source_id": "singh_rolling_friction_prl_2020_v1.4", + "source_name": "singh_rolling_friction_prl_2020", + "version": 1, + "ingest_date": "2021-06-29T20:02:02.948799Z", + "doi": "10.18126/oqop-2zxk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/singh_rolling_friction_prl_2020_v1.4/", + "metadata": { + "title": "Shear thickening and jamming of dense suspensions: The \u201croll\u201d of friction", + "authors": [ + { + "name": "Abhinendra Singh", + "given_name": "Abhinendra", + "family_name": "Singh", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Christopher Ness", + "given_name": "Christopher", + "family_name": "Ness", + "affiliations": [ + "University of Edinburgh" + ] + }, + { + "name": "Ryohei Seto", + "given_name": "Ryohei", + "family_name": "Seto", + "affiliations": [ + "Wenzhou Institute" + ] + }, + { + "name": "Juan J. de Pablo", + "given_name": "Juan J.", + "family_name": "de Pablo", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Heinrich M. Jaeger", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + } + ], + "description": "The following data is related to simulations of shear thickening systems with and without rolling friction. Each directory is related to the data used to create each figure (4 figures). Each directory contains sub-directories that contain the data for each subfigure reported in the paper. We have used open-source code to generate the data set. https://bitbucket.org/rmari/lf_dem/src/master/ ", + "keywords": [ + "simulation", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.248005", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "singh_rolling_friction_prl_2020_v1.4", + "mdf_source_name": "singh_rolling_friction_prl_2020" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/singh_rolling_friction_prl_2020_v1.4/", + "version": "1.0", + "root_version": "singh_rolling_friction_prl_2020_v1.4", + "latest": true + } + }, + { + "source_id": "pardue_controlling_symmetry_strain_v1.1", + "source_name": "pardue_controlling_symmetry_strain", + "version": 1, + "ingest_date": "2021-04-27T17:30:06.492729Z", + "doi": "10.18126/30vp-63nx", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/pardue_controlling_symmetry_strain_v1.1/", + "metadata": { + "title": "Controlling the symmetry of cadmium arsenide films by epitaxial strain", + "authors": [ + { + "name": "Pardue, Tyler", + "given_name": "Tyler", + "family_name": "Pardue", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Goyal, Manik", + "given_name": "Manik", + "family_name": "Goyal", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Guo, Binghao", + "given_name": "Binghao", + "family_name": "Guo", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Salmani-Rezaie, Salva", + "given_name": "Salva", + "family_name": "Salmani-Rezaie", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Kim, Honggyu", + "given_name": "Honggyu", + "family_name": "Kim", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Heinonen, Olle G.", + "given_name": "Olle G.", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Johannes, Michelle D.", + "given_name": "Michelle D.", + "family_name": "Johannes", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + }, + { + "name": "Stemmer, Susanne", + "given_name": "Susanne", + "family_name": "Stemmer", + "affiliations": [ + "Argonne National Laboratory", + "Naval Research Laboratory", + "University of California-Santa Barbara", + "University of Florida" + ] + } + ], + "description": "All the data files are in dm3 format\n\nFigure 2 and Figure 4 are from the main text\n\n\nFigure 2: Whole pattern (WP) and zero-order Laue zone (ZOLZ) CBED files are posted for three different zone axes ([uvw]) in a compressive (112)-oriented film. \n\n\nFigure 4: Whole pattern (WP) and zero-order Laue zone (ZOLZ) CBED files are posted for different zone axes ([uvw]) in both compressive and tensile (001)-oriented films. \n\nSupplementary figures:\n\nFigure S2: Additional wWhole pattern (WP) and zero-order Laue zone (ZOLZ) CBED files are posted for three different zone axes ([uvw]) in a compressive (112)-oriented film. \n\n\nFigure S3: Additional whole pattern (WP) and zero-order Laue zone (ZOLZ) CBED files are posted for different zone axes ([uvw]) in both compressive and tensile (001)-oriented films. ", + "keywords": [ + "microscopy", + "topological materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "pardue_controlling_symmetry_strain_v1.1", + "mdf_source_name": "pardue_controlling_symmetry_strain" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/pardue_controlling_symmetry_strain_v1.1/", + "version": "1.0", + "root_version": "pardue_controlling_symmetry_strain_v1.1", + "latest": true + } + }, + { + "source_id": "uncoatedfiberbeds_insitupyrolysis_v1.1", + "source_name": "uncoatedfiberbeds_insitupyrolysis", + "version": 1, + "ingest_date": "2020-08-02T11:57:10.844590Z", + "doi": "10.18126/wr84-hg0a", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/uncoatedfiberbeds_insitupyrolysis_v1.1/", + "metadata": { + "title": "X-ray computed tomography dataset for in-situ 3D visualization of composite microstructure during polymer-to-ceramic conversion in uncoated fiber beds", + "authors": [ + { + "name": "Larson, Natalie M.", + "given_name": "Natalie M.", + "family_name": "Larson", + "affiliations": [ + "University of California - Santa Barbara" + ] + }, + { + "name": "Zok, Frank W.", + "given_name": "Frank W.", + "family_name": "Zok", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "The dataset consists of X-ray Computed Tomography (XCT) scans for in-situ observation of composite microstructure during polymer-to-ceramic conversion in uncoated fiber beds.", + "keywords": [ + "X-ray computed tomography (XCT)", + "Polymer derived ceramic (PDC)", + "Ceramic matrix composite (CMC)", + "Precursor impregnation and pyrolysis (PIP)", + "Preceramic polymer", + "experiment", + "composites", + "ceramics", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2017.10.054", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.18126/M20355", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.18126/M2QM0Z", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.18126/M2V937", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1016/j.compositesa.2018.11.021", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1016/j.compositesa.2017.12.024", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "uncoatedfiberbeds_insitupyrolysis_v1.1", + "mdf_source_name": "uncoatedfiberbeds_insitupyrolysis" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/uncoatedfiberbeds_insitupyrolysis_v1.1/", + "version": "1.0", + "root_version": "uncoatedfiberbeds_insitupyrolysis_v1.1", + "latest": true + } + }, + { + "source_id": "zhao_twisted_ws2_wse2_v1.2", + "source_name": "zhao_twisted_ws2_wse2", + "version": 1, + "ingest_date": "2020-04-26T12:54:04.083750Z", + "doi": "10.18126/m8ug-yrk1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhao_twisted_ws2_wse2_v1.2/", + "metadata": { + "title": "Super-twisted spirals of layered materials due to non-Euclidean surfaces", + "authors": [ + { + "name": "Zhao, Yuzhou", + "given_name": "Yuzhou", + "family_name": "Zhao", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Zhang, Chenyu", + "given_name": "Chenyu", + "family_name": "Zhang", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Jin, Song", + "given_name": "Song", + "family_name": "Jin", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "4D STEM dataset on twisted WS_2 and WSe_2 nanostructures. Z-contrast / HAADF STEM images of Moire patterns from the nanostructures.", + "keywords": [ + "2D materials", + "microscopy", + "experiment", + "WS2", + "4D STEM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zhao_twisted_ws2_wse2_v1.2", + "mdf_source_name": "zhao_twisted_ws2_wse2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zhao_twisted_ws2_wse2_v1.2/", + "version": "1.0", + "root_version": "zhao_twisted_ws2_wse2_v1.2", + "latest": true + } + }, + { + "source_id": "xie_microscale_bendable_tissues_v1.1", + "source_name": "xie_microscale_bendable_tissues", + "version": 1, + "ingest_date": "2020-05-18T14:24:24.039298Z", + "doi": "10.18126/ccof-rzbg", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/xie_microscale_bendable_tissues_v1.1/", + "metadata": { + "title": "Microscale, bendable thermoreflectance sensor for local measurements of the thermal effusivity of biological fluids and tissues", + "authors": [ + { + "name": "Xie, Xu", + "given_name": "Xu", + "family_name": "Xie", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Diao, Zhu", + "given_name": "Zhu", + "family_name": "Diao", + "affiliations": [ + "Stockholm University" + ] + }, + { + "name": "Cahill, David G.", + "given_name": "David G.", + "family_name": "Cahill", + "affiliations": [ + "Florida A&M University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": " 10.1063/1.5141376 ", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "xie_microscale_bendable_tissues_v1.1", + "mdf_source_name": "xie_microscale_bendable_tissues" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/xie_microscale_bendable_tissues_v1.1/", + "version": "1.0", + "root_version": "xie_microscale_bendable_tissues_v1.1", + "latest": true + } + }, + { + "source_id": "l7_qmc_v1.1", + "source_name": "l7_qmc", + "version": 1, + "ingest_date": "2020-09-04T20:58:01.952578Z", + "doi": "10.18126/ooou-s47a", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/l7_qmc_v1.1/", + "metadata": { + "title": "Dataset for \"Quantum Monte Carlo benchmarking of large noncovalent complexes in the L7 benchmark set\"", + "authors": [ + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "Dataset for the L7 benchmark using DFT (B3LYP) and QMC(DMC). All files are input and output files are in the pyscf code format and QMCPACK format. ", + "keywords": [ + "simulation", + "molecular structures", + "DFT", + "QMCPack", + "QMC", + "van_der_Waals", + "Benchmark", + "L7" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "l7_qmc_v1.1", + "mdf_source_name": "l7_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/l7_qmc_v1.1/", + "version": "1.0", + "root_version": "l7_qmc_v1.1", + "latest": true + } + }, + { + "source_id": "guangming_si_molecules_v1_v1.1", + "source_name": "guangming_si_molecules_v1", + "version": 1, + "ingest_date": "2020-09-17T15:17:47.983755Z", + "doi": "10.18126/dxpz-vo2i", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/guangming_si_molecules_v1_v1.1/", + "metadata": { + "title": "Binding and excitations in SixHy molecular systems using quantum Monte Carlo", + "authors": [ + { + "name": "Wang, Guangming", + "given_name": "Guangming", + "family_name": "Wang" + }, + { + "name": "Annaberdiyev Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev" + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas" + } + ], + "description": "\nDataset for \"Binding and excitations in SixHy molecular systems using quantum Monte Carlo\".\n\nData is categorized by each molecule.", + "keywords": [ + "ccECPs", + " excitation energies", + "binding energies", + "fixed-node error" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "guangming_si_molecules_v1_v1.1", + "mdf_source_name": "guangming_si_molecules_v1" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/guangming_si_molecules_v1_v1.1/", + "version": "1.0", + "root_version": "guangming_si_molecules_v1_v1.1", + "latest": true + } + }, + { + "source_id": "chaitanya_borophene_v1.1", + "source_name": "chaitanya_borophene", + "version": 1, + "ingest_date": "2021-01-22T19:37:44.528308Z", + "doi": "10.18126/oizk-5a9l", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/chaitanya_borophene_v1.1/", + "metadata": { + "title": "Synthesis of borophane polymorphs via hydrogenation of borophene", + "authors": [ + { + "name": "Li, Qiucheng", + "given_name": "Qiucheng", + "family_name": "Li", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Kolluru, Venkata Surya Chaitanya", + "given_name": "Venkata Surya Chaitanya", + "family_name": "Kolluru", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Rahn, Matthew S.", + "given_name": "Matthew S.", + "family_name": "Rahn", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Schwenker, Eric", + "given_name": "Eric", + "family_name": "Schwenker", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Li, Shaowei", + "given_name": "Shaowei", + "family_name": "Li", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Hennig, Richard G.", + "given_name": "Richard G.", + "family_name": "Hennig", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Darancet, Pierre", + "given_name": "Pierre", + "family_name": "Darancet", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Chan, Maria K. Y.", + "given_name": "Maria K. Y.", + "family_name": "Chan", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ] + } + ], + "description": "### Description\n\nThe dataset consists of the structure files in POSCAR format of the bare v1/6 -30 borophene and the three rectangular lattice candidate structures of the H adsorption on borophene - Bridge-1H, Top-1H and 2H.\n
\n\nFurther, the vasprun.xml files from the VASP calculations of the different characterization methods which include -\n\n* Bader charge analysis\n* Projected density of states\n* Partial charge densities near Fermi level for STM simulation\n* Vibrational mode calculations\n* Wrok function calculations with dipole corrections\n\nof the three candidate structures. The vasprun.xml files contains all the relevant input settings used to perform these calculations. By using the same settings along with the provided structure files and pseudopotentials, one can reproduce the reported data. The results from these calculations are compared with experimental observations to determine the atomisitc structure of the observed rectangular borophane lattice.\n", + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chaitanya_borophene_v1.1", + "mdf_source_name": "chaitanya_borophene" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chaitanya_borophene_v1.1/", + "version": "1.0", + "root_version": "chaitanya_borophene_v1.1", + "latest": true + } + }, + { + "source_id": "structurefactorsilicajade_v2.1", + "source_name": "structurefactorsilicajade", + "version": 2, + "ingest_date": "2021-05-12T19:37:39.364275Z", + "doi": "10.18126/mnbf-k5kn", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/structurefactorsilicajade_v2.1/", + "metadata": { + "title": "Neutron Structure Factor of Glassy Silica and Jade Glass", + "authors": [ + { + "name": "Shi, Ying", + "given_name": "Ying", + "family_name": "Shi", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ] + }, + { + "name": "Neuefeind, J\u00f6rg", + "given_name": "J\u00f6rg", + "family_name": "Neuefeind", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ] + }, + { + "name": "Bauchy, Mathieu", + "given_name": "Mathieu", + "family_name": "Bauchy", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ] + }, + { + "name": "Zhou, Qi", + "given_name": "Qi", + "family_name": "Zhou", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ] + }, + { + "name": "Deng, Binghui", + "given_name": "Binghui", + "family_name": "Deng", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ] + } + ], + "description": "Room temperature reduced neutron structure factor of glassy silica and Jade Glass measured by Time-of-flight (TOF) neutron scattering. The measurements were performed on the Nanoscale-Ordered Materials Diffractometer (NOMAD) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory.", + "keywords": [ + "experiment", + "oxides", + "silicate", + "glass", + "neutron diffraction", + "structure factor", + "silica", + "calcium aluminosilicate" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1103/PhysRevB.101.134106", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.1016/j.jnoncrysol.2019.03.037", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "structurefactorsilicajade_v2.1", + "mdf_source_name": "structurefactorsilicajade" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/structurefactorsilicajade_v2.1/", + "version": "2.0", + "root_version": "structurefactorsilicajade", + "latest": true + } + }, + { + "source_id": "bcdb_v1.3", + "source_name": "bcdb", + "version": 1, + "ingest_date": "2021-07-20T03:53:55.339818Z", + "doi": "10.18126/w26l-fjts", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bcdb_v1.3/", + "metadata": { + "title": "BCDB: The Block Copolymer Phase Behavior Database", + "authors": [ + { + "name": "Rebello, Nathan J.", + "given_name": "Nathan J.", + "family_name": "Rebello", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "CRIPT", + "given_name": "CRIPT", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + } + ], + "description": "The Block Copolymer Database (BCDB) is a database platform that allows a user to search, submit, visualize, and download experimental phase measurements and their associated characterization information for di- and multi-block copolymers. This database template can accommodate any number of blocks and at the time of publication contains over 5,300 block copolymer melt phase measurements mined from literature and manually curated. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the search syntax SMARTS (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using the Structured Query Language (SQL). This platform is an important step in making polymer data more accessible to the broader community, facilitates benchmarking and visualization of new data against existing samples, enables to user to search and download custom sets of block copolymer data from the literature, and will drive the development of data-driven models and machine learning applications.", + "keywords": [ + "experiment", + "polymers", + "morphologies", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bcdb_v1.3", + "mdf_source_name": "bcdb" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bcdb_v1.3/", + "version": "1.0", + "root_version": "bcdb_v1.3", + "latest": true + } + }, + { + "source_id": "euin2as2_v1.1", + "source_name": "euin2as2", + "version": 1, + "ingest_date": "2020-12-16T21:21:56.339287Z", + "doi": "10.18126/u3j8-aplr", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/euin2as2_v1.1/", + "metadata": { + "title": "Data set for \"Magnetic crystalline-symmetry-protected axion electrodynamics and field-tunable unpinned Dirac cones in EuIn2As2\", published in Nature Communications", + "authors": [ + { + "name": "Riberolles, S.X.M.", + "given_name": "S.X.M.", + "family_name": "Riberolles", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Trevisan, T. V.", + "given_name": "T. V.", + "family_name": "Trevisan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Kuthanazhi, B.", + "given_name": "B.", + "family_name": "Kuthanazhi", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Heitmann, T. W.", + "given_name": "T. W.", + "family_name": "Heitmann", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Ye, F.", + "given_name": "F.", + "family_name": "Ye", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Johnston, D. C.", + "given_name": "D. C.", + "family_name": "Johnston", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Bud\u2019ko, S. L.", + "given_name": "S. L.", + "family_name": "Bud\u2019ko", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Ryan, D. H.", + "given_name": "D. H.", + "family_name": "Ryan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Canfield, P. C.", + "given_name": "P. C.", + "family_name": "Canfield", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Kreyssig, A.", + "given_name": "A.", + "family_name": "Kreyssig", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Vishwanath, A.", + "given_name": "A.", + "family_name": "Vishwanath", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "McQueeney, R. J.", + "given_name": "R. J.", + "family_name": "McQueeney", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Wang, L. L.", + "given_name": "L. L.", + "family_name": "Wang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Orth, P. P.", + "given_name": "P. P.", + "family_name": "Orth", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "name": "Ueland, B. G.", + "given_name": "B. G.", + "family_name": "Ueland", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + } + ], + "description": "EuIn2As2 single crystal neutron scattering data collected on the TRIAX instrument at the MURR facility (MO) and on the CORRELI instrument at the Oak Ridge SNS facility (TN), presented in \"Magnetic crystalline-symmetry-protected axion electrodynamics and field-tunable unpinned Dirac cones in EuIn2As2\" published in Nature Comm.", + "keywords": [ + "experiment", + "DFT", + "Topological materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "euin2as2_v1.1", + "mdf_source_name": "euin2as2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/euin2as2_v1.1/", + "version": "1.0", + "root_version": "euin2as2_v1.1", + "latest": true + } + }, + { + "source_id": "huang_correlation_symmetry_v1.1", + "source_name": "huang_correlation_symmetry", + "version": 1, + "ingest_date": "2021-06-01T22:05:36.960930Z", + "doi": "10.18126/y6l9-ts2n", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/huang_correlation_symmetry_v1.1/", + "metadata": { + "title": "Data for Correlation Symmetry Analysis of Electron Nanodiffraction from Amorphous Materials", + "authors": [ + { + "name": "Huang, Shuoyuan", + "given_name": "Shuoyuan", + "family_name": "Huang", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Francis, Carter", + "given_name": "Carter", + "family_name": "Francis", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Ketkaew, Jittisa", + "given_name": "Jittisa", + "family_name": "Ketkaew", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Schroers, Jan", + "given_name": "Jan", + "family_name": "Schroers", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + } + ], + "description": "Data for \"Correlation Symmetry Analysis of Electron Nanodiffraction from Amorphous Materials\" by Shuoyuan Huang, Carter Francis, Jittisa Ketkaew, Jan Schroers, Paul M. Voyles. \n\nData consist of experimental electron nanodiffraction patterns from metallic glasses. Experimental data were collected on Pd40Ni10Cu27P20 metallic glass nanowires with a 2 nm diameter, 200 kV electron probe beam on a Titan STEM using a Gatan US1000 CCD camera. The nanowires were 41 \u00b1 3 nm thick. Jupyter notebooks demonstrate analysis of this data for its rotation symmetries using the angular power spectrum and the correlation symmetry coefficients. \n", + "keywords": [ + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy", + "nanodiffraction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "huang_correlation_symmetry_v1.1", + "mdf_source_name": "huang_correlation_symmetry" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/huang_correlation_symmetry_v1.1/", + "version": "1.0", + "root_version": "huang_correlation_symmetry_v1.1", + "latest": true + } + }, + { + "source_id": "silicajade_v1.1", + "source_name": "silicajade", + "version": 1, + "ingest_date": "2021-05-12T16:29:59.113871Z", + "doi": "10.18126/rowi-jn5s", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/silicajade_v1.1/", + "metadata": { + "title": "Atomic Structures of Glassy Silica and Jade Glass", + "authors": [ + { + "name": "Zhou, Qi", + "given_name": "Qi", + "family_name": "Zhou", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Shi, Ying", + "given_name": "Ying", + "family_name": "Shi", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Deng, Binghui", + "given_name": "Binghui", + "family_name": "Deng", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Neuefeind, J\u00f6rg", + "given_name": "J\u00f6rg", + "family_name": "Neuefeind", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bauchy, Mathieu", + "given_name": "Mathieu", + "family_name": "Bauchy", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "The dataset contains the atomic configurations of glassy silica (SiO2) and Jade Glass generated by molecular dynamics (MD) and force-enhanced atomic refinement (FEAR). The configurations have the LAMMPS Data format. Computed neutron structure factors and ring size distribution (using the Guttman definition) are also provided as an Excel file.", + "keywords": [ + "simulation", + "glass", + "silica", + "molecular dynamics", + "atomic structure", + "calcium aluminosilicate", + "ring size distribution" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "silicajade_v1.1", + "mdf_source_name": "silicajade" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/silicajade_v1.1/", + "version": "1.0", + "root_version": "silicajade_v1.1", + "latest": true + } + }, + { + "source_id": "elder_solidification_al126wtcu_c_v1.1", + "source_name": "elder_solidification_al126wtcu_c", + "version": 1, + "ingest_date": "2021-04-27T16:56:26.017020Z", + "doi": "10.18126/yf3k-kcr0", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/elder_solidification_al126wtcu_c_v1.1/", + "metadata": { + "title": "Solidification of an Al-12.6wt%Cu alloy cooled from 628.0 C to 623.9 C", + "authors": [ + { + "name": "Elder, Kate L. M.", + "given_name": "Kate L. M.", + "family_name": "Elder", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "name": "Stan, Tiberiu", + "given_name": "Tiberiu", + "family_name": "Stan", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "name": "Sun, Yue", + "given_name": "Yue", + "family_name": "Sun", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "name": "Xiao, Xianghui", + "given_name": "Xianghui", + "family_name": "Xiao", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "name": "Voorhees, Peter W.", + "given_name": "Peter W.", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + } + ], + "description": "Reconstruction of X-ray projections collected as an Al-12.6wt%Cu alloy was cooled from 628.0 C to 623.9 C at a cooling rate of 1 C/min. The experiment shows dendritic growth as the sample solidifies.", + "keywords": [ + "experiment", + "metals and alloys", + "microstructures", + "tomography", + "dendrite" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "elder_solidification_al126wtcu_c_v1.1", + "mdf_source_name": "elder_solidification_al126wtcu_c" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/elder_solidification_al126wtcu_c_v1.1/", + "version": "1.0", + "root_version": "elder_solidification_al126wtcu_c_v1.1", + "latest": true + } + }, + { + "source_id": "tiihonen_forces_v1.3", + "source_name": "tiihonen_forces", + "version": 1, + "ingest_date": "2021-05-20T22:17:37.469914Z", + "doi": "10.18126/136a-d6x2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tiihonen_forces_v1.3/", + "metadata": { + "title": "Dataset for Towards Quantum Monte Carlo Forces on Heavier Ions: Scaling Properties", + "authors": [ + { + "name": "Tiihonen, Juha", + "given_name": "Juha", + "family_name": "Tiihonen", + "affiliations": [ + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Clay III, Raymond C.", + "given_name": "Raymond C.", + "family_name": "Clay III", + "affiliations": [ + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "QMCPACK input and output files for forces of atoms and dimers used to derive scaling properties of statistical variance and computational cost as a function of the effective pseudopotential core charge. Postprocessing facilities to analyze the data in a reblocked tail regression estimator framework.", + "keywords": [ + "simulation", + "QMC", + "DMC", + "Quantum Monte Carlo", + "QMCPACK", + "Forces" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "tiihonen_forces_v1.3", + "mdf_source_name": "tiihonen_forces" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/tiihonen_forces_v1.3/", + "version": "1.0", + "root_version": "tiihonen_forces_v1.3", + "latest": true + } + }, + { + "source_id": "bennett_origin_metalinsulator_metals_v1.1", + "source_name": "bennett_origin_metalinsulator_metals", + "version": 1, + "ingest_date": "2021-07-09T21:08:45.492803Z", + "doi": "10.18126/yec3-xn27", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bennett_origin_metalinsulator_metals_v1.1/", + "metadata": { + "title": "Origin of Metal-Insulator Transitions in Correlated Perovskite Metals", + "authors": [ + { + "name": "Bennett, M. Chandler", + "given_name": "M. Chandler", + "family_name": "Bennett", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Hu, Guoxiang", + "given_name": "Guoxiang", + "family_name": "Hu", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Wang, Guangming", + "given_name": "Guangming", + "family_name": "Wang", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Kent, Paul R.C.", + "given_name": "Paul R.C.", + "family_name": "Kent", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + }, + { + "name": "Ganesh, P", + "given_name": "P", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State", + "Argonne National Laboratory", + "Queens College, City University of New York" + ] + } + ], + "keywords": [ + "simulation", + "perovskite", + "metals and alloys", + "correlated materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bennett_origin_metalinsulator_metals_v1.1", + "mdf_source_name": "bennett_origin_metalinsulator_metals" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bennett_origin_metalinsulator_metals_v1.1/", + "version": "1.0", + "root_version": "bennett_origin_metalinsulator_metals_v1.1", + "latest": true + } + }, + { + "source_id": "exclaim_exploratory_v1.1", + "source_name": "exclaim_exploratory", + "version": 1, + "ingest_date": "2021-02-11T15:50:25.729976Z", + "doi": "10.18126/v7bl-lj1n", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/exclaim_exploratory_v1.1/", + "metadata": { + "title": "EXSCLAIM! 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This dataset illustrates how a sample query, submitted to the EXSCLAIM! pipeline, can be used to construct a sizable labeled dataset (> 280,000 images) of nanostructure images from Nature journals.", + "keywords": [ + "microscopy", + "machine learning", + "nanomaterials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "exclaim_exploratory_v1.1", + "mdf_source_name": "exclaim_exploratory" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/exclaim_exploratory_v1.1/", + "version": "1.0", + "root_version": "exclaim_exploratory_v1.1", + "latest": true + } + }, + { + "source_id": "exclaim_validation_v1.1", + "source_name": "exclaim_validation", + "version": 1, + "ingest_date": "2021-02-11T15:29:15.739732Z", + "doi": "10.18126/a6jr-yfoq", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/exclaim_validation_v1.1/", + "metadata": { + "title": "EXSCLAIM! 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Python toolkit for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific literature. We highlight the methodology behind the construction of EXSCLAIM! and demonstrate its ability to extract and label open-source scientific images at high volume. 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"University of Wisconsin-Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Schultz, Lane E.", + "given_name": "Lane E.", + "family_name": "Schultz", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "The best GB model from the paper \"Experimental Exploration of Characteristic Temperature Contributions to Metallic Glass Forming Ability.\"", + "keywords": [ + "Metallic glasses", + "machine learning", + "Characteristic temperatures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "schultz_gb_model_full_fit_v1.1", + "mdf_source_name": "schultz_gb_model_full_fit" + }, + "download_url": 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"name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "FF-HEDM data with systematic sample motions to characterize the angular and translational sensitivity of the APS HEDM instrument at the 1-ID beamline.", + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "park_sensitivity_characterization_source_v1.1", + "mdf_source_name": "park_sensitivity_characterization_source" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/park_sensitivity_characterization_source_v1.1/", + "version": "1.0", + "root_version": "park_sensitivity_characterization_source_v1.1", + "latest": true + } + }, + { + "source_id": "shenyolosi_v1.3", + "source_name": "shenyolosi", + "version": 1, + "ingest_date": "2020-11-11T00:10:57.551245Z", + "doi": "10.18126/n9dj-5mk0", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shenyolosi_v1.3/", + "metadata": { + "title": "A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations ", + "authors": [ + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Shen, Mingren", + "given_name": "Mingren", + "family_name": "Shen", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Codes and Data for A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations \n", + "keywords": [ + "machine learning", + "metals and alloys", + "microscopy", + "defects" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "shenyolosi_v1.3", + "mdf_source_name": "shenyolosi" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/shenyolosi_v1.3/", + "version": "1.0", + "root_version": "shenyolosi_v1.3", + "latest": true + } + }, + { + "source_id": "hanus_thermoreflectance_imaging_coatings_v1.1", + "source_name": "hanus_thermoreflectance_imaging_coatings", + "version": 1, + "ingest_date": "2022-02-23T22:26:17.300968Z", + "doi": "10.18126/emun-4lr3", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hanus_thermoreflectance_imaging_coatings_v1.1/", + "metadata": { + "title": "Thermoreflectance Imaging of (Ultra)wide Band-Gap Devices with MoS2 Enhancement Coatings", + "authors": [ + { + "name": "Hanus, Riley", + "given_name": "Riley", + "family_name": "Hanus", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Rangnekar, Sonal V.", + "given_name": "Sonal V.", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Mollah, Shahab", + "given_name": "Shahab", + "family_name": "Mollah", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Hussain, Kamal", + "given_name": "Kamal", + "family_name": "Hussain", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Hines, Nicholas", + "given_name": "Nicholas", + "family_name": "Hines", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Heller, Eric", + "given_name": "Eric", + "family_name": "Heller", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Khan, Asif", + "given_name": "Asif", + "family_name": "Khan", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "name": "Graham, Samuel", + "given_name": "Samuel", + "family_name": "Graham", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + } + ], + "description": "Measuring the maximum operating temperature within the channel of ultrawide band-gap transistors is critically important since the temperature dependence of the device reliability sets operational limits such as maximum operational power. Thermoreflectance imaging (TTI) is an optimal choice to measure the junction temperature due to its submicrometer spatial resolution and submicrosecond temporal resolution. Since TTI is an imaging technique, data acquisition is orders of magnitude faster than point measurement techniques such as Raman thermometry. Unfortunately, commercially available LED light sources used in thermoreflectance systems are limited to energies less than \u223c3.9 eV, which is below the band gap of many ultrawide band-gap semiconductors (>4.0 eV). Therefore, the semiconductors are transparent to the probing light sources, prohibiting the application of TTI. To address this thermal imaging challenge, we utilize an MoS2 coating as a thermoreflectance enhancement coating that allows for the measurement of the surface temperature of (ultra)wide band-gap materials. The coating consists of a network of MoS2 nanoflakes with the c axis aligned normal to the surface and is easily removable via sonication. The method is validated using electrical and thermal characterization of GaN and AlGaN devices. We demonstrate that this coating does not measurably influence the electrical performance or the measured operating temperature. A maximum temperature rise of 49 K at 0.59 W was measured within the channel of the AlGaN device, which is over double the maximum temperature rise obtained by measuring the thermoreflectance of the gate metal. The importance of accurately measuring the peak operational temperature is discussed in the context of accelerated stress testing.", + "keywords": [ + "experiment", + "2D materials", + "AlGaN", + "GaN", + "power electronics", + "radio frequency (RF) electronics", + "thermal imaging", + "thermoreflectance", + "ultra wide band gap", + "wide band gap", + "semiconductors" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsami.1c11528", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hanus_thermoreflectance_imaging_coatings_v1.1", + "mdf_source_name": "hanus_thermoreflectance_imaging_coatings" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hanus_thermoreflectance_imaging_coatings_v1.1/", + "version": "1.0", + "root_version": "hanus_thermoreflectance_imaging_coatings_v1.1", + "latest": true + } + }, + { + "source_id": "garrison_2d_v1.2", + "source_name": "garrison_2d", + "version": 1, + "ingest_date": "2022-02-23T22:54:57.837071Z", + "doi": "10.18126/2rpo-t1rn", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/garrison_2d_v1.2/", + "metadata": { + "title": "Accelerated Decomposition Kinetics of Ammonium Perchlorate via Conformal Graphene Coating", + "authors": [ + { + "name": "Garrison, Michael D", + "given_name": "Michael D", + "family_name": "Garrison", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Wallace, Shay Goff", + "given_name": "Shay Goff", + "family_name": "Wallace", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Baldwin, Lawrence C.", + "given_name": "Lawrence C.", + "family_name": "Baldwin", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Guo, Zixuan", + "given_name": "Zixuan", + "family_name": "Guo", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Kuo, Lidia", + "given_name": "Lidia", + "family_name": "Kuo", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Estevez, Joseph E.", + "given_name": "Joseph E.", + "family_name": "Estevez", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Briseno, Alejandro L.", + "given_name": "Alejandro L.", + "family_name": "Briseno", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "name": "Baca, Alfred J.", + "given_name": "Alfred J.", + "family_name": "Baca", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + } + ], + "description": "Ammonium perchlorate (AP) is an oxidizer material that is widely employed in applications ranging from rocketry to airbags. Previous research has suggested that efficient electron transfer plays a critical role in determining the kinetics of catalyzed AP decomposition reactions. Consequently, intimate contact between AP crystals and electron acceptors has the potential to accelerate decomposition kinetics, which motivates the development of conformal coatings with suitably tailored electronic structures. Here, we demonstrate a scalable method for conformally coating AP crystals with two atomically well-defined 2D materials with orthogonal electronic properties\u2500namely, pristine graphene, which is a zero-band gap semiconductor that has been shown to be an effective electron acceptor in diverse heterojunctions and hexagonal boron nitride (hBN), which is a wide-band gap electrical insulator. Consistent with an electron transfer mechanism, graphene-coated AP undergoes accelerated decomposition kinetics compared to uncoated (neat) or hBN-coated AP. Through extensive structural characterization including electron microscopy and X-ray diffraction, the effects of AP crystal size and crystallinity are examined. In addition, the accelerated decomposition kinetics of graphene-coated AP are quantified through thermogravimetric analysis, gas chromatography mass spectrometry, and kinetic modeling. Overall, this work establishes pristine graphene as an effective coating for promoting accelerated decomposition of AP, which enhances its utility in various applications.", + "keywords": [ + "experiment", + "2D materials", + "crystals", + "charge transfer" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.chemmater.1c03100", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "garrison_2d_v1.2", + "mdf_source_name": "garrison_2d" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/garrison_2d_v1.2/", + "version": "1.0", + "root_version": "garrison_2d_v1.2", + "latest": true + } + }, + { + "source_id": "yang_evidence_coupled_nbge2_v1.1", + "source_name": "yang_evidence_coupled_nbge2", + "version": 1, + "ingest_date": "2021-08-02T20:44:10.850958Z", + "doi": "10.18126/uftm-ny12", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/yang_evidence_coupled_nbge2_v1.1/", + "metadata": { + "title": "Evidence of a coupled electron-phonon liquid in NbGe2", + "authors": [ + { + "name": "Yang, Hung-Yu", + "given_name": "Hung-Yu", + "family_name": "Yang", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Yao, Xiaohan", + "given_name": "Xiaohan", + "family_name": "Yao", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Plisson, Vincent", + "given_name": "Vincent", + "family_name": "Plisson", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Tafti, Fazel", + "given_name": "Fazel", + "family_name": "Tafti", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Balicas, Luis", + "given_name": "Luis", + "family_name": "Balicas", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Burch, Kenneth", + "given_name": "Kenneth", + "family_name": "Burch", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Mozaffari, Shirin", + "given_name": "Shirin", + "family_name": "Mozaffari", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Choi, Eun Sang", + "given_name": "Eun Sang", + "family_name": "Choi", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "name": "Chan, Julia", + "given_name": "Julia", + "family_name": "Chan", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + } + ], + "description": "Comprehensive characterization of the intermetallic compounds NbGe2 to provide evidence of electron-phonon liquid. ", + "keywords": [ + "experiment", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "yang_evidence_coupled_nbge2_v1.1", + "mdf_source_name": "yang_evidence_coupled_nbge2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/yang_evidence_coupled_nbge2_v1.1/", + "version": "1.0", + "root_version": "yang_evidence_coupled_nbge2_v1.1", + "latest": true + } + }, + { + "source_id": "omor_apl_v1.1", + "source_name": "omor_apl", + "version": 1, + "ingest_date": "2021-10-12T16:23:36.051578Z", + "doi": "10.18126/x8xf-ld1l", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/omor_apl_v1.1/", + "metadata": { + "title": "Dataset for \"Detecting topological phase transitions in cadmium arsenide films via the transverse magnetoresistance\" paper in Applied Physics Letters", + "authors": [ + { + "name": "Shoron, Omor F.", + "given_name": "Omor F.", + "family_name": "Shoron", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + }, + { + "name": "Kealhofer, David A.", + "given_name": "David A.", + "family_name": "Kealhofer", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + }, + { + "name": "Goyal, Manik", + "given_name": "Manik", + "family_name": "Goyal", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + }, + { + "name": "Schumann, Timo", + "given_name": "Timo", + "family_name": "Schumann", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + }, + { + "name": "Burkov, Anton A.", + "given_name": "Anton A.", + "family_name": "Burkov", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + }, + { + "name": "Stemmer, Susanne", + "given_name": "Susanne", + "family_name": "Stemmer", + "affiliations": [ + "University of California, Santa Barbara", + "University of Waterloo" + ] + } + ], + "description": "Dataset for \"Detecting topological phase transitions in cadmium arsenide films via the transverse magnetoresistance\" paper in Applied Physics Letters", + "keywords": [ + "experiment", + "magnetoresistance", + "topological phases" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "omor_apl_v1.1", + "mdf_source_name": "omor_apl" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/omor_apl_v1.1/", + "version": "1.0", + "root_version": "omor_apl_v1.1", + "latest": true + } + }, + { + "source_id": "axmas_v1.1", + "source_name": "axmas", + "version": 1, + "ingest_date": "2021-10-26T22:10:43.234408Z", + "doi": "10.18126/iidy-30e7", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/axmas_v1.1/", + "metadata": { + "title": "AXMAS Data", + "authors": [ + { + "name": "Osborn, Raymond", + "given_name": "Raymond", + "family_name": "Osborn", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Rosenkranz, Stephan", + "given_name": "Stephan", + "family_name": "Rosenkranz", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Haley, Charlotte", + "given_name": "Charlotte", + "family_name": "Haley", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Rao, Vishwas", + "given_name": "Vishwas", + "family_name": "Rao", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Anitescu, Mihai", + "given_name": "Mihai", + "family_name": "Anitescu", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Krogstad, Matthew", + "given_name": "Matthew", + "family_name": "Krogstad", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Kim, Eun-Ah", + "given_name": "Eun-Ah", + "family_name": "Kim", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + }, + { + "name": "Mallayya, Krishnanand", + "given_name": "Krishnanand", + "family_name": "Mallayya", + "affiliations": [ + "Argonne National Laboratory", + "Cornell University" + ] + } + ], + "description": "This contains experimental data used in machine-learning and spectral analysis of single-crystal X-ray diffraction data collected at the Advanced Photon Source as part of the AXMAS project (https://cels.anl.gov/axmas). The data is made available for use in tutorial notebooks.", + "keywords": [ + "experiment", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "axmas_v1.1", + "mdf_source_name": "axmas" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/axmas_v1.1/", + "version": "1.0", + "root_version": "axmas_v1.1", + "latest": true + } + }, + { + "source_id": "shin_optimized_structure_methods_v1.1", + "source_name": "shin_optimized_structure_methods", + "version": 1, + "ingest_date": "2021-01-29T21:56:18.287971Z", + "doi": "10.18126/0l2d-tv94", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shin_optimized_structure_methods_v1.1/", + "metadata": { + "title": "Dataset for \"Optimized structure and electronic band gap of monolayer GeSe from quantum Monte Carlo methods\"", + "authors": [ + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Gasperich, Kevin", + "given_name": "Kevin", + "family_name": "Gasperich", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Kent, Paul R. C.", + "given_name": "Paul R. C.", + "family_name": "Kent", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory" + ] + } + ], + "keywords": [ + "simulation", + "semiconductors", + "QMCPack", + "QMC", + "Monte Carlo", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "shin_optimized_structure_methods_v1.1", + "mdf_source_name": "shin_optimized_structure_methods" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/shin_optimized_structure_methods_v1.1/", + "version": "1.0", + "root_version": "shin_optimized_structure_methods_v1.1", + "latest": true + } + }, + { + "source_id": "rai_anisotropic_thermal_selenide_v1.1", + "source_name": "rai_anisotropic_thermal_selenide", + "version": 1, + "ingest_date": "2021-02-02T22:15:03.029196Z", + "doi": "10.18126/lsg5-3utw", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rai_anisotropic_thermal_selenide_v1.1/", + "metadata": { + "title": "Anisotropic Thermal Conductivity of Layered Indium Selenide", + "authors": [ + { + "name": "Rai, Akash", + "given_name": "Akash", + "family_name": "Rai", + "affiliations": [ + "Northwestern University", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Gish, J. Tyler", + "given_name": "J. Tyler", + "family_name": "Gish", + "affiliations": [ + "Northwestern University", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Cahill, David G.", + "given_name": "David G.", + "family_name": "Cahill", + "affiliations": [ + "Northwestern University", + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + "University of Illinois at Urbana-Champaign" + ] + } + ], + "keywords": [ + "experiment", + "thermal conductivity", + "InS" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "rai_anisotropic_thermal_selenide_v1.1", + "mdf_source_name": "rai_anisotropic_thermal_selenide" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/rai_anisotropic_thermal_selenide_v1.1/", + "version": "1.0", + "root_version": "rai_anisotropic_thermal_selenide_v1.1", + "latest": true + } + }, + { + "source_id": "kononov_anomalous_stopping_graphene_v1.1", + "source_name": "kononov_anomalous_stopping_graphene", + "version": 1, + "ingest_date": "2021-03-12T15:38:33.374903Z", + "doi": "10.18126/xnxt-wn6i", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kononov_anomalous_stopping_graphene_v1.1/", + "metadata": { + "title": "Anomalous stopping and charge transfer in proton-irradiated graphene", + "authors": [ + { + "name": "Kononov, Alina", + "given_name": "Alina", + "family_name": "Kononov", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "keywords": [ + "simulation", + "machine learning", + "2D materials", + "graphene" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kononov_anomalous_stopping_graphene_v1.1", + "mdf_source_name": "kononov_anomalous_stopping_graphene" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kononov_anomalous_stopping_graphene_v1.1/", + "version": "1.0", + "root_version": "kononov_anomalous_stopping_graphene_v1.1", + "latest": true + } + }, + { + "source_id": "sangwan_intrinsic_carrier_ghz_v1.1", + "source_name": "sangwan_intrinsic_carrier_ghz", + "version": 1, + "ingest_date": "2022-02-25T22:44:56.875075Z", + "doi": "10.18126/ne02-lzps", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sangwan_intrinsic_carrier_ghz_v1.1/", + "metadata": { + "title": "Intrinsic carrier multiplication in layered Bi2O2Se avalanche photodiodes with gain bandwidth product exceeding 1 GHz", + "authors": [ + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Kang, Joohoon", + "given_name": "Joohoon", + "family_name": "Kang", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Lam, David", + "given_name": "David", + "family_name": "Lam", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Gish, J. Tyler", + "given_name": "J. Tyler", + "family_name": "Gish", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Wells, Spencer A.", + "given_name": "Spencer A.", + "family_name": "Wells", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Luxa, Jan", + "given_name": "Jan", + "family_name": "Luxa", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Male, James P.", + "given_name": "James P.", + "family_name": "Male", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Snyder, G. Jeffrey", + "given_name": "G. Jeffrey", + "family_name": "Snyder", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Sofer, Zden\u011bk", + "given_name": "Zden\u011bk", + "family_name": "Sofer", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + } + ], + "description": " Emerging layered semiconductors present multiple advantages for optoelectronic technologies including high carrier mobilities, strong light-matter interactions, and tunable optical absorption and emission. Here, metal-semiconductor-metal avalanche photodiodes (APDs) are fabricated from Bi2O2Se crystals, which consist of electrostatically bound [Bi2O2]2+ and [Se]2\u2212 layers. The resulting APDs possess an intrinsic carrier multiplication factor up to 400 at 7 K with a responsivity gain exceeding 3,000 A/W and bandwidth of ~ 400 kHz at a visible wavelength of 515.6 nm, ultimately resulting in a gain bandwidth product exceeding 1 GHz. Due to exceptionally low dark currents, Bi2O2Se APDs also yield high detectivities up to 4.6 \u00d7 1014 Jones. A systematic analysis of the photocurrent temperature and bias dependence reveals that the carrier multiplication process in Bi2O2Se APDs is consistent with a reverse biased Schottky diode model with a barrier height of ~ 44 meV, in contrast to the charge trapping extrinsic gain mechanism that dominates most layered semiconductor phototransistors. In this manner, layered Bi2O2Se APDs provide a unique platform that can be exploited in a diverse range of high-performance photodetector applications.", + "keywords": [ + "experiment", + "semiconductors", + "photodiodes" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1007/s12274-020-3059-3", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sangwan_intrinsic_carrier_ghz_v1.1", + "mdf_source_name": "sangwan_intrinsic_carrier_ghz" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sangwan_intrinsic_carrier_ghz_v1.1/", + "version": "1.0", + "root_version": "sangwan_intrinsic_carrier_ghz_v1.1", + "latest": true + } + }, + { + "source_id": "bennett_high_accuracy_carlo_v1.1", + "source_name": "bennett_high_accuracy_carlo", + "version": 1, + "ingest_date": "2021-12-08T23:15:11.440255Z", + "doi": "10.18126/j7cm-wh84", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bennett_high_accuracy_carlo_v1.1/", + "metadata": { + "title": "High Accuracy Transition Metal Effective Cores for Many-body Diffusion Monte Carlo", + "authors": [ + { + "name": "Bennett, M. Chandler", + "given_name": "M. Chandler", + "family_name": "Bennett", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Reboredo, Fernando A.", + "given_name": "Fernando A.", + "family_name": "Reboredo", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "North Carolina State University" + ] + } + ], + "description": "Practical applications of the real-space diffusion Monte Carlo (DMC) method require the removal of core electrons, where currently localization approximations of semi-local potentials are generally used in the projector. Accurate calculations of complex solids and large molecules demand minimizing the impact of approximated atomic cores. Prior works have shown the errors from such approximations can be sizable in both finite and periodic systems. In this work, we show that a class of differential pseudopotentials, known as pseudo-Hamiltonians, can be constructed for the $3d$ transition metal atoms, entirely removing the need for any localization scheme in the DMC projector. As a proof of principle, we demonstrate the approach for the case of Co. In order to minimize errors in the pseudo-Hamiltonian at the many-body level, we generalize the recently proposed correlation-consistent pseudopotential generation scheme to successively close semi-local representations of the differential potentials. Our generation scheme successfully produces potentials tailored specifically for real space projector quantum Monte Carlo methods with low error at the many-body level, i.e., with many-body scattering properties very close to relativistic all-electron results. In particular, we show that the agreement with respect to atomic and molecular quantities reach chemical accuracy in many cases -- on par with the most accurate semi-local pseudopotentials available. Further, our pseudo-Hamiltonian generation scheme utilizes standard quantum chemistry codes designed only to work with semi-local pseudopotentials, enabling straightforward generation of pseudo-Hamiltonians for additional elements in future works.The entire set of input and output files used in this project are provided here.", + "keywords": [ + "simulation", + "Monte Carlo" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bennett_high_accuracy_carlo_v1.1", + "mdf_source_name": "bennett_high_accuracy_carlo" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bennett_high_accuracy_carlo_v1.1/", + "version": "1.0", + "root_version": "bennett_high_accuracy_carlo_v1.1", + "latest": true + } + }, + { + "source_id": "phthalocyanine_mos2_v1.1", + "source_name": "phthalocyanine_mos2", + "version": 1, + "ingest_date": "2021-11-05T01:09:58.175851Z", + "doi": "10.18126/xkru-gan1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/phthalocyanine_mos2_v1.1/", + "metadata": { + "title": "Electronic Structure of Transition-Metal Phthalocyanine (MPc) Molecules and Mixed-Dimensional MPc-MoS2 Heterojunctions", + "authors": [ + { + "name": "Zhou, Qunfei", + "given_name": "Qunfei", + "family_name": "Zhou", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Liu, Zhen-Fei", + "given_name": "Zhen-Fei", + "family_name": "Liu", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Marks, Tobin", + "given_name": "Tobin", + "family_name": "Marks", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "name": "Darancet, Pierre", + "given_name": "Pierre", + "family_name": "Darancet", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "description": "Density functional calculations with different functionals, B3LYP, HSE, PBE, range-separated hybrid functionals for transition-metal phthalocyanine (MPc) molecules and mixed-dimensional MPc-MoS2 heterojunctions. Calculations of dielectric screening effects of the 2D MoS2 and other substrates on the energy levels of the organic molecules. Calculations and figure plotting in Python scripts are included in jupyter notebooks.", + "keywords": [ + "molecular structures", + "DFT", + "Electronic Structure", + " mixed-dimensional heterojunctions", + "transition-metal phthalocyanine" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.jpca.0c10766", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1021/acsnano.8b09166", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "\tarXiv:2107.08516", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "phthalocyanine_mos2_v1.1", + "mdf_source_name": "phthalocyanine_mos2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/phthalocyanine_mos2_v1.1/", + "version": "1.0", + "root_version": "phthalocyanine_mos2_v1.1", + "latest": true + } + }, + { + "source_id": "nature_ndbi_cats_v1.1", + "source_name": "nature_ndbi_cats", + "version": 1, + "ingest_date": "2021-12-13T15:35:20.099036Z", + "doi": "10.18126/p9b8-e7ms", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nature_ndbi_cats_v1.1/", + "metadata": { + "title": "Data Set for \"Emergence of Fermi arcs due to magnetic band splitting in an antiferromagnet\"", + "authors": [ + { + "name": "Schrunk, Benjamin", + "given_name": "Benjamin", + "family_name": "Schrunk", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Kushnirenko, Yevhen", + "given_name": "Yevhen", + "family_name": "Kushnirenko", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Kuthanazhi, Brinda", + "given_name": "Brinda", + "family_name": "Kuthanazhi", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Ahn, Junyeong", + "given_name": "Junyeong", + "family_name": "Ahn", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Wang, Lin-Lin", + "given_name": "Lin-Lin", + "family_name": "Wang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "O\u2019Leary, Evan", + "given_name": "Evan", + "family_name": "O\u2019Leary", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Lee, Kyungchan", + "given_name": "Kyungchan", + "family_name": "Lee", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Eaton, Andrew", + "given_name": "Andrew", + "family_name": "Eaton", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Fedorov, Alexander", + "given_name": "Alexander", + "family_name": "Fedorov", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Lou, Rui", + "given_name": "Rui", + "family_name": "Lou", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Voroshnin, Vladimir", + "given_name": "Vladimir", + "family_name": "Voroshnin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "Clark, Oliver J.", + "given_name": "Oliver J.", + "family_name": "Clark", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "name": "\u0301anchez-Barriga, J. 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Jeffrey", + "given_name": "G. Jeffrey", + "family_name": "Snyder", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Ozen, Melis", + "given_name": "Melis", + "family_name": "Ozen", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Saglik, Kivanc", + "given_name": "Kivanc", + "family_name": "Saglik", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Aydemir, Umut", + "given_name": "Umut", + "family_name": "Aydemir", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Cardoso-Gil, Ra\u00fal", + "given_name": "Ra\u00fal", + "family_name": "Cardoso-Gil", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Svanidze, E", + "given_name": "E", + "family_name": "Svanidze", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Funahashi, Ryoji", + "given_name": "Ryoji", + "family_name": "Funahashi", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Powell, Anthony V", + "given_name": "Anthony V", + "family_name": "Powell", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Mukherjee, Shriparna", + "given_name": "Shriparna", + "family_name": "Mukherjee", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Tippireddy, Sahil", + "given_name": "Sahil", + "family_name": "Tippireddy", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Vaqueiro, Paz", + "given_name": "Paz", + "family_name": "Vaqueiro", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Gascoin, Franck", + "given_name": "Franck", + "family_name": "Gascoin", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Kyratsi, Theodora", + "given_name": "Theodora", + "family_name": "Kyratsi", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Sauerschnig, Philipp", + "given_name": "Philipp", + "family_name": "Sauerschnig", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "name": "Mori, Takao", + "given_name": "Takao", + "family_name": "Mori", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + } + ], + "description": "This Dataset presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The 12 families of materials included in these tables are primarily selected on the basis of well established, internationally-recognised performance and promise for current and future applications: Tellurides, Skutterudites, Half Heuslers, Zintls, Mg-Sb Antimonides, Clathrates, FeGa3\u2013type materials, Actinides and Lanthanides, Oxides, Sulfides, Selenides, Silicides, Borides and Carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data. ", + "keywords": [ + "semiconductors", + "energy materials", + "oxides", + "Thermoelectrics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1088/2515-7655/ac49dc", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "robert_freer_inorganic_thermoelectric_materials_v1.1", + "mdf_source_name": "robert_freer_inorganic_thermoelectric_materials" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/robert_freer_inorganic_thermoelectric_materials_v1.1/", + "version": "1.0", + "root_version": "robert_freer_inorganic_thermoelectric_materials_v1.1", + "latest": true + } + }, + { + "source_id": "sangwan_visualizing_thermally_semiconductors_v1.1", + "source_name": "sangwan_visualizing_thermally_semiconductors", + "version": 1, + "ingest_date": "2022-02-25T22:51:53.513004Z", + "doi": "10.18126/v4tm-nw3i", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sangwan_visualizing_thermally_semiconductors_v1.1/", + "metadata": { + "title": "Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution\u2010Processed 2D Semiconductors", + "authors": [ + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Rangnekar, Sonal V.", + "given_name": "Sonal V.", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Kang, Joohoon", + "given_name": "Joohoon", + "family_name": "Kang", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Shen, Jianan", + "given_name": "Jianan", + "family_name": "Shen", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Lee, Hong\u2010Sub", + "given_name": "Hong\u2010Sub", + "family_name": "Lee", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Lam, David", + "given_name": "David", + "family_name": "Lam", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Shen, Junhua", + "given_name": "Junhua", + "family_name": "Shen", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Liu, Xiaolong", + "given_name": "Xiaolong", + "family_name": "Liu", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Moraes, Ana C. M.", + "given_name": "Ana C. M.", + "family_name": "Moraes", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Kuo, Lidia", + "given_name": "Lidia", + "family_name": "Kuo", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Gu, Jie", + "given_name": "Jie", + "family_name": "Gu", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Wang, Haihua", + "given_name": "Haihua", + "family_name": "Wang", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ] + } + ], + "description": "Memristive systems present a low-power alternative to silicon-based elec-tronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two-terminal vertical geometry. Here, the authors report thermally activated mem-ristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS2, ReS2, WS2, and InSe. The mechanisms underlying threshold switching and negative differential resistance are eluci-dated by designing large-area lateral memristors that allow the direct observa-tion of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 103) that are achieved at low fields (\u22484 kV cm\u22121) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution-processed 2D semiconductors as a platform for neuromorphic architectures.", + "keywords": [ + "experiment", + "2D materials", + "memristors", + "solution processing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/adfm.202107385", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sangwan_visualizing_thermally_semiconductors_v1.1", + "mdf_source_name": "sangwan_visualizing_thermally_semiconductors" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sangwan_visualizing_thermally_semiconductors_v1.1/", + "version": "1.0", + "root_version": "sangwan_visualizing_thermally_semiconductors_v1.1", + "latest": true + } + }, + { + "source_id": "moore_ultrasensitive_molecular_materials_v1.1", + "source_name": "moore_ultrasensitive_molecular_materials", + "version": 1, + "ingest_date": "2022-02-25T22:41:18.638663Z", + "doi": "10.18126/fipx-ox7u", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/moore_ultrasensitive_molecular_materials_v1.1/", + "metadata": { + "title": "Ultrasensitive Molecular Sensors Based on Real\u2010Time Impedance Spectroscopy in Solution\u2010Processed 2D Materials", + "authors": [ + { + "name": "Moore, David C.", + "given_name": "David C.", + "family_name": "Moore", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Jawaid, Ali", + "given_name": "Ali", + "family_name": "Jawaid", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Busch, Robert", + "given_name": "Robert", + "family_name": "Busch", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Brothers, Michael", + "given_name": "Michael", + "family_name": "Brothers", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Miesle, Paige", + "given_name": "Paige", + "family_name": "Miesle", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Miesle, Adam", + "given_name": "Adam", + "family_name": "Miesle", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Rao, Rahul", + "given_name": "Rahul", + "family_name": "Rao", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Lee, Jonghoon", + "given_name": "Jonghoon", + "family_name": "Lee", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Beagle, Lucas K.", + "given_name": "Lucas K.", + "family_name": "Beagle", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Motala, Michael", + "given_name": "Michael", + "family_name": "Motala", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Wallace, Shay Goff", + "given_name": "Shay Goff", + "family_name": "Wallace", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Downing, Julia R.", + "given_name": "Julia R.", + "family_name": "Downing", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Roy, Ajit", + "given_name": "Ajit", + "family_name": "Roy", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Muratore, Christopher", + "given_name": "Christopher", + "family_name": "Muratore", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Vaia, Richard", + "given_name": "Richard", + "family_name": "Vaia", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Kim, Steve", + "given_name": "Steve", + "family_name": "Kim", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "name": "Glavin, Nicholas R.", + "given_name": "Nicholas R.", + "family_name": "Glavin", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + } + ], + "description": "Chemical sensors based on solution-processed 2D nanomaterials represent an extremely attractive approach toward scalable and low-cost devices. Through the implementation of real-time impedance spectroscopy and development of a three-element circuit model, redox exfoliated MoS2 nanoflakes demonstrate an ultrasensitive empirical detection limit of NO2 gas at 1 ppb, with an extrapolated ultimate detection limit approaching 63 ppt. This sensor construct reveals a more than three orders of magnitude improvement from conventional direct current sensing approaches as the traditionally dominant interflake interactions are bypassed in favor of selectively extracting intraflake doping effects. This same approach allows for an all solution-processed, flexible 2D sensor to be fabricated on a polyimide substrate using a combination of graphene contacts and drop-casted MoS2 nanoflakes, exhibiting similar sensitivity limits. Finally, a thermal annealing strategy is used to explore the tunability of the nanoflake interactions and subsequent circuit model fit, with a demonstrated sensitivity improvement of 2\u00d7 with thermal annealing at 200 \u00b0C.", + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/adfm.202106830", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "moore_ultrasensitive_molecular_materials_v1.1", + "mdf_source_name": "moore_ultrasensitive_molecular_materials" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/moore_ultrasensitive_molecular_materials_v1.1/", + "version": "1.0", + "root_version": "moore_ultrasensitive_molecular_materials_v1.1", + "latest": true + } + }, + { + "source_id": "foam_db_v1.1", + "source_name": "foam_db", + "version": 1, + "ingest_date": "2022-06-04T05:26:26.663877Z", + "doi": "10.18126/6h74-leb4", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/foam_db_v1.1/", + "metadata": { + "title": "A Materials Dataset for Elastomeric Foam Impact Mitigating Materials", + "authors": [ + { + "name": "Landauer, Alexander K", + "given_name": "Alexander K", + "family_name": "Landauer", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "name": "Kafka, Orion L", + "given_name": "Orion L", + "family_name": "Kafka", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "name": "Moser, Newell H", + "given_name": "Newell H", + "family_name": "Moser", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "name": "Forster, Aaron M", + "given_name": "Aaron M", + "family_name": "Forster", + "affiliations": [ + "National Institute of Standards and Technology" + ] + } + ], + "description": "The database includes data for structure-properties relationships and mechanical modeling of elastic impact protection foams from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions and modes. The data repository includes directories for: dynamic mechanical analysis raw data, results, and analysis tools; intermediate rate (servo-hydraulic UTS based) raw data including 2D digital image correlation (DIC) images, results, and analysis tools; quasi-static rate (electro-mechanical UTS based) raw data including 2D digital image correlation (DIC), results, and analysis tools; and, micro-computed tomography data including raw volume images, filtered images, binarized images, other results, and analysis tools. For more information see the readme and data documentation in each respective directory. A complete manuscript describing data collection, analysis, and database documentation is to be published.", + "keywords": [ + "experiment", + "machine learning", + "microscopy", + "microstructures", + "foam", + "mechanical properties", + "vinyl nitrile", + "impact mitigation", + " elastomer", + " computer tomography", + " digital image correlation", + "impact protection", + " impact mitigating materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18434/mds2-2606", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "foam_db_v1.1", + "mdf_source_name": "foam_db" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/foam_db_v1.1/", + "version": "1.0", + "root_version": "foam_db_v1.1", + "latest": true + } + }, + { + "source_id": "doan_datasets_accelerating_representations_v1.1", + "source_name": "doan_datasets_accelerating_representations", + "version": 1, + "ingest_date": "2022-03-29T21:32:03.215104Z", + "doi": "10.18126/82c7-6cn8", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/doan_datasets_accelerating_representations_v1.1/", + "metadata": { + "title": "Datasets for Accelerating Catalysts Screening via Machine-learned Local Coordination Graph Representations", + "authors": [ + { + "name": "Doan, Hieu A.", + "given_name": "Hieu A.", + "family_name": "Doan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Li, Chenyang", + "given_name": "Chenyang", + "family_name": "Li", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Zhou, Mingxia", + "given_name": "Mingxia", + "family_name": "Zhou", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Curtiss, Larry A.", + "given_name": "Larry A.", + "family_name": "Curtiss", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + }, + { + "name": "Assary, Rajeev S.", + "given_name": "Rajeev S.", + "family_name": "Assary", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ] + } + ], + "description": "A priori catalyst designs from reliable first principles simulations and emerging artificial intelligence tools are desired to accelerate materials development. In the context of upgrading biomass materials via deoxygenation reaction to value-added chemicals, molybdenum carbides (Mo2C) have been considered among the best and economically viable catalysts. One of the bottlenecks related to longer term stability of Mo2C catalysts is the susceptibility to surface oxidation, which requires the use of excess hydrogen for active site regeneration. By using dopants to tune the oxygen affinity of Mo2C surfaces, it is possible to design new doped Mo2C catalysts with desired reactivity and stability. Here, we first employed Density Functional Theory (DFT) to perform high-throughput calculations of oxygen binding energies (BEO) on various pristine and doped Mo2C surfaces. We evaluated a total of 20,177 oxygen adsorption structures consisting of 7 low Miller-index surfaces, 23 d-block elements as single-atom dopants, all possible surface terminations, dopant locations, and adsorption sites. We make the outputs including 1865 pristine and 18312 doped relaxed geometries from VASP simulations available here.", + "keywords": [ + "machine learning", + "simulation", + "DFT", + "catalyst" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "doan_datasets_accelerating_representations_v1.1", + "mdf_source_name": "doan_datasets_accelerating_representations" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/doan_datasets_accelerating_representations_v1.1/", + "version": "1.0", + "root_version": "doan_datasets_accelerating_representations_v1.1", + "latest": true + } + }, + { + "source_id": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1", + "source_name": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph", + "version": 1, + "ingest_date": "2022-02-24T12:23:45.678463Z", + "doi": "10.18126/to2p-lmot", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1/", + "metadata": { + "title": "Datasets with uncertainty quantification from DFT, MD, and experiments and their rescaled uncertainties after Bayesian automated weighting", + "authors": [ + { + "name": "Gabriel, Joshua J.", + "given_name": "Joshua J.", + "family_name": "Gabriel", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Paulson, Noah H.", + "given_name": "Noah H.", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Duong, Thien C.", + "given_name": "Thien C.", + "family_name": "Duong", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Becker, Chandler A.", + "given_name": "Chandler A.", + "family_name": "Becker", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Tavazza, Francesca", + "given_name": "Francesca", + "family_name": "Tavazza", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Kattner, Ursula R.", + "given_name": "Ursula R.", + "family_name": "Kattner", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + }, + { + "name": "Stan, Marius", + "given_name": "Marius", + "family_name": "Stan", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ] + } + ], + "description": "Excel spreadsheet contains sub-sheets of \n1. the raw DFT, MD, and Experimental datasets named according to the convention adopted in https://doi.org/10.1016/j.mtla.2021.101216\n2. the mean rescaled uncertainty of each heat capacity dataset of the solid and liquid phase of aluminum (Cp_Solid and Cp_Liquid) \n3. the mean rescaled uncertainty of each heat capacity dataset of the solid and liquid phase of aluminum (H_Solid and H_Liquid)\n", + "keywords": [ + "simulation", + "experiment", + "metals and alloys", + "DFT", + "MD", + "Uncertainty Quantification", + "Bayesian automated weighting", + "heat capacity", + "enthalpy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.mtla.2021.101216", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1", + "mdf_source_name": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1/", + "version": "1.0", + "root_version": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1", + "latest": true + } + }, + { + "source_id": "3d_cnn_dendrites_v1.1", + "source_name": "3d_cnn_dendrites", + "version": 1, + "ingest_date": "2022-02-18T04:36:49.616796Z", + "doi": "10.18126/cxf3-hrum", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/3d_cnn_dendrites_v1.1/", + "metadata": { + "title": "3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization", + "authors": [ + { + "name": "James, Jim", + "given_name": "Jim", + "family_name": "James", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Pruyne, Nathan", + "given_name": "Nathan", + "family_name": "Pruyne", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Stan, Tiberiu", + "given_name": "Tiberiu", + "family_name": "Stan", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Schwarting, Marcus", + "given_name": "Marcus", + "family_name": "Schwarting", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Yeom, Jiwon", + "given_name": "Jiwon", + "family_name": "Yeom", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Hong, Seungbum", + "given_name": "Seungbum", + "family_name": "Hong", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Voorhees, Peter", + "given_name": "Peter", + "family_name": "Voorhees", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + } + ], + "description": "X-ray computed tomography reconstructions showing the dendritic growth of an Al-Zn alloy across time steps. Folders beginning with \"c-\" contain image volumes, while other folders contain manually segmented images used for training 3D convolutional neural networks. These networks are used to segment dendrites across all published image volumes.", + "keywords": [ + "machine learning", + "experiment", + "metals and alloys", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/M2RM08", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.18126/M2W93J", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1016/j.matchar.2020.110119", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "3d_cnn_dendrites_v1.1", + "mdf_source_name": "3d_cnn_dendrites" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/3d_cnn_dendrites_v1.1/", + "version": "1.0", + "root_version": "3d_cnn_dendrites_v1.1", + "latest": true + } + }, + { + "source_id": "cdznx_db_v1.2", + "source_name": "cdznx_db", + "version": 1, + "ingest_date": "2022-03-06T17:10:49.817847Z", + "doi": "10.18126/ak3i-s746", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cdznx_db_v1.2/", + "metadata": { + "title": "DFT Dataset of Ternary and Quaternary II\u2013VI Zincblende Semiconductor Alloys", + "authors": [ + { + "name": "Mannodi-Kanakkithodi, Arun", + "given_name": "Arun", + "family_name": "Mannodi-Kanakkithodi", + "affiliations": [ + "Purdue University" + ] + } + ], + "description": "This dataset contains high-throughput density functional theory (DFT) computations on ternary and quaternary alloys of II-VI zincblende semiconductors with Cd or Zn at the cation site and S, Se or Te at the anion site. Computed properties include formation and mixing energies, electronic band gaps, and optical absorption spectra, from different levels of theory (GGA-PBE, HSE06, with and without spin-orbit coupling).", + "keywords": [ + "simulation", + "semiconductors", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://iopscience.iop.org/article/10.1088/1361-651X/ac59d8", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "cdznx_db_v1.2", + "mdf_source_name": "cdznx_db" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/cdznx_db_v1.2/", + "version": "1.0", + "root_version": "cdznx_db_v1.2", + "latest": true + } + }, + { + "source_id": "marras_physical_property_micelles_v1.1", + "source_name": "marras_physical_property_micelles", + "version": 1, + "ingest_date": "2022-04-01T17:03:48.968297Z", + "doi": "10.18126/gtyw-0yag", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/marras_physical_property_micelles_v1.1/", + "metadata": { + "title": "Physical Property Scaling Relationships for Polyelectrolyte Complex Micelles", + "authors": [ + { + "name": "Marras, Alexander E.", + "given_name": "Alexander E.", + "family_name": "Marras", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Campagna, Trinity R.", + "given_name": "Trinity R.", + "family_name": "Campagna", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Vieregg, Jeffrey R.", + "given_name": "Jeffrey R.", + "family_name": "Vieregg", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Tirrell, Matthew V.", + "given_name": "Matthew V.", + "family_name": "Tirrell", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "Raw SAXS data used in this manuscript", + "keywords": [ + "polymers", + "polyelectrolytes", + "micelles", + "SAXS", + "experiment", + "CHiMaD" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.macromol.1c00743", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "marras_physical_property_micelles_v1.1", + "mdf_source_name": "marras_physical_property_micelles" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/marras_physical_property_micelles_v1.1/", + "version": "1.0", + "root_version": "marras_physical_property_micelles_v1.1", + "latest": true + } + }, + { + "source_id": "bahrami_first_demonstration_lattice_v1.1", + "source_name": "bahrami_first_demonstration_lattice", + "version": 1, + "ingest_date": "2022-01-26T20:09:49.237201Z", + "doi": "10.18126/ar66-63gd", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bahrami_first_demonstration_lattice_v1.1/", + "metadata": { + "title": "First Demonstration of Tuning Between the Kitaev and Ising Limits in a Honeycomb Lattice", + "authors": [ + { + "name": "Bahrami, Faranak", + "given_name": "Faranak", + "family_name": "Bahrami", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Hu, Xiaodong", + "given_name": "Xiaodong", + "family_name": "Hu", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Du, Yonghua", + "given_name": "Yonghua", + "family_name": "Du", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Lebedev, Oleg I.", + "given_name": "Oleg I.", + "family_name": "Lebedev", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Wang, Chennan", + "given_name": "Chennan", + "family_name": "Wang", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Luetkens, Hubertus", + "given_name": "Hubertus", + "family_name": "Luetkens", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Fabbris, Gilberto", + "given_name": "Gilberto", + "family_name": "Fabbris", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Graf, Michael J.", + "given_name": "Michael J.", + "family_name": "Graf", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Haskel, Daniel", + "given_name": "Daniel", + "family_name": "Haskel", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Ran, Ying", + "given_name": "Ying", + "family_name": "Ran", + "affiliations": [ + "Boston College" + ] + }, + { + "name": "Tafti, Fazel", + "given_name": "Fazel", + "family_name": "Tafti", + "affiliations": [ + "Boston College" + ] + } + ], + "description": "Recent observations of novel spin-orbit coupled states have generated tremendous interest in $4d/5d$ transition metal systems.\n\nA prime example is the $J_{\\text{eff}}=\\frac{1}{2}$ state in iridate materials and \\RCL\\ that drives Kitaev interactions. Here, by tuning the competition between spin-orbit interaction ($\\lambda_{\\text{SOC}}$) and trigonal crystal field splitting ($\\Delta_\\text{T}$), we restructure the spin-orbital wave functions into a novel $\\mu=\\frac{1}{2}$ state that drives Ising interactions. This is done via a topochemical reaction that converts \\LRO\\ to \\ALRO, leading to an enhanced trigonal distortion and a diminished spin-orbit coupling in the latter compound. Using perturbation theory, we present an explicit expression for the new $\\mu=\\frac{1}{2}$ state in the limit $\\Delta_\\text{T}\\gg \\lambda_{\\text{SOC}}$ realized in \\ALRO, different from the conventional $J_\\text{eff}=\\frac{1}{2}$ state in the limit $\\lambda_{\\text{SOC}}\\gg \\Delta_\\text{T}$ realized in \\LRO. The change of ground state is followed by a dramatic change of magnetism from a 6~K spin-glass in \\LRO\\ to a 94~K antiferromagnet in \\ALRO. These results open a pathway for tuning materials between the two limits and creating a rich magnetic phase diagram. ", + "keywords": [ + "simulation", + "experiment", + "topological", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "bahrami_first_demonstration_lattice_v1.1", + "mdf_source_name": "bahrami_first_demonstration_lattice" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/bahrami_first_demonstration_lattice_v1.1/", + "version": "1.0", + "root_version": "bahrami_first_demonstration_lattice_v1.1", + "latest": true + } + }, + { + "source_id": "dumi_dmc_hgraphene_v1.3", + "source_name": "dumi_dmc_hgraphene", + "version": 1, + "ingest_date": "2022-01-20T23:07:28.031464Z", + "doi": "10.18126/s1wc-tyac", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/dumi_dmc_hgraphene_v1.3/", + "metadata": { + "title": "Dataset for \"The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations\"", + "authors": [ + { + "name": "Dumi, Amanda", + "given_name": "Amanda", + "family_name": "Dumi", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + }, + { + "name": "Upadhyay, Shiv", + "given_name": "Shiv", + "family_name": "Upadhyay", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + }, + { + "name": "Bernasconi, Leonardo", + "given_name": "Leonardo", + "family_name": "Bernasconi", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + }, + { + "name": "Shin, Hyeondeok", + "given_name": "Heondeok", + "family_name": "Shin", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + }, + { + "name": "Jordan, Kenneth D.", + "given_name": "Kenneth D.", + "family_name": "Jordan", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ] + } + ], + "description": "The files and data used for \"The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations\".\n\nA.B. and H.S were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.\nAn award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program.\nThe DMC and PW-DFT calculations used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE-AC02-06CH11357. The DFT calculation using Gaussian orbitals were carried out on computing resources in the University of Pittsburgh's Center for Research Computing. K.D.J. acknowledges NSF (CBET-2028826) for partial support of this work. S.U. was supported in part by the Pittsburgh Quantum Institute (PQI) Graduate Quantum Leader Award. ", + "keywords": [ + "DMC", + "QMC", + "QMCPack", + "PWSCF", + "Nexus", + "graphene", + "diffusion Monte Carlo", + "variational Monte Carlo", + "VMC", + "quantum Monte Carlo", + "CPSFM", + "simulation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "dumi_dmc_hgraphene_v1.3", + "mdf_source_name": "dumi_dmc_hgraphene" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/dumi_dmc_hgraphene_v1.3/", + "version": "1.0", + "root_version": "dumi_dmc_hgraphene_v1.3", + "latest": true + } + }, + { + "source_id": "kumar_dft_wte2_v1.1", + "source_name": "kumar_dft_wte2", + "version": 1, + "ingest_date": "2022-08-12T05:40:42.433003Z", + "doi": "10.18126/p4z6-743i", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kumar_dft_wte2_v1.1/", + "metadata": { + "title": "A Gapped Phase in Semimetallic T\n d\n \u2010WTe\n 2\n Induced by Lithium Intercalation", + "authors": [ + { + "name": "Wang, Mengjing", + "given_name": "Mengjing", + "family_name": "Wang", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Kumar, Aakash", + "given_name": "Aakash", + "family_name": "Kumar", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Dong, Hao", + "given_name": "Hao", + "family_name": "Dong", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Woods, John M.", + "given_name": "John M.", + "family_name": "Woods", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Pondick, Joshua V.", + "given_name": "Joshua V.", + "family_name": "Pondick", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Xu, Shiyu", + "given_name": "Shiyu", + "family_name": "Xu", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Hynek, David J.", + "given_name": "David J.", + "family_name": "Hynek", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Guo, Peijun", + "given_name": "Peijun", + "family_name": "Guo", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Cha, Judy J.", + "given_name": "Judy J.", + "family_name": "Cha", + "affiliations": [ + "Yale University" + ] + } + ], + "description": "1. Files for the DFT calculations of i) Td phase (WTe2) - scf input, scf output, bands input , ii) Td' phase (WLi0.5Te2) - scf input, scf output, bands input, using Quantum Espresso software package\n2. Pseudopotential files for PAW-PBE, fully relativistic calculations", + "keywords": [ + "DFT", + "energy materials", + "semiconductors" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1002/adma.202200861", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kumar_dft_wte2_v1.1", + "mdf_source_name": "kumar_dft_wte2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kumar_dft_wte2_v1.1/", + "version": "1.0", + "root_version": "kumar_dft_wte2_v1.1", + "latest": true + } + }, + { + "source_id": "huang_symmetry_mapping_v1.1", + "source_name": "huang_symmetry_mapping", + "version": 1, + "ingest_date": "2022-06-10T15:31:48.485688Z", + "doi": "10.18126/oqy3-1cyv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/huang_symmetry_mapping_v1.1/", + "metadata": { + "title": "Data for Large Area, High Resolution Mapping of Approximate Rotational Symmetries in a Pd77.5Cu6Si16.5 Metallic Glass Thin Film", + "authors": [ + { + "name": "Shuoyuan, Huang.", + "given_name": "Huang.", + "family_name": "Shuoyuan", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Francis, Carter", + "given_name": "Carter", + "family_name": "Francis", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Sunderland, John", + "given_name": "John", + "family_name": "Sunderland", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Jambur, Vrishank", + "given_name": "Vrishank", + "family_name": "Jambur", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Szlufarska, Izabela", + "given_name": "Izabela", + "family_name": "Szlufarska", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "Data for \"Large Area, High Resolution Mapping of Approximate Rotational Symmetries in a Pd77.5Cu6Si16.5 Metallic Glass Thin Film\" by Shuoyuan Huang, Carter Francis, John Sunderland, Vrishank Jambur, Izabela Szlufarska, and Paul M Voyles. Contains molecular dynamics simulated structures of Al-Sm metallic glasses and electron nanodiffraction simulations from those structures, electron nanodiffraction data from Pd-Cu-Si metallic glass thin films under various experimental conditions, and analysis of those data using symmetry coefficients.", + "keywords": [ + "simulation", + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy", + "diffraction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "huang_symmetry_mapping_v1.1", + "mdf_source_name": "huang_symmetry_mapping" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/huang_symmetry_mapping_v1.1/", + "version": "1.0", + "root_version": "huang_symmetry_mapping_v1.1", + "latest": true + } + }, + { + "source_id": "geom_hessians_v1.1", + "source_name": "geom_hessians", + "version": 1, + "ingest_date": "2022-08-02T23:02:03.214158Z", + "doi": "10.18126/xm9b-ghae", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/geom_hessians_v1.1/", + "metadata": { + "title": "GEOM with Hessians", + "authors": [ + { + "name": "Axelrod, Simon", + "given_name": "Simon", + "family_name": "Axelrod", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "G\u00f3mez-Bombarelli, Rafael", + "given_name": "Rafael", + "family_name": "G\u00f3mez-Bombarelli", + "affiliations": [ + "Harvard University" + ] + } + ], + "description": "Here you can find 1.3 million Hessians from 1,511 species in the BACE dataset. The conformers were computed with CREST using xTB, and released as part of the GEOM dataset. If you use this data, please cite our paper: https://www.nature.com/articles/s41597-022-01288-4.", + "keywords": [ + "simulation", + "machine learning", + "DFT", + "molecules", + "generative" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/s41597-022-01288-4", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.7910/DVN/JNGTDF", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "geom_hessians_v1.1", + "mdf_source_name": "geom_hessians" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/geom_hessians_v1.1/", + "version": "1.0", + "root_version": "geom_hessians_v1.1", + "latest": true + } + }, + { + "source_id": "matthoffman_cebi_v1.1", + "source_name": "matthoffman_cebi", + "version": 1, 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University" + ] + }, + { + "name": "Drucker, Nathan C.", + "given_name": "Nathan C.", + "family_name": "Drucker", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Jo, Na Hyun", + "given_name": "Na Hyun", + "family_name": "Jo", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Kuthanazhi, Brinda", + "given_name": "Brinda", + "family_name": "Kuthanazhi", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Huang, Zhao", + "given_name": "Zhao", + "family_name": "Huang", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Lane, Christopher", + "given_name": "Christopher", + "family_name": "Lane", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Zhu, Jian-Xin", + "given_name": "Jian-Xin", + "family_name": "Zhu", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Canfield, Paul C.", + "given_name": "Paul C.", + "family_name": "Canfield", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + }, + { + "name": "Hoffman, Jennifer E.", + "given_name": "Jennifer E.", + "family_name": "Hoffman", + "affiliations": [ + "Los Alamos National Laboratory", + "Harvard University", + "Ames Laboratory", + "Iowa State University" + ] + } + ], + "description": "Dataset for article to appear in Phys Rev B", + "keywords": [ + "experiment", + "simulation", + "DFT", + "Weyl semimetal", + "spin-polarized scanning tunneling 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"Eric B.", + "family_name": "Issacs", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Annaberdiyev, Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Wolverton, Chris", + "given_name": "Chris", + "family_name": "Wolverton", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Heinonen, Olle", + "given_name": "Olle", + "family_name": "Heinonen", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Dataset for formation energies of VPt2 and CuI using DFT (PBE, SCAN, PBE0, and PBE0-SO) and QMC(DMC). All files are input and output files are in the Quantum espresso and DIRAC code format and QMCPACK format.", + "keywords": [ + "simulation", + "DFT", + "QMCPack", + "QMC", + "Monte Carlo" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "vpt2_cui_qmc_v1.1", + "mdf_source_name": "vpt2_cui_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/vpt2_cui_qmc_v1.1/", + "version": "1.0", + "root_version": "vpt2_cui_qmc_v1.1", + "latest": true + } + }, + { + "source_id": "orbital_mixer_v1.1", + "source_name": "orbital_mixer", + "version": 1, + "ingest_date": "2022-05-10T23:42:06.623814Z", + "doi": "10.18126/cu4h-d2mm", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/orbital_mixer_v1.1/", + "metadata": { + "title": "Supporting data for \"Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions\"", + "authors": [ + { + "name": "Shmilovich, Kirill", + "given_name": "Kirill", + "family_name": "Shmilovich", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + }, + { + "name": "Willmott, Devin", + "given_name": "Devin", + "family_name": "Willmott", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + }, + { + "name": "Batalov, Ivan", + "given_name": "Ivan", + "family_name": "Batalov", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + }, + { + "name": "Kornbluth, Mordechai", + "given_name": "Mordechai", + "family_name": "Kornbluth", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + }, + { + "name": "Mailoa, Jonathan", + "given_name": "Jonathan", + "family_name": "Mailoa", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + }, + { + "name": "Kolter, J. Zico", + "given_name": "J. Zico", + "family_name": "Kolter", + "affiliations": [ + "Bosch Center for Artificial Intelligence", + "University of Chicago", + "Bosch Research and Technology Center", + "Carnegie Mellon University", + "Tencent Quantum laboratory" + ] + } + ], + "description": "This repository contains supporting data and code for the paper titled \"Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions\" by Kirill Shmilovich, Devin Willmott, Ivan Batalov, Mordechai Kornbluth, Jonathan Mailoa, and J. 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"family_name": "Krogstad", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Rosenkranz, Stephan", + "given_name": "Stephan", + "family_name": "Rosenkranz", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Wozniak, Justin", + "given_name": "Justin", + "family_name": "Wozniak", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Ruff, Jacob", + "given_name": "Jacob", + "family_name": "Ruff", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "John, Vaughey", + "given_name": "Vaughey", + "family_name": "John", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Jennings, Guy", + "given_name": "Guy", + "family_name": "Jennings", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "These files contain data sets used in the 3D \u0394PDF analysis of Na0.45V2O5, reported in \u201cReciprocal Space Imaging of Ionic Correlations in 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Within the file there are three data groups and a group containing sample information.\n\nPath: /entry/data - 3D S(Q)\nPath: /entry/dpdf - 3D \u0394PDF\nPath: /entry/tpdf - 3D Total PDF (at 30K, 150K, and 275K only)\nPath: /entry/sample - crystallographic information (lattice parameters, angles, symmetry)\n\nThe data groups contain external links to files stored in a subdirectory, e.g., na04v2o5_30K.nxs references files in the 30K/ subdirectory. Therefore, to view data at a particular temperature, please download the subdirectory as well. The files will still open in NeXpy without the linked files, but the data cannot be plotted.", + "keywords": [ + "battery materials", + "experiment", + "intercalation compounds", + "single crystal", + "x-ray scattering" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "osborn_reciprocal_space_compounds_v1.1", + "mdf_source_name": "osborn_reciprocal_space_compounds" + }, + "download_url": "https://data.materialsdatafacility.org/osborn_reciprocal_space_compounds_v1.1", + "version": "1.0", + "root_version": "osborn_reciprocal_space_compounds_v1.1", + "latest": true + } + }, + { + "source_id": "foundry_wei_atom_locating_benchmark_v1.1", + "source_name": "foundry_wei_atom_locating_benchmark", + "version": 1, + "ingest_date": "2021-07-15T14:24:40.739344Z", + "doi": "10.18126/e73h-3w6n", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/foundry_wei_atom_locating_benchmark_v1.1/", + "metadata": { + "title": "Benchmark Dataset for Locating Atoms in STEM images", + "authors": [ + { + "name": "Wei, Jingrui", + "given_name": "Jingrui", + "family_name": "Wei", + "affiliations": [ + "University of Wisconsin - Madison" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin - Madison" + ] + }, + { + "name": "Voyles, Paul", + "given_name": "Paul", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin - Madison" + ] + } + ], + "keywords": [ + "machine learning", + "foundry", + "atom finding", + "materials", + "STEM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "foundry_wei_atom_locating_benchmark_v1.1", + "mdf_source_name": "foundry_wei_atom_locating_benchmark" + }, + "ml": { + "data_format": "hdf5", + "short_name": "wei_atom_locating", + "n_items": 299, + "splits": [ + { + "type": "train", + "path": "wei_atom.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "imgs", + "role": "input", + "units": "", + "description": "input, unlabeled images" + }, + { + "name": "raw", + "role": "input", + "units": "", + "description": "raw, unlabeled images" + }, + { + "name": "metadata", + "role": "input", + "units": "", + "description": "tabular information describing the experimental conditions and materials for each image" + }, + { + "name": "coords", + "role": "target", + "units": "", + "description": "(x,y) coordinates for labeled atoms for each image" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/foundry_wei_atom_locating_benchmark_v1.1/", + "version": "1.0", + "root_version": "foundry_wei_atom_locating_benchmark_v1.1", + "latest": true + } + }, + { + "source_id": "shmilovich_supporting_temporally_resolution_v1.6", + "source_name": "shmilovich_supporting_temporally_resolution", + "version": 1, + "ingest_date": "2022-05-10T18:52:33.109856Z", + "doi": "10.18126/tf0h-w0jz", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shmilovich_supporting_temporally_resolution_v1.6/", + "metadata": { + "title": "Supporting data for \"Temporally coherent backmapping of molecular trajectories from coarse-grain to atomistic resolution\"", + "authors": [ + { + "name": "Shmilovich, Kirill", + "given_name": "Kirill", + "family_name": "Shmilovich", + "affiliations": [ + "Freie Universit\u00e4t Berlin" + ] + }, + { + "name": "Stieffenhofer, Marc", + "given_name": "Marc", + "family_name": "Stieffenhofer", + "affiliations": [ + "Rice University" + ] + }, + { + "name": "Charron, Nicholas E.", + "given_name": "Nicholas E.", + "family_name": "Charron", + "affiliations": [ + "Max Planck Institute for Polymer Research" + ] + }, + { + "name": "Hoffmann, Moritz", + "given_name": "Moritz", + "family_name": "Hoffmann", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "This repository contains supporting data and code for the paper titled \"Temporally coherent backmapping of molecular trajectories from coarse-grain to atomistic resolution\" by Kirill Shmilovich, Marc Stieffenhofer, Nicholas E. 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"affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Thompson, Zach", + "given_name": "Zach", + "family_name": "Thompson", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Voorhees, Peter", + "given_name": "Peter", + "family_name": "Voorhees", + "affiliations": [ + "Northwestern University" + ] + } + ], + "keywords": [ + "machine learning", + "foundry", + "segmentation", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "foundry_stan_segmentation_v1.1", + "mdf_source_name": "foundry_stan_segmentation" + }, + "ml": { + "data_format": "hdf5", + "short_name": "segmentation", + "n_items": 30, + "splits": [ + { + "type": "train", + "path": "foundry.hdf5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "train/input", + "role": 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}, + { + "name": "Malone, Fionn D.", + "given_name": "Fionn D.", + "family_name": "Malone", + "affiliations": [ + "Lawrence Livermore National Laboratory" + ] + } + ], + "description": "Input files and qmcpack output for MgO, LiH and Si", + "keywords": [ + "simulation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "morales_opt_basis_v1.1", + "mdf_source_name": "morales_opt_basis" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/morales_opt_basis_v1.1/", + "version": "1.0", + "root_version": "morales_opt_basis_v1.1", + "latest": true + } + }, + { + "source_id": "foundry_qmc_ml_v1.1", + "source_name": "foundry_qmc_ml", + "version": 1, + "ingest_date": "2022-10-12T14:54:23.830372Z", + "doi": "10.18126/wg30-95z0", + "endpoint_path": 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the B3LYP functional" + }, + { + "name": "DMC(HF)", + "role": "target", + "units": "Ha", + "description": "All-electron calculations with cc-pvtz basis set, Diffusion Monte Carlo with the corresponding HF trial wavefunction" + }, + { + "name": "DMC(PBE)", + "role": "target", + "units": "Ha", + "description": "All-electron calculations with cc-pvtz basis set, Diffusion Monte Carlo with the corresponding DFT-PBE trial wavefunction" + }, + { + "name": "DMC(PBE0)", + "role": "target", + "units": "Ha", + "description": "All-electron calculations with cc-pvtz basis set, Diffusion Monte Carlo with the corresponding DFT-PBE0 trial wavefunction" + }, + { + "name": "DMC(B3LYP)", + "role": "target", + "units": "Ha", + "description": "All-electron calculations with cc-pvtz basis set, Diffusion Monte Carlo with the corresponding DFT-B3LYP trial wavefunction" + }, + { + "name": "DMC(HF)_err", + "role": "target", + "units": "Ha", + "description": "Error associated with, DMC(HF)" + }, + { + "name": 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Gilad", + "family_name": "Kusne", + "affiliations": [ + "University of Maryland and NIST" + ] + }, + { + "name": "Rodriguez, Efrain", + "given_name": "Efrain", + "family_name": "Rodriguez", + "affiliations": [ + "University of Maryland" + ] + }, + { + "name": "Paglione, Johnpierre", + "given_name": "Johnpierre", + "family_name": "Paglione", + "affiliations": [ + "University of Maryland" + ] + }, + { + "name": "Curtarolo, Stefano", + "given_name": "Stefano", + "family_name": "Curtarolo", + "affiliations": [ + "Duke University" + ] + }, + { + "name": "Takeuchi, Ichiro", + "given_name": "Ichiro", + "family_name": "Takeuchi", + "affiliations": [ + "University of Maryland" + ] + } + ], + "description": "Dataset containing experimentally measured superconducting critical temperatures for 16414 materials", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 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+ "name": "Lin, Shih-kang", + "given_name": "Shih-kang", + "family_name": "Lin", + "affiliations": [ + "National Cheng Kung University" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Dataset containing effective charge values for 49 metal host-impurity pairs", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "electromigration_v1.1", + "mdf_source_name": "electromigration" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_electromigration", + "n_items": 49, + "splits": [ + { + "type": "train", + "path": "Dataset_electromigration_v1.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": 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"name": "Chatterjee, Debaditya", + "given_name": "Debaditya", + "family_name": "Chatterjee", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Huang, Shuoyuan", + "given_name": "Shuoyuan", + "family_name": "Huang", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Gu, Kaichen", + "given_name": "Kaichen", + "family_name": "Gu", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Ju, Jianzhu", + "given_name": "Jianzhu", + "family_name": "Ju", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Yu, Junguang", + "given_name": "Junguang", + "family_name": "Yu", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Bock, Harald", + "given_name": "Harald", + "family_name": "Bock", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Yu, Lian", + "given_name": "Lian", + "family_name": "Yu", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Ediger, M. D.", + "given_name": "M. D.", + "family_name": "Ediger", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "Universit\u00e9 de Bordeaux", + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "4D STEM data for orientation mapping of phenanthroperylene ester anisotropic molecular glass thin films. Raw nanodiffraction data, intermediate analyses, and derived orientation maps for samples with varying domain size as a function of substrate temperature during deposition. Additional datasets from samples cooled for a higher temperature liquid crystal phase.", + "keywords": [ + "experiment", + "microscopy", + "microstructures", + "4D STEM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chatterjee_phenester_orientation_v1.3", + "mdf_source_name": "chatterjee_phenester_orientation" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chatterjee_phenester_orientation_v1.3/", + "version": "1.0", + "root_version": "chatterjee_phenester_orientation_v1.3", + "latest": true + } + }, + { + "source_id": "sdsa_tsaxs_map_v1.1", + "source_name": "sdsa_tsaxs_map", + "version": 1, + "ingest_date": "2022-10-07T18:30:44.314600Z", + "doi": "10.18126/qboh-6fav", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sdsa_tsaxs_map_v1.1/", + "metadata": { + "title": "Autonomous TSAXS mapping of sDSA sample", + "authors": [ + { + "name": "Gregory S. Doerk Marcus M. Noack, Masafumi Fukuto, Kevin G. Yager, Aaron Stein", + "given_name": "Aaron Stein", + "family_name": "Gregory S. Doerk Marcus M. Noack, Masafumi Fukuto, Kevin G. Yager", + "affiliations": [ + "Brookhaven National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "Autonomous SAXS mapping of sDSA sample. Experimental transmission small-angle x-ray scattering (TSAXS) data collected as a function of position for a selective directed self-assembly (sDSA) combinatorial sample. Sample was a grid of lithographically-defined chemical patterns (varying pitch and dose) with a blend of block copolymer materials cast on top. Measurement positions were selected machine-learning algorithm implementing a Gaussian Process (GP) method. Dataset contains raw SAXS detector images, as well as analysis results converting the image into linecuts in various azimuthal directions, and fitting those linecuts to extract peak position, width, and intensity.", + "keywords": [ + "experiment", + "polymers", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sdsa_tsaxs_map_v1.1", + "mdf_source_name": "sdsa_tsaxs_map" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sdsa_tsaxs_map_v1.1/", + "version": "1.0", + "root_version": "sdsa_tsaxs_map_v1.1", + "latest": true + } + }, + { + "source_id": "darpa_sd2_perovskites_v1.2", + "source_name": "darpa_sd2_perovskites", + "version": 1, + "ingest_date": "2020-02-25T18:50:54.909613Z", + "doi": null, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/darpa_sd2_perovskites_v1.2/", + "metadata": { + "title": "Untangling How Machines 'Learn' Perovskite Crystallization Chemistry Through Stepwise Data Sample Comparisons", + "authors": [ + { + "name": "Pendleton, Ian M.", + "given_name": "Ian M.", + "family_name": "Pendleton", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Caucci, Mary K.", + "given_name": "Mary K.", + "family_name": "Caucci", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Tynes, Michael", + "given_name": "Michael", + "family_name": "Tynes", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Dharna, Aaron", + "given_name": "Aaron", + "family_name": "Dharna", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Najeeb, Mansoor Ani", + "given_name": "Mansoor Ani", + "family_name": "Najeeb", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Chan, Emory M.", + "given_name": "Emory M.", + "family_name": "Chan", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Norquist, Alexander J.", + "given_name": "Alexander J.", + "family_name": "Norquist", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Schrier, Joshua", + "given_name": "Joshua", + "family_name": "Schrier", + "affiliations": [ + "Haverford College", + "Fordham University", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "## Included in this content:\n * 0045.perovksitedata.csv - main dataset used in this article. A more detailed description can be found in the \u201cdataset overview\u201d section below\n * Chemical Inventory.csv - the hand curated file of all chemicals used in the construction of the perovskite dataset. This file includes identifiers, chemical properties, and other information.\n * ExcessMolarVolumeData.xlsx - record of experimental data, computations, and final dataset used in the generation of the excess molar volume plots.\n * MLModelMetrics.xlsx - all of the ML metrics organized in one place (excludes reactant set specific breakdown, see ML_Logs.zip for those files).\n * OrganoammoniumDensityDataset.xlsx - complete set of the data used to generate the density values. Example calculations included.\n * model_matchup_main.py - python pipeline used to generate all of the ML runs associated with the article. More detailed instructions on the operation of this code is included in the \u201cML Code\u201d Section below. This file is also hosted on\n * GIT: https://github.com/ipendlet/MLScripts/blob/master/temp_densityconc/model_matchup_main_20191231.py \n * SolutionVolumeDataset - complete set of 219 solutions in the perovskite dataset. Tabs include the automatically generated reagent information from ESCALATE, hand curated reagent information from early runs, and the generation of the dataset used in the creation of Figures\n * error_auditing.zip - code and historical datasets used for reporting the dataset auditing.\n * \u201cAllCode.zip\u201d which contains:\n * model_matchup_main_20191231.py - python pipeline used to generate all of the ML runs associated with the article. More detailed instructions on the operation of this code is included in the \u201cML Code\u201d Section below. This file is also hosted on\n * GIT: https://github.com/ipendlet/MLScripts/blob/master/temp_densityconc/0045.perovskitedata.csv \n * VmE_CurveFitandPlot.py - python code for generating the third order polynomial fit to the VmE vs mole fraction of FAH included in the main text. Requires the \u2018MolFractionResults.csv\u2019 to function (also included).\n * Calculation_Vm_Ve_CURVEFITTING.nb - mathematica code for generating the third order polynomial fit to the VmE vs mole fraction of FAH included in the main text. \n * Covariance_Analysis.py - python code for ingesting and plotting the covariance of features and volumes in the perovskite dataset. Includes renaming dictionaries used for the publication.\n * FeatureComparison_Plotting.py - python code for reading in and plotting features for the \u2018GBT\u2019 and \u2018OHGBT\u2019 folders in this directory. The code parses the contents of these folders and generates feature comparison metrics used for Figure 9 and the associated Figure S8. Some assembly required.\n * Requirements.txt - all of the packages used in the generation of this paper\n * 0045.perovskitedata.csv - the main dataset described throughout the article. This file is required to run some of the code and is therefore kept near the code. \n * \u201cML_Logs.zip\u201d which contains:\n * A folder describing every model generated for this article. In each folder there are a number of files:\n * Features_named_important.csv and features_value_importance.csv - these files are linked together and describe the weighted feature contributions from features (only present for GBT models)\n * AnalysisLog.txt - Log file of the run including all options, data curation and model training summaries \n * LeaveOneOut_Summary.csv - Results of the leave-one-reactant set-out studies on the model (if performed)\n * LOOModelInfo.txt - Hyperparameter information for each model in the study (associated with the given dataset, sometimes includes duplicate runs).\n * STTSModelInfo.txt - Hyperparameter information for each model in the study (associated with the given dataset, sometimes includes duplicate runs).\n * StandardTestTrain_Summary.csv - Results of the 6 fold cross validation ML performance (for the hold out case)\n * LeaveOneOut_FullDataset_ByAmine.csv - Results of the leave-one-reactant set-out studies performed on the full dataset (all experiments) specified by reactant set (delineated by the amine)\n * LeaveOneOut_StratifiedData_ByAmine.csv - Results of the leave-one-reactant set-out studies performed on a random stratified sample (96 random experiments) specified by reactant set (delineated by the amine)\n * model_matchup_main_*.py - code used to generate all of the runs contained in a particular folder. The code is exactly what was used at run time to generate a given dataset (requires 0045.perovskitedata.csv file to run).", + "keywords": [ + "perovskite", + "experiment", + "machine learning", + "energy materials", + "high-throughput" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.26434/chemrxiv.10013090", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "http://doi.org/10.1557/mrc.2019.72", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "darpa_sd2_perovskites_v1.2", + "mdf_source_name": "darpa_sd2_perovskites" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/darpa_sd2_perovskites_v1.2/", + "version": "1.0", + "root_version": "darpa_sd2_perovskites_v1.2", + "latest": true + } + }, + { + "source_id": "swatek_prb_rapids_2020_v1.1", + "source_name": "swatek_prb_rapids_2020", + "version": 1, + "ingest_date": "2020-03-06T19:10:47.176848Z", + "doi": "10.18126/x3ye-f0tl", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/swatek_prb_rapids_2020_v1.1/", + "metadata": { + "title": "Data set for \"Gapless Dirac surface states in the antiferromagnetic topological insulator MnBi2Te4\" published in the Phsycial Review B(R) 2020", + "authors": [ + { + "name": "Swatek, Przemyslaw", + "given_name": "Przemyslaw", + "family_name": "Swatek", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Wu, Yun", + "given_name": "Yun", + "family_name": "Wu", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Wang, Lin-Lin", + "given_name": "Lin-Lin", + "family_name": "Wang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Lee, Kyungchan", + "given_name": "Kyungchan", + "family_name": "Lee", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Schrunk, Benjamin", + "given_name": "Benjamin", + "family_name": "Schrunk", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Yan, Jiaqiang", + "given_name": "Jiaqiang", + "family_name": "Yan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + }, + { + "name": "Kaminski, Adam", + "given_name": "Adam", + "family_name": "Kaminski", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ] + } + ], + "description": "Data set for \"Gapless Dirac surface states in the antiferromagnetic topological insulator MnBi2Te4\" by P. Swatek et al., published in the Physical Review B(R) 2020. \nThe data is provided for each figure panel as indicated by file name. The data is in following forms:\n\n1) ARPES color plot: 2D matrix data in delimited format\n2) Calculation - constant energy plots: vectors for XY type plots\n3) Calculation - band dispersion: first column is momentum (x-axis) others are binding energies for various bands\n4) Energy distribution plots: first column is binding energy, rest are the intensities for each energy point.", + "keywords": [ + "simulation", + "experiment", + "DFT", + "ARPES", + "topological insulator" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "swatek_prb_rapids_2020_v1.1", + "mdf_source_name": "swatek_prb_rapids_2020" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/swatek_prb_rapids_2020_v1.1/", + "version": "1.0", + "root_version": "swatek_prb_rapids_2020_v1.1", + "latest": true + } + }, + { + "source_id": "matt_hoffman_smb6_t4_v1.1", + "source_name": "matt_hoffman_smb6_t4", + "version": 1, + "ingest_date": "2020-02-18T15:57:45.261420Z", + "doi": "10.18126/cjyc-7ugb", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/matt_hoffman_smb6_t4_v1.1/", + "metadata": { + "title": "Data set for \"Consistency between ARPES and STM measurements on SmB6\"", + "authors": [ + { + "name": "Matt, Christian E.", + "given_name": "Christian E.", + "family_name": "Matt", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Pirie, Harris", + "given_name": "Harris", + "family_name": "Pirie", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Soumyanarayanan, Anjan", + "given_name": "Anjan", + "family_name": "Soumyanarayanan", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "He, Yang", + "given_name": "Yang", + "family_name": "He", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Yee, Michael M.", + "given_name": "Michael M.", + "family_name": "Yee", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Chen, Pengcheng", + "given_name": "Pengcheng", + "family_name": "Chen", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Liu, Yu", + "given_name": "Yu", + "family_name": "Liu", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Larson, Daniel T.", + "given_name": "Daniel T.", + "family_name": "Larson", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Paz, Wendel S.", + "given_name": "Wendel S.", + "family_name": "Paz", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Palacios, J. J.", + "given_name": "J. J.", + "family_name": "Palacios", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Hamidian, M. H.", + "given_name": "M. H.", + "family_name": "Hamidian", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + }, + { + "name": "Hoffman, Jennifer E.", + "given_name": "Jennifer E.", + "family_name": "Hoffman", + "affiliations": [ + "Harvard University", + "Universidade Federal do Esp\u00edrito Santo", + "Universidade Federal do Rio de Janeiro", + "Universidad Aut\u00f3noma de Madrid" + ] + } + ], + "description": "Data set for \"Consistency between ARPES and STM measurements on SmB6\" to be published in the Physical Review B, 2020\n", + "keywords": [ + "simulation", + "experiment", + "Topological matter" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "matt_hoffman_smb6_t4_v1.1", + "mdf_source_name": "matt_hoffman_smb6_t4" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/matt_hoffman_smb6_t4_v1.1/", + "version": "1.0", + "root_version": "matt_hoffman_smb6_t4_v1.1", + "latest": true + } + }, + { + "source_id": "lee_si_si_e_dyn_tddft_v1.1", + "source_name": "lee_si_si_e_dyn_tddft", + "version": 1, + "ingest_date": "2020-02-19T16:21:25.093296Z", + "doi": "10.18126/5jmh-o56p", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lee_si_si_e_dyn_tddft_v1.1/", + "metadata": { + "title": "First-principles simulation of electron dynamics in self-irradiated silicon", + "authors": [ + { + "name": "Lee, Cheng-Wei", + "given_name": "Cheng-Wei", + "family_name": "Lee", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Schleife, Andre", + "given_name": "Andre", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This dataset contains the simulation details (input, output, and analysis) for the manuscript", + "keywords": [ + "simulation", + "RT-TDDFT ", + "ion beam", + "radiation damage", + "electronic stopping", + "charge equilibration" + ], + "publisher": "Materials Data Facility", + "publication_year": 2020, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://arxiv.org/abs/2001.10162", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "lee_si_si_e_dyn_tddft_v1.1", + "mdf_source_name": "lee_si_si_e_dyn_tddft" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/lee_si_si_e_dyn_tddft_v1.1/", + "version": "1.0", + "root_version": "lee_si_si_e_dyn_tddft_v1.1", + "latest": true + } + }, + { + "source_id": "tsopanidis_training_validation_networks_v1.1", + "source_name": "tsopanidis_training_validation_networks", + "version": 1, + "ingest_date": "2019-07-30T17:15:49.002974Z", + "doi": "10.18126/vu60-4htj", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/tsopanidis_training_validation_networks_v1.1/", + "metadata": { + "title": "Training and Validation Dataset of MgAl2O4 SEM Fracture Images for \"Toward Quantitative Fractography Using Convolutional Neural Networks\"", + "authors": [ + { + "name": "Tsopanidis, Stylianos", + "given_name": "Stylianos", + "family_name": "Tsopanidis", + "affiliations": [ + "University Carlos III of Madrid" + ] + }, + { + "name": "Osovski, Shmuel", + "given_name": "Shmuel", + "family_name": "Osovski", + "affiliations": [ + "Israel Institute of Technology" + ] + } + ], + "description": "This is a dataset of SEM fracture images for training and validation of deep learning algorithms. The fracture surfaces of the MgAl2O4 ceramic material can be used to train a convolutional neural network for identifying the intergranular and transgranular fracture modes.\n\nEuropean Union's Horizon2020 Programme (Excellent Science, Marie-Sklodowska-Curie Actions) under REA grant agreement 675602 (Project OUTCOME)\n\nContact: Stylianos Tsopanidis or Shmuel Osovski ", + "keywords": [ + "experiment", + "machine learning", + "microscopy", + "ceramic", + "training set", + "scanning electron microscopy", + "SEM", + "fracture", + "fractography", + "intergranular", + "transgranular", + "validation set" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "tsopanidis_training_validation_networks_v1.1", + "mdf_source_name": "tsopanidis_training_validation_networks" + }, + "download_url": "https://data.materialsdatafacility.org/tsopanidis_training_validation_networks_v1.1/", + "version": "1.0", + "root_version": "tsopanidis_training_validation_networks_v1.1", + "latest": true + } + }, + { + "source_id": "kaufman_firstprinciples_investigation_naxcro2_v1.1", + "source_name": "kaufman_firstprinciples_investigation_naxcro2", + "version": 1, + "ingest_date": "2022-10-18T00:34:12.116273Z", + "doi": "10.18126/a1g5-gx7c", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kaufman_firstprinciples_investigation_naxcro2_v1.1/", + "metadata": { + "title": "First-principles investigation of phase stability in layered NaxCrO2", + "authors": [ + { + "name": "Kaufman, Jonas L.", + "given_name": "Jonas L.", + "family_name": "Kaufman", + "affiliations": [ + "University of California, Santa Barbara" + ] + }, + { + "name": "Van der Ven, Anton", + "given_name": "Anton", + "family_name": "Van der Ven", + "affiliations": [ + "University of California, Santa Barbara" + ] + } + ], + "description": "The dataset contains VASP calculations for Na-vacancy orderings in layered host structures of NaxCrO2 enumerated using the CASM code (https://github.com/prisms-center/CASMcode), as well as calculations of magnetic orderings and CrO2 structures with Cr migrated to tetrahedral sites.", + "keywords": [ + "density functional theory", + "oxides", + "VASP", + "layered intercalation compounds", + "CASM", + "Na-ion batteries", + "simulation", + "DFT", + "energy materials", + "crystal structures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kaufman_firstprinciples_investigation_naxcro2_v1.1", + "mdf_source_name": "kaufman_firstprinciples_investigation_naxcro2" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kaufman_firstprinciples_investigation_naxcro2_v1.1/", + "version": "1.0", + "root_version": "kaufman_firstprinciples_investigation_naxcro2_v1.1", + "latest": true + } + }, + { + "source_id": "annaberdiyev_accurate_atomic_potentials_v1.1", + "source_name": "annaberdiyev_accurate_atomic_potentials", + "version": 1, + "ingest_date": "2019-07-30T18:46:59.341686Z", + "doi": "10.18126/rubn-1mnf", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/annaberdiyev_accurate_atomic_potentials_v1.1", + "metadata": { + "title": "Dataset for \"Accurate atomic total energies for correlation consistent effective core potentials\"", + "authors": [ + { + "name": "Annaberdiyev, Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Melton, Cody A.", + "given_name": "Cody A.", + "family_name": "Melton", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bennett, M. Chandler", + "given_name": "M. Chandler", + "family_name": "Bennett", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Wang, Guangming", + "given_name": "Guangming", + "family_name": "Wang", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "\nDataset for \"Accurate atomic total energies for correlation consistent effective core potentials\"\n\nIncluded are input and output files for various codes. For each code, outputs are organized per atom.\n\nFunding: U.S. Department of Energy\n\nContact: Abdulgani Annaberdiyev ", + "keywords": [ + "ccECP", + "ECP", + "pseudopotential", + "correlation energy", + "atomic energy", + "FN-DMC", + "kinetic energy", + "CPSFM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2019, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "annaberdiyev_accurate_atomic_potentials_v1.1", + "mdf_source_name": "annaberdiyev_accurate_atomic_potentials" + }, + "download_url": "https://data.materialsdatafacility.org/annaberdiyev_accurate_atomic_potentials_v1.1", + "version": "1.0", + "root_version": "annaberdiyev_accurate_atomic_potentials_v1.1", + "latest": true + } + }, + { + "source_id": "sdsa_sem_images_v1.2", + "source_name": "sdsa_sem_images", + "version": 1, + "ingest_date": "2022-10-12T16:58:28.681199Z", + "doi": "10.18126/09ow-8g8k", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sdsa_sem_images_v1.2/", + "metadata": { + "title": "SEM images of sDSA morphologies", + "authors": [ + { + "name": "Doerk, Gregory S.", + "given_name": "Gregory S.", + "family_name": "Doerk", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Stein, Aaron", + "given_name": "Aaron", + "family_name": "Stein", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Yager, Kevin G.", + "given_name": "Kevin G.", + "family_name": "Yager", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "description": "Scanning electron microscope (SEM) images collected on a combinatorial samplel used in an autonomous SAXS mapping experiment. The sample is a selective directed self-assembly (sDSA) combinatorial sample. Sample was a grid of lithographically-defined chemical patterns (varying pitch and dose) with a blend of block copolymer materials cast on top.", + "keywords": [ + "polymer", + "experiment", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sdsa_sem_images_v1.2", + "mdf_source_name": "sdsa_sem_images" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sdsa_sem_images_v1.2/", + "version": "1.0", + "root_version": "sdsa_sem_images_v1.2", + "latest": true + } + }, + { + "source_id": "manna_sio2_polymorphs_functional_v1.1", + "source_name": "manna_sio2_polymorphs_functional", + "version": 1, + "ingest_date": "2022-10-27T20:26:21.405338Z", + "doi": "10.18126/8wuy-z519", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manna_sio2_polymorphs_functional_v1.1/", + "metadata": { + "title": "Dataset for SiO2 polymorphs using metaGGA functional", + "authors": [ + { + "name": "Manna, Sukriti", + "given_name": "Sukriti", + "family_name": "Manna", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Sankaranarayanan, Subramanian KRS", + "given_name": "Subramanian KRS", + "family_name": "Sankaranarayanan", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Darancet, Pierre", + "given_name": "Pierre", + "family_name": "Darancet", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + } + ], + "description": "VASP run directory for SiO2 polymorphs", + "keywords": [ + "simulation", + "ceramics", + "semiconductors", + "oxides", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "manna_sio2_polymorphs_functional_v1.1", + "mdf_source_name": "manna_sio2_polymorphs_functional" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/manna_sio2_polymorphs_functional_v1.1/", + "version": "1.0", + "root_version": "manna_sio2_polymorphs_functional_v1.1", + "latest": true + } + }, + { + "source_id": "kuo_mos2_mse_v1.1", + "source_name": "kuo_mos2_mse", + "version": 1, + "ingest_date": "2023-03-25T21:48:44.452514Z", + "doi": "10.18126/d5dp-eglo", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kuo_mos2_mse_v1.1/", + "metadata": { + "title": "All\u2010Printed Ultrahigh\u2010Responsivity MoS2 Nanosheet Photodetectors Enabled by Megasonic Exfoliation", + "authors": [ + { + "name": "Kuo, Lidia", + "given_name": "Lidia", + "family_name": "Kuo", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Rangnekar, Sonal V.", + "given_name": "Sonal V.", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Chu, Ting\u2010Ching", + "given_name": "Ting\u2010Ching", + "family_name": "Chu", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Lam, David", + "given_name": "David", + "family_name": "Lam", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Zhu, Zhehao", + "given_name": "Zhehao", + "family_name": "Zhu", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Richter, Lee J.", + "given_name": "Lee J.", + "family_name": "Richter", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Li, Ruipeng", + "given_name": "Ruipeng", + "family_name": "Li", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Szyd\u0142owska, Beata M.", + "given_name": "Beata M.", + "family_name": "Szyd\u0142owska", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Downing, Julia R.", + "given_name": "Julia R.", + "family_name": "Downing", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Luijten, Benjamin J.", + "given_name": "Benjamin J.", + "family_name": "Luijten", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ] + } + ], + "description": "Printed 2D materials, derived from solution-processed inks, offer scalable and cost-effective routes to mechanically flexible optoelectronics. With micrometer-scale control and broad processing latitude, aerosol-jet printing (AJP) is of particular interest for all-printed circuits and systems. Here, AJP is utilized to achieve ultrahigh-responsivity photodetectors consisting of well-aligned, percolating networks of semiconducting MoS2 nanosheets and graphene electrodes on flexible polyimide substrates. Ultrathin (\u22481.2 nm thick) and high-aspect-ratio (\u22481 \u03bcm lateral size) MoS2 nanosheets are obtained by electrochemical intercalation followed by megasonic atomization during AJP, which not only aerosolizes the inks but also further exfoliates the nanosheets. The incorporation of the high-boiling-point solvent terpineol into the MoS2ink is critical for achieving a highly aligned and flat thin-film morphology following AJP as confirmed by grazing-incidence wide-angle X-ray scattering and atomic force microscopy. Following AJP, curing is achieved with photonic annealing, which yields quasi-ohmic contacts and photoactive channels with responsivities exceeding 103 A W\u22121 that outperform previously reported all-printed visible-light photodetectors by over three orders of magnitude. Megasonic exfoliation coupled with properly designed AJP ink formula-tions enables the superlative optoelectronic properties of ultrathin MoS2nanosheets to be preserved and exploited for the scalable additive manufac-turing of mechanically flexible optoelectronics", + "keywords": [ + "semiconductors", + "2D materials", + "additive manufacturing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/adma.202203772", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kuo_mos2_mse_v1.1", + "mdf_source_name": "kuo_mos2_mse" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kuo_mos2_mse_v1.1/", + "version": "1.0", + "root_version": "kuo_mos2_mse_v1.1", + "latest": true + } + }, + { + "source_id": "gr_covid_biosensors_2022_v1.2", + "source_name": "gr_covid_biosensors_2022", + "version": 1, + "ingest_date": "2023-03-29T19:45:48.704630Z", + "doi": "10.18126/d1vz-v939", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gr_covid_biosensors_2022_v1.2/", + "metadata": { + "title": "Aerosol-jet-printed graphene electrochemical immunosensors for rapid and label-free detection of SARS-CoV-2 in saliva", + "authors": [ + { + "name": "Pola, C\u00edcero C", + "given_name": "C\u00edcero C", + "family_name": "Pola", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Rangnekar, Sonal V", + "given_name": "Sonal V", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Sheets, Robert", + "given_name": "Robert", + "family_name": "Sheets", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Szyd\u0142owska, Beata M", + "given_name": "Beata M", + "family_name": "Szyd\u0142owska", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Downing, Julia R", + "given_name": "Julia R", + "family_name": "Downing", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Parate, Kshama W", + "given_name": "Kshama W", + "family_name": "Parate", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Wallace, Shay G", + "given_name": "Shay G", + "family_name": "Wallace", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Tsai, Daphne", + "given_name": "Daphne", + "family_name": "Tsai", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Hersam, Mark C", + "given_name": "Mark C", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Gomes, Carmen L", + "given_name": "Carmen L", + "family_name": "Gomes", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "name": "Claussen, Jonathan C", + "given_name": "Jonathan C", + "family_name": "Claussen", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + } + ], + "description": "Rapid, inexpensive, and easy-to-use coronavirus disease 2019 (COVID-19) home tests are key tools in addition to vaccines in the world wide fight to eliminate national and local shutdowns. However, currently available tests for SARS-CoV-2, the virus that causes COVID-19, are too expensive,\npainful, and irritating, or not sufficiently sensitive for routine, accurate home testing. Herein, we employ custom-formulated graphene inks and aerosol jet printing to create a rapid electrochemical immunosensor for direct detection of SARS-CoV-2 spike receptor-binding domain (RBD) in saliva samples acquired noninvasively. This sensor demonstrated limits of detection that are considerably lower than most commercial SARS-CoV-2 antigen tests (22.91 \u00b1 4.72 pg ml\u22121 for spike RBD and 110.38 \u00b1 9.00 pg ml\u22121 for spike S1) as well as fast response time (\u223c30 min), which was facilitated by the functionalization of printed graphene electrodes in a single-step with SARS-CoV-2 polyclonal antibody through the carbodiimide reaction without the need for nanoparticle functionalization or secondary antibody or metallic nanoparticle labels. This immunosensor presents a wide linear sensing range from 1 to 1000 ng ml\u22121 and does not react with other coexisting influenza viruses such as H1N1 hemagglutinin. By combining high-yield graphene ink synthesis, automated printing, high antigen selectivity, and rapid testing capability, this work offers a promising alternative to current SARS-CoV-2 antigen tests.", + "keywords": [ + "experiment", + "2d materials", + "sensors" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1088/2053-1583/ac7339", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "gr_covid_biosensors_2022_v1.2", + "mdf_source_name": "gr_covid_biosensors_2022" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/gr_covid_biosensors_2022_v1.2/", + "version": "1.0", + "root_version": "gr_covid_biosensors_2022_v1.2", + "latest": true + } + }, + { + "source_id": "wallace_biosensor_2022_v1.3", + "source_name": "wallace_biosensor_2022", + "version": 1, + "ingest_date": "2023-03-30T21:49:36.199770Z", + "doi": "10.18126/7s4s-6p2s", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/wallace_biosensor_2022_v1.3/", + "metadata": { + "title": "Fully printed and flexible multi-material electrochemical aptasensor platform enabled by selective graphene biofunctionalization", + "authors": [ + { + "name": "Wallace, Shay Goff", + "given_name": "Shay Goff", + "family_name": "Wallace", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Brothers, Michael C", + "given_name": "Michael C", + "family_name": "Brothers", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Brooks, Zachary E", + "given_name": "Zachary E", + "family_name": "Brooks", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Rangnekar, Sonal V", + "given_name": "Sonal V", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Lam, David", + "given_name": "David", + "family_name": "Lam", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Lawrence, Michael J St", + "given_name": "Michael J St", + "family_name": "Lawrence", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Rojas, William A Gaviria", + "given_name": "William A Gaviria", + "family_name": "Rojas", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Putz, Karl W", + "given_name": "Karl W", + "family_name": "Putz", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Kim, Steve S", + "given_name": "Steve S", + "family_name": "Kim", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + }, + { + "name": "Hersam, Mark C", + "given_name": "Mark C", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ] + } + ], + "description": "The demand for flexible biochemical sensors has increased with advances in computational functionality and wireless communication. Advances in materials science and biochemistry have enabled the development and fabrication of biosensors for selective detection of biological analytes leveraging ink-printed technologies, including in flexible form-factors. However, despite these advances, minimal effort has been devoted to translating the multi-material, three-electrode electrochemical cell, which is widely regarded as the standard for laboratory-scale studies, into a flexible form-factor for use in immunosensors, especially in a manner that is compatible with rapid and scalable additive manufacturing. Here, we report a fully printed and flexible electrochemical non-enzymatic immunosensor platform that integrates four chemically compatible inks and a non-covalent, two-step biofunctionalization scheme. The robustness of the platform is demonstrated using a model aptasensor that enables lysozyme detection using both electrochemical impedance spectroscopy and square wave voltammetry. The flexible, fully ink-printed aptasensor shows competitive performance to commercially available rod/disc electrodes in a bath cell. Overall, this work establishes a methodology for high-throughput fabrication of robust, flexible, multi-material, three-electrode immunosensors that can be generalized to a range of biosensor applications.", + "keywords": [ + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1088/2631-8695/ac5e27", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "wallace_biosensor_2022_v1.3", + "mdf_source_name": "wallace_biosensor_2022" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/wallace_biosensor_2022_v1.3/", + "version": "1.0", + "root_version": "wallace_biosensor_2022_v1.3", + "latest": true + } + }, + { + "source_id": "hyun_nanoletters2022_v1.1", + "source_name": "hyun_nanoletters2022", + "version": 1, + "ingest_date": "2023-03-29T03:36:29.959095Z", + "doi": "10.18126/32mg-akx2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hyun_nanoletters2022_v1.1/", + "metadata": { + "title": "Screen-Printable Hexagonal Boron Nitride Ionogel Electrolytes for Mechanically Deformable Solid-State Lithium-Ion Batteries", + "authors": [ + { + "name": "Hyun, Woo Jin", + "given_name": "Woo Jin", + "family_name": "Hyun", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Thomas, Cory M.", + "given_name": "Cory M.", + "family_name": "Thomas", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Chaney, Lindsay E.", + "given_name": "Lindsay E.", + "family_name": "Chaney", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Mazarin de Moraes, Ana Carolina", + "given_name": "Ana Carolina", + "family_name": "Mazarin de Moraes", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Ionogel electrolytes present several benefits for solid-state lithium-ion batteries including nonflammability, favorable electrochemical properties, and high thermal stability. However, limited processing methods are currently available for ionogel electrolytes, restricting their practical applications. Here, we present a screen-printable ionogel electrolyte formulation based on hexagonal boron nitride (hBN) nanoplatelets. To achieve screen-printable rheological properties, hBN nanoplatelets are mixed with an imidazolium ionic liquid in ethyl lactate. Following screen printing, the resulting spatially uniform and mechanically flexible hBN ionogel electrolytes achieve high room-temperature ionic conductivities >1 mS cm\u20131 and stiff mechanical moduli >1 MPa. These hBN ionogel electrolytes enable the fabrication of fully screen-printed lithium-ion batteries with high cycling stability, rate performance, and mechanical resilience against flexion and external forces, thus providing a robust energy storage solution that is compatible with scalable additive manufacturing.", + "keywords": [ + "experiment", + "batteries" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.nanolett.2c01364", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "hyun_nanoletters2022_v1.1", + "mdf_source_name": "hyun_nanoletters2022" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/hyun_nanoletters2022_v1.1/", + "version": "1.0", + "root_version": "hyun_nanoletters2022_v1.1", + "latest": true + } + }, + { + "source_id": "thomas_bladecoat_hbn_v1.1", + "source_name": "thomas_bladecoat_hbn", + "version": 1, + "ingest_date": "2023-03-29T17:30:32.502464Z", + "doi": "10.18126/6fqv-23mo", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/thomas_bladecoat_hbn_v1.1/", + "metadata": { + "title": "Blade-Coatable Hexagonal Boron Nitride Ionogel Electrolytes for Scalable Production of Lithium Metal Batteries", + "authors": [ + { + "name": "Thomas, Cory M.", + "given_name": "Cory M.", + "family_name": "Thomas", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hyun, Woo Jin", + "given_name": "Woo Jin", + "family_name": "Hyun", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Huang, Hsien Cheng", + "given_name": "Hsien Cheng", + "family_name": "Huang", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Zeng, Davy", + "given_name": "Davy", + "family_name": "Zeng", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Solid-state electrolytes have attracted significant attention for rechargeable lithium-ion batteries due to their potential to enable higher energy density technologies and improve cell safety by removing volatile liquid electrolytes. However, existing solid-state electrolyte materials lack sufficient electrochemical performance or require expensive and time-consuming processing methods that have prevented their wide-scale adoption. Here, a blade-coatable hexagonal boron nitride ionogel electrolyte is introduced that exhibits high room temperature ionic conductivity (>1 mS cm\u20131), is stable against lithium metal anodes, and can be applied over a wide area in a thin (<40 \u03bcm) and crack-free film. Furthermore, this blade-coatable slurry has a tunable viscosity to enable its use in existing battery manufacturing infrastructure. The resulting blade-coated hBN ionogel electrolyte is employed in a lithium metal battery with a LiFePO4 cathode, exhibiting superlative rate capability at room temperature with a 78% capacity retention after 500 cycles at a rate of 1C.", + "keywords": [ + "experiment", + "batteries" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsenergylett.2c00535", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "thomas_bladecoat_hbn_v1.1", + "mdf_source_name": "thomas_bladecoat_hbn" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/thomas_bladecoat_hbn_v1.1/", + "version": "1.0", + "root_version": "thomas_bladecoat_hbn_v1.1", + "latest": true + } + }, + { + "source_id": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1", + "source_name": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure", + "version": 1, + "ingest_date": "2023-04-05T02:18:44.231364Z", + "doi": "10.18126/r6dh-lser", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1/", + "metadata": { + "title": "AtomVison: Atomistic STM images of 2D materials with unique chemical compositions and structure", + "authors": [ + { + "name": "Choudhary, Kamal", + "given_name": "Kamal", + "family_name": "Choudhary" + }, + { + "name": "Gurunathan, Ramya", + "given_name": "Ramya", + "family_name": "Gurunathan" + }, + { + "name": "DeCost, Brian", + "given_name": "Brian", + "family_name": "DeCost" + }, + { + "name": "Biacchi, Adam", + "given_name": "Adam", + "family_name": "Biacchi" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1", + "mdf_source_name": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure" + }, + "ml": { + "data_format": "hdf5", + "short_name": "AtomVison_STM", + "n_items": 1400, + "splits": [ + { + "type": "train", + "path": "stmimages.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "images", + "role": "input", + "units": null, + "description": "simulated STM images of various 2D materials using the Tersoff-Hamann approach" + }, + { + "name": "bias_voltage", + "role": "input", + "units": null, + "description": "bias of STM, negative or positive" + }, + { + "name": "database_id", + "role": "input", + "units": null, + "description": "id of each 2D material in the JARVIS-DFT database" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1/", + "version": "1.0", + "root_version": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1", + "latest": true + } + }, + { + "source_id": "axelrod_photodock_docking_drugs_v1.1", + "source_name": "axelrod_photodock_docking_drugs", + "version": 1, + "ingest_date": "2023-01-10T20:36:56.673546Z", + "doi": "10.18126/41tz-igf4", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/axelrod_photodock_docking_drugs_v1.1/", + "metadata": { + "title": "PhotoDock: Docking scores and poses of photoactive drugs", + "authors": [ + { + "name": "Axelrod, Simon", + "given_name": "Simon", + "family_name": "Axelrod", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Shakhnovich, Eugene", + "given_name": "Eugene", + "family_name": "Shakhnovich", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Gomez-Bombarelli, Rafael", + "given_name": "Rafael", + "family_name": "Gomez-Bombarelli", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + } + ], + "description": "Here you can find the docking data from our paper, \"Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling\" (https://arxiv.org/abs/2302.11490). If you use this data, please cite our paper.\n", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "axelrod_photodock_docking_drugs_v1.1", + "mdf_source_name": "axelrod_photodock_docking_drugs" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/axelrod_photodock_docking_drugs_v1.1/", + "version": "1.0", + "root_version": "axelrod_photodock_docking_drugs_v1.1", + "latest": true + } + }, + { + "source_id": "clement_benchmark_aflow_all_v1.1", + "source_name": "clement_benchmark_aflow_all", + "version": 1, + "ingest_date": "2023-03-08T05:13:14.491530Z", + "doi": "10.18126/990d-7b8r", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/clement_benchmark_aflow_all_v1.1/", + "metadata": { + "title": "Benchmark AFLOW Data Sets for Machine Learning (All)", + "authors": [ + { + "name": "Clement, Conrad L.", + "given_name": "Conrad L.", + "family_name": "Clement", + "affiliations": [ + "University of Utah" + ] + }, + { + "name": "Kauwe, Steven K.", + "given_name": "Steven K.", + "family_name": "Kauwe", + "affiliations": [ + "University of Utah" + ] + }, + { + "name": "Sparks, Taylor D.", + "given_name": "Taylor D.", + "family_name": "Sparks", + "affiliations": [ + "University of Utah" + ] + } + ], + "keywords": [ + "simulation", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1007/s40192-020-00174-4", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "clement_benchmark_aflow_all_v1.1", + "mdf_source_name": "clement_benchmark_aflow_all" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/clement_benchmark_aflow_all_v1.1/", + "version": "1.0", + "root_version": "clement_benchmark_aflow_all_v1.1", + "latest": true + } + }, + { + "source_id": "atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_based_on_convolution_approximation_v1.1", + "source_name": "atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_based_on_convolution_approximation", + "version": 1, + "ingest_date": "2023-04-05T02:12:24.869375Z", + "doi": "10.18126/raou-p0vg", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_based_on_convolution_approximation_v1.1/", + "metadata": { + "title": "AtomVison: Simulated Atomistic STEM images of 2D materials with unique chemical compositions and structure based on convolution approximation", + "authors": [ + { + "name": "Choudhary, Kamal", + "given_name": "Kamal", + "family_name": "Choudhary" + }, + { + "name": "Gurunathan, Ramya", + "given_name": "Ramya", + "family_name": "Gurunathan" + }, + { + "name": "DeCost, Brian", + "given_name": "Brian", + "family_name": "DeCost" + }, + { + "name": "Biacchi, Adam", + "given_name": "Adam", + "family_name": "Biacchi" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_based_on_convolution_approximation_v1.1", + "mdf_source_name": "atomvison_simulated_atomistic_stem_images_2d_materials_unique_chemical_compositions_structure_based_on_convolution_approximation" + }, + "ml": { + "data_format": "hdf5", + "short_name": "AtomVison_STEM", + "n_items": 9150, + "splits": [ + { + "type": "train", + "path": "stemimages.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "images", + "role": "input", + "units": null, + "description": "simulated STEM images of various 2D materials. 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Filament with a diameter of 2.85 \u00b1 0.06 mm was extruded from strand-cut pellets of Technomelt PA 6910 using a Dr. Collin single screw extruder (COLLIN Lab & Pilot Solutions GmbH).", + "keywords": [ + "experiment", + "polymers", + "additive manufacturing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.addma.2021.102110", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "peterson_technomelt_v1.1", + "mdf_source_name": "peterson_technomelt" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/peterson_technomelt_v1.1/", + "version": "1.0", + "root_version": "peterson_technomelt_v1.1", + "latest": true + } + }, + { + "source_id": "defecttrack_training_v1.2", + "source_name": "defecttrack_training", + "version": 1, + "ingest_date": "2023-01-20T21:45:09.384353Z", + "doi": "10.18126/71ek-dyij", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/defecttrack_training_v1.2/", + "metadata": { + "title": "DefectTrack training dataset", + "authors": [ + { + "name": "Sainju, Rajat", + "given_name": "Rajat", + "family_name": "Sainju" + }, + { + "name": "Chen, Wei-Ying", + "given_name": "Wei-Ying", + "family_name": "Chen" + }, + { + "name": "Schaefer, Samuel", + "given_name": "Samuel", + "family_name": "Schaefer" + }, + { + "name": "Yang, Qian", + "given_name": "Qian", + "family_name": "Yang" + }, + { + "name": "Ding, Caiwen", + "given_name": "Caiwen", + "family_name": "Ding" + }, + { + "name": "Li, Meimei", + "given_name": "Meimei", + "family_name": "Li" + }, + { + "name": "Zhu, Yuanyuan", + "given_name": "Yuanyuan", + "family_name": "Zhu" + } + ], + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "defecttrack_training_v1.2", + "mdf_source_name": "defecttrack_training" + }, + "ml": { + "data_format": "hdf5", + "short_name": "Defect_track", + "n_items": 1200, + "splits": [ + { + "type": "train", + "path": "data2foundry/Defecttrack.h5", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "images", + "role": "input", + "units": null, + "description": "input in situ TEM images of annealed nickel during irradiation" + }, + { + "name": "groundtruths", + "role": "target", + "units": null, + "description": "dataframes of ground truth for each image, i.e. boxes of tracked defects.The columns are image_id, defect_id, top, left, width, height" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org/foundry/defecttrack_training_v1.2/", + "version": "1.0", + "root_version": "defecttrack_training_v1.2", + "latest": true + } + }, + { + "source_id": "hsiao_library_highentropyalloy_catalysis_v1.1", + "source_name": "hsiao_library_highentropyalloy_catalysis", + "version": 1, + "ingest_date": "2023-04-24T20:41:21.979818Z", + "doi": "10.18126/aub9-ty47", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hsiao_library_highentropyalloy_catalysis_v1.1/", + "metadata": { + "title": "A Library of High-Entropy-Alloy Nanocrystals with Controlled Surface Atomic Arrangements for Catalysis", + "authors": [ + { + "name": "Hsiao, Yueh-Chun", + "given_name": "Yueh-Chun", + "family_name": "Hsiao" + }, + { + "name": "Wu, Cheng-Yu", + "given_name": "Cheng-Yu", + "family_name": "Wu" + }, + { + "name": "Huang, Wen-Yang", + "given_name": "Wen-Yang", + "family_name": "Huang" + }, + { + "name": "Thang, Ho Viet", + "given_name": "Ho Viet", + "family_name": "Thang" + }, + { + "name": "Chi, Chong-Chi", + "given_name": "Chong-Chi", + "family_name": "Chi" + }, + { + "name": "Zeng, Wen-Jing", + "given_name": "Wen-Jing", + "family_name": "Zeng" + }, + { + "name": "Gao, Jia-Qi", + "given_name": "Jia-Qi", + "family_name": "Gao" + }, + { + "name": "Lin, Chih-Yi", + "given_name": "Chih-Yi", + "family_name": "Lin" + }, + { + "name": "Lin, Jui-Tai", + "given_name": "Jui-Tai", + "family_name": "Lin" + }, + { + "name": "Lee, Chih-Heng", + "given_name": "Chih-Heng", + "family_name": "Lee" + }, + { + "name": "Mekhemer, Islam M. 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"source_name": "ingan_on_crystallized_sam", + "version": 1, + "ingest_date": "2023-04-18T15:13:49.033862Z", + "doi": "10.18126/4gh1-vqqo", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ingan_on_crystallized_sam_v1.1/", + "metadata": { + "title": "MOCVD of InGaN on ScAlMgO4 on Al2O3 Substrates with Improved Surface Morphology and Crystallinity", + "authors": [ + { + "name": "Wang, Guangying", + "given_name": "Guangying", + "family_name": "Wang", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Li, Yuting", + "given_name": "Yuting", + "family_name": "Li", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Kirch, Jeremy", + "given_name": "Jeremy", + "family_name": "Kirch", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Han, Yizhou", + "given_name": "Yizhou", + "family_name": "Han", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Chen, Jiahao", + "given_name": "Jiahao", + "family_name": "Chen", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Marks, Samuel", + "given_name": "Samuel", + "family_name": "Marks", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Mukhopadhyay, Swarnav", + "given_name": "Swarnav", + "family_name": "Mukhopadhyay", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Liu, Rui", + "given_name": "Rui", + "family_name": "Liu", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Liu, Cheng", + "given_name": "Cheng", + "family_name": "Liu", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Evans, Paul G.", + "given_name": "Paul G.", + "family_name": "Evans", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Pasayat, Shubhra S.", + "given_name": "Shubhra S.", + "family_name": "Pasayat", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "All the AFM, XRD and PL raw data files related to the published work", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": " https://doi.org/10.3390/cryst13030446", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": " https://doi.org/10.3390/cryst13030446", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ingan_on_crystallized_sam_v1.1", + "mdf_source_name": "ingan_on_crystallized_sam" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ingan_on_crystallized_sam_v1.1/", + "version": "1.0", + "root_version": "ingan_on_crystallized_sam_v1.1", + "latest": true + } + }, + { + "source_id": "rucl3_lpe_2022_v1.1", + "source_name": "rucl3_lpe_2022", + "version": 1, + "ingest_date": "2023-03-25T20:12:25.065039Z", + "doi": "10.18126/9z5q-zwzu", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rucl3_lpe_2022_v1.1/", + "metadata": { + "title": "Liquid-Phase Exfoliation of Magnetically and Optoelectronically Active Ruthenium Trichloride Nanosheets", + "authors": [ + { + "name": "Lam, David", + "given_name": "David", + "family_name": "Lam", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Lebedev, Dmitry", + "given_name": "Dmitry", + "family_name": "Lebedev", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Kuo, Lidia", + "given_name": "Lidia", + "family_name": "Kuo", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Szyd\u0142owska, Beata M.", + "given_name": "Beata M.", + "family_name": "Szyd\u0142owska", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Ferraresi, Filippo", + "given_name": "Filippo", + "family_name": "Ferraresi", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "S\u00f6ll, Aljoscha", + "given_name": "Aljoscha", + "family_name": "S\u00f6ll", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Sofer, Zden\u011bk", + "given_name": "Zden\u011bk", + "family_name": "Sofer", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ] + } + ], + "description": "\u03b1-RuCl3 is a layered transition metal halide that possesses a range of exotic magnetic, optical, and electronic properties including fractional excitations indicative of a proximate Kitaev quantum spin liquid (QSL). While previous reports have explored these properties on idealized single crystals or mechanically exfoliated samples, the scalable production of \u03b1-RuCl3 nanosheets has not yet been demonstrated. Here, we perform liquid-phase exfoliation (LPE) of \u03b1-RuCl3 through an electrochemically assisted approach, which yields ultrathin, electron-doped \u03b1-RuCl3 nanosheets that are then assembled into electrically conductive large-area thin films. The crystalline integrity of the \u03b1-RuCl3 nanosheets following LPE is confirmed through a wide range of structural and chemical analyses. Moreover, the physical properties of the LPE \u03b1-RuCl3 nanosheets are investigated through electrical, optical, and magnetic characterization methods, which reveal a structural phase transition at 230 K that is consistent with the onset of Kitaev paramagnetism in addition to an antiferromagnetic transition at 2.6 K. Intercalated ions from the electrochemical LPE protocol favorably alter the optical response of the \u03b1-RuCl3 nanosheets, enabling large-area Mott insulator photodetectors that operate at telecommunications-relevant infrared wavelengths near 1.55 \u03bcm. These photodetectors show a linear photocurrent response as a function of incident power, which suggests negligible trap-mediated recombination or photothermal effects, ultimately resulting in a photoresponsivity of \u22482 mA/W.", + "keywords": [ + "experiment", + "2D materials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsnano.2c04888", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "rucl3_lpe_2022_v1.1", + "mdf_source_name": "rucl3_lpe_2022" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/rucl3_lpe_2022_v1.1/", + "version": "1.0", + "root_version": "rucl3_lpe_2022_v1.1", + "latest": true + } + }, + { + "source_id": "multiresource_ai_v2.1", + "source_name": "multiresource_ai", + "version": 2, + "ingest_date": "2023-03-15T13:57:14.838475Z", + "doi": "10.18126/ld7m-5bb5", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/multiresource_ai_v2.1/", + "metadata": { + "title": "Dataset for Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources", + "authors": [ + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Pauloski, J. Gregory", + "given_name": "J. Gregory", + "family_name": "Pauloski", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Hayot-Sasson, Valerie", + "given_name": "Valerie", + "family_name": "Hayot-Sasson", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Chard, Ryan", + "given_name": "Ryan", + "family_name": "Chard", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Babuji, Yadu", + "given_name": "Yadu", + "family_name": "Babuji", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Sivaraman, Ganesh", + "given_name": "Ganesh", + "family_name": "Sivaraman", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Choudhury, Sutanay", + "given_name": "Sutanay", + "family_name": "Choudhury", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Chard, Kyle", + "given_name": "Kyle", + "family_name": "Chard", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Thakur, Rajeev", + "given_name": "Rajeev", + "family_name": "Thakur", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ] + } + ], + "description": "The data and software needed leveraging multiple computing resources together to design molecules and fit interatomic potentials. We also include the data from runs of the application described in our associated paper.", + "keywords": [ + "machine learning", + "simulation", + "energy materials", + "molecular structures", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/hy6c-vyh6", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "multiresource_ai_v2.1", + "mdf_source_name": "multiresource_ai" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/multiresource_ai_v2.1/", + "version": "2.0", + "root_version": "multiresource_ai", + "latest": true + } + }, + { + "source_id": "axelrod_azoflip_spinflip_derivatives_v1.3", + "source_name": "axelrod_azoflip_spinflip_derivatives", + "version": 1, + "ingest_date": "2021-10-01T03:33:09.861988Z", + "doi": "10.18126/unc8-336t", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/axelrod_azoflip_spinflip_derivatives_v1.3/", + "metadata": { + "title": "AzoFlip: Spin-flip TDDFT Data for Non-adiabatic Dynamics of Azobenzene Derivatives", + "authors": [ + { + "name": "Axelrod, Simon", + "given_name": "Simon", + "family_name": "Axelrod", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Shakhnovich, Eugene", + "given_name": "Eugene", + "family_name": "Shakhnovich", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Gomez-Bombarelli, Rafael", + "given_name": "Rafael", + "family_name": "Gomez-Bombarelli", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + } + ], + "description": "Here you can find information about the quantum chemistry data accompanying our papers, \"Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential\" (https://www.nature.com/articles/s41467-022-30999-w), and \"Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential\" (https://pubs.acs.org/doi/10.1021/acscentsci.2c00897). If you use this data, please cite our papers.\n", + "keywords": [ + "machine learning", + "simulation", + "TDDFT", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "axelrod_azoflip_spinflip_derivatives_v1.3", + "mdf_source_name": "axelrod_azoflip_spinflip_derivatives" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/axelrod_azoflip_spinflip_derivatives_v1.3/", + "version": "1.0", + "root_version": "axelrod_azoflip_spinflip_derivatives_v1.3", + "latest": true + } + }, + { + "source_id": "zhu_transistor_v1.1", + "source_name": "zhu_transistor", + "version": 1, + "ingest_date": "2023-03-19T20:56:16.859508Z", + "doi": "10.18126/e9d0-pc68", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhu_transistor_v1.1/", + "metadata": { + "title": "Edge and Interface Resistances Create Distinct Trade-Offs When Optimizing the Microstructure of Printed van der Waals Thin-Film Transistors", + "authors": [ + { + "name": "Zhu, Zhehao", + "given_name": "Zhehao", + "family_name": "Zhu", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kim, Joon-Seok", + "given_name": "Joon-Seok", + "family_name": "Kim", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Moody, Michael J.", + "given_name": "Michael J.", + "family_name": "Moody", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Lauhon, Lincoln J.", + "given_name": "Lincoln J.", + "family_name": "Lauhon", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "Raw experimental data, and Python notebooks for data analysis and device simulation. ", + "keywords": [ + "simulation", + "experiment", + "semiconductors", + "microscopy", + "printed electronics", + "thin-film transistors", + "2D materials", + "transition metal dichalcogenides", + "Kelvin probe force microscopy", + "finite element simulation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://pubs.acs.org/doi/10.1021/acsnano.2c09527", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zhu_transistor_v1.1", + "mdf_source_name": "zhu_transistor" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zhu_transistor_v1.1/", + "version": "1.0", + "root_version": "zhu_transistor_v1.1", + "latest": true + } + }, + { + "source_id": "mbsmd_v1.2", + "source_name": "mbsmd", + "version": 1, + "ingest_date": "2023-02-10T15:42:32.989087Z", + "doi": "10.18126/g26r-f6oz", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mbsmd_v1.2/", + "metadata": { + "title": "Datasets for: \"Many-Body Mechanochemistry: Intra-molecular Strain in Condensed Matter Chemistry\"", + "authors": [ + { + "name": "Hamilton, Brenden W.", + "given_name": "Brenden W.", + "family_name": "Hamilton", + "affiliations": [ + "Purdue University" + ] + }, + { + "name": "Strachan, Alejandro", + "given_name": "Alejandro", + "family_name": "Strachan", + "affiliations": [ + "Purdue University" + ] + } + ], + "description": "Dataset includes all activation barriers for mechanochemical simulations of TATB and Spiropyran using the MBsMD method. TATB reaction counts and Spiropyran free energy are also included.", + "keywords": [ + "simulation", + "mechanochemistry", + "TATB", + "spiropyran", + "activation energy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.26434/chemrxiv-2022-bvdmp", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mbsmd_v1.2", + "mdf_source_name": "mbsmd" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/mbsmd_v1.2/", + "version": "1.0", + "root_version": "mbsmd_v1.2", + "latest": true + } + }, + { + "source_id": "kononov_identifying_native_\u03b1alumina_v1.1", + "source_name": "kononov_identifying_native_\u03b1alumina", + "version": 1, + "ingest_date": "2023-03-23T21:34:40.319895Z", + "doi": "10.18126/bwgz-gel3", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kononov_identifying_native_\u03b1alumina_v1.1/", + "metadata": { + "title": "Identifying native point defect configurations in \u03b1-alumina", + "authors": [ + { + "name": "Kononov, Alina", + "given_name": "Alina", + "family_name": "Kononov", + "affiliations": [ + "Sandia National Laboratories" + ] + }, + { + "name": "Lee, Cheng-Wei", + "given_name": "Cheng-Wei", + "family_name": "Lee", + "affiliations": [ + "Colorado School of Mines" + ] + }, + { + "name": "Shapera, Ethan", + "given_name": "Ethan", + "family_name": "Shapera", + "affiliations": [ + "Graz University of Technology" + ] + }, + { + "name": "Schleife, Andre", + "given_name": "Andre", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "DFT calculations for native point defects in aluminum oxide performed using VASP", + "keywords": [ + "simulation", + "oxides", + "defects", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kononov_identifying_native_\u03b1alumina_v1.1", + "mdf_source_name": "kononov_identifying_native_\u03b1alumina" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kononov_identifying_native_\u03b1alumina_v1.1/", + "version": "1.0", + "root_version": "kononov_identifying_native_\u03b1alumina_v1.1", + "latest": true + } + }, + { + "source_id": "stanley_machine_learning_photovoltaics_v1.1", + "source_name": "stanley_machine_learning_photovoltaics", + "version": 1, + "ingest_date": "2023-04-24T21:00:09.085121Z", + "doi": "10.18126/tu66-3hpl", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/stanley_machine_learning_photovoltaics_v1.1/", + "metadata": { + "title": "Dataset for 'Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics'", + "authors": [ + { + "name": "Stanley, Jared C.", + "given_name": "Jared C.", + "family_name": "Stanley" + }, + { + "name": "Gagliardi, Alessio", + "given_name": "Alessio", + "family_name": "Gagliardi" + } + ], + "description": "\nDatasets used in the publication \"Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics\" [doi:10.1002/adts.201900178].\n\nAll structures were relaxed with the following parameters using Quantumwise QATK 2017:\n\n- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)\n- 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4\u00c5x5.7\u00c5x11.4\u00c5-structures (forces < 0.01eV/\u00c5)\n- 300K Fermi-Dirac-smearing\n- a 6x12x6 k-point grid (Monkhorst-Pack)\n\n\nSpecifically, the included files are:\n\ndb_2.data: the actual database used for model building (json-format)\nlead_set.data: the \"external\" test set used to test predictive power with out of sample compounds (json-format)\nload_stanley_c.py: a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as ASE-atoms\n\nThe format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):\n{\n \"\" : {\n \"trajectory\" : n/a,\n \"energy\" : total DFT energy in eV,\n \"rstruc\" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),\n \"gaps\" : { \"opt_gap\", \"ind_gap } - both direct and indirect gap,\n \"effective_mass\" : n/a,\n \"iterations\" : number of relaxation steps,\n \"calc\" : some calculation metadata,\n \"ustruc\" : unrelaxed input structure,\n \n }\n}\nMissing ids relate to structures filtered out, because the calculation didn't converge.\n\nSome code which works with a different representation of this data can be found at https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics\n", + "keywords": [ + "perovskite", + "machine learning", + "materials science" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.4043330", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "stanley_machine_learning_photovoltaics_v1.1", + "mdf_source_name": "stanley_machine_learning_photovoltaics" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/stanley_machine_learning_photovoltaics_v1.1/", + "version": "1.0", + "root_version": "stanley_machine_learning_photovoltaics_v1.1", + "latest": true + } + }, + { + "source_id": "lee_repository_machinelearning_antiferromagnet_v1.1", + "source_name": "lee_repository_machinelearning_antiferromagnet", + "version": 1, + "ingest_date": "2023-04-24T20:50:49.440512Z", + "doi": "10.18126/grx6-uzq5", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lee_repository_machinelearning_antiferromagnet_v1.1/", + "metadata": { + "title": "Data Repository for: Machine-learning the spectral function of a hole in a quantum antiferromagnet", + "authors": [ + { + "name": "Lee, Jackson", + "given_name": "Jackson", + "family_name": "Lee" + }, + { + "name": "Carbone, Matthew R.", + "given_name": "Matthew R.", + "family_name": "Carbone" + }, + { + "name": "Yin, Weiguo", + "given_name": "Weiguo", + "family_name": "Yin" + } + ], + "description": "\n The machine-learning dataset of 51^3 ~1.3 \u00d7 10^5 density of states (DOS) of a mobile hole in the t-t'-t''-J model theoretically generated by using the self-consistent Born approximation in the three-dimensional parameter space of t\u2032 \u2208 [\u22120.5, 0.5], t\u2032\u2032 \u2208 [\u22120.5, 0.5] and J \u2208 [0.2, 1.0], with each parameter sampled on a 51-point uniform grid. The dataset is randomly partitioned into an 80/10/10 training (T), validation (V), and testing T split. Note that each DOS A(\u03c9) was calculated on a 1201-point uniform grid of \u03c9 \u2208 [\u22126t, 6t], then it was resampled on a 301-point uniform grid for the forward problem and on a 354-point uniform grid for the inverse problem. The dataset used in the inverse problem is limited to the parameter space of t\u2032 \u2208 [\u22120.5, 0], t\u2032\u2032 \u2208 [0, 0.5] and J \u2208 [0.2, 1.0]. To open the enclosed .npz files, use numpy.load() in python3.\n \n This work was supported by U.S. Department of Energy (DOE) the Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-SC0012704. This project was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI). This project was supported in part by the Brookhaven National Laboratory (BNL), Condensed Matter Physics and Materials Science Division under the BNL Supplemental Undergraduate Research Program (SURP).\n", + "keywords": [ + "machine learning", + "spectral function", + "correlated materials", + "t-J model" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7527378", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "lee_repository_machinelearning_antiferromagnet_v1.1", + "mdf_source_name": "lee_repository_machinelearning_antiferromagnet" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/lee_repository_machinelearning_antiferromagnet_v1.1/", + "version": "1.0", + "root_version": "lee_repository_machinelearning_antiferromagnet_v1.1", + "latest": true + } + }, + { + "source_id": "rojas_chemtastesdb_curated_tastants_v1.1", + "source_name": "rojas_chemtastesdb_curated_tastants", + "version": 1, + "ingest_date": "2023-04-24T21:14:39.407456Z", + "doi": "10.18126/5pq3-zbho", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rojas_chemtastesdb_curated_tastants_v1.1/", + "metadata": { + "title": "ChemTastesDB: A Curated Database of Molecular Tastants", + "authors": [ + { + "name": "Rojas, Cristian", + "given_name": "Cristian", + "family_name": "Rojas" + }, + { + "name": "Ballabio, Davide", + "given_name": "Davide", + "family_name": "Ballabio" + }, + { + "name": "Pacheco Sarmiento, Karen", + "given_name": "Karen", + "family_name": "Pacheco Sarmiento" + }, + { + "name": "Pacheco Jaramillo, Elisa", + "given_name": "Elisa", + "family_name": "Pacheco Jaramillo" + }, + { + "name": "Mendoza, Mateo", + "given_name": "Mateo", + "family_name": "Mendoza" + }, + { + "name": "Garc\u00eda, Fernando", + "given_name": "Fernando", + "family_name": "Garc\u00eda" + } + ], + "description": "\nChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR) studies for taste prediction by means of diverse machine learning approaches.\n\nMolecules are labelled in one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, multitaste and miscellaneous). ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (.hin) format of each chemical is provided.\n\nThis is version 1.2 of the ChemTastesDB.\n\nWhat's new in version 1.2:\n\nChemical information (for instance, name, PubChem CID or CAS number) for some tastants has been included.\n\nThe database is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper:\nRojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & Garc\u00eda, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090.\n", + "keywords": [ + "chemistry", + "machine learning", + "taste" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.6528835", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "rojas_chemtastesdb_curated_tastants_v1.1", + "mdf_source_name": "rojas_chemtastesdb_curated_tastants" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/rojas_chemtastesdb_curated_tastants_v1.1/", + "version": "1.0", + "root_version": "rojas_chemtastesdb_curated_tastants_v1.1", + "latest": true + } + }, + { + "source_id": "zheng_iupac_dissociationconstants_v10_v1.1", + "source_name": "zheng_iupac_dissociationconstants_v10", + "version": 1, + "ingest_date": "2023-04-24T20:58:49.353339Z", + "doi": "10.18126/cy58-tndq", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zheng_iupac_dissociationconstants_v10_v1.1/", + "metadata": { + "title": "IUPAC Dissociation-Constants: v1.0", + "authors": [ + { + "name": "Zheng, Jonathan", + "given_name": "Jonathan", + "family_name": "Zheng" + } + ], + "description": "\nThis release includes the labeled data, digitization report, method keys, and partial reference keys as part of the IUPAC Digitized pKa Dataset.\n\nThis is the first release of this dataset. The detailed process of digitization and curation of this dataset can be found in the IUPAC pKa Data Digitization Report attached in this repository. Our validation process is ongoing and will continue. Please be advised that a few errors and inconsistencies may still exist.\n\nThe digitized books in this collection include:\n\nSerjeant: International Union of Pure and Applied Chemistry, E. P Serjeant and Boyd Dempsey. Ionisation Constants of Organic Acids in Aqueous Solution; Oxford/Pergamon, 1979 (Oxford IUPAC chemical data series)\nPerrin: International Union of Pure and Applied Chemistry, DD Perrin. Dissociation Constants of Organic Bases in Aqueous Solution; Butterworths, 1965\n", + "keywords": [ + "pKa", + "machine learning", + "dissociation", + "chemistry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.7236453", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "zheng_iupac_dissociationconstants_v10_v1.1", + "mdf_source_name": "zheng_iupac_dissociationconstants_v10" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/zheng_iupac_dissociationconstants_v10_v1.1/", + "version": "1.0", + "root_version": "zheng_iupac_dissociationconstants_v10_v1.1", + "latest": true + } + }, + { + "source_id": "banszerus_particlehole_symmetry_dots_v1.3", + "source_name": "banszerus_particlehole_symmetry_dots", + "version": 1, + "ingest_date": "2023-04-24T20:22:58.571840Z", + "doi": "10.18126/a3gy-rufd", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/banszerus_particlehole_symmetry_dots_v1.3/", + "metadata": { + "title": "Particle\u2013hole symmetry protects spin-valley blockade in graphene quantum dots", + "authors": [ + { + "name": "Banszerus, L.", + "given_name": "L.", + "family_name": "Banszerus" + }, + { + "name": "M\u00f6ller, S.", + "given_name": "S.", + "family_name": "M\u00f6ller" + }, + { + "name": "Hecker, K.", + "given_name": "K.", + "family_name": "Hecker" + }, + { + "name": "Icking, E.", + "given_name": "E.", + "family_name": "Icking" + }, + { + "name": "Watanabe, K.", + "given_name": "K.", + "family_name": "Watanabe" + }, + { + "name": "Taniguchi, T.", + "given_name": "T.", + "family_name": "Taniguchi" + }, + { + "name": "Hassler, F.", + "given_name": "F.", + "family_name": "Hassler" + }, + { + "name": "Volk, C.", + "given_name": "C.", + "family_name": "Volk" + }, + { + "name": "Stampfer, C.", + "given_name": "C.", + "family_name": "Stampfer" + } + ], + "description": "\n Experimental data and Python scripts used to evaluate the data and to perform simulations for the publication 'Particle-hole symmetry protects spin-valley blockade in graphene quantum dots' in Nature.\n", + "keywords": [ + "bilayer graphene", + "quantum dots", + "electron hole symmetry", + "Pauli blockade" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1038/s41586-023-05953-5", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "banszerus_particlehole_symmetry_dots_v1.3", + "mdf_source_name": "banszerus_particlehole_symmetry_dots" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/banszerus_particlehole_symmetry_dots_v1.3/", + "version": "1.0", + "root_version": "banszerus_particlehole_symmetry_dots_v1.3", + "latest": true + } + }, + { + "source_id": "nazarova_polymeric_property_optimizations_v1.1", + "source_name": "nazarova_polymeric_property_optimizations", + "version": 1, + "ingest_date": "2023-04-24T20:36:40.766236Z", + "doi": "10.18126/6xyv-r29h", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nazarova_polymeric_property_optimizations_v1.1/", + "metadata": { + "title": "Polymeric Property Prediction Using Recurrent Neural Networks with Optimizations", + "authors": [ + { + "name": "Nazarova, Antonina", + "given_name": "Antonina", + "family_name": "Nazarova" + }, + { + "name": "Nakano, Aiichiro", + "given_name": "Aiichiro", + "family_name": "Nakano" + }, + { + "name": "Mishra, Ankit", + "given_name": "Ankit", + "family_name": "Mishra" + } + ], + "description": "\n Database of the Dielectric Polymers used for training and testing of the developed Recurrent Neural Network models.\n", + "keywords": [ + "dielectric 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"globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kino_characterization_descriptors_prediction_v1.1/", + "metadata": { + "title": "Characterization of descriptors in machine learning for data-based sputtering yield prediction", + "authors": [ + { + "name": "Kino, Hiori", + "given_name": "Hiori", + "family_name": "Kino" + } + ], + "description": "\ndescriptor and target variables\n\n", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.5281/zenodo.3702043", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kino_characterization_descriptors_prediction_v1.1", + "mdf_source_name": "kino_characterization_descriptors_prediction" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kino_characterization_descriptors_prediction_v1.1/", + "version": "1.0", + "root_version": "kino_characterization_descriptors_prediction_v1.1", + "latest": true + } + }, + { + "source_id": "cb4438e5-e2d0-4834-b94a-4e16953e7645", + "source_name": "cb4438e5-e2d0-4834-b94a-4e16953e7645", + "version": "1.0", + "ingest_date": "2024-09-03T16:41:51.839817Z", + "doi": "10.18126/9181-re95", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/cb4438e5-e2d0-4834-b94a-4e16953e7645/1.0/", + "metadata": { + "title": "Thermal Contact Resistance, Geometry, and Tensile Properties Measurements for Thermal and Mechanical Modeling of Polycarbonate Material Extrusion Additive Manufacturing", + "authors": [ + { + "name": "Adisa, Ahmed O.", + "given_name": "Ahmed O.", + "family_name": "Adisa", + "affiliations": [ + "University of Massachusetts Lowell" + ] + }, + { + "name": "Kazmer, David O.", + "given_name": "David O.", + "family_name": "Kazmer", + "affiliations": [ + "University of Massachusetts Lowell" + ] + }, + { + "name": "Peterson, Amy M.", + "given_name": "Amy M.", + "family_name": "Peterson", + "affiliations": [ + "University of Massachusetts Lowell" + ] + } + ], + "description": "Experimental data includes conductivity and contact resistance data, tensile properties, and cross-sectional microscopy of material extrusion additive manufacturing of polycarbonate. Modeling includes predicted temperature and tensile strength profiles of material extrusion additive manufacturing of polycarbonate with different print speeds, layer times, extrusion temperatures, and numbers of roads in a layer.", + "keywords": [ + "experiment", + "polymers", + "microscopy", + "numerical modeling" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/p8jh-shxc", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "cb4438e5-e2d0-4834-b94a-4e16953e7645", + "mdf_source_name": "cb4438e5-e2d0-4834-b94a-4e16953e7645" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/cb4438e5-e2d0-4834-b94a-4e16953e7645/1.0/", + "version": "1.0", + "root_version": 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"affiliations": [ + "Sandia National Laboratories" + ] + } + ], + "description": "This dataset consists of multiple microstructure evolution trajectories, grouped by physical phenomenon \n- Physical phenomena: Spinodal decomposition, Physical Vapor Deposition (PVD), Dendritic evolution, Grain growth", + "keywords": [ + "simulation", + "machine learning", + "metals and alloys", + "microscopy", + "phase field" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.actamat.2020.02.011", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "microstructure_evolution_phase_field_v1.1", + "mdf_source_name": "microstructure_evolution_phase_field" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/microstructure_evolution_phase_field_v1.1/", + "version": "1.0", + "root_version": 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Michael", + "given_name": "Michael", + "family_name": "van der Naald", + "affiliations": [ + "University of Chicago", + "Institut Prime, CNRS-Universit\u00e9 de Poitiers-ISAE ENSMA" + ] + }, + { + "name": "Kim, Hojin", + "given_name": "Hojin", + "family_name": "Kim", + "affiliations": [ + "University of Chicago", + "Institut Prime, CNRS-Universit\u00e9 de Poitiers-ISAE ENSMA" + ] + }, + { + "name": "Atis, S\u00e9verine", + "given_name": "S\u00e9verine", + "family_name": "Atis", + "affiliations": [ + "University of Chicago", + "Institut Prime, CNRS-Universit\u00e9 de Poitiers-ISAE ENSMA" + ] + }, + { + "name": "Jaeger, Heinrich M.", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago", + "Institut Prime, CNRS-Universit\u00e9 de Poitiers-ISAE ENSMA" + ] + } + ], + "description": "Description of contents:\n------------------------------------------------\nFolder: Code\n- Contains all analysis files for foam motion tracking and fluid PIV\n------------------------------------------------\nFolder: Rheology\n- Contains Excel file of shear thickening rheology\n------------------------------------------------\nFolder: Zwick files\n- Contains all stress-strain measurements of fluid-filled foams\n------------------------------------------------\nFile: integrating stress strain data.xlsx\n- Integrated stress-strain curves as a function of impact speed\n", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "67b75e10-6d2a-4165-aadd-d3580dd59376", + "mdf_source_name": "67b75e10-6d2a-4165-aadd-d3580dd59376" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/67b75e10-6d2a-4165-aadd-d3580dd59376/1.0/", + "version": "1.0", + "root_version": "67b75e10-6d2a-4165-aadd-d3580dd59376_v1.0", + 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"Contains Faraday cup electrostatic data for volcanic ash samples, for the investigation of their triboelectric charging properties. There is also density, shape, and size data for the volcanic ash samples.", + "keywords": [ + "simulation", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "9cde500f-87e2-493b-aa9c-b31a8346b045", + "mdf_source_name": "9cde500f-87e2-493b-aa9c-b31a8346b045" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/9cde500f-87e2-493b-aa9c-b31a8346b045/1.0/", + "version": "1.0", + "root_version": "9cde500f-87e2-493b-aa9c-b31a8346b045", + "latest": true + } + }, + { + "source_id": "d138e31b-2f7f-49c4-a9ac-775675b449cb", + "source_name": "d138e31b-2f7f-49c4-a9ac-775675b449cb", + "version": "1.0", + "ingest_date": "2024-09-04T19:48:26.713484Z", + "doi": "10.18126/5zts-cy47", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/d138e31b-2f7f-49c4-a9ac-775675b449cb/1.0/", + "metadata": { + "title": "Original Data from \"Dynamic Surface Incorporation of Pb2+ Ions at the Actively Dissolving Calcite (104) Surface\"", + "authors": [ + { + "name": "Abdilla, Bektur", + "given_name": "Bektur", + "family_name": "Abdilla", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + }, + { + "name": "Lee, Sang Soo", + "given_name": "Sang Soo", + "family_name": "Lee", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + }, + { + "name": "Sturchio, Neil C.", + "given_name": "Neil C.", + "family_name": "Sturchio", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + }, + { + "name": "Fenter, Paul", + "given_name": "Paul", + "family_name": "Fenter", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + } + ], + "keywords": [ + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1021/acs.est.4c03567", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "d138e31b-2f7f-49c4-a9ac-775675b449cb", + "mdf_source_name": "d138e31b-2f7f-49c4-a9ac-775675b449cb" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/d138e31b-2f7f-49c4-a9ac-775675b449cb/1.0/", + "version": "1.0", + "root_version": "d138e31b-2f7f-49c4-a9ac-775675b449cb", + "latest": true + } + }, + { + "source_id": "64d19901-f3a7-4ed6-937a-65a6108abe34", + "source_name": "64d19901-f3a7-4ed6-937a-65a6108abe34", + "version": "1.1", + "ingest_date": "2024-09-20T04:19:45.571253Z", + "doi": "10.18126/ncqt-rh18", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/64d19901-f3a7-4ed6-937a-65a6108abe34/1.1/", + "metadata": { + "title": "Data ver 2 for: Learning Orderings in Crystalline Materials with Symmetry-Aware Graph Neural Networks", + "authors": [ + { + "name": "Peng, Jiayu", + "given_name": "Jiayu", + "family_name": "Peng", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Damewood, James", + "given_name": "James", + "family_name": "Damewood", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Karaguesian, Jessica", + "given_name": "Jessica", + "family_name": "Karaguesian", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Lunger, Jaclyn R.", + "given_name": "Jaclyn R.", + "family_name": "Lunger", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "G\u00f3mez-Bombarelli, Rafael", + "given_name": "Rafael", + "family_name": "G\u00f3mez-Bombarelli", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + } + ], + "description": "Data for our paper \"Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks\". If you use this data, please cite our paper.", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "64d19901-f3a7-4ed6-937a-65a6108abe34", + "mdf_source_name": "64d19901-f3a7-4ed6-937a-65a6108abe34" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/64d19901-f3a7-4ed6-937a-65a6108abe34/1.1/", + "version": "1.1", + "previous_version": "64d19901-f3a7-4ed6-937a-65a6108abe34_v1.0", + "root_version": "64d19901-f3a7-4ed6-937a-65a6108abe34_v1.0", + "latest": true + } + }, + { + "source_id": "e32dae46-863d-4c13-a9ef-a36eefa38a78", + "source_name": "e32dae46-863d-4c13-a9ef-a36eefa38a78", + "version": "1.0", + "ingest_date": "2025-05-06T03:33:38.958356Z", + "doi": "10.18126/1f89-fc48", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/e32dae46-863d-4c13-a9ef-a36eefa38a78/1.0/", + "metadata": { + "title": "Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations", + "authors": [ + { + "name": "Hou, Bowen", + "given_name": "Bowen", + "family_name": "Hou", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Wu, Jinyuan", + "given_name": "Jinyuan", + "family_name": "Wu", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Lee, Victor Chang", + "given_name": "Victor Chang", + "family_name": "Lee", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Guo, Jiaxuan", + "given_name": "Jiaxuan", + "family_name": "Guo", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Liu, Luna Y.", + "given_name": "Luna Y.", + "family_name": "Liu", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Yale University" + ] + } + ], + "description": "Many-body electron-hole interactions are essential for understanding non-linear optical processes and ultrafast spectroscopy of materials. Recent first principles approaches based on nonequilibrium Green\u2019s function formalisms, such as the time-dependent adiabatic GW (TD-aGW) approach, can predict the nonequilibrium dynamics of excited states including electron-hole interactions. However, the high dimensionality of the electron-hole kernel poses significant computational challenges for scalability. Here, we develop a data-driven low-rank approximation for the electron-hole kernel, leveraging localized excitonic effects in the Hilbert space of crystalline systems. Through singular value decomposition (SVD) analysis, we show that the subspace of non-zero singular values, containing the key information of the electron-hole kernel, retains a small size even as the k-grid grows, ensuring computational feasibility with extremely dense k-grids for converged calculations. Utilizing this low-rank property, we achieve at least 95% compression of the kernel and an orderof-magnitude speedup of TD-aGW calculations. Our method, rooted in physical interpretability, outperforms existing machine learning approaches by avoiding intensive training processes and eliminating time-accumulated errors, providing a general framework for high-throughput, nonequilibrium simulation of light-driven dynamics in materials.\n\nThis dataset includes all the raw data of MoS2 BSE kernel from BerkeleyGW, the TD-aGW generated data, and post-processed data used for plot.", + "keywords": [ + "machine learning", + "TD-aGW", + "SVD", + "Non-equilibrium Dynamics", + "Electron-hole Interaction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "e32dae46-863d-4c13-a9ef-a36eefa38a78", + "mdf_source_name": "e32dae46-863d-4c13-a9ef-a36eefa38a78" + }, + "download_url": 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Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Semeraro, Federico", + "given_name": "Federico", + "family_name": "Semeraro", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Ferguson, Joseph C.", + "given_name": "Joseph C.", + "family_name": "Ferguson", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Barnard, Harold S.", + "given_name": "Harold S.", + "family_name": "Barnard", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Dias, Bruno", + "given_name": "Bruno", + "family_name": "Dias", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Schlep\u00fctz, Christian M.", + "given_name": "Christian M.", + "family_name": "Schlep\u00fctz", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Barnard, Edward S.", + "given_name": "Edward S.", + "family_name": "Barnard", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Schickler, Sam", + "given_name": "Sam", + "family_name": "Schickler", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Levy, Kara", + "given_name": "Kara", + "family_name": "Levy", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Shacterman, Shawn", + "given_name": "Shawn", + "family_name": "Shacterman", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Benioff-White, Talia", + "given_name": "Talia", + "family_name": "Benioff-White", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Davis, Julian", + "given_name": "Julian", + "family_name": "Davis", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "MacDowell, Alastair A.", + "given_name": "Alastair A.", + "family_name": "MacDowell", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Parkinson, Dilworth Y.", + "given_name": "Dilworth Y.", + "family_name": "Parkinson", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "name": "Panerai, Francesco", + "given_name": "Francesco", + "family_name": "Panerai", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "3bcaac03-f9de-44f7-a31d-a532d324e4ad", + "mdf_source_name": "3bcaac03-f9de-44f7-a31d-a532d324e4ad" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/3bcaac03-f9de-44f7-a31d-a532d324e4ad/1.0/", + "version": "1.0", + "root_version": "3bcaac03-f9de-44f7-a31d-a532d324e4ad", + "latest": true + } + }, + { + "source_id": "34c73a10-2b22-404c-9c6f-652569720a28", + "source_name": "34c73a10-2b22-404c-9c6f-652569720a28", + "version": "1.1", + "ingest_date": "2025-05-16T14:42:37.707474Z", + "doi": "10.18126/ccqv-t437", + 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Each file is a Numpy array corresponding to one simulation frame. The first section each file name is the simulation number, from 0000_out to 0199_out. The second section of each file name is the tracked phase: composition, ostwald 1, ostwald 2, ostwald 3, or ostwald 4. The last digits of each file name is the simulation timestep, from 0 to 30,000,000 with an interval of 30,000 for a total of 101 timesteps included per simulation per phase. Each array contains only 0s and 1s, with 0 corresponding to out of phase and 1 corresponding to in phase. At each timestep, the sum of all four individual phases will equal the compositional phase. The simulation parameters used were randomized but checked to ensure mictrostructures generated. A dump with these parameters is available in excel format from the authors.", + "keywords": [ + "simulation", + "machine learning", + "metals and alloys", + "diffusion", + "microstructures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "34c73a10-2b22-404c-9c6f-652569720a28", + "mdf_source_name": "34c73a10-2b22-404c-9c6f-652569720a28" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/34c73a10-2b22-404c-9c6f-652569720a28/1.1/", + "version": "1.1", + "root_version": "34c73a10-2b22-404c-9c6f-652569720a28", + "latest": true + } + }, + { + "source_id": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18", + "source_name": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18", + "version": "1.1", + "ingest_date": "2025-05-19T20:36:33.362989Z", + "doi": 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"affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Kwon, Yongkyung", + "given_name": "Yongkyung", + "family_name": "Kwon", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + } + ], + "description": "Datasets for Datasets for Stacking polymorphism of PtSe2: its implication to layer-dependent metal-insulator transitions published on npj 2D Mater Appl 9, 34 (2025). https://doi.org/10.1038/s41699-025-00544-6 ", + "keywords": [ + "simulation", + "DFT", + "QMCPack", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1038/s41699-025-00544-6", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18", + "mdf_source_name": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/be9d86d2-dbc9-4b0a-813f-6bd2784aaa18/1.1/", + "version": "1.1", + "root_version": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18", + "latest": true + } + }, + { + "source_id": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "source_name": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "version": "1.2", + "ingest_date": "2025-05-13T15:20:24.247558Z", + "doi": "10.18126/t1j5-4111", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ad2a1532-e3dd-4b12-bded-8653466b3a9b/1.2/", + "metadata": { + "title": "Differential Scanning Calorimetry of Thermosets Polymerized via Chemical Activation of Frontal Ring-Opening Metathesis Polymerization", + "authors": [ + { + "name": "Cramblitt, Anna C.", + "given_name": "Anna C.", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Lessard, Jacob J.", + "given_name": "Jacob J.", + "family_name": "Lessard", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Differential Scanning Calorimetry (DSC) experiments were conducted on samples taken from thermoset polymer specimens after frontal ring-opening metathesis polymerization (FROMP) to determine glass transition temperature and evaluate for plasticization. Polymerization resins comprised of dicyclopentadiene (DCPD) with 5 wt% 5-ethylidene-2-norbornene (ENB) added, grubbs\u2019 second generation catalyst, and tributyl phosphite (TBP) inhibitor (8000:1:1 monomer:initiator:inhibitor ratio). Polymerization fronts were triggered using concentrated solutions of grubbs\u2019 third generation catalyst in various solvents.", + "keywords": [ + "experiment", + "polymers", + "DSC", + "properties", + "characterization", + "chemistry", + "glass transition temperature", + "FROMP" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "mdf_source_name": "ad2a1532-e3dd-4b12-bded-8653466b3a9b" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/ad2a1532-e3dd-4b12-bded-8653466b3a9b/1.2/", + "version": "1.2", + "root_version": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "latest": true + } + }, + { + "source_id": "442f453e-e296-455a-a200-6ca6779cefca", + "source_name": "442f453e-e296-455a-a200-6ca6779cefca", + "version": "1.1", + "ingest_date": "2025-05-13T15:05:58.688071Z", + "doi": "10.18126/183e-2r15", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/442f453e-e296-455a-a200-6ca6779cefca/1.1/", + "metadata": { + "title": "Videos of Chemically Activated Frontal Polymerization of Thermosets ", + "authors": [ + { + "name": "Cramblitt, Anna C.", + "given_name": "Anna C.", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Lessard, Jacob J.", + "given_name": "Jacob J.", + "family_name": "Lessard", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Video recordings of the frontal ring-opening metathesis polymerization (FROMP) of thermoset polymer specimens in 10mm glass test tubes were collected in order to measure the front speed resulting from varied front initiation methods. Maximum front temperature was also measured. Polymerization resins comprised of dicyclopentadiene (DCPD) with 5 wt% 5-ethylidene-2-norbornene (ENB) added, grubbs\u2019 second generation catalyst, and tributyl phosphite (TBP) inhibitor (8000:1:1 monomer:initiator:inhibitor ratio). Polymerization fronts were triggered using concentrated solutions of grubbs\u2019 third generation catalyst in various solvents.", + "keywords": [ + "experiment", + "polymers", + "video", + "chemistry", + "front speed", + "front temperature", + "FROMP" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "442f453e-e296-455a-a200-6ca6779cefca", + "mdf_source_name": "442f453e-e296-455a-a200-6ca6779cefca" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/442f453e-e296-455a-a200-6ca6779cefca/1.1/", + "version": "1.1", + "root_version": "442f453e-e296-455a-a200-6ca6779cefca", + "latest": true + } + }, + { + "source_id": "1be66992-4956-44a6-834a-c790ca0559f9", + "source_name": "1be66992-4956-44a6-834a-c790ca0559f9", + "version": "1.1", + "ingest_date": "2025-05-21T21:15:45.321016Z", + "doi": "10.18126/gmnn-3z95", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/1be66992-4956-44a6-834a-c790ca0559f9/1.1/", + "metadata": { + "title": "Datasets for \"Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo\"", + "authors": [ + { + "name": "Lopez, Annette", + "given_name": "Annette", + "family_name": "Lopez", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Melton, Cody A.", + "given_name": "Cody A.", + "family_name": "Melton", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ahn, Jeonghwan", + "given_name": "Jeonghwan", + "family_name": "Ahn", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Rubenstein, Brenda M.", + "given_name": "Brenda M.", + "family_name": "Rubenstein", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Datasets for \"Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo\". Includes inputs and outputs for Quantum Espresso and QMCPACK codes. Nexus workflow scripts are also included wherever possible.", + "keywords": [ + "Quantum Monte Carlo", + "QMC", + "QMCPack", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "1be66992-4956-44a6-834a-c790ca0559f9", + "mdf_source_name": "1be66992-4956-44a6-834a-c790ca0559f9" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/1be66992-4956-44a6-834a-c790ca0559f9/1.1/", + "version": "1.1", + "root_version": "1be66992-4956-44a6-834a-c790ca0559f9", + "latest": true + } + }, + { + "source_id": "4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb", + "source_name": "4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb", + "version": "1.3", + "ingest_date": "2025-05-21T09:49:17.959020Z", + "doi": "10.18126/fyac-7s19", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb/1.3/", + "metadata": { + "title": "A new generation of effective core potentials: Selected lanthanides and heavy elements II", + "authors": [ + { + "name": "Madany, Omar", + "given_name": "Omar", + "family_name": "Madany", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Kincaid, Benjamin", + "given_name": "Benjamin", + "family_name": "Kincaid", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Shaikh, Aqsa", + "given_name": "Aqsa", + "family_name": "Shaikh", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Morningstar, Elizabeth", + "given_name": "Elizabeth", + "family_name": "Morningstar", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University" + ] + } + ], + "description": "All of the inputs and outputs used in the JCP paper \"A new generation of effective core potentials: Selected lanthanides and heavy elements II\" by O. Madany et. al.", + "keywords": [ + "ccECP", + "Pseudopotential", + "QMC", + "CPSFM", + "atomic spectra", + "binding energy", + "simulation", + "Effective Core Potentials", + "Spin-orbit" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb", + "mdf_source_name": "4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb/1.3/", + "version": "1.3", + "root_version": "4719f4f4-1f2a-4f0e-a351-06cb8c35e5eb", + "latest": true + } + }, + { + "source_id": "accelerated_electroplating_characterization_v1.1", + "source_name": "accelerated_electroplating_characterization", + "version": 1, + "ingest_date": "2023-10-11T18:07:40.999779Z", + "doi": "10.18126/v84f-we14", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/accelerated_electroplating_characterization_v1.1/", + "metadata": { + "title": "Accelerated Multimodal Characterization of Ni and Ni-Fe Electrodeposited Films", + "authors": [ + { + "name": "Basset, Kimberly L.", + "given_name": "Kimberly L.", + "family_name": "Basset", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Watkins, Tylan", + "given_name": "Tylan", + "family_name": "Watkins", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Coleman, Jonathan", + "given_name": "Jonathan", + "family_name": "Coleman", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Bianco, Nathan", + "given_name": "Nathan", + "family_name": "Bianco", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Bailey, Lauren S.", + "given_name": "Lauren S.", + "family_name": "Bailey", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Williams, Samuel Garrett", + "given_name": "Samuel Garrett", + "family_name": "Williams", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Babuska, Tomas F.", + "given_name": "Tomas F.", + "family_name": "Babuska", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Curry, John", + "given_name": "John", + "family_name": "Curry", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "DelRio, Frank W.", + "given_name": "Frank W.", + "family_name": "DelRio", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Henriksen, Amelia A.", + "given_name": "Amelia A.", + "family_name": "Henriksen", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Garland, Anthony", + "given_name": "Anthony", + "family_name": "Garland", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Hall, Justin", + "given_name": "Justin", + "family_name": "Hall", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Krick, Brandon A.", + "given_name": "Brandon A.", + "family_name": "Krick", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Boyce, Brad L.", + "given_name": "Brad L.", + "family_name": "Boyce", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "This dataset constitutes the output from a novel workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni-Fe thin films, as outlined in accompanying paper \u201cA Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films.\u201d Approximately four films were created for each unique set of process conditions. These process conditions span seven different chemical bath solutions (and one repeated solution with the addition of agitation), and current densities varied between 1 and 200 mA/cm^2. The in-situ voltage time series data for these electrodepositions is included to capture process information. Six characterization modalities are represented: X-ray diffraction, X-ray fluorescence, nanoindentation, optical microscopy, laser profilometry, and tribological (friction and wear) testing.\n\nWe acknowledge the Laboratory Directed Research and Development program for providing funding for this study. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy\u2019s National Nuclear Security Administration under contract DE-NA0003525.\n", + "keywords": [ + "electroplating", + "machine learning", + "multi-modal data", + " characterization", + " " + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "accelerated_electroplating_characterization_v1.1", + "mdf_source_name": "accelerated_electroplating_characterization" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/accelerated_electroplating_characterization_v1.1/", + "version": "1.0", + "root_version": "accelerated_electroplating_characterization_v1.1", + "latest": true + } + }, + { + "source_id": "sandia_vdos_xrd_si_fe_mg_si_v1.1", + "source_name": "sandia_vdos_xrd_si_fe_mg_si", + "version": 1, + "ingest_date": "2023-10-10T22:46:32.983304Z", + "doi": "10.18126/tacz-v14v", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sandia_vdos_xrd_si_fe_mg_si_v1.1/", + "metadata": { + "title": "Simulated Vibrational Density of States and X-ray Diffraction Profiles of Mechanically Deformed and Disordered Atomic Structures in Gold, Iron, Magnesium, and Silicon", + "authors": [ + { + "name": "Vizoso, Daniel", + "given_name": "Daniel", + "family_name": "Vizoso", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Dingreville, Remi", + "given_name": "Remi", + "family_name": "Dingreville", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "Raw molecular dynamics (LAMMPS) dump files, X-ray diffraction (XRD) compute outputs, and text files containing velocity autocorrelation function (VACF) measurements, computed vibrational density of states (VDoS) profiles, and microstructural descriptors of the dump files.\nData was collected using the molecular dynamics code LAMMPS from simulations of bulk silicon, gold, magnesium, and iron with and without disorder insertion and with and without mechanical loading.\nA portion of the Si data provided in this dataset was used for a previous publication (10.1021/acs.chemmater.2c03207).", + "keywords": [ + "simulation", + "metals and alloys", + "defects", + "high-throughput", + "microstructures", + "molecular dynamics", + "diffraction", + "spectroscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.chemmater.2c03207", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sandia_vdos_xrd_si_fe_mg_si_v1.1", + "mdf_source_name": "sandia_vdos_xrd_si_fe_mg_si" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sandia_vdos_xrd_si_fe_mg_si_v1.1/", + "version": "1.0", + "root_version": "sandia_vdos_xrd_si_fe_mg_si_v1.1", + "latest": true + } + }, + { + "source_id": "schleife_accurate_atomistic_stopping_v1.1", + "source_name": "schleife_accurate_atomistic_stopping", + "version": 1, + "ingest_date": "2023-10-24T15:43:09.755935Z", + "doi": "10.18126/uy0k-bwkq", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/schleife_accurate_atomistic_stopping_v1.1/", + "metadata": { + "title": "Accurate atomistic first-principles calculations of electronic stopping", + "authors": [ + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "University of North Carolina at Chapel Hill" + ] + }, + { + "name": "Kanai, Yosuke", + "given_name": "Yosuke", + "family_name": "Kanai", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "University of North Carolina at Chapel Hill" + ] + }, + { + "name": "Correa, Alfredo A.", + "given_name": "Alfredo A.", + "family_name": "Correa", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "University of North Carolina at Chapel Hill" + ] + } + ], + "description": "This dataset contains the outputs of real time time-dependent density functional theory (RT-TDDFT) calculations of a proton traveling at high velocities through face-centered cubic aluminum.", + "keywords": [ + "simulation", + "metals and alloys", + "nuclear materials", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1103/PhysRevB.91.014306", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "schleife_accurate_atomistic_stopping_v1.1", + "mdf_source_name": "schleife_accurate_atomistic_stopping" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/schleife_accurate_atomistic_stopping_v1.1/", + "version": "1.0", + "root_version": "schleife_accurate_atomistic_stopping_v1.1", + "latest": true + } + }, + { + "source_id": "pyebsdindexdemo_v1.4", + "source_name": "pyebsdindexdemo", + "version": 1, + "ingest_date": "2023-10-02T19:54:52.354854Z", + "doi": "10.18126/h2vw-rrzu", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/pyebsdindexdemo_v1.4/", + "metadata": { + "title": "PyEBSDIndex Demo", + "authors": [ + { + "name": "Rowenhorst, David", + "given_name": "David", + "family_name": "Rowenhorst", + "affiliations": [ + "US Naval Research Laboratory" + ] + }, + { + "name": "Callahan, Patrick", + "given_name": "Patrick", + "family_name": "Callahan", + "affiliations": [ + "US Naval Research Laboratory" + ] + } + ], + "description": "Data used to demonstrate PyEBSDIndex, a electron backscattered diffraction (EBSD) indexing algorithm. Jupyter notebooks are included to create simulated patterns of known orientation with varying levels of noise, which then can be indexed, with the index orientation compared to the simulated orientation. Additionally, a test Ti64 dataset is included, with a Jupyter notebook, that is used to demonstrate typical usage of PyEBSDIndex. ", + "keywords": [ + "experiment", + "EBSD" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.5281/zenodo.8400425", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "pyebsdindexdemo_v1.4", + "mdf_source_name": "pyebsdindexdemo" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/pyebsdindexdemo_v1.4/", + "version": "1.0", + "root_version": "pyebsdindexdemo_v1.4", + "latest": true + } + }, + { + "source_id": "jang_vu_vitrimer_calculations_v1.4", + "source_name": "jang_vu_vitrimer_calculations", + "version": 1, + "ingest_date": "2023-09-21T16:38:42.803367Z", + "doi": "10.18126/c9zx-r4gs", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/jang_vu_vitrimer_calculations_v1.4/", + "metadata": { + "title": "Control of lithium salt partitioning, coordination, and solvation in vitrimer electrolytes", + "authors": [ + { + "name": "Jang, Seongon", + "given_name": "Seongon", + "family_name": "Jang", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Alvarez, Erick I. Hernandez", + "given_name": "Erick I. Hernandez", + "family_name": "Alvarez", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Chen, Chen", + "given_name": "Chen", + "family_name": "Chen", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Jing, Brian B.", + "given_name": "Brian B.", + "family_name": "Jing", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Shen, Chengtian", + "given_name": "Chengtian", + "family_name": "Shen", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Braun, Paul V.", + "given_name": "Paul V.", + "family_name": "Braun", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Schroeder, Charles M.", + "given_name": "Charles M.", + "family_name": "Schroeder", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Evans, Christopher M.", + "given_name": "Christopher M.", + "family_name": "Evans", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Structures for polyethylene oxide (PEO) molecules (repeating units 2, 4, and 6) with a vinylogous urethane cross linker. Pre-relaxed and relaxed structures are provided. A Li+ ion is placed along different sites of the polymer backbone to calculate the binding energies at different oxygen sites.", + "keywords": [ + "simulation", + "polymers", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "jang_vu_vitrimer_calculations_v1.4", + "mdf_source_name": "jang_vu_vitrimer_calculations" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/jang_vu_vitrimer_calculations_v1.4/", + "version": "1.0", + "root_version": "jang_vu_vitrimer_calculations_v1.4", + "latest": true + } + }, + { + "source_id": "nagarajan_2023_v1.1", + "source_name": "nagarajan_2023", + "version": 1, + "ingest_date": "2023-10-16T21:27:42.646406Z", + "doi": "10.18126/3a2c-lkac", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nagarajan_2023_v1.1/", + "metadata": { + "title": "Biomimetic Human Skin Model Patterned with Rete Ridges", + "authors": [ + { + "name": "Maxwell B Nagarajan Daniel S Reynolds, Sebastien G M Uel, Jason W Bjork, Bryan A Baker, Amy K McNulty, Susan L Woulfe, Jennifer A Lewis, Alexander J Ainscough", + "given_name": "Alexander J Ainscough", + "family_name": "Maxwell B Nagarajan Daniel S Reynolds, Sebastien G M Uel, Jason W Bjork, Bryan A Baker, Amy K McNulty, Susan L Woulfe, Jennifer A Lewis", + "affiliations": [ + "Harvard University", + "3M Company" + ] + } + ], + "description": "Image data from Biomimetic human skin model patterned with rete ridges by Nagarajan et al., 2023. Data is organized by figure. ", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1088/1758-5090/acfc29", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "nagarajan_2023_v1.1", + "mdf_source_name": "nagarajan_2023" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/nagarajan_2023_v1.1/", + "version": "1.0", + "root_version": "nagarajan_2023_v1.1", + "latest": true + } + }, + { + "source_id": "lyc_v1.3", + "source_name": "lyc", + "version": 1, + "ingest_date": "2023-09-25T22:27:30.418890Z", + "doi": "10.18126/csff-i4um", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lyc_v1.3/", + "metadata": { + "title": "Raw data set for LYC paper", + "authors": [ + { + "name": "Ballal Ahammed, Elif Ertekin", + "given_name": "Elif Ertekin", + "family_name": "Ballal Ahammed", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Raw dataset for LYC paper", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "lyc_v1.3", + "mdf_source_name": "lyc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/lyc_v1.3/", + "version": "1.0", + "root_version": "lyc_v1.3", + "latest": true + } + }, + { + "source_id": "smc2023_v1.1", + "source_name": "smc2023", + "version": 1, + "ingest_date": "2023-07-25T15:12:13.879352Z", + "doi": "10.18126/gmi9-62tm", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/smc2023_v1.1/", + "metadata": { + "title": "Sandia Mechanics Challenge 2023 Information Packet", + "authors": [ + { + "name": "Kramer, Sharlotte L. B.", + "given_name": "Sharlotte L. B.", + "family_name": "Kramer", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Ivanoff A., Thomas", + "given_name": "Thomas", + "family_name": "Ivanoff A.", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Corona, Edmundo", + "given_name": "Edmundo", + "family_name": "Corona", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "\nThe Sandia Mechanics Challenge (SMC) provides the solid-mechanics community a forum for assessing its ability to predict mechanical behavior in structures and materials through a blind, round-robin format. Computationalists are asked to predict the behavior of an unfamiliar geometry given experimental calibration data, their predictions are compared to experimental measurements of the SMC scenario, and then the participants assess and compare their approaches, documenting their findings. The SMC broadens the scope of Sandia-hosted benchmarking problems that previously focused on ductile failure through the Sandia Fracture Challenges, enabling an enduring, community-wide self-assessment of predictive capabilities for a variety of mechanics topics. The SMC is part of the Structural Reliability Partnership, which offers other benchmarking challenges hosted by several participating institutions.\n\nThe topic of the SMC2023 is threaded fastener joints, specifically the deformation and failure of a structure with a threaded fastener joint in a drop scenario. Much of the research on predicting threaded fastener joint behavior has been on joints in vibrational environments or loss of joint preload. Less attention has been paid to the large deformation and failure of threaded fastener joints, even though analyses of such scenarios are vital to safety assessments of structures with threaded fastener joints. These analyses require modeling of several phenomena including multi-axial joint mechanics, material deformation and failure at intermediate strain rates, and contact mechanics. This SMC is calling attention to these diverse and challenging analyses, rallying the solid-mechanics community to identify and address potential shortcomings.\n\nThis information packet enables participants to perform their predictions. The SMC scenario is a drop of a metal structure with a cantilevered cylinder attached to a block by threaded fasteners. The information packet includes a description of the SMC scenario and geometry, calibration data for the metal materials and fasteners, and quantities of interest from the predictions that serve as the basis for comparison to the experimental data of the SMC scenario. Participants will be asked to perform their predictions in a limited amount of time, on the order of a few months, to mimic real-world time constraints of safety assessments. After the predictions are returned, the SMC organizers will conduct the initial comparisons of the predictions and experiments, and then all participants will work together to assess the predictive capabilities and report the results. The SMC will enable the solid-mechanics community to define the state-of-the-art in modeling the behavior and failure of structures with threaded fastener joints, highlighting where future research could improve the approaches.", + "keywords": [ + "SMC", + "SMC2023", + "Sandia", + "Benchmark Challenge", + "Simulation", + "Experiment", + "Fasteners", + "Al2024" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "smc2023_v1.1", + "mdf_source_name": "smc2023" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/smc2023_v1.1/", + "version": "1.0", + "root_version": "smc2023_v1.1", + "latest": true + } + }, + { + "source_id": "kroll_tcbpaper_v1.1", + "source_name": "kroll_tcbpaper", + "version": 1, + "ingest_date": "2023-07-24T16:45:24.114244Z", + "doi": "10.18126/e8y5-ayft", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kroll_tcbpaper_v1.1/", + "metadata": { + "title": "Dataset for Kroll, Mata, Homan et al. \"Immune-infiltrated Kidney Organoid-on-Chip Model for Assessing T Cell Bispecific Antibodies\", PNAS, 2023", + "authors": [ + { + "name": "Kroll, Katharina T", + "given_name": "Katharina T", + "family_name": "Kroll", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Mata, Mariana M", + "given_name": "Mariana M", + "family_name": "Mata", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Homan, Kimberly A", + "given_name": "Kimberly A", + "family_name": "Homan", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Micallef, Virginie", + "given_name": "Virginie", + "family_name": "Micallef", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Carpy, Alejandro", + "given_name": "Alejandro", + "family_name": "Carpy", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Hiratsuka, Ken", + "given_name": "Ken", + "family_name": "Hiratsuka", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Morizane, Ryuji", + "given_name": "Ryuji", + "family_name": "Morizane", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Moisan, Annie", + "given_name": "Annie", + "family_name": "Moisan", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Gubler, Marcel", + "given_name": "Marcel", + "family_name": "Gubler", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Walz, Antje-Christine", + "given_name": "Antje-Christine", + "family_name": "Walz", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Marrer-Berger, Estelle", + "given_name": "Estelle", + "family_name": "Marrer-Berger", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "name": "Lewis, Jennifer A", + "given_name": "Jennifer A", + "family_name": "Lewis", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + } + ], + "description": "This directory contains the raw flow cytometry and imaging data as well as Matlab code for\nKroll, Mata, Homan et al 2023 \u201cImmune-infiltrated Kidney Organoid-on-Chip Model for\nAssessing T Cell Bispecific Antibodies\u201d\nPlease refer to the manuscript for a full description of methods and data processing, external\ncode/data sources, and primary references.\nThe directory is split up by figure and supplemental figure.", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "kroll_tcbpaper_v1.1", + "mdf_source_name": "kroll_tcbpaper" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/kroll_tcbpaper_v1.1/", + "version": "1.0", + "root_version": "kroll_tcbpaper_v1.1", + "latest": true + } + }, + { + "source_id": "ht_ebsd_additive_kovar_v1.1", + "source_name": "ht_ebsd_additive_kovar", + "version": 1, + "ingest_date": "2023-07-14T17:40:06.922511Z", + "doi": "10.18126/7d9u-edev", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ht_ebsd_additive_kovar_v1.1/", + "metadata": { + "title": "High Throughput EBSD Characterization of Additive Kovar", + "authors": [ + { + "name": "Fowler, J. Elliott", + "given_name": "J. Elliott", + "family_name": "Fowler", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Ruggles, Tim J.", + "given_name": "Tim J.", + "family_name": "Ruggles", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Cillessen, Dale E.", + "given_name": "Dale E.", + "family_name": "Cillessen", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Johnson, Kyle L.", + "given_name": "Kyle L.", + "family_name": "Johnson", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Jauregui, Luis J.", + "given_name": "Luis J.", + "family_name": "Jauregui", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Henriksen, Amelia A.", + "given_name": "Amelia A.", + "family_name": "Henriksen", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Bianco, Nathan R.", + "given_name": "Nathan R.", + "family_name": "Bianco", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Boyce, Brad L.", + "given_name": "Brad L.", + "family_name": "Boyce", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "For this dataset, 607 unique kovar samples were manufactured using Laser Powder Bed Fusion (LPBF) over 304 unique sets of LPBF process parameters. Electron Backscatter Diffraction (EBSD) data was collected for each sample using one of two methods, called here \"Version 1\" and \"Version 2.\" For version 1 samples, two kovar specimens were created for each set of LPBF process parameters--one ground and one unground prior to EBSD collection. For version 2 samples, two kovar specimens were created for each set of LPBF process parameters--one horizontally oriented and one vertically oriented. Other methodological distinctions between version 1 and version 2 samples are highlighted in the methods section of our Readme.\n\nThe dataset consists of raw channel text files (ctf), usable header information and EBSD data points extracted from each ctf, and LPBF build process parameters for each additively manufactured sample. The high throughput EBSD innovations used to generate this data are thoroughly described in our corresponding paper, \"High Throughput Microstructural Characterization using Automated Electron Backscatter Diffraction.\"", + "keywords": [ + "EBSD", + "high-throughput", + "laser powder bed fusion", + "Kovar", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ht_ebsd_additive_kovar_v1.1", + "mdf_source_name": "ht_ebsd_additive_kovar" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ht_ebsd_additive_kovar_v1.1/", + "version": "1.0", + "root_version": "ht_ebsd_additive_kovar_v1.1", + "latest": true + } + }, + { + "source_id": "exp_dl_electron_counting_v1.1", + "source_name": "exp_dl_electron_counting", + "version": 1, + "ingest_date": "2023-07-28T21:57:58.173247Z", + "doi": "10.18126/xv6r-y3lr", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/exp_dl_electron_counting_v1.1/", + "metadata": { + "title": "Experimental data for evaluation of deep learning approach for high-accuracy electron counting of MAPS-type direct electron detectors", + "authors": [ + { + "name": "Wei, Jingrui", + "given_name": "Jingrui", + "family_name": "Wei", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Hagopian, Nicholas", + "given_name": "Nicholas", + "family_name": "Hagopian", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Voyles, Paul", + "given_name": "Paul", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "Experimental data of Celeritas XS direct detector for evaluation of deep learning counting approach as well as conventional connected component labeling counting and integrating. The dataset contains uniformly illuminated data with and without beam stop for MTF/DQE/DCE calculation, 4DSTEM datasets of monolayer WS2 before and after counting, and 4DSTEM datasets of SrTiO3[100] before and after counting. The zspy files can be loaded using Hyperspy(https://hyperspy.org)", + "keywords": [ + "experiment", + "machine learning", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/rgew-xbw4", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1093/micmic/ozad067.346", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "exp_dl_electron_counting_v1.1", + "mdf_source_name": "exp_dl_electron_counting" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/exp_dl_electron_counting_v1.1/", + "version": "1.0", + "root_version": "exp_dl_electron_counting_v1.1", + "latest": true + } + }, + { + "source_id": "spacetimeformer_battery_v1.2", + "source_name": "spacetimeformer_battery", + "version": 1, + "ingest_date": "2023-09-13T14:20:24.338346Z", + "doi": "10.18126/ckt2-g8j2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/spacetimeformer_battery_v1.2/", + "metadata": { + "title": "Prognosis of Multivariate Battery State of Performance and Health via Transformers - Data", + "authors": [ + { + "name": "Paulson, Noah H.", + "given_name": "Noah H.", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Kubal, Joseph", + "given_name": "Joseph", + "family_name": "Kubal", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Babinec, Susan J.", + "given_name": "Susan J.", + "family_name": "Babinec", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "This dataset contains the processed files required to reproduce results in the Prognosis of Multivariate Battery State of Performance and Health via Transformers work. CSV files are included for a lithium-iron-phosphate fast charging dataset and a six cathode chemistry (HE5050, 5VSpinel, NMC111, NMC532, NMC622, NMC811) cycling dataset.", + "keywords": [ + "machine learning", + "energy materials", + "batteries" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.18126/fdxq-7yul", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.1016/j.jpowsour.2022.231127", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.1038/s41560-019-0356-8", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.1038/s41586-020-1994-5", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "spacetimeformer_battery_v1.2", + 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"University of Massachusetts Lowell" + ] + }, + { + "name": "Kazmer, David O.", + "given_name": "David O.", + "family_name": "Kazmer", + "affiliations": [ + "University of Massachusetts Lowell" + ] + }, + { + "name": "Peterson, Amy M.", + "given_name": "Amy M.", + "family_name": "Peterson", + "affiliations": [ + "University of Massachusetts Lowell" + ] + } + ], + "description": "Material extrusion additive manufacturing of polycarbonate, including tensile properties, cross-sectional microscopy, and fracture surfaces for single road width boxes printed with different print speeds, layer times, and extrusion temperatures.", + "keywords": [ + "experiment", + "polymers", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "DOI: 10.1002/pen.26495", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": 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"given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Yale University" + ] + } + ], + "description": "Inputs for Quantum Espresso and BerkeleyGW to compute the bandstructures mentioned in \"Minimal Molecular Building Blocks for Screening in Quasi-Two-Dimensional Organic\u2013Inorganic Lead Halide Perovskites\", for full calculations and calculations with IPSA-2C.", + "keywords": [ + "simulation", + "perovskites", + "GW", + "energy materials", + "semiconductors" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1021/acs.nanolett.3c00082", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "ipsa_2c_v1.1", + "mdf_source_name": "ipsa_2c" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/ipsa_2c_v1.1/", + "version": "1.0", + "root_version": "ipsa_2c_v1.1", + "latest": true + } + }, + { + "source_id": "furanthiophenenanothreads_v1.2", + "source_name": "furanthiophenenanothreads", + "version": 1, + "ingest_date": "2023-09-20T18:58:42.855561Z", + "doi": "10.18126/s9fs-ob68", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/furanthiophenenanothreads_v1.2/", + "metadata": { + "title": "CIF files of isolated nanothreads formed by furan and thiophene derivatives", + "authors": [ + { + "name": "Reynoso, Andrew", + "given_name": "Andrew", + "family_name": "Reynoso", + "affiliations": [ + "Pennsylvania State University", + "University of California, Berkeley" + ] + }, + { + "name": "Xu, Bohan", + "given_name": "Bohan", + "family_name": "Xu", + "affiliations": [ + "Pennsylvania State University", + "University of California, Berkeley" + ] + }, + { + "name": "Crespi, Vincent H.", + "given_name": "Vincent H.", + "family_name": "Crespi", + "affiliations": [ + "Pennsylvania State University", + "University of California, Berkeley" + ] + } + ], + "description": "DFT simulations of isolated furan-based and thiophene-based nanothreads; structure CIF files and energy txt files;", + "keywords": [ + "simulation", + "polymers", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.48550/arXiv.2302.07482", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "furanthiophenenanothreads_v1.2", + "mdf_source_name": "furanthiophenenanothreads" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/furanthiophenenanothreads_v1.2/", + "version": "1.0", + "root_version": "furanthiophenenanothreads_v1.2", + "latest": true + } + }, + { + "source_id": "mofa_geom_v1.1", + "source_name": "mofa_geom", + "version": 1, + "ingest_date": "2023-12-15T22:39:59.417030Z", + "doi": "10.18126/tmk5-zf6h", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mofa_geom_v1.1/", + "metadata": { + "title": "Mof-generation-at-scale (mofa) GEOM dataset", + "authors": [ + { + "name": "Park, Hyun", + "given_name": "Hyun", + "family_name": "Park", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Illinois at Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Yan, Xiaoli", + "given_name": "Xiaoli", + "family_name": "Yan", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Illinois at Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Illinois at Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Huerta, Eliu", + "given_name": "Eliu", + "family_name": "Huerta", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Illinois at Chicago", + "Argonne National Laboratory" + ] + } + ], + "description": "Training data (GEOM) for GHP_MOFassemble code. \nGHP_MOFassemble is used as part of MOF-generation-at-scale project. \nTraining data was generated using the codes from DiffLinker paper.", + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/s41597-022-01288-4", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.48550/arXiv.2306.08695", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mofa_geom_v1.1", + "mdf_source_name": "mofa_geom" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/mofa_geom_v1.1/", + "version": "1.0", + "root_version": "mofa_geom_v1.1", + "latest": true + } + }, + { + "source_id": "phatak_micromagnetic_simulation_tem_v1.1", + "source_name": "phatak_micromagnetic_simulation_tem", + "version": 1, + "ingest_date": "2024-01-10T02:53:57.871032Z", + "doi": "10.18126/k9al-w012", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/phatak_micromagnetic_simulation_tem_v1.1/", + "metadata": { + "title": "Micromagnetic simulation training data for skyrmion detection in Lorentz TEM", + "authors": [ + { + "name": "Phatak, Charudatta", + "given_name": "Charudatta", + "family_name": "Phatak", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "McCray, Arthur", + "given_name": "Arthur", + "family_name": "McCray", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "Bender, Alec", + "given_name": "Alec", + "family_name": "Bender", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + }, + { + "name": "Petford-Long, Amanda", + "given_name": "Amanda", + "family_name": "Petford-Long", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ] + } + ], + "keywords": [ + "simulation", + "microscopy", + "micromagnetic", + "skyrmions" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "phatak_micromagnetic_simulation_tem_v1.1", + "mdf_source_name": "phatak_micromagnetic_simulation_tem" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/phatak_micromagnetic_simulation_tem_v1.1/", + "version": "1.0", + "root_version": "phatak_micromagnetic_simulation_tem_v1.1", + "latest": true + } + }, + { + "source_id": "automatic_identification_crystal_tructures", + "source_name": "automatic_identification_crystal_tructures", + "version": "1.0", + "ingest_date": "2024-02-06T16:40:12.454946Z", + "doi": "10.18126/424k-7s66", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/automatic_identification_crystal_tructures/1.0/", + "metadata": { + "title": "Training data for automatic identification of crystal structures and interfaces in HR-STEM images", + "authors": [ + { + "name": "Leitherer, Andreas", + "given_name": "Andreas", + "family_name": "Leitherer", + "affiliations": [ + "NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and IRIS-Adlershof of the Humboldt University" + ] + }, + { + "name": "Yeo, Byung Chul", + "given_name": "Byung Chul", + "family_name": "Yeo", + "affiliations": [ + "Department of Energy Resources Engineering, Pukyong National University" + ] + }, + { + "name": "Liebscher, Christian H.", + "given_name": "Christian H.", + "family_name": "Liebscher", + "affiliations": [ + "Max-Planck-Institut f\u00fcr Eisenforschung" + ] + }, + { + "name": "Ghiringhelli, Luca M.", + "given_name": "Luca M.", + "family_name": "Ghiringhelli", + "affiliations": [ + "NOMAD Laboratory" + ] + } + ], + "description": "This repository contains HAADF images and FFT-HAADF descriptors, 80/20 splits for train and validation. See more details about the work at https://doi.org/10.48550/arXiv.2303.12702", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "automatic_identification_crystal_tructures", + "mdf_source_name": "automatic_identification_crystal_tructures" + }, + "ml": { + "data_format": "hdf5", + "short_name": "automatic_identification_crystal_tructures", + "n_items": 31470, + "splits": [ + { + "type": "train", + "path": "train.h5", + "label": "train", + "n_items": null + }, + { + "type": "test", + "path": "val.h5", + "label": "validation", + "n_items": null + } + ], + "keys": [ + { + "name": "haadf", + "role": "input", + "units": null, + "description": "input, simulated HAADF images of difference crystal lattice" + }, + { + "name": "fft_haadf", + "role": "input", + "units": null, + "description": "fast fourier transform power spectrum of the HAADF images with hann windowing and thresholding" + }, + { + "name": "label_onehot", + "role": "target", + "units": null, + "description": "output, one hot label for class of crystal structure" + }, + { + "name": "label_encoding", + "role": "input", + "units": null, + "description": "tabular information describing the classes of crystal structure" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org//foundry/automatic_identification_crystal_tructures/1.0/", + "version": "1.0", + "root_version": "automatic_identification_crystal_tructures", + "latest": true + } + }, + { + "source_id": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "source_name": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "version": "1.0", + "ingest_date": "2024-02-15T01:59:57.122214Z", + "doi": "10.18126/82fx-q319", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/287cae7a-8304-4e0e-a13d-cf4effbdfc36/1.0/", + "metadata": { + "title": "In-Situ MCT of Loaded Parachute Textiles: MIL-C-7020 T.III and MIL-C-44378 T.II", + "authors": [ + { + "name": "Phillippe, Cutler", + "given_name": "Cutler", + "family_name": "Phillippe", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Villafa\u00f1e, Laura", + "given_name": "Laura", + "family_name": "Villafa\u00f1e", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Panerai, Francesco", + "given_name": "Francesco", + "family_name": "Panerai", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "In situ X-ray micro-tomography imaging of textiles under loaded conditions. Results are presented for two textiles commonly used in parachute systems, MIL-C-7020 Type III and MIL-C-44378 Type II. The materials are subjected to incremental tension using a custom apparatus that loads the fabric radially in-plane via a plunger, and the material microstructure is imaged sequentially at steady load conditions. Micro-tomography images are processed using learning-aided segmentation. \n\nIncluded are:\n\nThe raw x-ray reconstructions (tif stacks) with x-ray meta-data.\n\nSegmented warp and weft tows (both in tif stack form).\n\nU-NET 3D models trained for segmentation with validation loss meta-data.\n\nLabelled warp and weft tows (both in tif stack form).\n\nTensile tester plunger deflection measurements and associated radiographs for deflection measurement.\n\nRaw and processed load cell data with estimated radial stress boundary condition at lip of plunger.", + "keywords": [ + "experiment", + "machine learning", + "microstructures", + "microscopy", + "polymers" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.2514/6.2024-1003", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "mdf_source_name": "287cae7a-8304-4e0e-a13d-cf4effbdfc36" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/287cae7a-8304-4e0e-a13d-cf4effbdfc36/1.0/", + "version": "1.0", + "root_version": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "latest": true + } + }, + { + "source_id": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "source_name": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "version": "1.0", + "ingest_date": "2024-02-16T19:31:16.102110Z", + "doi": "10.18126/wjxd-j196", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bff0b7c9-7eda-4008-97ea-8603c1883230/1.0/", + "metadata": { + "title": "Effects of Dynamic Disulfide Bonds on Mechanical Behavior in Glassy Epoxy Thermosets", + "authors": [ + { + "name": "Lewis, Broderick", + "given_name": "Broderick", + "family_name": "Lewis", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Dennis, Joseph M.", + "given_name": "Joseph M.", + "family_name": "Dennis", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Shull, Kenneth R.", + "given_name": "Kenneth R.", + "family_name": "Shull", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cEffects of Dynamic Disulfide Bonds on Mechanical Behavior in Glassy Epoxy Thermosets.\u201d B. Lewis, J.M. Dennis & K.R. Shull, ACS Appl. Polym. Mater. 2023, 2583\u20132595 (2023) (http://dx.doi.org/10.1021/acsapm.2c02194).\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Lewis et al. - 2023 - Effects of Dynamic Disulfide Bonds on Mechanical B.pdf: the journal-formatted pdf.\n3. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n4. data: directory of raw data files called by the python scripts.\n5. figures: the figures generated by the makeplots.py in the python directory. These figures will all be regenerated when makeplots.py is run. ", + "keywords": [ + "experiment", + "polymers", + "fracture toughness" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsapm.2c02194", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "mdf_source_name": "bff0b7c9-7eda-4008-97ea-8603c1883230" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/bff0b7c9-7eda-4008-97ea-8603c1883230/1.0/", + "version": "1.0", + "root_version": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "latest": true + } + }, + { + "source_id": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "source_name": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "version": "1.0", + "ingest_date": "2024-02-16T03:46:58.398478Z", + "doi": "10.18126/7kfk-jr95", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/d58bad80-138c-4fb4-afb5-6786dc0bc7d4/1.0/", + "metadata": { + "title": "Investigations of the high-frequency dynamic properties of polymeric systems with quartz crystal resonators", + "authors": [ + { + "name": "Shull, Kenneth R.", + "given_name": "Kenneth R.", + "family_name": "Shull", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Taghon, Meredith", + "given_name": "Meredith", + "family_name": "Taghon", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Wang, Qifeng", + "given_name": "Qifeng", + "family_name": "Wang", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cInvestigations of the high-frequency dynamic properties of polymeric systems with quartz crystal resonators.\u201d K.R. Shull, M. Taghon & Q. Wang, Biointerphases 15, 021012 (2020) (http://dx.doi.org/10.1116/1.5142762)\n.\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Shull et al. - 2020 - Investigations of the high-frequency dynamic prope.pdf: the journal-formatted pdf.\n3. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n4. data: directory of raw data files called by the python scripts.\n5. figures: the figures generated by the makeplots.py in the python directory. These figures will all be regenerated when makeplots.py is run. ", + "keywords": [ + "QCM", + "experiment", + "polymers", + "rheology" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1116/1.5142762", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "mdf_source_name": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/d58bad80-138c-4fb4-afb5-6786dc0bc7d4/1.0/", + "version": "1.0", + "root_version": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "latest": true + } + }, + { + "source_id": "58d58130-8356-4290-9baf-5b614bc3d712", + "source_name": "58d58130-8356-4290-9baf-5b614bc3d712", + "version": "1.0", + "ingest_date": "2024-02-15T16:11:24.037039Z", + "doi": "10.18126/kqj3-jg60", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/58d58130-8356-4290-9baf-5b614bc3d712/1.0/", + "metadata": { + "title": "Processing Polyelectrolyte Complexes with Deep Eutectic Solvents", + "authors": [ + { + "name": "Chen, YuLing", + "given_name": "YuLing", + "family_name": "Chen", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Shull, Kenneth R.", + "given_name": "Kenneth R.", + "family_name": "Shull", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cProcessing Polyelectrolyte Complexes with Deep Eutectic Solvents.\u201d Y. Chen & K.R. Shull, ACS Macro Lett. 10, 1243\u20131247 (2021) (http://dx.doi.org/10.1021/acsmacrolett.1c00494).\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Chen and Shull - 2021 - Processing Polyelectrolyte Complexes with Deep Eut.pdf: the journal-formatted pdf.\n3. SI.pdf: published supporting information.\n4. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n5. data: directory of raw data files called by the python scripts.\n\n", + "keywords": [ + "experiment", + "polymers", + "polyelectrolytes", + "ionic liquids" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsmacrolett.1c00494", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "58d58130-8356-4290-9baf-5b614bc3d712", + "mdf_source_name": "58d58130-8356-4290-9baf-5b614bc3d712" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/58d58130-8356-4290-9baf-5b614bc3d712/1.0/", + "version": "1.0", + "root_version": "58d58130-8356-4290-9baf-5b614bc3d712", + "latest": true + } + }, + { + "source_id": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "source_name": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "version": "1.0", + "ingest_date": "2024-02-22T16:44:51.677848Z", + "doi": "10.18126/b873-4886", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/790c76bc-c489-4d86-b861-0afd1bea21bb/1.0/", + "metadata": { + "title": "Using X-ray computed microtomography to determine subsample-specific cosmogenic noble gas production rates of E (enstatite) chondrites", + "authors": [ + { + "name": "Mijjum, Moshammat", + "given_name": "Moshammat", + "family_name": "Mijjum", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "name": "Andrews, Benjamin J.", + "given_name": "Benjamin J.", + "family_name": "Andrews", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "name": "McCoy, Timothy J.", + "given_name": "Timothy J.", + "family_name": "McCoy", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "name": "Corrigan, Catherine M.", + "given_name": "Catherine M.", + "family_name": "Corrigan", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "name": "Caffee, Marc W.", + "given_name": "Marc W.", + "family_name": "Caffee", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "name": "Tremblay, Marissa M.", + "given_name": "Marissa M.", + "family_name": "Tremblay", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + } + ], + "description": "This repository contains X-ray computed microtomography .DCM files of the following meteorites: CEC 024, CEC 025, CEC 028, NWA 974, Indarch (USNM 449), and Ufana (USNM 6006). The images can be compiled to generate 3D renderings of subsamples. Segmented data can be located in the Dragonfly ORS files. ", + "keywords": [ + "microtomography", + "meteorites", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "mdf_source_name": "790c76bc-c489-4d86-b861-0afd1bea21bb" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/790c76bc-c489-4d86-b861-0afd1bea21bb/1.0/", + "version": "1.0", + "root_version": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "latest": true + } + }, + { + "source_id": "02b4ceb5-9263-40ae-acd5-136210301819", + "source_name": "02b4ceb5-9263-40ae-acd5-136210301819", + "version": "1.0", + "ingest_date": "2024-02-20T22:09:51.734136Z", + "doi": "10.18126/z20s-pg52", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/02b4ceb5-9263-40ae-acd5-136210301819/1.0/", + "metadata": { + "title": "Systematic Improvement of DMC Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gaps ", + "authors": [ + { + "name": "Shin, Hyeondeok", + "given_name": "Hyeondeok", + "family_name": "Shin", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Gasperich, Kevin", + "given_name": "Kevin", + "family_name": "Gasperich", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Rojas, Tomas", + "given_name": "Tomas", + "family_name": "Rojas", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Ngo, Anh T.", + "given_name": "Anh T.", + "family_name": "Ngo", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + } + ], + "description": "Data Description for \"Systematic Improvement of DMC Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gaps\"\nRefer to README File in top directory to navigate the data", + "keywords": [ + "simulation", + "energy materials", + "oxides", + "QMCPack", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": 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Optical Materials Science and Engineering Technologies (COMSET)", + "Department of Bioengineering" + ] + }, + { + "name": "Bandera, Yuriy", + "given_name": "Yuriy", + "family_name": "Bandera", + "affiliations": [ + "Clemson University", + "Department of Materials Science and Engineering", + "Center for Optical Materials Science and Engineering Technologies (COMSET)", + "Department of Bioengineering" + ] + }, + { + "name": "Foulger, Stephen H.", + "given_name": "Stephen H.", + "family_name": "Foulger", + "affiliations": [ + "Clemson University", + "Department of Materials Science and Engineering", + "Center for Optical Materials Science and Engineering Technologies (COMSET)", + "Department of Bioengineering" + ] + } + ], + "description": "Main manuscript and supplementary materials raw data for \"Probing Light-Matter Interactions: Fluorescence Lifetime Manipulation in Crystalline Colloidal Arrays\".", + "publisher": "Materials Data Facility", + "publication_year": 2024, + 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Carlo", + "authors": [ + { + "name": "Krongchon, Kittithat", + "given_name": "Kittithat", + "family_name": "Krongchon", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + }, + { + "name": "Rakib, Tawfiqur", + "given_name": "Tawfiqur", + "family_name": "Rakib", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + }, + { + "name": "Pathak, Shivesh", + "given_name": "Shivesh", + "family_name": "Pathak", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + }, + { + "name": "Ertekin, Elif", + "given_name": "Elif", + "family_name": "Ertekin", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + }, + { + "name": "Johnson, Harley T.", + "given_name": "Harley T.", + "family_name": "Johnson", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + }, + { + "name": "Wagner, Lucas K.", + "given_name": "Lucas K.", + "family_name": "Wagner", + "affiliations": [ + "University of Illinois Urbana Champaign" + ] + } + ], 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University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Sharma, Hemant", + "given_name": "Hemant", + "family_name": "Sharma", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Niezgoda, Stephen R.", + "given_name": "Stephen R.", + "family_name": "Niezgoda", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Mills, Michael J.", + "given_name": "Michael J.", + "family_name": "Mills", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Uchic, Michael D.", + "given_name": "Michael D.", + "family_name": "Uchic", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Shade, Paul A.", + "given_name": "Paul A.", + "family_name": "Shade", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "name": "Obstalecki, Mark", + "given_name": "Mark", + "family_name": "Obstalecki", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + } + ], + "description": "High-energy diffraction microscopy (HEDM) combined with \\insitu{} mechanical testing is a powerful non-destructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional (3D) reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed ``low solvus, high refractory'' (LSHR) nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available on the Materials Data Facility (MDF) for further investigation.", + "keywords": [ + "experiment", + "metals and alloys", + "high energy diffraction microscopy", + "hedm", + "ebsd", + "electron backscatter diffraction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8f586539-f397-4af1-9c52-3c2e0da796c8", + "mdf_source_name": "8f586539-f397-4af1-9c52-3c2e0da796c8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8f586539-f397-4af1-9c52-3c2e0da796c8/1.0/", + "version": "1.0", + "root_version": "8f586539-f397-4af1-9c52-3c2e0da796c8", + "latest": true + } + }, + { + "source_id": "Dataset_perovskite_tec", + "source_name": "Dataset_perovskite_tec", + 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shear thickening rheology\n------------------------------------------------\nFolder: Zwick files\n- Contains all stress-strain measurements of fluid-filled foams\n------------------------------------------------\nFile: integrating stress strain data.xlsx\n- Integrated stress-strain curves as a function of impact speed\n", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "67b75e10-6d2a-4165-aadd-d3580dd59376", + "mdf_source_name": "67b75e10-6d2a-4165-aadd-d3580dd59376" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/67b75e10-6d2a-4165-aadd-d3580dd59376/1.1/", + "version": "1.1", + "previous_version": "67b75e10-6d2a-4165-aadd-d3580dd59376_v1.0", + "root_version": "67b75e10-6d2a-4165-aadd-d3580dd59376_v1.0", + "latest": true + } + }, + { + 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University Bloomington" + ] + }, + { + "name": "Perkins, Maxwell P.", + "given_name": "Maxwell P.", + "family_name": "Perkins", + "affiliations": [ + "Indiana University Bloomington" + ] + }, + { + "name": "Chen, Jun", + "given_name": "Jun", + "family_name": "Chen", + "affiliations": [ + "Indiana University Bloomington" + ] + }, + { + "name": "Skrabalak, Sara E.", + "given_name": "Sara E.", + "family_name": "Skrabalak", + "affiliations": [ + "Indiana University Bloomington" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "72118a2f-5038-4a42-a13d-ab8204176a8d", + "mdf_source_name": "72118a2f-5038-4a42-a13d-ab8204176a8d" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/72118a2f-5038-4a42-a13d-ab8204176a8d/1.0/", + "version": 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"blockheads", + "foundry", + "test_data" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "CC-BY 4.0", + "url": null + }, + "organization": "Foundry", + "extensions": { + "mdf_source_id": "Datasets_for_Halide_Perovskite_Screening", + "mdf_source_name": "Datasets_for_Halide_Perovskite_Screening" + }, + "download_url": "https://data.materialsdatafacility.org//foundry/Datasets_for_Halide_Perovskite_Screening/1.0/", + "version": "1.0", + "root_version": "Datasets_for_Halide_Perovskite_Screening", + "latest": true + } + }, + { + "source_id": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "source_name": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "version": "1.1", + "ingest_date": "2024-12-20T18:27:54.020216Z", + "doi": "10.18126/v5sj-6q93", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/9bb8800f-9cda-4957-ad12-60ad2a381177/1.1/", + "metadata": { + "title": "D3TaLES: DFT Computations for Redox-Active Small Organic Molecule", + "authors": [ + { + "name": "Duke-Crockett, Rebekah", + "given_name": "Rebekah", + "family_name": "Duke-Crockett", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "name": "Bhat, Vinayak", + "given_name": "Vinayak", + "family_name": "Bhat", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "name": "Sornberger, Parker", + "given_name": "Parker", + "family_name": "Sornberger", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "name": "Odom, Susan A.", + "given_name": "Susan A.", + "family_name": "Odom", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "name": "Risko, Chad", + "given_name": "Chad", + "family_name": "Risko", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + } + ], + "description": "This dataset contains cleaned data for 35,729 molecules marked as `public` in the D3TaLES database at \nthe time of this dataset's creation. The calculations data were generated through a high-throughput molecular computational workflow \nusing density functional theory (DFT) carried out at the (IP-tuned) LC-\u03c9HPBE/Def2SVP level of \ntheory via the Gaussian16 (rev A.03) software suite. More details can be found in the associated\npublication: [https://doi.org/10.1039/D3DD00081H](https://doi.org/10.1039/D3DD00081H). \nThis dataset excludes molecules with outlier properties. An outlier \nis considered a property value greater than 3 standard deviations from the mean. \n\n\nThe dataset contains over 90 possible properties for each molecule. All energy properties are given \nin eV. Properties that begin with `solv_` were calculated in implicit solvent conditions simulating \nacetonitrile (dielectric constant 35.688). More information can be found in the README.md file, and \ncomplete documentation for molecular and species properties can \nbe found in the D3TaLES documentation at [https://d3tales.as.uky.edu/docs](https://d3tales.as.uky.edu/docs).", + "keywords": [ + "simulation", + "energy materials", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1039/D3DD00081H", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "mdf_source_name": "9bb8800f-9cda-4957-ad12-60ad2a381177" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/9bb8800f-9cda-4957-ad12-60ad2a381177/1.1/", + "version": "1.1", + "root_version": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "latest": true + } + }, + { + "source_id": "7f70db70-4550-4286-9744-71566906d8b6", + "source_name": "7f70db70-4550-4286-9744-71566906d8b6", + "version": "1.1", + "ingest_date": "2025-01-07T17:31:24.764609Z", + "doi": "10.18126/4c5v-p868", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/7f70db70-4550-4286-9744-71566906d8b6/1.1/", + "metadata": { + "title": "Differential Scanning Calorimetry to Determine Reactivity of Stable Viscosity-modified Thermoset Resins in Frontal Polymerization", + "authors": [ + { + "name": "Krishnan, Pranav", + "given_name": "Pranav", + "family_name": "Krishnan", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "name": "Lessard, Jacob", + "given_name": "Jacob", + "family_name": "Lessard", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "name": "Suslick, Benjamin", + "given_name": "Benjamin", + "family_name": "Suslick", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "name": "Cramblitt, Anna", + "given_name": "Anna", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "name": "Balta, Javier", + "given_name": "Javier", + "family_name": "Balta", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + } + ], + "description": "Differential Scanning Calorimetry (DSC) experiments were conducted on thermoset resins (95 wt% dicyclopentadiene (DCPD), 5 wt% 5-ethylidene-2-norbornene (ENB)) containing varying concentrations of catalyst and inhibitor. The samples were tested at a fixed heating rate in order to compare the enthalpy of reaction (\u2206Hr) released during the ring-opening metathesis (ROMP) reaction. This dataset is intended to be used as a reference for the range of \u2206Hr values typically achieved using the 95:5 wt% DCPD:ENB monomer resin. ", + "keywords": [ + "experiment", + "polymers", + "DSC", + "chemistry", + "reactivity" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "7f70db70-4550-4286-9744-71566906d8b6", + "mdf_source_name": "7f70db70-4550-4286-9744-71566906d8b6" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/7f70db70-4550-4286-9744-71566906d8b6/1.1/", + "version": "1.1", + "root_version": "7f70db70-4550-4286-9744-71566906d8b6", + "latest": true + } + }, + { + "source_id": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "source_name": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "version": "1.0", + "ingest_date": "2025-01-16T17:05:38.659630Z", + "doi": "10.18126/ka47-wp46", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ebcfac93-17d0-418f-b3ce-fd4d8a851674/1.0/", + "metadata": { + "title": "Pressure-Driven Solid\u2013Solid Phase Transformations of Isotropic Particles Across Diverse Crystal Structure Types", + "authors": [ + { + "name": "Du, Hongjin", + "given_name": "Hongjin", + "family_name": "Du", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Pan, Hillary", + "given_name": "Hillary", + "family_name": "Pan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by H. Du, H. Pan, and J. Dshemuchadse,\u201cPressure-Driven Solid\u2013Solid Phase Transformations of Isotropic Particles Across Diverse Crystal Structure Types\u201d, in publication (2025). In this work, we investigated the influence of pressure on the behavior of 16 crystal structure types that have been shown to self-assemble in molecular dynamics simulations using isotropic, pairwise interaction potentials. We studied these diverse structures using a range of computational models as a function of pressure, characterized the high-pressure phases, identified four previously unknown crystal structure types, and categorized the observed phase transformations. This dataset includes the representative simulation trajectories (in .gsd file format) mentioned in the main text and the Supplemental Material. A README.txt file is included to assist with parsing the data. We hope that this dataset will be useful for future research on pressure-induced phase transformations in both experimental and simulation studies.", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "mdf_source_name": "ebcfac93-17d0-418f-b3ce-fd4d8a851674" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/ebcfac93-17d0-418f-b3ce-fd4d8a851674/1.0/", + "version": "1.0", + "root_version": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "latest": true + } + }, + { + "source_id": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "source_name": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "version": "1.0", + "ingest_date": "2024-12-21T14:15:31.270585Z", + "doi": "10.18126/bhj3-3c20", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/81d55710-5bec-4e71-91b0-6f269e8da85a/1.0/", + "metadata": { + "title": "Data set for \"\u201cAssessment of diagenesis in archeological human second metacarpal bones using the intensity of the small angle x- ray scattering D-period peak\"", + "authors": [ + { + "name": "Gardner, L.L.", + "given_name": "L.L.", + "family_name": "Gardner", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Abramovich, W.", + "given_name": "W.", + "family_name": "Abramovich", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Romanov, M.", + "given_name": "M.", + "family_name": "Romanov", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Flohr, S.", + "given_name": "S.", + "family_name": "Flohr", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Kierdorf, U.", + "given_name": "U.", + "family_name": "Kierdorf", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Kierdorf, H.", + "given_name": "H.", + "family_name": "Kierdorf", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Almer, J.D.", + "given_name": "J.D.", + "family_name": "Almer", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Jacobsen, S.D.", + "given_name": "S.D.", + "family_name": "Jacobsen", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Gonzalez Aviles, G.", + "given_name": "G.", + "family_name": "Gonzalez Aviles", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Park, J.-S.", + "given_name": "J.-S.", + "family_name": "Park", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "name": "Stock, S.R.", + "given_name": "S.R.", + "family_name": "Stock", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + } + ], + "description": "This data set contains raw wide angle and small angle x-ray scattering patterns from archaeological human metacarpal bones. These scattering patterns were analyzed using GSAS-2 focusing on the D-periods to assess the diagenesis in these bones. ", + "keywords": [ + "experiment", + "biomaterials", + "microstructures", + "x-ray diffraction", + "wide angle x-ray scattering", + "WAXS", + "small angle x-ray scattering", + "SAXS", + "diagenesis", + "bone" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "mdf_source_name": "81d55710-5bec-4e71-91b0-6f269e8da85a" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/81d55710-5bec-4e71-91b0-6f269e8da85a/1.0/", + "version": "1.0", + "root_version": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "latest": true + } + }, + { + "source_id": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "source_name": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "version": "1.0", + "ingest_date": "2024-12-18T22:25:25.590549Z", + "doi": "10.18126/jezj-6852", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/5eba44ad-00f7-474c-8d72-7134cfd4f091/1.0/", + "metadata": { + "title": "Dataset of Simulated Shock Deformed Microstructures and X-ray Diffraction Patterns for Single Crystal and Nanocrystalline Cu for use with Machine-Learning", + "authors": [ + { + "name": "Vizoso, Daniel", + "given_name": "Daniel", + "family_name": "Vizoso", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "name": "Tsurkan, Phillip", + "given_name": "Phillip", + "family_name": "Tsurkan", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "name": "Ma, Ke", + "given_name": "Ke", + "family_name": "Ma", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "name": "Dongare, Avinash M.", + "given_name": "Avinash M.", + "family_name": "Dongare", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "name": "Dingreville, R\u00e9mi", + "given_name": "R\u00e9mi", + "family_name": "Dingreville", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + } + ], + "description": "Molecular dynamics (LAMMPS) dump files, simulated X-ray diffraction (XRD) profiles, and microstructural descriptor text files. Systems used are single crystal Cu shocked along the [100], [110], [111], and [112] directions, as well as a shocked nanocrystalline Cu system with random grain orientations. Scripts and an organized Python dataset are provided for generating data and running machine-learning models. \n", + "keywords": [ + "simulation", + "machine learning", + "metals", + "defects", + "microstructures", + "molecular dynamics", + "diffraction", + "shock" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "mdf_source_name": "5eba44ad-00f7-474c-8d72-7134cfd4f091" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/5eba44ad-00f7-474c-8d72-7134cfd4f091/1.0/", + "version": "1.0", + "root_version": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "latest": true + } + }, + { + "source_id": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "source_name": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "version": "1.0", + "ingest_date": "2024-12-18T18:01:17.009105Z", + "doi": "10.18126/y1j1-g019", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/77552592-f0b9-4df2-bcd5-8f470026c26e/1.0/", + "metadata": { + "title": "Neural Networks for Prediction of Electronic Excitation Dynamics", + "authors": [ + { + "name": "Shapera, Ethan P.", + "given_name": "Ethan P.", + "family_name": "Shapera", + "affiliations": [ + "University at Buffalo" + ] + }, + { + "name": "Lee, Cheng-Wei", + "given_name": "Cheng-Wei", + "family_name": "Lee", + "affiliations": [ + "Colorado School of Mines" + ] + } + ], + "description": "Dataset for \"Neural Networks for Prediction of Electronic Excitation Dynamics\". Contains rt-TDDFT data and descriptor files.", + "keywords": [ + "simulation", + "machine learning", + "molecular structures", + "high-throughput", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": " \t https://doi.org/10.48550/arXiv.2409.14042", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "mdf_source_name": "77552592-f0b9-4df2-bcd5-8f470026c26e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/77552592-f0b9-4df2-bcd5-8f470026c26e/1.0/", + "version": "1.0", + "root_version": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "latest": true + } + }, + { + "source_id": "7c20de4a-22fc-45d8-a918-dcd0925f5427", + "source_name": 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Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Zhernenkov, Mikhail", + "given_name": "Mikhail", + "family_name": "Zhernenkov", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "W\u0105sik, Patryk", + "given_name": "Patryk", + "family_name": "W\u0105sik", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Yavitt, Benjamin M.", + "given_name": "Benjamin M.", + "family_name": "Yavitt", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Barrera, Jorge-Luis", + "given_name": "Jorge-Luis", + "family_name": "Barrera", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Cook, Caitlyn C.", + "given_name": "Caitlyn C.", + "family_name": "Cook", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Pindak, Ronald", + "given_name": "Ronald", + "family_name": "Pindak", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Davidson, Emily C.", + "given_name": "Emily C.", + "family_name": "Davidson", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + }, + { + "name": "Lewis, Jennifer A.", + "given_name": "Jennifer A.", + "family_name": "Lewis", + "affiliations": [ + "Harvard University", + "Brookhaven National Laboratory", + "Lawrence Livermore National Laboratory" + ] + } + ], + "description": "Refer to README for navigating files.", + "keywords": [ + "direct ink writing", + "liquid crystal elastomers", + "x-ray scattering", + "shape-morphing" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "7c20de4a-22fc-45d8-a918-dcd0925f5427", + "mdf_source_name": "7c20de4a-22fc-45d8-a918-dcd0925f5427" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/7c20de4a-22fc-45d8-a918-dcd0925f5427/1.0/", + "version": "1.0", + "root_version": "7c20de4a-22fc-45d8-a918-dcd0925f5427", + "latest": true + } + }, + { + "source_id": "894f9192-ada3-40c3-9b6c-ca77eb3cb90e", + "source_name": "894f9192-ada3-40c3-9b6c-ca77eb3cb90e", + "version": "1.0", + "ingest_date": "2025-01-19T03:00:47.257956Z", + "doi": "10.18126/9wfh-k148", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/894f9192-ada3-40c3-9b6c-ca77eb3cb90e/1.0/", + "metadata": { + "title": "Scattering versus interference contribution to spectral iSCAT of plasmonic nanoparticles", + "authors": [ + { + "name": "Sridhar, Sanjay", + "given_name": "Sanjay", + "family_name": "Sridhar", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Nikolov, Marie", + "given_name": "Marie", + "family_name": "Nikolov", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Beutler, Elliot", + "given_name": "Elliot", + "family_name": "Beutler", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Knobeloch, Megan", + "given_name": "Megan", + "family_name": "Knobeloch", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Bianca, Paranzino", + "given_name": "Paranzino", + "family_name": "Bianca", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Vernon, Kelly", + "given_name": "Kelly", + "family_name": "Vernon", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Zhong, Yaxu", + "given_name": "Yaxu", + "family_name": "Zhong", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Ye, Xingchen", + "given_name": "Xingchen", + "family_name": "Ye", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Baker, Lane", + "given_name": "Lane", + "family_name": "Baker", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Skrabalak, Sara", + "given_name": "Sara", + "family_name": "Skrabalak", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Masiello, David", + "given_name": "David", + "family_name": "Masiello", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + }, + { + "name": "Willets, Katherine", + "given_name": "Katherine", + "family_name": "Willets", + "affiliations": [ + "Temple University", + "Indiana University Bloomington", + "Texas A&M University", + "University of Washington" + ] + } + ], + "description": "Spectral iSCAT data includes optical recordings, codes and electro microscopy images", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "894f9192-ada3-40c3-9b6c-ca77eb3cb90e", + "mdf_source_name": "894f9192-ada3-40c3-9b6c-ca77eb3cb90e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/894f9192-ada3-40c3-9b6c-ca77eb3cb90e/1.0/", + "version": "1.0", + "root_version": "894f9192-ada3-40c3-9b6c-ca77eb3cb90e", + "latest": true + } + }, + { + "source_id": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "source_name": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "version": "1.0", + "ingest_date": "2025-01-06T22:45:53.112981Z", + "doi": "10.18126/k8k2-ad62", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c7cd2c77-5607-4681-9955-f0a7f9f4d78c/1.0/", + "metadata": { + "title": "Dataset for \"Impact of Doping and Disorder on the Electronic Structure of Kagome Metal CoSn\"", + "authors": [ + { + "name": "Annaberdiyev, Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Karabin, Mariia", + "given_name": "Mariia", + "family_name": "Karabin", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Eisenbach, Markus", + "given_name": "Markus", + "family_name": "Eisenbach", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ganesh, P.", + "given_name": "P.", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Dataset for \"Impact of Doping and Disorder on the Electronic Structure of Kagome Metal CoSn\". It includes inputs and outputs for Quantum Espresso, RMG, and QMCPACK codes. Nexus workflow scripts are also included wherever possible.", + "keywords": [ + "DFT", + "QMC", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "mdf_source_name": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/c7cd2c77-5607-4681-9955-f0a7f9f4d78c/1.0/", + "version": "1.0", + "root_version": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "latest": true + } + }, + { + "source_id": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "source_name": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "version": "1.0", + "ingest_date": "2025-07-09T17:51:25.065040Z", + "doi": "10.18126/75tn-px24", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/1d473c7a-1028-4868-bfdd-ca20afa16d81/1.0/", + "metadata": { + "title": "A Coarse-Grained Simulation Toolkit for Metal\u2013Organic Framework Synthesis", + "authors": [ + { + "name": "Scott, Reum N.", + "given_name": "Reum N.", + "family_name": "Scott", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Milner, Phillp J.", + "given_name": "Phillp J.", + "family_name": "Milner", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by Reum N. Scott, Phillip J. Milner, and Julia Dshemuchadse, \"A Coarse-Grained Simulation Toolkit for Metal\u2013Organic Framework Synthesis\", in publication (2025). In this study, the self-assembly of different MOF structures is simulated from the MOFs' components (metal nodes and organic linkers), which were constructed in a coarse-grained model from isotropic beads, reflecting the symmetries of the molecular components. In addition to simulating the spontaneous growth of 34 different MOF nets (with linear, trigonal planar, tetragonal planar, as well as tetrahedral linkers), the effects of isoreticular relationships (linker length), competing interactions in layered MOFs, and the assembly of nets with additional degrees of freedom are demonstrated. This dataset includes representative simulation trajectories (in .gsd file format) documenting the growth of each of the MOF nets. A README.txt file is included to assist with parsing the data.\n\nThis material is based upon work supported by the National Science Foundation MPS-Ascend Postdoctoral Research Fellowship under Grant No. DMR-2139237. This work was supported by the donors of ACS Petroleum Research Fund under Grant 66310-DNI10. P.J.M. acknowledges support from the National Science Foundation (CBET-2047627).", + "keywords": [ + "simulation", + "experiment", + "Molecular Dynamics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1039/d5cp02121a", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "mdf_source_name": "1d473c7a-1028-4868-bfdd-ca20afa16d81" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/1d473c7a-1028-4868-bfdd-ca20afa16d81/1.0/", + "version": "1.0", + "root_version": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "latest": true + } + }, + { + "source_id": "3c249c48-eaa0-488b-866b-57ca9a53f39a", + "source_name": "3c249c48-eaa0-488b-866b-57ca9a53f39a", + "version": "1.0", + "ingest_date": "2025-07-07T23:17:59.855094Z", + "doi": "10.18126/32z7-7c09", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/3c249c48-eaa0-488b-866b-57ca9a53f39a/1.0/", + "metadata": { + "title": "Kinetic pathways of solid\u2013solid phase transitions dictated by short-range interactions", + "authors": [ + { + "name": "Pan, Hillary", + "given_name": "Hillary", + "family_name": "Pan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by H. Pan and J. Dshemuchadse, ``Kinetic pathways of solid--solid phase transitions dictated by short-range interactions'' Proc. Natl. Acad. Sci. USA (2025). In this study, we determine three distinct transformation pathways from body-centered cubic (bcc) to face-centered cubic (fcc) crystal structures upon cooling in minimal, one-component systems governed by isotropic pair potentials. These include: (1) a two-step bcc-to-bct-to-fcc transition, (2) a direct bcc-to-fcc transition, and (3) a microstructure-dependent transition with three main pathways. We show that in each case, particle-level rearrangements follow distinct kinetic pathways, leading to different microstructures of the resulting fcc phase. We also characterize the reverse transitions upon heating and report a previously unobserved fcc-to-bcc transformation pathway. \n\nThis dataset includes all seven high-resolution solid--solid phase transition simulations referenced in the manuscript, provided as GSD files:\n\n- bcc-to-bct: LJGP_2well_bcc_bct.gsd\n- bct-to-fcc: LJGP_2well_bct_fcc.gsd\n- direct bcc-to-fcc: OPP_bcc_fcc.gsd\n- three microstructure-dependent transformations: LJGP_1well_mix_fcc.gsd, LJGP_1well_gb_fcc.gsd, LJGP_1well_bcc_hcp.gsd\n- reverse fcc-to-bcc: LJGP_2well_fcc_bcc.gsd\n\nWe hope that this dataset will be useful for theorists, computationalists, and experimentalists interested in the mechanisms of solid--solid phase transitions. ", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "3c249c48-eaa0-488b-866b-57ca9a53f39a", + "mdf_source_name": "3c249c48-eaa0-488b-866b-57ca9a53f39a" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/3c249c48-eaa0-488b-866b-57ca9a53f39a/1.0/", + "version": "1.0", + "root_version": "3c249c48-eaa0-488b-866b-57ca9a53f39a", + "latest": true + } + }, + { + "source_id": "bbaf329c-ce49-440f-a9da-2141b913d880", + "source_name": "bbaf329c-ce49-440f-a9da-2141b913d880", + "version": "1.0", + "ingest_date": "2025-06-19T08:08:46.724918Z", + "doi": "10.18126/8ta9-3w37", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bbaf329c-ce49-440f-a9da-2141b913d880/1.0/", + "metadata": { + "title": "The Scaling of Triboelectric Charging Powder Drops for Industrial Applications", + "authors": [ + { + "name": "O'Hara, Tom F.", + "given_name": "Tom F.", + "family_name": "O'Hara", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Player, Ellen", + "given_name": "Ellen", + "family_name": "Player", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Ackroyd, Graham", + "given_name": "Graham", + "family_name": "Ackroyd", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Caine, Peter J.", + "given_name": "Peter J.", + "family_name": "Caine", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Aplin, Karen L.", + "given_name": "Karen L.", + "family_name": "Aplin", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + } + ], + "description": "The dataset used to produce the paper: \"The Scaling of Triboelectric Charging Powder Drops for Industrial Applications\", submitted to the Journal of Electrostatics. The files are predominantly .txt output files for Faraday cup measurements of charged powders, accompanied by some SEM images and particle size data for the materials investigated. README files explain the data structure within each directory. ", + "keywords": [ + "experiment", + "morphologies", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.5281/ZENODO.15684070", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "bbaf329c-ce49-440f-a9da-2141b913d880", + "mdf_source_name": "bbaf329c-ce49-440f-a9da-2141b913d880" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/bbaf329c-ce49-440f-a9da-2141b913d880/1.0/", + "version": "1.0", + "root_version": "bbaf329c-ce49-440f-a9da-2141b913d880", + "latest": true + } + }, + { + "source_id": "a65168f7-8f13-4552-b660-c1565f6d093e", + "source_name": "a65168f7-8f13-4552-b660-c1565f6d093e", + "version": "1.0", + "ingest_date": "2025-06-20T23:25:28.649391Z", + "doi": "10.18126/jy2f-2f17", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a65168f7-8f13-4552-b660-c1565f6d093e/1.0/", + "metadata": { + "title": "A Panoramic View of MXenes via an Atomic Coordination-Based Design Strategy", + "authors": [ + { + "name": "Noah Oyeniran Chongze Hu*, Traian Dumitrica, Panchapakesan Ganesh, Jacek Jakowski, Zhongfang Chen, Raymond R. Unocic, Michael Naguib, Vincent Meunier, Yury Gogotsi, Paul R. C. Kent, Bobby G. Sumpter, Jingsong Huang*, Oyshee Chowdhury", + "given_name": "Oyshee Chowdhury", + "family_name": "Noah Oyeniran Chongze Hu*, Traian Dumitrica, Panchapakesan Ganesh, Jacek Jakowski, Zhongfang Chen, Raymond R. Unocic, Michael Naguib, Vincent Meunier, Yury Gogotsi, Paul R. C. Kent, Bobby G. Sumpter, Jingsong Huang*", + "affiliations": [ + "University of Alabama", + "University of Minnesota", + "Oak Ridge National Laboratory", + " University of Puerto Rico", + " North Carolina State University", + "Tulane University", + "The Pennsylvania State University", + "Drexel University" + ] + } + ], + "keywords": [ + "MXenes", + " Coordination", + "2D materials", + "Computational simulation", + "Phase stability" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "doi.org/10.48550/arXiv.2501.15390", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "a65168f7-8f13-4552-b660-c1565f6d093e", + "mdf_source_name": "a65168f7-8f13-4552-b660-c1565f6d093e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/a65168f7-8f13-4552-b660-c1565f6d093e/1.0/", + "version": "1.0", + "root_version": "a65168f7-8f13-4552-b660-c1565f6d093e", + "latest": true + } + }, + { + "source_id": "b8084219-ce76-48a8-9638-1c4e258fa6cf", + "source_name": "b8084219-ce76-48a8-9638-1c4e258fa6cf", + "version": "1.0", + "ingest_date": "2025-06-13T16:25:38.677787Z", + "doi": "10.18126/cy5v-pj53", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b8084219-ce76-48a8-9638-1c4e258fa6cf/1.0/", + "metadata": { + "title": "Autonomous Mapping via SAXS of Nanoparticle Film", + "authors": [ + { + "name": "Yager, Kevin G.", + "given_name": "Kevin G.", + "family_name": "Yager", + "affiliations": [ + "Brookhaven National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Fukuto, Masafumi", + "given_name": "Masafumi", + "family_name": "Fukuto", + "affiliations": [ + "Brookhaven National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "name": "Noack, Marcus M.", + "given_name": "Marcus M.", + "family_name": "Noack", + "affiliations": [ + "Brookhaven National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "description": "Small-angle x-ray scattering (SAXS) images collected as a function of (x,y) spatial coordinates on a thin film of nanoparticles deposited on a substrate.", + "keywords": [ + "experiment", + "machine learning", + "autonomous experimentation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1038/s41598-019-48114-3", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1038/s41598-020-57887-x", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "b8084219-ce76-48a8-9638-1c4e258fa6cf", + "mdf_source_name": "b8084219-ce76-48a8-9638-1c4e258fa6cf" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/b8084219-ce76-48a8-9638-1c4e258fa6cf/1.0/", + "version": "1.0", + "root_version": "b8084219-ce76-48a8-9638-1c4e258fa6cf", + "latest": true + } + }, + { + "source_id": "8534ba1a-8553-4dde-b7f0-d70b2fcb43af", + "source_name": "8534ba1a-8553-4dde-b7f0-d70b2fcb43af", + "version": "1.0", + "ingest_date": "2025-06-24T16:09:30.955301Z", + "doi": "10.18126/hc1f-8y48", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8534ba1a-8553-4dde-b7f0-d70b2fcb43af/1.0/", + "metadata": { + "title": "W edge dislocation relaxation with LAMMPS", + "authors": [ + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "# Data set for W edge dislocation relaxation with LAMMPS\n\nAll calculations are performed using the `w_eam2.fs` EAM potential with LAMMPS. An a0 [100] (010) edge dislocation is created using elasticity theory: both the classical (TLE) solution, and the new (PNAS) solution. The overall workflow is straightforward; the initial positions are in the `Initial-positions` directory, which contains a Jupyter notebook to construct the isotropic edge dislocation solutions in a self-consistent manner. Self-consistency is required, as the displacements from the fictious reference crystal are defined in terms of the _defect_ positions, and so must be found iteratively from the elastic solution.", + "keywords": [ + "simulation", + "metals and alloys", + "defects", + "LAMMPS" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "GNU General Public License v3.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8534ba1a-8553-4dde-b7f0-d70b2fcb43af", + "mdf_source_name": "8534ba1a-8553-4dde-b7f0-d70b2fcb43af" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8534ba1a-8553-4dde-b7f0-d70b2fcb43af/1.0/", + "version": "1.0", + "root_version": "8534ba1a-8553-4dde-b7f0-d70b2fcb43af", + "latest": true + } + }, + { + "source_id": "74b15b28-ed14-4025-98a1-77ffac505f92", + "source_name": "74b15b28-ed14-4025-98a1-77ffac505f92", + "version": "1.0", + "ingest_date": "2025-06-24T12:55:22.070433Z", + "doi": "10.18126/6qer-bz95", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/74b15b28-ed14-4025-98a1-77ffac505f92/1.0/", + "metadata": { + "title": "Preparation of copper-zinc oxide pn-heterojunction catalysts by photo-deposition of [Cu(EDTA)]2+ complexes over ZnO nanorods with well-developed interfaces ", + "authors": [ + { + "name": "Svensson, Fredric G.", + "given_name": "Fredric G.", + "family_name": "Svensson", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Djurberg, Erik", + "given_name": "Erik", + "family_name": "Djurberg", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Yan, Yige", + "given_name": "Yige", + "family_name": "Yan", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Kim, Seohan", + "given_name": "Seohan", + "family_name": "Kim", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Henych, Jiri", + "given_name": "Jiri", + "family_name": "Henych", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Tolasz, Jakub", + "given_name": "Jakub", + "family_name": "Tolasz", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Dappozze, Frederic", + "given_name": "Frederic", + "family_name": "Dappozze", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Parola, Stephane", + "given_name": "Stephane", + "family_name": "Parola", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Guillard, Chantal", + "given_name": "Chantal", + "family_name": "Guillard", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "Stefanov, Bozhidar I.", + "given_name": "Bozhidar I.", + "family_name": "Stefanov", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + }, + { + "name": "\u00d6sterlund, Lars", + "given_name": "Lars", + "family_name": "\u00d6sterlund", + "affiliations": [ + "Uppsala University", + "University Lyon", + "ENS de Lyon", + "Pusan National University", + "Jan Evangelista Purkune University", + "Technical University of Sofia", + "institute of Inorganic Chemistry of the Czech Academy of Science" + ] + } + ], + "description": "XRD and XPS raw data. ", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "74b15b28-ed14-4025-98a1-77ffac505f92", + "mdf_source_name": "74b15b28-ed14-4025-98a1-77ffac505f92" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/74b15b28-ed14-4025-98a1-77ffac505f92/1.0/", + "version": "1.0", + "root_version": "74b15b28-ed14-4025-98a1-77ffac505f92", + "latest": true + } + }, + { + "source_id": "31913208-2ccc-463b-a4f6-ad9869f99ba4", + "source_name": "31913208-2ccc-463b-a4f6-ad9869f99ba4", + "version": "1.0", + "ingest_date": "2025-06-11T10:28:38.903579Z", + "doi": "10.18126/z7ax-6f35", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/31913208-2ccc-463b-a4f6-ad9869f99ba4/1.0/", + "metadata": { + "title": "Structural and transport data In2Cu2-xAgxSe4", + "authors": [ + { + "name": "Caro-Campos1 2, Victor Posligua3, Jes\u00fas Prado-Gonjal1, Oscar J. Dura4, Norbert M. Nemes5, Javier Gainza6, Jos\u00e9 L. Mart\u00ednez2, Jos\u00e9 A. Alonso2, Antonio M. M\u00e1rquez3, Jos\u00e9 J. Plata3, Federico Serrano-Sanchez2, *, Irene", + "given_name": "Irene", + "family_name": "Caro-Campos1 2, Victor Posligua3, Jes\u00fas Prado-Gonjal1, Oscar J. Dura4, Norbert M. Nemes5, Javier Gainza6, Jos\u00e9 L. Mart\u00ednez2, Jos\u00e9 A. Alonso2, Antonio M. M\u00e1rquez3, Jos\u00e9 J. Plata3, Federico Serrano-Sanchez2, *", + "affiliations": [ + "1Departamento de Qu\u00edmica Inorg\u00e1nica, Universidad Complutense de Madrid, Ciudad Universitaria s/n, Madrid E-28040, Spain 2Instituto de Ciencia de Materiales de Madrid, CSIC, Cantoblanco, Madrid E-28049, Spain 3Departamento de Qu\u00edmica F\u00edsica, Facultad de Qu\u00edmica, Universidad de Sevilla, Seville 41012, Spain 4Departamento de F\u00edsica Aplicada, Universidad de Castilla-La Mancha, E-13071 Ciudad Real, Spain 5GFMC, Departamento de F\u00edsica de Materiales, Universidad Complutense de Madrid, Madrid E-28040, Spain 6European Synchrotron Radiation Facility (ESRF), 71 Avenue des Martyrs, 38000 Grenoble, France" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "31913208-2ccc-463b-a4f6-ad9869f99ba4", + "mdf_source_name": "31913208-2ccc-463b-a4f6-ad9869f99ba4" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/31913208-2ccc-463b-a4f6-ad9869f99ba4/1.0/", + "version": "1.0", + "root_version": "31913208-2ccc-463b-a4f6-ad9869f99ba4", + "latest": true + } + }, + { + "source_id": "566d3ffd-b098-408a-93f7-fc4a826433b8", + "source_name": "566d3ffd-b098-408a-93f7-fc4a826433b8", + "version": "1.0", + "ingest_date": "2025-07-06T21:04:13.388911Z", + "doi": "10.18126/z9ne-mn12", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/566d3ffd-b098-408a-93f7-fc4a826433b8/1.0/", + "metadata": { + "title": "Surface Material Dependence in Powder Triboelectric Charging", + "authors": [ + { + "name": "O'Hara, Tom F.", + "given_name": "Tom F.", + "family_name": "O'Hara", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Player, Ellen", + "given_name": "Ellen", + "family_name": "Player", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Ackroyd, Graham", + "given_name": "Graham", + "family_name": "Ackroyd", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Caine, Peter J.", + "given_name": "Peter J.", + "family_name": "Caine", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + }, + { + "name": "Aplin, Karen L.", + "given_name": "Karen L.", + "family_name": "Aplin", + "affiliations": [ + "University of Bristol", + "Syngenta" + ] + } + ], + "description": "All the data used for the conference paper 'Surface Material Dependence in Powder Triboelectric Charging'.", + "keywords": [ + "experiment", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "GNU General Public License v3.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "566d3ffd-b098-408a-93f7-fc4a826433b8", + "mdf_source_name": "566d3ffd-b098-408a-93f7-fc4a826433b8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/566d3ffd-b098-408a-93f7-fc4a826433b8/1.0/", + "version": "1.0", + "root_version": "566d3ffd-b098-408a-93f7-fc4a826433b8", + "latest": true + } + }, + { + "source_id": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "source_name": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "version": "1.3", + "ingest_date": "2024-07-11T17:49:09.427501Z", + "doi": "10.18126/rpa1-yp91", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b483f85e-9af3-4cd8-a83b-ea178c925312/1.3/", + "metadata": { + "title": "Unsupervised Learning of Individual Kohn-Sham States: Interpretable Representations and Consequences for Downstream Predictions of Many-Body Effects", + "authors": [ + { + "name": "Hou, Bowen", + "given_name": "Bowen", + "family_name": "Hou", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Wu, Jinyuan", + "given_name": "Jinyuan", + "family_name": "Wu", + "affiliations": [ + "Yale University" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Yale University" + ] + } + ], + "description": "Representation learning for the electronic structure problem is a major challenge of machine learning in computational condensed matter and materials physics. Within quantum mechanical first principles approaches, Kohn-Sham density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional wavefunctions calculated in this approach serve as the building block for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of high-dimensional DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within the latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering and selection in prior work. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of the quasiparticle bandstructures within the GW formalism, which includes many-electron correlations beyond DFT. The GW prediction achieves a low error of 0.11 eV for a combined test set of metals and semiconductors drawn from the Computational 2D Materials Database (C2DB), suggesting that latent space representation captures key physical information from the original data. Finally, we explore the interpretability of the VAE representation and show that the successful representation learning and downstream prediction by our model is derived from the smoothness of the VAE latent space, which also enables the generation of wavefunctions on arbitrary points in latent space. Our work provides a novel and general machine-learning framework for investigating electronic structure and many-body physics.", + "keywords": [ + "DFT", + "machine learning", + "VAE", + "Many-body Interaction", + "GW", + "Unsupervised Learning", + "Representation Learning", + "Simulation", + "Condensed Matter Physics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "mdf_source_name": "b483f85e-9af3-4cd8-a83b-ea178c925312" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/b483f85e-9af3-4cd8-a83b-ea178c925312/1.3/", + "version": "1.3", + "root_version": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "latest": true + } + }, + { + "source_id": "6eb6f3b6-8e8f-4454-b998-591da2419ba8", + "source_name": "6eb6f3b6-8e8f-4454-b998-591da2419ba8", + "version": "1.1", + "ingest_date": "2024-07-31T11:24:42.618370Z", + "doi": "10.18126/vxx9-qw76", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6eb6f3b6-8e8f-4454-b998-591da2419ba8/1.1/", + "metadata": { + "title": "Quartz Crystal Microbalance with Dissipation Monitoring for Studying Soft Matter at Interfaces", + "authors": [ + { + "name": "Johannsmann, Diethelm", + "given_name": "Diethelm", + "family_name": "Johannsmann", + "affiliations": [ + "Institute of Physical Chemistry, Clausthal University of Technology, Clausthal-Zellerfeld, Germany", + "Department of Bioengineering, University of Washington, Seattle, Washington, USA", + "Advanced Wave Sensors S.L., Valencia, Spain" + ] + }, + { + "name": "Reviakine, Ilya", + "given_name": "Ilya", + "family_name": "Reviakine", + "affiliations": [ + "Institute of Physical Chemistry, Clausthal University of Technology, Clausthal-Zellerfeld, Germany", + "Department of Bioengineering, University of Washington, Seattle, Washington, USA", + "Advanced Wave Sensors S.L., Valencia, Spain" + ] + } + ], + "description": "The two files contain raw QCM-D data for adsorption of streptavidin on biotinylated SAM on gold and adsorption of coffee cream on bare gold surface, plots, input for PyQTM, and the output of the fitting to the viscoelastic model performed in PyQTM. 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"affiliations": [ + "Academia Sinica", + "Louisiana State University", + "Yale University" + ] + }, + { + "name": "Chan, Yang-hao", + "given_name": "Yang-hao", + "family_name": "Chan", + "affiliations": [ + "Academia Sinica", + "Louisiana State University", + "Yale University" + ] + }, + { + "name": "Qiu, Diana Y.", + "given_name": "Diana Y.", + "family_name": "Qiu", + "affiliations": [ + "Academia Sinica", + "Louisiana State University", + "Yale University" + ] + } + ], + "description": "This file contains all the raw data used in all the results presented in the paper.", + "keywords": [ + "simulation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "c6a9203c-c4dd-4b20-8eff-c75f9077f0b5", + "mdf_source_name": "c6a9203c-c4dd-4b20-8eff-c75f9077f0b5" + }, + 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He", + "given_name": "He", + "family_name": "Liu", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Yang, Lin", + "given_name": "Lin", + "family_name": "Yang", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Conry, Bryan", + "given_name": "Bryan", + "family_name": "Conry", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Marvel, Christopher J.", + "given_name": "Christopher J.", + "family_name": "Marvel", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Harmer, Martin P.", + "given_name": "Martin P.", + "family_name": "Harmer", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Rohrer, Gregory S.", + "given_name": "Gregory S.", + "family_name": "Rohrer", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Tonks, Michael R.", + "given_name": "Michael R.", + "family_name": "Tonks", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Suter, Robert M.", + "given_name": "Robert M.", + "family_name": "Suter", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Krill, Carl E.", + "given_name": "Carl E.", + "family_name": "Krill", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Park, Jun-Sang", + "given_name": "Jun-Sang", + "family_name": "Park", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Kenesei, Peter", + "given_name": "Peter", + "family_name": "Kenesei", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + }, + { + "name": "Krause, Amanda R.", + "given_name": "Amanda R.", + "family_name": "Krause", + "affiliations": [ + "University of Florida", + "Carnegie Mellon University", + "Lehigh University", + "Ulm University", + "Argonne National Laboratory" + ] + } + ], + "description": "3D microstructure datasets for SrTiO3 polycrystal acquired by high energy x-ray diffraction microscopy (HEDM) at the Advanced Photon Source (APS) at beamline 1ID. The first dataset (t0) was collected after the microstructure was sintered (1425\u00b0C, 1hr, oxygen) and pre-annealed (1400\u00b0C, 10 hr, forming gas). The second dataset (t1) was collected after an additional annealing step at 1400\u00b0C for 70 hr under forming gas. The data is provided in a .dream3D file, which includes post-processing information completed in Dream3d and grain information (orientation, centroid, grain size) in voxel arrays. See Dream3D for more information about the file format and storage. For more details about the data cleanup and material processing, please see the papers listed below.\n\nV. Muralikrishnan, H. Liu, B. Conry, C.J. Marvel, M.P. Harmer, G.S. Rohrer, M.R. Tonks, R.M. Suter, C.E. Krill III, A.R. Krause, Observations of unexpected grain boundary migration in SrTiO3. Scripta Materialia. 2023; 222:115022. 10.1016/j.scriptamat.2022.115055.\nV. Muralikrishnan, Z. Xu, G.S. Rohrer, A.R. Krause, The effect of local geometry and relative energy on grain boundary area changes during grain growth in SrTiO3. Journal of the American Ceramic Society. 2025; 108[4]:e20319.\n", + "keywords": [ + "ceramics", + "oxides", + "3D microstructure evolution", + "high energy x-ray diffraction microscopy", + "experiment", + "microstructures", + "strontium titanate" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.scriptamat.2022.115055", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1111/jace.20319", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "12b54dfc-0584-4665-9a56-595ec6dcac85", + "mdf_source_name": "12b54dfc-0584-4665-9a56-595ec6dcac85" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/12b54dfc-0584-4665-9a56-595ec6dcac85/1.0/", + "version": "1.0", + "root_version": "12b54dfc-0584-4665-9a56-595ec6dcac85", + "latest": true + } + }, + { + "source_id": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8", + "source_name": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8", + "version": "1.0", + "ingest_date": "2025-07-31T23:37:22.281952Z", + "doi": "10.18126/6w8c-by76", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8/1.0/", + "metadata": { + "title": "MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials", + "authors": [ + { + "name": "Maxson, Tristan", + "given_name": "Tristan", + "family_name": "Maxson", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "name": "Soyemi, Ademola", + "given_name": "Ademola", + "family_name": "Soyemi", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "name": "Zhang, Xinglong", + "given_name": "Xinglong", + "family_name": "Zhang", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "name": "Chen, Benjamin Wei Jie", + "given_name": "Benjamin Wei Jie", + "family_name": "Chen", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "name": "Szilvasi, Tibor", + "given_name": "Tibor", + "family_name": "Szilvasi", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + } + ], + "description": "Here we provide the following zipped to facilitate easy downloading.\n\n- `Analysis.zip`: Additional analysis scripts and minor datasets used to validate observable properties. ~11 MB\n- `Speed.zip`: The LAMMPS input for performing our speed tests for each MLIP. ~55 KB\n\nThe datasets are provided unzipped as the file size is quite large and only particular datasets may be of interest.\n- `Datasets/`: The database of datasets used in the benchmark with 3 splits. ~3.8 GB\n\nPlease note that the Zr-O dataset is derived from the referenced work by Waters et. al with permission.\n- Waters, M. J.; Rondinelli, J. M. Benchmarking Structural Evolution Methods for Training of Machine Learned Interatomic Potentials. J. Phys.: Condens. Matter 2022, 34 (38), 385901", + "keywords": [ + "machine learning", + "metals and alloys", + "oxides", + "simulation", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.5281.zenodo.14584499", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1021/acs.jcim.5c01262", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1088/1361-648X/ac7f73", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8", + "mdf_source_name": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8/1.0/", + "version": "1.0", + "root_version": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8", + "latest": true + } + }, + { + "source_id": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "source_name": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "version": "1.1", + "ingest_date": "2025-07-14T14:10:09.707689Z", + "doi": "10.18126/e1f9-bn46", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/7e599fc3-f015-4d16-9082-101b2d3b54c6/1.1/", + "metadata": { + "title": "Characterization Data of Laser-Induced Graphene/Polydimethylsiloxane Nanocomposite Strain Sensors", + "authors": [ + { + "name": "Jacquin, Tom", + "given_name": "Tom", + "family_name": "Jacquin", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Wanstall, Simon", + "given_name": "Simon", + "family_name": "Wanstall", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Park, Inkyu", + "given_name": "Inkyu", + "family_name": "Park", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Stokes, Adam A.", + "given_name": "Adam A.", + "family_name": "Stokes", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Heidari, Hadi", + "given_name": "Hadi", + "family_name": "Heidari", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Lim, Theodore", + "given_name": "Theodore", + "family_name": "Lim", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "name": "Amjadi, Morteza", + "given_name": "Morteza", + "family_name": "Amjadi", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + } + ], + "description": "This repository contains experimental data acquired for the characterization of strain sensors and self-sensing actuators based on laser-induced graphene (LIG) and polydimethylsiloxane (PDMS) nanocomposites. The layout and file names of the included files are based on an accompanying publication covering the topic of \"Wearable, Near Temperature Insensitive Laser-Induced Graphene Nanocomposite Strain Sensors\".\n\nSimon Wanstall and Tom Jacquin contributed equally to this work.", + "keywords": [ + "laser-induced graphene", + "nanocomposites", + "soft wearable sensors", + "temperature insensitivity", + "soft robotics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "mdf_source_name": "7e599fc3-f015-4d16-9082-101b2d3b54c6" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/7e599fc3-f015-4d16-9082-101b2d3b54c6/1.1/", + "version": "1.1", + "root_version": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "latest": true + } + }, + { + "source_id": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "source_name": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "version": "1.0", + "ingest_date": "2024-05-02T02:19:16.267445Z", + "doi": "10.18126/wy01-4e11", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/e52f77de-6756-4ca9-8fdb-f4791b395c1f/1.0/", + "metadata": { + "title": "Local structural features elucidate crystallization of complex structures", + "authors": [ + { + "name": "Martirossyan, Maya M.", + "given_name": "Maya M.", + "family_name": "Martirossyan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Spellings, Matthew", + "given_name": "Matthew", + "family_name": "Spellings", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Pan, Hillary", + "given_name": "Hillary", + "family_name": "Pan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the \u201cLocal structural features elucidate crystallization of complex structures\u201d https://doi.org/10.1021/acsnano.4c01290 (Martirossyan, et al. ACS Nano 2024, 18, 14989\u221215002) by M. M. Martirossyan, M. Spellings, H. Pan, and J. Dshemuchadse. This dataset is built to be used in conjunction with the GitHub code (https://github.com/capecrystal/local-structural-features) for training order metrics with machine learning methods. In this work, we show that this method can distinguish different crystallographic sites in highly complex structures of varying complexity and coordination number, and it can be used to study the growth trajectories of such structures. The dataset includes self-assembly trajectories from 10 different crystal structures and 2 trajectories of the same structure assembling via different crystallization pathways. A README.txt file is included for parsing the data.", + "keywords": [ + "simulation", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": " https://doi.org/10.48550/arXiv.2401.13765", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "mdf_source_name": "e52f77de-6756-4ca9-8fdb-f4791b395c1f" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/e52f77de-6756-4ca9-8fdb-f4791b395c1f/1.0/", + "version": "1.0", + "root_version": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "latest": true + } + }, + { + "source_id": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "source_name": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "version": "1.0", + "ingest_date": "2025-07-16T14:57:00.160394Z", + "doi": "10.18126/fttq-w045", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/37a80cc7-5b0d-42ee-9d79-c4c6dac507a4/1.0/", + "metadata": { + "title": "Spin-space averaged Fe\u2013C diffusion calculations from density functional theory", + "authors": [ + { + "name": "Wirth, Luke J.", + "given_name": "Luke J.", + "family_name": "Wirth", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Data set for spin-space averaged Fe\u2013C diffusion calculations with VASP. Output is from two sets of calculations. Monte Carlo simulations of a Heisenberg model yield configurations of individual atomic magnetic moments in bcc Fe at 1043 K with and without an external 6 T field. VASP calculations use these configurations to provide spin-space averaged forces and energies for octahedral and tetrahedral carbon in bcc Fe. Activation energies from these inform diffusion calculations of carbon in bcc Fe at finite temperatures and under field.", + "keywords": [ + "simulation", + "metals and alloys", + "DFT", + "diffusion" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "mdf_source_name": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/37a80cc7-5b0d-42ee-9d79-c4c6dac507a4/1.0/", + "version": "1.0", + "root_version": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "latest": true + } + }, + { + "source_id": "6114d6c9-6c76-497f-bf4b-f1033c4b1679", + "source_name": "6114d6c9-6c76-497f-bf4b-f1033c4b1679", + "version": "1.0", + "ingest_date": "2025-07-28T14:33:55.443129Z", + "doi": "10.18126/dqxb-9m77", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6114d6c9-6c76-497f-bf4b-f1033c4b1679/1.0/", + "metadata": { + "title": "Atom Probe Tomography dataset", + "authors": [ + { + "name": "Ulfig, Robert", + "given_name": "Robert", + "family_name": "Ulfig", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Reinhard, David", + "given_name": "David", + "family_name": "Reinhard", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Jacobs, Ryan", + "given_name": "Ryan", + "family_name": "Jacobs", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Voyles, Paul", + "given_name": "Paul", + "family_name": "Voyles", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "Collection of Atom Probe Tomography (APT) dataset files for various materials. Each .hdf5 file includes information from an .apt file and .rrng file, which includes peak ranging and ion labels for each APT experiment.", + "keywords": [ + "atom probe tomography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "6114d6c9-6c76-497f-bf4b-f1033c4b1679", + "mdf_source_name": "6114d6c9-6c76-497f-bf4b-f1033c4b1679" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/6114d6c9-6c76-497f-bf4b-f1033c4b1679/1.0/", + "version": "1.0", + "root_version": "6114d6c9-6c76-497f-bf4b-f1033c4b1679", + "latest": true + } + }, + { + "source_id": "atmospheric_tide_entrain_bioclocks", + "source_name": "atmospheric_tide_entrain_bioclocks", + "version": "1.0", + "ingest_date": "2025-07-14T16:36:45.788147Z", + "doi": "10.18126/zyra-qh71", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/atmospheric_tide_entrain_bioclocks/1.0/", + "metadata": { + "title": "atmospheric_tides_entrain_circadianr", + "authors": [ + { + "name": "Gon\u00e7alves, Felipe Goulart", + "given_name": "Felipe Goulart", + "family_name": "Gon\u00e7alves", + "affiliations": [ + "Universidade de S\u00e3o Paulo" + ] + } + ], + "description": "Insects behavioral observations on atmospheric pressure treatments.", + "keywords": [ + "Insect behavior", + "circadian rhythm", + "atmospheric tide", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "atmospheric_tide_entrain_bioclocks", + "mdf_source_name": "atmospheric_tide_entrain_bioclocks" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/atmospheric_tide_entrain_bioclocks/1.0/", + "version": "1.0", + "root_version": "atmospheric_tide_entrain_bioclocks", + "latest": true + } + }, + { + "source_id": "22b509c6-55dc-492e-a080-ea000f07d65f", + "source_name": "22b509c6-55dc-492e-a080-ea000f07d65f", + "version": "1.0", + "ingest_date": "2025-07-28T20:28:06.800012Z", + "doi": "10.18126/sadd-1e78", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/22b509c6-55dc-492e-a080-ea000f07d65f/1.0/", + "metadata": { + "title": "Investigating the effects of local environment on nitrogen vacancies in high entropy metal nitrides - Data ", + "authors": [ + { + "name": "DeSilva, Charith R.", + "given_name": "Charith R.", + "family_name": "DeSilva", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Sandia National Laboratory" + ] + }, + { + "name": "Witman, Matthew D.", + "given_name": "Matthew D.", + "family_name": "Witman", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Sandia National Laboratory" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Sandia National Laboratory" + ] + } + ], + "keywords": [ + "ceramics", + "DFT", + "defects" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "22b509c6-55dc-492e-a080-ea000f07d65f", + "mdf_source_name": "22b509c6-55dc-492e-a080-ea000f07d65f" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/22b509c6-55dc-492e-a080-ea000f07d65f/1.0/", + "version": "1.0", + "root_version": "22b509c6-55dc-492e-a080-ea000f07d65f", + "latest": true + } + }, + { + "source_id": "632ade4b-f035-47b8-83c3-d4256c3f0c59", + "source_name": "632ade4b-f035-47b8-83c3-d4256c3f0c59", + "version": "1.0", + "ingest_date": "2025-07-14T02:29:29.841541Z", + "doi": "10.18126/cwz9-6y89", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/632ade4b-f035-47b8-83c3-d4256c3f0c59/1.0/", + "metadata": { + "title": "X-ray tomography dataset for the Sartorius cellulose nitrate membrane filter ", + "authors": [ + { + "name": "Sima Zeinali Danalou Arash Rabbani, Patrick C. Lee, Jason Hattrick-Simpers, Jay R Werber, Hooman Chamani", + "given_name": "Hooman Chamani", + "family_name": "Sima Zeinali Danalou Arash Rabbani, Patrick C. Lee, Jason Hattrick-Simpers, Jay R Werber", + "affiliations": [ + "University of Toronto " + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "632ade4b-f035-47b8-83c3-d4256c3f0c59", + "mdf_source_name": "632ade4b-f035-47b8-83c3-d4256c3f0c59" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/632ade4b-f035-47b8-83c3-d4256c3f0c59/1.0/", + "version": "1.0", + "root_version": "632ade4b-f035-47b8-83c3-d4256c3f0c59", + "latest": true + } + }, + { + "source_id": "final_frames_metadata_v1.1", + "source_name": "final_frames_metadata", + "version": 1, + "ingest_date": "2023-11-13T16:19:47.248373Z", + "doi": "10.18126/b3yc-1mnp", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/final_frames_metadata_v1.1/", + "metadata": { + "title": "Targeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions", + "authors": [ + { + "name": "Pan, Hillary", + "given_name": "Hillary", + "family_name": "Pan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by H. Pan and J. Dshemuchadse, \u201cTargeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions\u201d ACS Nano 17(8), 7157\u20137169 (2023). In this work, we designed a new isotropic pair potential, in which all energy wells and maxima can be tuned independently. We use our interaction potential design to systematically explore self-assembled crystal structure configurations, specifically targeting low-coordinated structures. We report the computational self-assembly of 20 new crystal structure types, 14 of which are low-coordinated, and we investigate the relationship between features of the interaction potential and the resulting structures. This dataset includes over 2000 final simulation frames of the resulting crystal droplets (.gsd) and the corresponding interaction potential and simulation parameters (.json) used in this study. A README.txt file is included for parsing the data. We hope that this dataset will be useful for a variety of future work, both fundamental in nature as well as data-centric applications related to self-assembly, interaction potentials, and complex crystal structures. ", + "keywords": [ + "simulation", + "metamaterials" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1021/acsnano.2c09131", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "final_frames_metadata_v1.1", + "mdf_source_name": "final_frames_metadata" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/final_frames_metadata_v1.1/", + "version": "1.0", + "root_version": "final_frames_metadata_v1.1", + "latest": true + } + }, + { + "source_id": "rscott_2d_coarsegrained_mof_simulation_v1.1", + "source_name": "rscott_2d_coarsegrained_mof_simulation", + "version": 1, + "ingest_date": "2023-11-15T02:02:25.153181Z", + "doi": "10.18126/a6sr-0s9p", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rscott_2d_coarsegrained_mof_simulation_v1.1/", + "metadata": { + "title": "Two-Dimensional Metal\u2013Organic Framework Self-Assembly and Defect Engineering Studied via Coarse-Grained Simulations", + "authors": [ + { + "name": "Scott, Reum N.", + "given_name": "Reum N.", + "family_name": "Scott", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Martirossyan, Maya M.", + "given_name": "Maya M.", + "family_name": "Martirossyan", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Frank, Claire E.", + "given_name": "Claire E.", + "family_name": "Frank", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Milner, Phillip J.", + "given_name": "Phillip J.", + "family_name": "Milner", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "These files contain the data supporting all of the results in Reum N. Scott, et. al, Metal\u2013organic frameworks (MOFs) are crystalline materials that self-assemble from inorganic nodes and organic linkers, and isoreticular chemistry allows for modular and synthetic reagents of various sizes. In this study, a MOF's components\u2014metal nodes and organic linkers\u2014are constructed in a coarse-grained model from isotropic beads, retaining the basic symmetries of the molecular components. Lennard-Jones and Weeks\u2013Chandler\u2013Andersen pair potentials are used to model attractive and repulsive particle interactions, respectively. We analyze the crystallinity of the self-assembled products and explore the role of modulators\u2014molecules that compete with the organic linkers in binding to the metal nodes, and which we construct analogously\u2014during the self-assembly process of defect-engineered MOFs. Coarse-grained simulation allows for the uncoupling of experimentally interdependent variables to broadly map and determine essential MOF self-assembly conditions, among which are properties of the modulator: binding strength, size (steric hindrance), and concentration. Of these, the simulated modulator's binding strength has the most pronounced effect on the resulting MOF's crystal size.", + "keywords": [ + "simulation", + "Molecular Dynamics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "rscott_2d_coarsegrained_mof_simulation_v1.1", + "mdf_source_name": "rscott_2d_coarsegrained_mof_simulation" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/rscott_2d_coarsegrained_mof_simulation_v1.1/", + "version": "1.0", + "root_version": "rscott_2d_coarsegrained_mof_simulation_v1.1", + "latest": true + } + }, + { + "source_id": "schleife2018_v1.1", + "source_name": "schleife2018", + "version": 1, + "ingest_date": "2023-10-24T16:38:18.431004Z", + "doi": "10.18126/2kdh-3lwi", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/schleife2018_v1.1/", + "metadata": { + "title": "Time-Dependent Density Functional Theory Calculations of Proton Radiation in Aluminum", + "authors": [ + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + } + ], + "description": "Input file and outputs of Real Time Time-Dependent Density Functional Data (RT-TDDFT) computations of proton radiation in Aluminum. This dataset contains four additional trajectories in Aluminum using the same settings as in [Schleife et al. (2015)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.91.014306).", + "keywords": [ + "simulation", + "nuclear materials", + "time dependent density functional theory", + "aluminum" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.18126/UY0K-BWKQ", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "10.1103/PhysRevB.91.014306", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "schleife2018_v1.1", + "mdf_source_name": "schleife2018" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/schleife2018_v1.1/", + "version": "1.0", + "root_version": "schleife2018_v1.1", + "latest": true + } + }, + { + "source_id": "alcu_annealing_labdct_11steps_v1.1", + "source_name": "alcu_annealing_labdct_11steps", + "version": 1, + "ingest_date": "2023-10-25T18:21:14.816496Z", + "doi": "10.18126/5q8s-3ef9", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/alcu_annealing_labdct_11steps_v1.1/", + "metadata": { + "title": "Grain structure evolution during heat treatment of a Al-5wt%Cu alloy studied with lab-based diffraction contrast tomography", + "authors": [ + { + "name": "Sun, Jun", + "given_name": "Jun", + "family_name": "Sun", + "affiliations": [ + "Xnovo Technology", + "Ulm University" + ] + }, + { + "name": "Oddershede, Jette", + "given_name": "Jette", + "family_name": "Oddershede", + "affiliations": [ + "Xnovo Technology", + "Ulm University" + ] + }, + { + "name": "Dake, Jules", + "given_name": "Jules", + "family_name": "Dake", + "affiliations": [ + "Xnovo Technology", + "Ulm University" + ] + } + ], + "description": "### Sample and materials\n* Sample: Al-wt%Cu alloy\n* Dimension: 1.4mm in diameter, 6.5mm in length\n\n### Experiment and characterisation\n* Heat treatment: 15min annealing at 630\u00b0C in a muffle furnace, air cooling to room temperature\n* X-ray imaging equipment: ZEISS Xradia 520 Versa X-ray microscope with LabDCT pro module\n* Absorption contrast tomography scan: 80kV-7W, 4X detector, 1601 projections, 1.84\u00a0\u00b5m voxel size\n* Diffraction contrast tomography scan: 110kV-10W, flat panel detector, helical phyllotaxis scanning scheme, 2900 projections\n\n### Data \n* Number of datasets: 11 \n* Format: .dream3d \n* Naming: t0 - initial state, t10 - after the 10th annealing treatment\n", + "keywords": [ + "metals and alloys", + "microscopy", + "high-throughput", + "microstructures", + "lab-based DCT", + "X-ray imaging" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "alcu_annealing_labdct_11steps_v1.1", + "mdf_source_name": "alcu_annealing_labdct_11steps" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/alcu_annealing_labdct_11steps_v1.1/", + "version": "1.0", + "root_version": "alcu_annealing_labdct_11steps_v1.1", + "latest": true + } + }, + { + "source_id": "5decm_1_v1.4", + "source_name": "5decm_1", + "version": 1, + "ingest_date": "2023-11-17T02:40:03.425766Z", + "doi": "10.18126/y2vi-ntsk", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/5decm_1_v1.4/", + "metadata": { + "title": "Data for Momentum Transfer Resolved Electron Correlation Microscopy", + "authors": [ + { + "name": "Huang, Shuoyuan", + "given_name": "Shuoyuan", + "family_name": "Huang", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + }, + { + "name": "Voyles, Paul M.", + "given_name": "Paul M.", + "family_name": "Voyles", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "5D electron correlation microscopy data from a Pt57.5Cu14.7Ni5.2P22.5 metallic glass. Associated with the publication \"Momentum Transfer Resolved Electron Correlation Microscopy\" by Shuoyuan Huang and Paul Voyles, Ultramicroscopy 113886 (2023) DOI: 10.1016/j.ultramic.2023.113886", + "keywords": [ + "experiment", + "metals and alloys", + "metallic glasses", + "microscopy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.ultramic.2023.113886", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "5decm_1_v1.4", + "mdf_source_name": "5decm_1" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/5decm_1_v1.4/", + "version": "1.0", + "root_version": "5decm_1_v1.4", + "latest": true + } + }, + { + "source_id": "rtdftplusai_v1.1", + "source_name": "rtdftplusai", + "version": 1, + "ingest_date": "2023-10-27T14:31:17.660497Z", + "doi": "10.18126/r7lp-z2wj", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rtdftplusai_v1.1/", + "metadata": { + "title": "Software and Datasets for \"Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning\"", + "authors": [ + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Blaiszik, Ben", + "given_name": "Ben", + "family_name": "Blaiszik", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Lee, Cheng-Wei", + "given_name": "Cheng-Wei", + "family_name": "Lee", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Martin, Troy", + "given_name": "Troy", + "family_name": "Martin", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Foster, Ian", + "given_name": "Ian", + "family_name": "Foster", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Schleife, Andr\u00e9", + "given_name": "Andr\u00e9", + "family_name": "Schleife", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "The software associated with a publication showing how to quickly assess the electronic stopping power of a material using a combination of simulation and machine learning. 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"10.1002/adma.202305161", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "chaney_fully_printed_electrolyte_v1.1", + "mdf_source_name": "chaney_fully_printed_electrolyte" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/chaney_fully_printed_electrolyte_v1.1/", + "version": "1.0", + "root_version": "chaney_fully_printed_electrolyte_v1.1", + "latest": true + } + }, + { + "source_id": "gcta_v1.1", + "source_name": "gcta", + "version": 1, + "ingest_date": "2024-01-10T20:08:52.935013Z", + "doi": "10.18126/aut7-zp3j", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gcta_v1.1/", + "metadata": { + "title": "Dataset for the article \"Enhanced Twist-Averaging Technique for Magnetic Metals: Applications using Quantum Monte Carlo\".", + "authors": [ + { + "name": "Annaberdiyev, Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Krogel, Jaron T.", + "given_name": "Jaron T.", + "family_name": "Krogel", + "affiliations": [ + "Oak Ridge National Laboratory" + ] + } + ], + "description": "Dataset for the article \"Enhanced Twist-Averaging Technique for Magnetic Metals: Applications using Quantum Monte Carlo.\"", + "keywords": [ + "simulation", + "metals and alloys", + "DFT", + "QMCPack", + "QMC", + "Monte Carlo" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "gcta_v1.1", + "mdf_source_name": "gcta" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/gcta_v1.1/", + "version": "1.0", + "root_version": "gcta_v1.1", + "latest": true + } + }, + { + "source_id": "mcqueeney_chiral_flatband_layers_v1.1", + "source_name": "mcqueeney_chiral_flatband_layers", + "version": 1, + "ingest_date": "2024-01-14T19:00:25.135372Z", + "doi": "10.18126/fu9m-y02f", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mcqueeney_chiral_flatband_layers_v1.1/", + "metadata": { + "title": "Chiral and flat-band magnetic quasiparticles in ferromagnetic and metallic kagome layers", + "authors": [ + { + "name": "McQueeney, Robert J", + "given_name": "Robert J", + "family_name": "McQueeney", + "affiliations": [ + "Ames Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "mcqueeney_chiral_flatband_layers_v1.1", + "mdf_source_name": "mcqueeney_chiral_flatband_layers" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/mcqueeney_chiral_flatband_layers_v1.1/", + "version": "1.0", + "root_version": "mcqueeney_chiral_flatband_layers_v1.1", + "latest": true + } + }, + { + "source_id": "adams_sputter_deposited_morphology_v1.2", + "source_name": "adams_sputter_deposited_morphology", + "version": 1, + "ingest_date": "2022-12-21T03:11:24.678906Z", + "doi": "10.18126/io99-ldj2", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/adams_sputter_deposited_morphology_v1.2/", + "metadata": { + "title": "Sputter-deposited Mo thin films: multimodal characterization of structure, surface morphology, density, residual stress, electrical resistivity, and mechanical response", + "authors": [ + { + "name": "Adams, David P.", + "given_name": "David P.", + "family_name": "Adams", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Addamane, Sadhvikas J.", + "given_name": "Sadhvikas J.", + "family_name": "Addamane", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Custer, Joyce O.", + "given_name": "Joyce O.", + "family_name": "Custer", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Delrio, Frank W.", + "given_name": "Frank W.", + "family_name": "Delrio", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Jauregui, Luis J.", + "given_name": "Luis J.", + "family_name": "Jauregui", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Kalaswad, Matias S.", + "given_name": "Matias S.", + "family_name": "Kalaswad", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Khan, Ryan M.", + "given_name": "Ryan M.", + "family_name": "Khan", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Henriksen, Amelia A.", + "given_name": "Amelia A.", + "family_name": "Henriksen", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Babuska, Tomas F.", + "given_name": "Tomas F.", + "family_name": "Babuska", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Kothari, Rishabh S.", + "given_name": "Rishabh S.", + "family_name": "Kothari", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Ruggles, Tim", + "given_name": "Tim", + "family_name": "Ruggles", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Kotula, Paul G.", + "given_name": "Paul G.", + "family_name": "Kotula", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Hinojos, Alejandro E.", + "given_name": "Alejandro E.", + "family_name": "Hinojos", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process-structure-property relationships and for designing materials with specific structures and/or properties. Here, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature applications, photovoltaics, and microelectronics. A process space consisting of 27 unique combinations of sputter power and Argon (Ar) deposition pressure was explored. We include an extensive set of eight experimental characterization modalities for each unique power/pressure combination: X-ray diffraction, scanning electron microscopy, residual stress, resistivity, atomic force microscopy, rutherford backscattering, nanoindentation, and transmission electron microscopy with automated crystal orientation mapping data. We also include simulated experiments over an expanded set of powers, pressures and working distances to capture process data in addition to this experimental structure and property data. We acknowledge the Laboratory Directed Research and Development program for providing funding for this study. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the view of the U.S. DOE or the United States Government.", + "keywords": [ + "thin films", + "molybednum", + "magnetron sputter deposition", + "multimodal data", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2022, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "adams_sputter_deposited_morphology_v1.2", + "mdf_source_name": "adams_sputter_deposited_morphology" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/adams_sputter_deposited_morphology_v1.2/", + "version": "1.0", + "root_version": "adams_sputter_deposited_morphology_v1.2", + "latest": true + } + }, + { + "source_id": "82393b42-7252-4d33-ad97-dfda59f22e33", + "source_name": "82393b42-7252-4d33-ad97-dfda59f22e33", + "version": "1.0", + "ingest_date": "2024-12-03T05:10:10.531823Z", + "doi": "10.18126/qpb3-7246", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/82393b42-7252-4d33-ad97-dfda59f22e33/1.0/", + "metadata": { + "title": "Electron Microscopy and White Light Interferometry Fractography Images of Ti64 Compact Tension Specimens", + "authors": [ + { + "name": "Jones, Katelyn", + "given_name": "Katelyn", + "family_name": "Jones", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Shade, Paul A.", + "given_name": "Paul A.", + "family_name": "Shade", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "John, Reji", + "given_name": "Reji", + "family_name": "John", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Musinski, William D.", + "given_name": "William D.", + "family_name": "Musinski", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Pilchak, Adam L.", + "given_name": "Adam L.", + "family_name": "Pilchak", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Chapman, Michael G.", + "given_name": "Michael G.", + "family_name": "Chapman", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Holm, Elizabeth A.", + "given_name": "Elizabeth A.", + "family_name": "Holm", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "name": "Rollett, Anthony D.", + "given_name": "Anthony D.", + "family_name": "Rollett", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + } + ], + "description": "The study of fatigue fracture surfaces presents a perfect opportunity for machine learning (ML) and computer vision (CV) techniques. This work compiles a multimodal set of images taken on the fractured surfaces of fatigue crack growth experiments on Ti-6Al-4V compact tension specimens. Secondary electron (SE), back scattered electron (BSE), and scanning white-light interferometry (SWLI) images were taken on the samples. A set of 102 SE images was taken from near the crack initiation site up to 14mm from the initiation site in regular 1mm increments. The other set of SE images, in addition to the BSE and SWLI, were from large area scans that were stitched together but can be cropped for future ML usage. Labeled data sets such as this can improve the development of ML tools for fractography studies.", + "keywords": [ + "machine learning", + "ti64", + "fatigue", + "microscopy", + "metals and alloys", + "SEM", + "BSE", + "Scanning white light interferometry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.ijfatigue.2023.107915", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "82393b42-7252-4d33-ad97-dfda59f22e33", + "mdf_source_name": "82393b42-7252-4d33-ad97-dfda59f22e33" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/82393b42-7252-4d33-ad97-dfda59f22e33/1.0/", + "version": "1.0", + "root_version": "82393b42-7252-4d33-ad97-dfda59f22e33", + "latest": true + } + }, + { + "source_id": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "source_name": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "version": "1.1", + "ingest_date": "2024-11-25T22:23:01.887936Z", + "doi": "10.18126/ga5y-dh42", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/3034ae5d-f0a8-439d-908b-e1d41d275279/1.1/", + "metadata": { + "title": "Dataset for Thermogravimetric analysis of commercial tungsten molecular precursors for vapor phase materials growth processes", + "authors": [ + { + "name": "Currie, Taylor M.", + "given_name": "Taylor M.", + "family_name": "Currie", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "McNealy-James, Terrick", + "given_name": "Terrick", + "family_name": "McNealy-James", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "Berriel, S. Novia", + "given_name": "S. Novia", + "family_name": "Berriel", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "Preradovic, Konstantin", + "given_name": "Konstantin", + "family_name": "Preradovic", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "Sattelberger, Alfred P.", + "given_name": "Alfred P.", + "family_name": "Sattelberger", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "Banerjee, Parag", + "given_name": "Parag", + "family_name": "Banerjee", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "name": "Jurca, Titel", + "given_name": "Titel", + "family_name": "Jurca", + "affiliations": [ + "University of Central Florida" + ] + } + ], + "description": "This dataset is associated with the following publication: T. M. Currie, T. McNealy-James, S. N. Berriel, K. Preradovic, A. P. Sattelberger, P. Banerjee, and T. Jurca, RSC Advances, 2024.\n\nThis dataset includes relevant TGA data, Temperature (\u00b0C) vs. Mass (%), for all eight precursors discussed in our publication. Please see our publication and associated supplementary information for more details. \n\nIf using this dataset, please cite both this page and our publication.", + "keywords": [ + "thermogravimetric analysis", + "precursor chemistry", + "atomic layer deposition", + "chemical vapor deposition" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "mdf_source_name": "3034ae5d-f0a8-439d-908b-e1d41d275279" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/3034ae5d-f0a8-439d-908b-e1d41d275279/1.1/", + "version": "1.1", + "root_version": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "latest": true + } + }, + { + "source_id": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "source_name": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "version": "1.0", + "ingest_date": "2024-12-18T18:08:51.505959Z", + "doi": "10.18126/xr9e-js38", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ace76b96-9175-4e1a-810f-ef0a54fc2c45/1.0/", + "metadata": { + "title": "Asymmetric porous catalyst structures for low temperature photocatalytic dry reforming of methane", + "authors": [ + { + "name": "Moore, William", + "given_name": "William", + "family_name": "Moore", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Shoji, Shusaku", + "given_name": "Shusaku", + "family_name": "Shoji", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Tsaur, Lieihn", + "given_name": "Lieihn", + "family_name": "Tsaur", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Yu, Fei", + "given_name": "Fei", + "family_name": "Yu", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Thedford, R. Paxton", + "given_name": "R. Paxton", + "family_name": "Thedford", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Tait, William R", + "given_name": "William R", + "family_name": "Tait", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Raisi, M. Sadegh", + "given_name": "M. Sadegh", + "family_name": "Raisi", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Saha, Aniruddha", + "given_name": "Aniruddha", + "family_name": "Saha", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Hur, Kayhun", + "given_name": "Kayhun", + "family_name": "Hur", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Reese, Austin", + "given_name": "Austin", + "family_name": "Reese", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Kozbek, Ali Y.", + "given_name": "Ali Y.", + "family_name": "Kozbek", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Hesse, Sarah", + "given_name": "Sarah", + "family_name": "Hesse", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Gruner, Sol M.", + "given_name": "Sol M.", + "family_name": "Gruner", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Yeghiazarian, Lilit", + "given_name": "Lilit", + "family_name": "Yeghiazarian", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Sobhani, Sadaf", + "given_name": "Sadaf", + "family_name": "Sobhani", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Suntivich, Jin", + "given_name": "Jin", + "family_name": "Suntivich", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "name": "Wiesner, Ulrich B.", + "given_name": "Ulrich B.", + "family_name": "Wiesner", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + } + ], + "description": "Data associated with the article titled \"Asymmetric porous catalyst structures for low temperature photocatalytic dry reforming of methane\"", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "mdf_source_name": "ace76b96-9175-4e1a-810f-ef0a54fc2c45" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/ace76b96-9175-4e1a-810f-ef0a54fc2c45/1.0/", + "version": "1.0", + "root_version": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "latest": true + } + }, + { + "source_id": "55490765-6506-4532-9e80-4f8c32195466", + "source_name": "55490765-6506-4532-9e80-4f8c32195466", + "version": "1.0", + "ingest_date": "2024-11-19T18:19:58.885764Z", + "doi": "10.18126/93xq-zt25", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/55490765-6506-4532-9e80-4f8c32195466/1.0/", + "metadata": { + "title": "Dataset for Quantifying the Effect of Ultrasonic Scattering from a Distribution of Grain Sizes", + "authors": [ + { + "name": "Islam, Showmic", + "given_name": "Showmic", + "family_name": "Islam", + "affiliations": [ + "University of Nebraska\u2013Lincoln" + ] + }, + { + "name": "Turner, Joseph A.", + "given_name": "Joseph A.", + "family_name": "Turner", + "affiliations": [ + "University of Nebraska-Lincoln" + ] + } + ], + "description": "The dataset consists of synthetic polycrystals created using DREAM.3D. All synthetics have the same mean grain size. There are sets of thirty synthetics and each set has a different grain-size distribution width. Both cropped and uncropped synthetics are included as described in the article \"Dataset for Quantifying the Effect of Ultrasonic Scattering from a Distribution of Grain Sizes.\"", + "keywords": [ + "simulation", + "microstructures", + "metals and alloys" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "55490765-6506-4532-9e80-4f8c32195466", + "mdf_source_name": "55490765-6506-4532-9e80-4f8c32195466" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/55490765-6506-4532-9e80-4f8c32195466/1.0/", + "version": "1.0", + "root_version": "55490765-6506-4532-9e80-4f8c32195466", + "latest": true + } + }, + { + "source_id": "camp_2023", + "source_name": "camp_2023", + "version": "1.1", + "ingest_date": "2024-11-19T19:08:20.970886Z", + "doi": "10.18126/s90e-dq90", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/camp_2023/1.1/", + "metadata": { + "title": "Dataset of NMC battery Tests from CAMP, 2023 Release", + "authors": [ + { + "name": "Ward, Logan", + "given_name": "Logan", + "family_name": "Ward", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Kubal, Joseph", + "given_name": "Joseph", + "family_name": "Kubal", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Babinec, Susan J.", + "given_name": "Susan J.", + "family_name": "Babinec", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Lu, Wenquan", + "given_name": "Wenquan", + "family_name": "Lu", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Dunlop, Allison", + "given_name": "Allison", + "family_name": "Dunlop", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Trask, Steve", + "given_name": "Steve", + "family_name": "Trask", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Polzin, Bryant", + "given_name": "Bryant", + "family_name": "Polzin", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Jansen, Andrew", + "given_name": "Andrew", + "family_name": "Jansen", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Paulson, Noah H.", + "given_name": "Noah H.", + "family_name": "Paulson", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "Collection of cycle life tests of Li-ion batteries from the Cell Analysis, Modeling, and Prototyping (CAMP) Facility at Argonne National Laboratory. The dataset contains all 300 cells used by Paulson et al. to study the effect of feature engineering on machine learning models to predict the life of batteries, which were selected because they have a graphite anode, used low charging rates, and were tested for at least 100 cycles. We also include 302 cells that failed to meet acceptence criteria for that study. Each cell is stored in the HDF5 format of Argonne's Battery Data Toolkit v0.4.0, which includes battery metadata, the raw signal from the testing equipment, and per-cycle summaries of battery performance.", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1016/j.jpowsour.2022.231127", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "camp_2023", + "mdf_source_name": "camp_2023" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/camp_2023/1.1/", + "version": "1.1", + "root_version": "camp_2023", + "latest": true + } + }, + { + "source_id": "237d3189-ae38-496a-88c9-760c5fa25419", + "source_name": "237d3189-ae38-496a-88c9-760c5fa25419", + "version": "1.0", + "ingest_date": "2024-12-05T19:05:45.795399Z", + "doi": "10.18126/g7qt-bp50", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/237d3189-ae38-496a-88c9-760c5fa25419/1.0/", + "metadata": { + "title": "Improvement of Cellular Pattern Organization and Clarity Through Centrifugal Force ", + "authors": [ + { + "name": "Mehanna, Lauren E.", + "given_name": "Lauren E.", + "family_name": "Mehanna", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Boyd, James D.", + "given_name": "James D.", + "family_name": "Boyd", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Remus-Williams, Shelley", + "given_name": "Shelley", + "family_name": "Remus-Williams", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Racca, Nicole M.", + "given_name": "Nicole M.", + "family_name": "Racca", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Spraggins P., Dawson", + "given_name": "Dawson", + "family_name": "Spraggins P.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Grady, Martha E.", + "given_name": "Martha E.", + "family_name": "Grady", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "name": "Berron, Brad J.", + "given_name": "Brad J.", + "family_name": "Berron", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + } + ], + "keywords": [ + "cell patterning", + "adhesion", + "centrifugation", + "shear flow", + "streptavidin", + "cell density" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "237d3189-ae38-496a-88c9-760c5fa25419", + "mdf_source_name": "237d3189-ae38-496a-88c9-760c5fa25419" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/237d3189-ae38-496a-88c9-760c5fa25419/1.0/", + "version": "1.0", + "root_version": "237d3189-ae38-496a-88c9-760c5fa25419", + "latest": true + } + }, + { + "source_id": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "source_name": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "version": "1.0", + "ingest_date": "2025-04-26T02:16:13.283743Z", + "doi": "10.18126/0a1r-zt14", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/e6d1f230-74af-4f53-9f5b-836aa5efe72e/1.0/", + "metadata": { + "title": "A Programmable Nanoparticle Conversion Pathway to Monodisperse Polyelemental High Entropy Alloy, Intermetallic, and Multiphase Nanoparticles", + "authors": [ + { + "name": "Kar, Nabojit", + "given_name": "Nabojit", + "family_name": "Kar", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "name": "Leonardi, Alberto", + "given_name": "Alberto", + "family_name": "Leonardi", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "name": "McCoy, Maximilian", + "given_name": "Maximilian", + "family_name": "McCoy", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "name": "Selvaraj, Rukshanthan", + "given_name": "Rukshanthan", + "family_name": "Selvaraj", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "name": "Skrabalak, Sara E.", + "given_name": "Sara E.", + "family_name": "Skrabalak", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + } + ], + "description": "Data Collection associated with the above titled paper, published in Angewandte Chemie. Abstract: Polyelemental nanoparticles (PE NPs), those consisting of four or more elements, exhibit unique properties from synergistic compositional effects. Examples include high entropy alloys, high entropy intermetallics, and multiphase types, including Janus and core-shell architectures. Although colloidal syntheses offer excellent structural control for mono- and bi-elemental compositions, achieving the same control for PE NPs remains challenging. Here, this challenge is addressed\nwith a NP conversion strategy wherein different types of PE NPs \u2013 including high entropy alloy, high entropy intermetallic, and multiphase Janus nanoparticles \u2013 are achieved through thermal transformation of readily synthesized colloidal coreshell NPs. Through systematic variations in stoichiometry and metal identity to the core-shell precursor NPs, along with atomistic simulations that probe phase stabilities, we deduce that the final mixing states of the various NPs are governed by the balance between the enthalpy and entropy of mixing. Moreover, our annealing method allows us to trap NPs at intermediate states of mixing, creating distinct surface ensembles that were evaluated as catalysts for the hydrogen evolution reaction. This study is the first, to our knowledge, to report colloidally derived precursor NPs enabling the synthesis of all types of PE NPs in a single process. This NP conversion strategy offers a general route to diverse PE NPs.", + "keywords": [ + "inorganic Nanoparticles", + "high entropy alloy", + "Polyelemental nanoparticle" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1002/anie.202505523", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "doi.org/10.1002/anie.202505523", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "mdf_source_name": "e6d1f230-74af-4f53-9f5b-836aa5efe72e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/e6d1f230-74af-4f53-9f5b-836aa5efe72e/1.0/", + "version": "1.0", + "root_version": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "latest": true + } + }, + { + "source_id": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "source_name": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "version": "1.0", + "ingest_date": "2025-04-16T17:00:39.946154Z", + "doi": "10.18126/1v02-w971", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c4cf2068-fc7a-469e-af39-0aa60bf20cf4/1.0/", + "metadata": { + "title": "Terahertz spectroscopic characterization of Ge3Sb2Te6 compounds for active applications", + "authors": [ + { + "name": "Kumar, Krishna", + "given_name": "Krishna", + "family_name": "Kumar", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "name": "Zabyako, Miroslavna Kovylina", + "given_name": "Miroslavna Kovylina", + "family_name": "Zabyako", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "name": "Alvarez-Sanchis, Jose Antonio", + "given_name": "Jose Antonio", + "family_name": "Alvarez-Sanchis", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "name": "De Zarate Diaz, David Ortiz", + "given_name": "David Ortiz", + "family_name": "De Zarate Diaz", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "name": "Vidal, Borja", + "given_name": "Borja", + "family_name": "Vidal", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "name": "Garcia-Meca, Carlos", + "given_name": "Carlos", + "family_name": "Garcia-Meca", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + } + ], + "description": "The uploaded data comprises three primary folders titled: THz-TDS, Raman data, and XRD data\n\nTHz-TDS\nThis folder has four subfolders, each contains terahertz time-domain transmission data. In every data file, the first column represents the time delay, and the second column corresponds to the transmitted electric field (e-field). \nThe measurements are named in the format: \"LnpmXYi_11h_1\", where:\n n = 1 for GST225, 3 for GST326;\nm =1 for as-grown, 2 for sample annealed at 155 C, 3 at 205 C, 4 at 255 C, 5 at 305 C, 6 at 355 C, and 7 at 405 C;\nX = Z indicates sapphire substrate and without X is silicon substrate;\nY = M for thin-film+substrate, S for bare substrate only, R for reference;\ni = 1, 2 or 3 indicates measurement spots 1, 2 and 3 respectively. \nIn cases where the file name is prefixed by 'replica', for example replicaLnpmXYi_11h_1, it refers to newly fabricated samples measured under the same conditions. \n\n\nRaman data\nRaman measurements are named in the format: \"Ln_i_laser 785nm_F25_r50_250_ob10_g600_t2s_ac20_spotj\", where:\nn = 2 for GST225, 3 for GST326;\ni = 1 to 7, representing the as-grown state and annealed samples at 155 C, 205 C, 255 C, 305 C, 355 C and 405 C, respectively;\nj = 1 to 3, indicating the three different measurement spots per sample.\nEach Raman dataset contains two columns: the first column is the Raman shift, and second column is the corresponding intensity. \n\nXRD data\nXRD files are named in the format: \"Ln.i_exported\", where:\nn = 2 for GST225, 3 for GST326;\ni = 1 to 7, corresponding to the as-grown sample and annealed samples at 155 C, 205 C, 255 C, 305 C, 355 C and 405 C, respectively.\nEach XRD file contains two columns: the first column is the diffraction angle (2 theta), and the second column is the intensity. ", + "keywords": [ + "spectroscopy", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "MIT License", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "mdf_source_name": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/c4cf2068-fc7a-469e-af39-0aa60bf20cf4/1.0/", + "version": "1.0", + "root_version": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "latest": true + } + }, + { + "source_id": "0989ee07-e015-494d-8805-8e2703e76763", + "source_name": "0989ee07-e015-494d-8805-8e2703e76763", + "version": "1.0", + "ingest_date": "2025-03-26T08:46:40.678208Z", + "doi": "10.18126/yc1m-nk41", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/0989ee07-e015-494d-8805-8e2703e76763/1.0/", + "metadata": { + "title": "LabDCT data for investigation of 3D strain heterogeneity and fracture studied by X-ray tomography and crystal plasticity in an aluminium alloy", + "authors": [ + { + "name": "Gille, Maryse", + "given_name": "Maryse", + "family_name": "Gille", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "name": "Proudhon, Henry", + "given_name": "Henry", + "family_name": "Proudhon", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "name": "Oddershede, Jette", + "given_name": "Jette", + "family_name": "Oddershede", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "name": "Quey, Romain", + "given_name": "Romain", + "family_name": "Quey", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "name": "Morgeneyer, Thilo F.", + "given_name": "Thilo F.", + "family_name": "Morgeneyer", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + } + ], + "description": "Sample and materials\n* Material: 6016 T4 aluminum alloy\n* Average grain size: 40 \u00b5m\n* Shape: Wide dogbone to fulfil plane strain conditions during tensile deformation\n\n\nExperiment and characterisation\n* LabDCT grain map of interior volume of undeformed sample\n* X-ray imaging equipment: Zeiss Xradia 520 Versa X-ray microscope with LabDCT Pro module\n* Absorption contrast scan: 60kV-4W, 4X detector, 1601 projections, 4.45 \u00b5m voxel size\n* Diffraction contrast scan: 110kV-9W, flat panel detector, helical phyllotaxis HART scanning scheme, 1800 projections\n\n\nData\n* Map dimensions: \n - x = 0.44 mm (sample thickness)\n - y = 0.24 mm (direction of near-zero strain in the plane strain condition, ~6 grain diameters width)\n - z = 1.00 mm (tensile direction)\n* Voxel size: 4 \u00b5m\n* Number of grains: 1666, all of which are at least 27 voxels, and no voids\n* Context: This is a cropped volume at the center of the sample, not the full volume analysed in the associated publication \n* Format: .h5", + "keywords": [ + "LabDCT", + "plane strain deformation", + "CP-FE simulations", + "metals and alloys", + "microstructures", + "X-ray imaging" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1016/j.ijplas.2024.104146", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "0989ee07-e015-494d-8805-8e2703e76763", + "mdf_source_name": "0989ee07-e015-494d-8805-8e2703e76763" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/0989ee07-e015-494d-8805-8e2703e76763/1.0/", + "version": "1.0", + "root_version": "0989ee07-e015-494d-8805-8e2703e76763", + "latest": true + } + }, + { + "source_id": "8d0831be-baf6-4829-8187-e2e45192df1e", + "source_name": "8d0831be-baf6-4829-8187-e2e45192df1e", + "version": "1.0", + "ingest_date": "2025-05-05T19:14:19.780879Z", + "doi": "10.18126/8h2q-g409", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8d0831be-baf6-4829-8187-e2e45192df1e/1.0/", + "metadata": { + "title": "Molecular dynamics trajectories of ZnCl2 aqueous solutions from 1 m to 30 m ", + "authors": [ + { + "name": "Cao, Chuntian", + "given_name": "Chuntian", + "family_name": "Cao", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Zhang, Chunyi", + "given_name": "Chunyi", + "family_name": "Zhang", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Wu, Xifan", + "given_name": "Xifan", + "family_name": "Wu", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Carbone, Matthew R.", + "given_name": "Matthew R.", + "family_name": "Carbone", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "van Dam, Hubertus", + "given_name": "Hubertus", + "family_name": "van Dam", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Yoo, Shinjae", + "given_name": "Shinjae", + "family_name": "Yoo", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "name": "Lu, Deyu", + "given_name": "Deyu", + "family_name": "Lu", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "description": "This dataset contains molecular dynamics (MD) trajectories of aqueous zinc chloride (ZnCl\u2082) solutions at concentrations ranging from 1 mol/kg to 30 mol/kg, with 30 mol/kg representing the \"water-in-salt\" regime. Simulations were performed using deep neural network potential trained with DFT data. Details about the neural network potential can be found in the publication: PRX Energy 4, 023004 (10.1103/PRXEnergy.4.023004). \n\nThe simulation used a 0.5 fs timestep. For each concentration, the dataset includes three 100-ns trajectories for a smaller system (about 1600 atoms) and one 20-ns trajectory for a larger system (about 15,000 atoms). These trajectories can be used to study ion solvation, structural organization, and dynamical behavior as a function of concentration.", + "keywords": [ + "simulation", + "machine learning", + "energy materials", + "diffusion" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1103/PRXEnergy.4.023004", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.24435/materialscloud:xb-4f", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8d0831be-baf6-4829-8187-e2e45192df1e", + "mdf_source_name": "8d0831be-baf6-4829-8187-e2e45192df1e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8d0831be-baf6-4829-8187-e2e45192df1e/1.0/", + "version": "1.0", + "root_version": "8d0831be-baf6-4829-8187-e2e45192df1e", + "latest": true + } + }, + { + "source_id": "915db26c-222b-4f06-9352-643a2f36415d", + "source_name": "915db26c-222b-4f06-9352-643a2f36415d", + "version": "1.0", + "ingest_date": "2025-03-13T23:09:13.052385Z", + "doi": "10.18126/1423-4046", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/915db26c-222b-4f06-9352-643a2f36415d/1.0/", + "metadata": { + "title": "Glassy Dynamics of Epoxy-Amine Thermosets Containing Dynamic, Aromatic Disulfides", + "authors": [ + { + "name": "Broderick Lewis Cheol Park, and Kenneth R. Shull, Joseph M. Dennis", + "given_name": "Joseph M. Dennis", + "family_name": "Broderick Lewis Cheol Park, and Kenneth R. Shull", + "affiliations": [ + "Northwestern University", + "Army Research Laboratory", + "NASA Langley Research Center" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \u201cGlassy Dynamics of Epoxy-Amine Thermosets Containing Dynamic, Aromatic Disulfides.\u201d B. Lewis, J.M. Dennis, C. Park & K.R. Shull, Macromolecules 57, 7112\u20137122 (2024) (http://dx.doi.org/10.1021/acs.macromol.4c01012). The dataset includes the following files and directories:\n- readme.md: Markdown file of this description. \n- Lewis et al. - 2024 - Glassy Dynamics of Epoxy-Amine Thermosets Containi.pdf: the journal-formatted pdf. \n- ma4c01012_si_001.pdf: Published supporting information. \n- python: python scripts used to generate the figures in the paper and in the supporting information.\n- data: raw data accessed by the python scripts.\n- figures: figures generated by the python scripts. ", + "keywords": [ + "experiment", + "polymers", + "glass transition" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.macromol.4c01012", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "915db26c-222b-4f06-9352-643a2f36415d", + "mdf_source_name": "915db26c-222b-4f06-9352-643a2f36415d" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/915db26c-222b-4f06-9352-643a2f36415d/1.0/", + "version": "1.0", + "root_version": "915db26c-222b-4f06-9352-643a2f36415d", + "latest": true + } + }, + { + "source_id": "607759eb-e2da-4782-959c-d21b3449f320", + "source_name": "607759eb-e2da-4782-959c-d21b3449f320", + "version": "1.0", + "ingest_date": "2025-03-13T22:23:10.335395Z", + "doi": "10.18126/97p3-hb33", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/607759eb-e2da-4782-959c-d21b3449f320/1.0/", + "metadata": { + "title": "Thermomechanical Characterization of High Tg Disulfide-Containing Thermoplastic Polyimides", + "authors": [ + { + "name": "Margaret A. Hall and Kenneth R. Shull, Broderick Lewis", + "given_name": "Broderick Lewis", + "family_name": "Margaret A. Hall and Kenneth R. Shull", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \u201cThermomechanical Characterization of High Tg Disulfide-Containing Thermoplastic Polyimides.\u201d M.A. Hall, B. Lewis & K.R. Shull, Macromolecules 58, 2065\u20132072 (2025) (http://dx.doi.org/10.1021/acs.macromol.4c02693)\n- The dataset includes the following files and directories:\n- readme.md: Markdown file of this description. \n- Hall et al. - 2025 - Thermomechanical Characterization of High Tg Disulfide-Containing Thermoplastic Polyimide. pdf. \n- supporting_info.pdf: Published supporting information. \n- python directory: python scripts for generating Figures 2,4 and 5 in the main body of the paper, and Figures 12-15 in the supporting information.\n- DMA directory: raw dynamic mechanical data accessed by the python scripts\n- QCM directory: raw quartz crystal microblance data accessed by the python scripts.", + "keywords": [ + "experiment", + "polymers", + "QCM", + "Mechanics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "607759eb-e2da-4782-959c-d21b3449f320", + "mdf_source_name": "607759eb-e2da-4782-959c-d21b3449f320" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/607759eb-e2da-4782-959c-d21b3449f320/1.0/", + "version": "1.0", + "root_version": "607759eb-e2da-4782-959c-d21b3449f320", + "latest": true + } + }, + { + "source_id": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "source_name": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "version": "1.0", + "ingest_date": "2025-03-31T03:39:19.643863Z", + "doi": "10.18126/91ax-1e09", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e/1.0/", + "metadata": { + "title": "The emergence of bulk structure in clusters via isotropic multi-well pair potentials", + "authors": [ + { + "name": "Doyle, Jennifer E.", + "given_name": "Jennifer E.", + "family_name": "Doyle", + "affiliations": [ + "Wellesley College", + "Cornell University" + ] + }, + { + "name": "Martirossyan, Maya M.", + "given_name": "Maya M.", + "family_name": "Martirossyan", + "affiliations": [ + "Wellesley College", + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Wellesley College", + "Cornell University" + ] + }, + { + "name": "Teich, Erin G.", + "given_name": "Erin G.", + "family_name": "Teich", + "affiliations": [ + "Wellesley College", + "Cornell University" + ] + } + ], + "description": "Dataset for \"The emergence of bulk structure in clusters via isotropic multi-well pair potentials\" (arXiv:2412.04588), consisting of all final frames of all crystallized clusters that were analyzed in the paper. Files are in GSD (https://gsd.readthedocs.io) format, and are able to be rendered by OVITO (https://www.ovito.org/).", + "keywords": [ + "simulation" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.48550/arXiv.2412.04588", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "mdf_source_name": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e/1.0/", + "version": "1.0", + "root_version": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "latest": true + } + }, + { + "source_id": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "source_name": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "version": "1.2", + "ingest_date": "2025-03-21T19:19:14.334322Z", + "doi": "10.18126/p0gx-tt30", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/0f2c24a3-71fa-49cc-8339-df4f9d443f95/1.2/", + "metadata": { + "title": "Fe-Cu-Ni liquid ternary calculations with EAM", + "authors": [ + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "# Data set for Fe-Cu-Ni liquid ternary calculations with LAMMPS\n\nOutput from a series of runs using the `FeCuNi.eam.alloy` EAM potential with LAMMPS to compute Gibbs free energies. The runs are from 1200K to 3000K in 300K increments, over a variety of concentrations. Chemical potential differences are extracted and analyzed to produce a ternary phase diagram. Nonequilibrium Hamiltonian integration is used to compute absolute Gibbs free energies for the pure liquids.", + "keywords": [ + "simulation", + "metals and alloys", + "EAM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "mdf_source_name": "0f2c24a3-71fa-49cc-8339-df4f9d443f95" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/0f2c24a3-71fa-49cc-8339-df4f9d443f95/1.2/", + "version": "1.2", + "root_version": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "latest": true + } + }, + { + "source_id": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "source_name": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "version": "1.0", + "ingest_date": "2025-04-02T18:23:30.570894Z", + "doi": "10.18126/hpbm-f471", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8550cbeb-e710-439f-96c3-2aaa5ded6a30/1.0/", + "metadata": { + "title": "Block copolymer self-assembly directed mesoporous gyroidal strontium titanate with room-temperature ferromagnetism", + "authors": [ + { + "name": "Moore, William", + "given_name": "William", + "family_name": "Moore", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Yang, Ningning", + "given_name": "Ningning", + "family_name": "Yang", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Nason, Abigail K.", + "given_name": "Abigail K.", + "family_name": "Nason", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Freychet, Guillaume", + "given_name": "Guillaume", + "family_name": "Freychet", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Beaucage, Peter A.", + "given_name": "Peter A.", + "family_name": "Beaucage", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Thom-Levy, Julia", + "given_name": "Julia", + "family_name": "Thom-Levy", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Gruner, Sol M.", + "given_name": "Sol M.", + "family_name": "Gruner", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "name": "Wiesner, Ulrich", + "given_name": "Ulrich", + "family_name": "Wiesner", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + } + ], + "description": "Data supporting Block copolymer self-assembly directed mesoporous gyroidal strontium titanate with room-temperature ferromagnetism", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "mdf_source_name": "8550cbeb-e710-439f-96c3-2aaa5ded6a30" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8550cbeb-e710-439f-96c3-2aaa5ded6a30/1.0/", + "version": "1.0", + "root_version": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "latest": true + } + }, + { + "source_id": "jolley_correlative_v1.2", + "source_name": "jolley_correlative", + "version": 1, + "ingest_date": "2024-01-26T15:03:14.322348Z", + "doi": "10.18126/t27t-9iao", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/jolley_correlative_v1.2/", + "metadata": { + "title": "Correlative X-ray Computed Tomography and Optical Microscopy Serial Sectioning Data of Additive Manufactured Ti-6Al-4V using External Fiducial Markers", + "authors": [ + { + "name": "Jolley, Bryce R.", + "given_name": "Bryce R.", + "family_name": "Jolley", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ] + }, + { + "name": "Uchic, Michael D.", + "given_name": "Michael D.", + "family_name": "Uchic", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ] + }, + { + "name": "Sparkman, Daniel", + "given_name": "Daniel", + "family_name": "Sparkman", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ] + }, + { + "name": "Chapman, Michael", + "given_name": "Michael", + "family_name": "Chapman", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ] + }, + { + "name": "Schwalbach, Edwin", + "given_name": "Edwin", + "family_name": "Schwalbach", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ] + } + ], + "description": "The x-ray computed tomography (XCT) data from five different scans are available for a volume of interest (VOI). Mechanical polish serial sectioning optical microscopy data (SS) for that same VOI is also provided. Each computed tomography system collected data with different processing parameters and acquired different voxel sizes for the sample. The reconstruction from each XCT scan is presented as a stack of TIFs registered with every other data set. These registered TIF stacks only represent the VOI that will correlate with the provided SS data. \nThe projections for each individual scan are provided in the format they were exported as. Scans A, B, and C contain projection images in 32-bit float format and are provided with corresponding reference and dark current images. Scan C projections are rotated by 90 degrees. Scan D consists of transmission radiographs scaled by 100 in 16-bit integer format with no reference image or dark current image. Scan E consists of transmission radiographs in 16-bit integer format scaled by 65535. Scans A and B include a \u2018_shifts.txt\u2019 text file containing the x and y shift of the detector for each projection. Scan A also consisted of an image taken at four different detector positions for each projection angle, and the detector position is included for each of these. The projections are over views from 0 to 360 degrees in a nominally circular trajectory. The corresponding view angle for each projection is provided in the \u2018_angles.txt\u2019 file for each scan. If reconstruction algorithms are to be used on these projections, registration of a generated reconstruction to the SS data will need to be accomplished independently. A portion of the projections (1812) for data set E was lost due to a data transfer error. The remaining 1329 projections are provided. \nThe SS data is presented as a stack of TIFs for the VOI that correlates with the multiple XCT scans. The in-plane resolution of the SS data is 2.8 \u03bcm. The average slice thickness for this stack of TIFs is 2.01 \u03bcm. The z-height map data is provided as a text file for the actual thicknesses. Accordingly, to effectively transfer the stack of SS images into a 3D data volume, the slices of the stack should be spaced vertically with the average of 2.01 \u03bcm between images or according to the actual thicknesses as given from the z-height map. \n", + "keywords": [ + "Serial Sectioning Data", + "Additive Manufacturing", + "Computed Tomography Data", + " Data Registration" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "jolley_correlative_v1.2", + "mdf_source_name": "jolley_correlative" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/jolley_correlative_v1.2/", + "version": "1.0", + "root_version": "jolley_correlative_v1.2", + "latest": true + } + }, + { + "source_id": "4dclustering_v1.1", + "source_name": "4dclustering", + "version": 1, + "ingest_date": "2024-01-22T01:14:33.920211Z", + "doi": "10.18126/z6m9-o3hv", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/4dclustering_v1.1/", + "metadata": { + "title": "Clustering Diffraction Vectors from Overlapping Structures in 4D STEM Data Sets", + "authors": [ + { + "name": "Carter Francis, Paul M. Voyles", + "given_name": "Paul M. Voyles", + "family_name": "Carter Francis", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ] + } + ], + "description": "The following contains two folders containing the raw and processed data for the Paper ``Clustering Diffraction Vectors from Overlapping Structures in 4-D STEM Data Sets`` There are 4 datasets included.\nThe first three datasets are from a PdCuSi thin film sputtered metallic glasses:\n1. 4nmSample2pt5mradCompressed.zspy\n2. 9nmSample2pt5mradCompressed.zspy\n3. 13nmSample2pt5mradCompressed.zspy\nThe last dataset is an Al nano crystal standard sample.\nAll of the datasets are saved in the ``.zspy`` file format (defined in the ``rosettasciio`` package) using the ``Zarr.ZipStore`` formalism.\nIf you want to load the data first install hyperspy\n```\n$pip install hyperspy\n# or\n$conda install hyperspy\n```\nThen you can load the data using:\n```python\nimport hyperspy.api as hs\nimport zarr\nstore = zarr.ZipStore(\"file_you_want_to_read.zspy\")\ns = hs.load(store, lazy=True) # lazy so you don't load the entire data into memory", + "keywords": [ + "microscopy", + "metallic glasses", + "4DSTEM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "4dclustering_v1.1", + "mdf_source_name": "4dclustering" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/4dclustering_v1.1/", + "version": "1.0", + "root_version": "4dclustering_v1.1", + "latest": true + } + }, + { + "source_id": "sotck_nov17_mummy_v1.1", + "source_name": "sotck_nov17_mummy", + "version": 1, + "ingest_date": "2024-01-31T03:21:43.899548Z", + "doi": "10.18126/o00b-7reh", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sotck_nov17_mummy_v1.1/", + "metadata": { + "title": "Combined computed tomography and position-resolved X-ray diffraction of an intact Roman-era Egyptian portrait mummy", + "authors": [ + { + "name": "Stock, S. R.", + "given_name": "S. R.", + "family_name": "Stock", + "affiliations": [ + "Metropolitan State Univ. of Denver" + ] + }, + { + "name": "Stock, M. K.", + "given_name": "M. K.", + "family_name": "Stock", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Almer, J. D.", + "given_name": "J. D.", + "family_name": "Almer", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "Diffraction data and associated metadata for \nStock SR, Stock MK, Almer JD. 2020 Combined computed tomography and position-resolved X-ray diffraction of an intact Roman-era Egyptian portrait mummy. J. R. Soc. Interface 17: 20200686. http://dx.doi.org/10.1098/rsif.2020.0686", + "keywords": [ + "experiment", + " diffraction" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "http://dx.doi.org/10.1098/rsif.2020.0686", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "sotck_nov17_mummy_v1.1", + "mdf_source_name": "sotck_nov17_mummy" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/sotck_nov17_mummy_v1.1/", + "version": "1.0", + "root_version": "sotck_nov17_mummy_v1.1", + "latest": true + } + }, + { + "source_id": "rangnekar_electroluminescence_megasonically_films_v1.1", + "source_name": "rangnekar_electroluminescence_megasonically_films", + "version": 1, + "ingest_date": "2024-01-22T14:11:28.058528Z", + "doi": "10.18126/pviz-km4p", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rangnekar_electroluminescence_megasonically_films_v1.1/", + "metadata": { + "title": "Electroluminescence from Megasonically Solution-Processed MoS2 Nanosheet Films", + "authors": [ + { + "name": "Rangnekar, Sonal V.", + "given_name": "Sonal V.", + "family_name": "Rangnekar", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Jin, Mengru", + "given_name": "Mengru", + "family_name": "Jin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Khalaj, Maryam", + "given_name": "Maryam", + "family_name": "Khalaj", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Szyd\u0142owska, Beata M.", + "given_name": "Beata M.", + "family_name": "Szyd\u0142owska", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Dasgupta, Anushka", + "given_name": "Anushka", + "family_name": "Dasgupta", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kuo, Lidia", + "given_name": "Lidia", + "family_name": "Kuo", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kurtz, Heather E.", + "given_name": "Heather E.", + "family_name": "Kurtz", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsnano.3c06034", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "rangnekar_electroluminescence_megasonically_films_v1.1", + "mdf_source_name": "rangnekar_electroluminescence_megasonically_films" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/rangnekar_electroluminescence_megasonically_films_v1.1/", + "version": "1.0", + "root_version": "rangnekar_electroluminescence_megasonically_films_v1.1", + "latest": true + } + }, + { + "source_id": "felements_v1.1", + "source_name": "felements", + "version": 1, + "ingest_date": "2024-01-19T20:48:33.414902Z", + "doi": "10.18126/hfzz-ull5", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/felements_v1.1/", + "metadata": { + "title": "Dataset for \"A new generation of effective core potentials: selected lanthanides and heavy elements\"", + "authors": [ + { + "name": "Zhou, Haihan", + "given_name": "Haihan", + "family_name": "Zhou", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Kincaid, Benjamin E.", + "given_name": "Benjamin E.", + "family_name": "Kincaid", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Wang, Guangming", + "given_name": "Guangming", + "family_name": "Wang", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Annaberdiyev, Abdulgani", + "given_name": "Abdulgani", + "family_name": "Annaberdiyev", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Ganesh, Panchapakesan", + "given_name": "Panchapakesan", + "family_name": "Ganesh", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Mitas, Lubos", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "All of the inputs and outputs used in the JCP paper \"A new generation of effective core potentials: selected lanthanides and heavy elements\" by H. Zhou et. al.", + "keywords": [ + "simulation", + "DFT", + "QMCPack", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "felements_v1.1", + "mdf_source_name": "felements" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/felements_v1.1/", + "version": "1.0", + "root_version": "felements_v1.1", + "latest": true + } + }, + { + "source_id": "quinone_qmc_v1.2", + "source_name": "quinone_qmc", + "version": 1, + "ingest_date": "2024-01-19T19:45:49.150512Z", + "doi": "10.18126/13xf-0nxn", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/quinone_qmc_v1.2/", + "metadata": { + "title": "The Paraquinone + H2 \u2192 Hydroquinone Reaction: A Challenge for Diffusion Monte Carlo Calculations ", + "authors": [ + { + "name": "Mauger, Nastasia", + "given_name": "Nastasia", + "family_name": "Mauger", + "affiliations": [ + "Argonne National Laboratory", + "University of Pittsburgh" + ] + }, + { + "name": "Jordan, Kenneth D.", + "given_name": "Kenneth D.", + "family_name": "Jordan", + "affiliations": [ + "Argonne National Laboratory", + "University of Pittsburgh" + ] + }, + { + "name": "Benali, Anouar", + "given_name": "Anouar", + "family_name": "Benali", + "affiliations": [ + "Argonne National Laboratory", + "University of Pittsburgh" + ] + } + ], + "description": "This data set contains input and outfiles for the simulations associated with the paper: \"The Paraquinone + H2 \u2192 Hydroquinone Reaction: A Challenge for Diffusion Monte Carlo Calculations.\" N, Mauger A. Benali and K. D. Jordan ", + "keywords": [ + "simulation", + "QMCPack", + "QMC" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "quinone_qmc_v1.2", + "mdf_source_name": "quinone_qmc" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/quinone_qmc_v1.2/", + "version": "1.0", + "root_version": "quinone_qmc_v1.2", + "latest": true + } + }, + { + "source_id": "li_what_is_efficiency_v1.4", + "source_name": "li_what_is_efficiency", + "version": 1, + "ingest_date": "2024-01-22T14:22:05.893557Z", + "doi": "10.18126/tbna-f89g", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/li_what_is_efficiency_v1.4/", + "metadata": { + "title": "What is the role of non-fullerene acceptor symmetry in polymer solar cell efficiency?", + "authors": [ + { + "name": "Li, Guoping", + "given_name": "Guoping", + "family_name": "Li", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Qin, Fei", + "given_name": "Fei", + "family_name": "Qin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Jacobberger, Robert M.", + "given_name": "Robert M.", + "family_name": "Jacobberger", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Mukherjee, Subhrangsu", + "given_name": "Subhrangsu", + "family_name": "Mukherjee", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Jones, Leighton O.", + "given_name": "Leighton O.", + "family_name": "Jones", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Young, Ryan M.", + "given_name": "Ryan M.", + "family_name": "Young", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Pankow, Robert M.", + "given_name": "Robert M.", + "family_name": "Pankow", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kerwin, Brendan P.", + "given_name": "Brendan P.", + "family_name": "Kerwin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Flagg, Lucas Q.", + "given_name": "Lucas Q.", + "family_name": "Flagg", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Zheng, Ding", + "given_name": "Ding", + "family_name": "Zheng", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Feng, Liang-Wen", + "given_name": "Liang-Wen", + "family_name": "Feng", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Kohlstedt, Kevin L.", + "given_name": "Kevin L.", + "family_name": "Kohlstedt", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Sangwan, Vinod K.", + "given_name": "Vinod K.", + "family_name": "Sangwan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Hersam, Mark C.", + "given_name": "Mark C.", + "family_name": "Hersam", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Schatz, George C.", + "given_name": "George C.", + "family_name": "Schatz", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "DeLongchamp, Dean M.", + "given_name": "Dean M.", + "family_name": "DeLongchamp", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Wasielewski, Michael R.", + "given_name": "Michael R.", + "family_name": "Wasielewski", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Zhou, Yinhua", + "given_name": "Yinhua", + "family_name": "Zhou", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Facchetti, Antonio", + "given_name": "Antonio", + "family_name": "Facchetti", + "affiliations": [ + "Northwestern University" + ] + }, + { + "name": "Marks, Tobin J.", + "given_name": "Tobin J.", + "family_name": "Marks", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "MDF Open", + "extensions": { + "mdf_source_id": "li_what_is_efficiency_v1.4", + "mdf_source_name": "li_what_is_efficiency" + }, + "download_url": "https://data.materialsdatafacility.org/mdf_open/li_what_is_efficiency_v1.4/", + "version": "1.0", + "root_version": "li_what_is_efficiency_v1.4", + "latest": true + } + }, + { + "source_id": "Dataset_Li_conductivity", + "source_name": "Dataset_Li_conductivity", + "version": "1.0", + "ingest_date": "2024-03-28T19:58:58.942413Z", + "doi": "10.18126/k9tn-3965", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/Dataset_Li_conductivity/1.0/", + "metadata": { + "title": "A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning", + "authors": [ + { + "name": "Hargreaves, Cameron J.", + "given_name": "Cameron J.", + "family_name": "Hargreaves", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Gaultois, Michael W.", + "given_name": "Michael W.", + "family_name": "Gaultois", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Daniels, Luke M.", + "given_name": "Luke M.", + "family_name": "Daniels", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Watts, Emma J.", + "given_name": "Emma J.", + "family_name": "Watts", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Kurlin, Vitaliy A.", + "given_name": "Vitaliy A.", + "family_name": "Kurlin", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Moran, Michael", + "given_name": "Michael", + "family_name": "Moran", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Dang, Yun", + "given_name": "Yun", + "family_name": "Dang", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Morris, Rhun", + "given_name": "Rhun", + "family_name": "Morris", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Morscher, Alexandra", + "given_name": "Alexandra", + "family_name": "Morscher", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Thompson, Kate", + "given_name": "Kate", + "family_name": "Thompson", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Wright, Matthew A.", + "given_name": "Matthew A.", + "family_name": "Wright", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Prasad, Beluvalli-Eshwarappa", + "given_name": "Beluvalli-Eshwarappa", + "family_name": "Prasad", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Blanc, Fr\u00e9d\u00e9ric", + "given_name": "Fr\u00e9d\u00e9ric", + "family_name": "Blanc", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Collins, Chris M.", + "given_name": "Chris M.", + "family_name": "Collins", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": "Crawford, Catriona A.", + "given_name": "Catriona A.", + "family_name": "Crawford", + "affiliations": [ + "University of Liverpool" + ] + }, + { + "name": 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[ + "University of California- Santa Barbara" + ] + }, + { + "name": "Yamamoto, Takuya", + "given_name": "Takuya", + "family_name": "Yamamoto", + "affiliations": [ + "University of California- Santa Barbara" + ] + }, + { + "name": "Odette, G. Robert", + "given_name": "G. Robert", + "family_name": "Odette", + "affiliations": [ + "University of California- Santa Barbara" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of California- Santa Barbara" + ] + } + ], + "description": "Dataset containing 4535 transition temperature shifts of reactor pressure vessel steels", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2023, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "Dataset_RPV_TTS", + "mdf_source_name": "Dataset_RPV_TTS" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_RPV_TTS", + "splits": [ + { + "type": "train", + "path": "RPV_UCSB_Plotter_combined_2023-04-17_quad2term_noATR1_dropdups.xlsx", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "temperature_C", + "role": "input", + "units": "degC", + "description": "Temperature of measurement" + }, + { + "name": "wt_percent_Cu", + "role": "input", + "units": "wt%", + "description": "Amount of Cu" + }, + { + "name": "wt_percent_Ni", + "role": "input", + "units": "wt%", + "description": "Amount of Ni" + }, + { + "name": "wt_percent_Mn", + "role": "input", + "units": "wt%", + "description": "Amount of Mn" + }, + { + "name": "wt_percent_P", + "role": "input", + "units": "wt%", + "description": "Amount of P" + }, + { + "name": "wt_percent_Si", + "role": "input", + "units": "wt%", + "description": "Amount of Si" + }, + { + "name": "wt_percent_C", + "role": "input", + "units": "wt%", + "description": "Amount of C" + }, + { + "name": "log(fluence_n_cm2)", + "role": "input", + "units": "n/cm2", + "description": "Irradiation fluence (log scale)" + }, + { + "name": "log(flux_n_cm2_sec)", + "role": "input", + "units": "n/cm2-s", + "description": "Irradiation flux (log scale)" + }, + { + "name": "datatype", + "role": "input", + "units": "", + "description": "Data subtype" + }, + { + "name": "Measured DT41J [C]", + "role": "target", + "units": "degC", + "description": "Ductile-to-brittle transition temperature shift" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org//foundry/Dataset_RPV_TTS/1.0/", + "version": "1.0", + "root_version": "Dataset_RPV_TTS", + "latest": true + } + }, + { + "source_id": "Dataset_perovskite_conductivity", + "source_name": "Dataset_perovskite_conductivity", + "version": "1.0", + "ingest_date": "2024-03-28T21:00:24.900714Z", + "doi": "10.18126/f1vb-et73", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/Dataset_perovskite_conductivity/1.0/", + "metadata": { + "title": "Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning", + "authors": [ + { + "name": "Priya, Pikee", + "given_name": "Pikee", + "family_name": "Priya", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Aluru, N. R.", + "given_name": "N. R.", + "family_name": "Aluru", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "Dataset containing 7230 perovskite conductivity data points", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2021, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "Dataset_perovskite_conductivity", + "mdf_source_name": "Dataset_perovskite_conductivity" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_perovskite_conductivity", + "splits": [ + { + "type": "train", + "path": "perovskite_conductivity_mlready.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "Composition", + "role": "input", + "units": "", + "description": "Material composition" + }, + { + "name": "Temperature (degC)", + "role": "input", + "units": "degC", + "description": "Temperature of measurement" + }, + { + "name": "PO2 (%)", + "role": "input", + "units": "", + "description": "Oxygen partial pressure" + }, + { + "name": "PH2O (%)", + "role": "input", + "units": "", + "description": "Water partial pressure" + }, + { + "name": "Grainsize", + "role": "input", + "units": "microns", + "description": "Grain size" + }, + { + "name": "Ref", + "role": "input", + "units": "", + "description": "Original reference" + }, + { + "name": "Carrier Type", + "role": "input", + "units": "", + "description": "Designation of dominant carrier type" + }, + { + "name": "Conductivity (S/cm)", + "role": "target", + "units": "S/cm", + "description": "Total conductivity" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org//foundry/Dataset_perovskite_conductivity/1.0/", + "version": "1.0", + "root_version": "Dataset_perovskite_conductivity", + "latest": true + } + }, + { + "source_id": "Dataset_perovskite_formationE", + "source_name": "Dataset_perovskite_formationE", + "version": "1.0", + "ingest_date": "2024-03-28T21:08:23.494135Z", + "doi": "10.18126/xmh8-d711", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/Dataset_perovskite_formationE/1.0/", + "metadata": { + "title": "Computational screening of perovskite metal oxides for optimal solar light capture", + "authors": [ + { + "name": "Castelli, Ivano E.", + "given_name": "Ivano E.", + "family_name": "Castelli", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Olsen, Thomas", + "given_name": "Thomas", + "family_name": "Olsen", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Datta, Soumendu", + "given_name": "Soumendu", + "family_name": "Datta", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Landis, David D.", + "given_name": "David D.", + "family_name": "Landis", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Dahl, S\u00f8ren", + "given_name": "S\u00f8ren", + "family_name": "Dahl", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Thygesena, Kristian S.", + "given_name": "Kristian S.", + "family_name": "Thygesena", + "affiliations": [ + "Technical University of Denmark" + ] + }, + { + "name": "Jacobsen, Karsten W.", + "given_name": "Karsten W.", + "family_name": "Jacobsen", + "affiliations": [ + "Technical University of Denmark" + ] + } + ], + "description": "Dataset containing 9646 perovskite formation energy data points", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2011, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "Dataset_perovskite_formationE", + "mdf_source_name": "Dataset_perovskite_formationE" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_perovskite_formationE", + "splits": [ + { + "type": "train", + "path": "castelli_cleaned.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "formula", + "role": "input", + "units": "", + "description": "Material composition" + }, + { + "name": "target", + "role": "target", + "units": "eV/atom", + "description": "Formation energy" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org//foundry/Dataset_perovskite_formationE/1.0/", + "version": "1.0", + "root_version": "Dataset_perovskite_formationE", + "latest": true + } + }, + { + "source_id": "Dataset_metallicglass_Rc_LLM", + "source_name": "Dataset_metallicglass_Rc_LLM", + "version": "1.0", + "ingest_date": "2024-03-28T20:29:42.785083Z", + "doi": "10.18126/ndyp-yv32", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/Dataset_metallicglass_Rc_LLM/1.0/", + "metadata": { + "title": "Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering", + "authors": [ + { + "name": "Polak, Maciej P.", + "given_name": "Maciej P.", + "family_name": "Polak", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "name": "Morgan, Dane", + "given_name": "Dane", + "family_name": "Morgan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "description": "Dataset containing LLM-derived experimental critical cooling rates of 297 metallic glasses", + "keywords": [ + "machine learning", + "foundry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "organization": "Foundry", + "extensions": { + "mdf_source_id": "Dataset_metallicglass_Rc_LLM", + "mdf_source_name": "Dataset_metallicglass_Rc_LLM" + }, + "ml": { + "data_format": "tabular", + "short_name": "Dataset_metallicglass_Rc_LLM", + "splits": [ + { + "type": "train", + "path": "Metallic_glass_Rc.csv", + "label": "train", + "n_items": null + } + ], + "keys": [ + { + "name": "Passage", + "role": "input", + "units": "", + "description": "Text passage LLM found to get data" + }, + { + "name": "DOI", + "role": "input", + "units": "", + "description": "Original data reference" + }, + { + "name": "Material", + "role": "input", + "units": "", + "description": "Material composition" + }, + { + "name": "Rc", + "role": "input", + "units": "K/s", + "description": "Critical cooling rate" + }, + { + "name": "Unit", + "role": "input", + "units": "", + "description": "Critical cooling rate units" + }, + { + "name": "log(Rc)", + "role": "target", + "units": "K/s", + "description": "Critical cooling rate (log scale)" + } + ] + }, + "download_url": "https://data.materialsdatafacility.org//foundry/Dataset_metallicglass_Rc_LLM/1.0/", + "version": "1.0", + "root_version": "Dataset_metallicglass_Rc_LLM", + "latest": true + } + }, + { + "source_id": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "source_name": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "version": "1.0", + "ingest_date": "2024-06-07T06:26:51.155530Z", + "doi": "10.18126/b2sj-nt57", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6d571f53-a034-47e5-b3e3-d01adf437ae8/1.0/", + "metadata": { + "title": "Data for: A methodology for robust multislice ptychography", + "authors": [ + { + "name": "Gilgenbach, Colin", + "given_name": "Colin", + "family_name": "Gilgenbach", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "Chen, Xi", + "given_name": "Xi", + "family_name": "Chen", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "name": "LeBeau, James M.", + "given_name": "James M.", + "family_name": "LeBeau", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + } + ], + "description": "This repository contains simulated and experimental datasets from the paper \"A methodology for robust multislice ptychography\".\n\n# File layout\n\nThis repository is structured with the following subdirectories:\n\n - exp-data: Raw experimental datasets, metadata and reconstruction parameters\n - sim-data: Simulated datasets, metadata and reconstruction parameters\n - code: Code for reconstruction & analysis\n\n# Metadata format\n\n4D STEM files are provided in the EMPAD raw data format, accompanied with metadata provided in JSON format. A description of this metadata format is provided in the notebook `code/data_format.ipynb`, and a Python schema is provided in `code/metadata.py`.\n\n# Simulated data\n\nIn addition to raw datasets + metadata, simulated datasets contain additional information. This information is summarized in the `code/data_format.ipynb` notebook. The supercell used for simulation is provided in the XCrysDen/XSF file format as well as the Prismatic-flavor XYZ file format.\n\n# License information & acknowledements\n\nThe MATLAB code provided is a modified fork of Yi Jiang's `fold_slice`, which is based on the PtychoShelves package developed at the Paul Scherrer Institut. In addition to the authors at PSI, the code contains work from the following authors:\n\n - Zhen Chen\n - Yi Jiang\n - Colin Gilgenbach\n - Michael Xu\n - Xi Chen\n\nThe code can be found on GitHub at https://github.com/LeBeauGroup/fold_slice, commit 7c9816f0584ff142c90aa836ed744a299397dad6.\n\nThe code is licensed under the same license as the original PtychoShelves code, which is a academic non-commercial license from the Paul Scherr Institut. The complete license agreement can be found in the `LICENSE.md` file in `code/fold_slice`. All reproductions of this code must be distributed under this same license.\n\nThe remainder of the code and data in this repository is licensed under the [Creative Commons Attribution Sharealike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) (CC BY-SA 4.0) license. This license allows for copying, reproduction, and transformation, but requires modified versions to be released under the same license with proper attribution.", + "keywords": [ + "simulation", + "experiment", + "semiconductors", + "microscopy", + "ptychography" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1093/mam/ozae055", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "mdf_source_name": "6d571f53-a034-47e5-b3e3-d01adf437ae8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/6d571f53-a034-47e5-b3e3-d01adf437ae8/1.0/", + "version": "1.0", + "root_version": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "latest": true + } + }, + { + "source_id": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "source_name": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "version": "1.0", + "ingest_date": "2024-05-28T21:05:56.674298Z", + "doi": "10.18126/2zmk-n291", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/4f8e7a42-09a2-45aa-af07-0994d14d2a54/1.0/", + "metadata": { + "title": "Adhesion and Motility Quantification to Assess Cancer Cell Aggression", + "authors": [ + { + "name": "Mehanna, Lauren E.", + "given_name": "Lauren E.", + "family_name": "Mehanna", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "name": "Boyd, James D.", + "given_name": "James D.", + "family_name": "Boyd", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "name": "Walker, Chloe G.", + "given_name": "Chloe G.", + "family_name": "Walker", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "name": "Osborne, Adrianna R.", + "given_name": "Adrianna R.", + "family_name": "Osborne", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "name": "Grady, Martha E.", + "given_name": "Martha E.", + "family_name": "Grady", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "name": "Berron, Brad J.", + "given_name": "Brad J.", + "family_name": "Berron", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + } + ], + "description": "During epithelial-to-mesenchymal transition (EMT), cancer cells lose their cell-cell adhesion junctions as they become more metastatic, altering cell motility and focal adhesion disassembly associated with increased detachment from the primary tumor and a migratory response into nearby tissue and vasculature. Current in vitro strategies characterizing a cell\u2019s metastatic potential heavily favor quantifying the presence of cell adhesion biomarkers through biochemical analysis; however, mechanical cues such as adhesion and motility directly relate to cancer metastasis and can be quantified without needing to first identify a cell specific biomarker for a particular type of cancer. This paper presents a comprehensive comparison of two functional metrics of cancer aggression, wound closure migration velocity and cell detachment from a culture surface, for three pairs of epithelial cancer cell lines (breast, endometrium, tongue tissue origins). On average, cell lines with low metastatic potential (MCF-7, Ishikawa, and Cal-27) were more aggressive through wound closure migration compared to loss of cell adhesion. On the other hand, cell lines with high metastatic potential (MDA-MB-231, KLE, and SCC-25) were on average more aggressive through loss of cell adhesion compared to wound closure migration. This trend was true independent of the tissue type where the cells originated, indicating that there is a relationship between metastatic potential and the predominate type of cancer aggression. Our work presents one of the first combined studies observing functional cell migration and adhesion across cancer cell lines from selected tissue origins, without needing to identify tissue-specific biomarkers to achieve success. Using functional metrics to assess cancer cell aggression provides a powerful clinical tool for future label-free bioseparation platforms.", + "keywords": [ + "adhesion", + "shear flow", + "cancer metastasis", + "metastatic potential", + "wound closure" + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "mdf_source_name": "4f8e7a42-09a2-45aa-af07-0994d14d2a54" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/4f8e7a42-09a2-45aa-af07-0994d14d2a54/1.0/", + "version": "1.0", + "root_version": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "latest": true + } + }, + { + "source_id": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e", + "source_name": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e", + "version": "1.1", + "ingest_date": "2024-06-06T08:35:07.095739Z", + "doi": "10.18126/vzbt-a750", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e/1.1/", + "metadata": { + "title": "Insights into the Nucleation and Growth of BiOCl Nanoparticles by In Situ X- ray Pair Distribution Function Analysis and In Situ Liquid Cell TEM", + "authors": [ + { + "name": "Gordon, Matthew N.", + "given_name": "Matthew N.", + "family_name": "Gordon", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Junkers, Laura S.", + "given_name": "Laura S.", + "family_name": "Junkers", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Googasian, Jack S.", + "given_name": "Jack S.", + "family_name": "Googasian", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Mathiesen, Jette K.", + "given_name": "Jette K.", + "family_name": "Mathiesen", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Zhan, Xun", + "given_name": "Xun", + "family_name": "Zhan", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Morgan, David Gene", + "given_name": "David Gene", + "family_name": "Morgan", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Jensen, Kirsten M. \u00d8.", + "given_name": "Kirsten M. \u00d8.", + "family_name": "Jensen", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "name": "Skrabalak, Sara E.", + "given_name": "Sara E.", + "family_name": "Skrabalak", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + } + ], + "description": "Data Collection associated with the above titled paper, pulished in Nanoscale.\nAbstract: The synthesis of bismuth oxyhalides as defined nanostructures is hindered by their fast nucleation and growth in aqueous solutions. Using our recently developed single-source precursor, the formation of bismuth oxychloride in such solutions can be slowed significantly. As reported herein, this advance enables BiOCl formation to be investigated by in situ X-ray total scattering and in situ liquid cell transmission electron microscopy. In situ pair distribution function analysis of X-ray total scattering data reveals the local order of atomic structures throughout the synthesis, while in situ liquid cell transmission electron microscopy allows for tracking the growth of individual nanoparticles. Through this work, the precursor complex is shown to give rise to BiOCl upon heating in solution without the observation of structurally distinct intermediates. The emerging nanoparticles have a widened interlayer spacing, which moderately decreases as the particles grow. Mechanistic insights into the formation of bismuth oxyhalide nanoparticles, including the absence of distinct intermediates within the available time resolution, will help facilitate future design of controlled BiOX nanostructures. 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}, + { + "name": "Badr, Hussein", + "given_name": "Hussein", + "family_name": "Badr", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "name": "Cope, Jacob", + "given_name": "Jacob", + "family_name": "Cope", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "name": "Ferral, Emilio", + "given_name": "Emilio", + "family_name": "Ferral", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "name": "Barsoum, Michel", + "given_name": "Michel", + "family_name": "Barsoum", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "name": "Hu, Yong-Jie", + "given_name": "Yong-Jie", + "family_name": "Hu", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "name": "Klie, Robert", + "given_name": "Robert", + "family_name": "Klie", + "affiliations": [ + "University of Illinois at 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Function of Materials (IEK-2)" + ] + }, + { + "name": "Schwaiger, Ruth", + "given_name": "Ruth", + "family_name": "Schwaiger", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + }, + { + "name": "Hodge, Andrea", + "given_name": "Andrea", + "family_name": "Hodge", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + } + ], + "description": "XRD dataset for 169 unique CuNiAl compositions analyzed as-sputtered and annealed. 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F.", + "affiliations": [ + "University of Illinois at Chicago", + "Argonne National Laboratory", + "Arizona State University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "cf3ddbe3-2911-486e-9961-8b98385451c7", + "mdf_source_name": "cf3ddbe3-2911-486e-9961-8b98385451c7" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/cf3ddbe3-2911-486e-9961-8b98385451c7/1.0/", + "version": "1.0", + "root_version": "cf3ddbe3-2911-486e-9961-8b98385451c7", + "latest": true + } + }, + { + "source_id": "9d4b8454-b8eb-4767-a2d7-ad7e1d423c29", + "source_name": "9d4b8454-b8eb-4767-a2d7-ad7e1d423c29", + "version": "1.0", + "ingest_date": "2024-06-01T15:24:49.785654Z", + "doi": "10.18126/3j2f-1h39", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/9d4b8454-b8eb-4767-a2d7-ad7e1d423c29/1.0/", + "metadata": { + "title": "Chemically synthesized poly(3,4-ethylenedioxythiophene) conducting polymer as a robust electrocatalyst for highly efficient dye-sensitized solar cells", + "authors": [ + { + "name": "Masud", + "given_name": "Masud", + "affiliations": [ + "POSTECH" + ] + }, + { + "name": "Kim, Youn Soo", + "given_name": "Youn Soo", + "family_name": "Kim", + "affiliations": [ + "Dongguk University" + ] + }, + { + "name": "Kim, YHwan Kyu", + "given_name": "YHwan Kyu", + "family_name": "Kim", + "affiliations": [ + "Korea University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "9d4b8454-b8eb-4767-a2d7-ad7e1d423c29", + "mdf_source_name": "9d4b8454-b8eb-4767-a2d7-ad7e1d423c29" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/9d4b8454-b8eb-4767-a2d7-ad7e1d423c29/1.0/", + "version": "1.0", + "root_version": "9d4b8454-b8eb-4767-a2d7-ad7e1d423c29", + "latest": true + } + }, + { + "source_id": "7337da5b-e9c1-4f6e-b3e3-fe1a45984d29", + "source_name": "7337da5b-e9c1-4f6e-b3e3-fe1a45984d29", + "version": "1.0", + "ingest_date": "2024-06-21T19:28:02.242059Z", + "doi": "10.18126/nr56-ew87", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/7337da5b-e9c1-4f6e-b3e3-fe1a45984d29/1.0/", + "metadata": { + "title": "Coexistence of ferromagnetism and antiferromagnetic dimers in topological insulators", + "authors": [ + { + "name": "McQueeney, Robert", + "given_name": "Robert", + "family_name": "McQueeney", + "affiliations": [ + "Ames Laboratory", + "Iowa State University" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "7337da5b-e9c1-4f6e-b3e3-fe1a45984d29", + "mdf_source_name": "7337da5b-e9c1-4f6e-b3e3-fe1a45984d29" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/7337da5b-e9c1-4f6e-b3e3-fe1a45984d29/1.0/", + "version": "1.0", + "root_version": "7337da5b-e9c1-4f6e-b3e3-fe1a45984d29", + "latest": true + } + }, + { + "source_id": "35597120-2082-4d46-b054-16d625cf43b8", + "source_name": "35597120-2082-4d46-b054-16d625cf43b8", + "version": "1.0", + "ingest_date": "2025-03-13T18:22:40.762976Z", + "doi": "10.18126/85t4-vc27", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/35597120-2082-4d46-b054-16d625cf43b8/1.0/", + "metadata": { + "title": "Using the Quartz Crystal Microbalance to Monitor the Curing of Drying Oils", + "authors": [ + { + "name": "Gwen dePolo Marco Faustini, Lucie Laporte, C\u00f4me Thillaye du Boullay, \u00c9tienne Barthel, Joen Hermans, Piet D. Iedema, Laurence de Viguerie, * and Kenneth R. Shull, Arnaud Lesaine", + "given_name": "Arnaud Lesaine", + "family_name": "Gwen dePolo Marco Faustini, Lucie Laporte, C\u00f4me Thillaye du Boullay, \u00c9tienne Barthel, Joen Hermans, Piet D. Iedema, Laurence de Viguerie, * and Kenneth R. Shull", + "affiliations": [ + "Northwestern University", + "Sorbonne Universit\u00e9", + "University of Amsterdam" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \u201cUsing the Quartz Crystal Microbalance to Monitor the Curing of Drying Oils.\u201d G. dePolo, A. Lesaine, M. Faustini, L. Laporte, C. Thillaye du Boullay, \u00c9. Barthel, J. Hermans, P.D. Iedema, L. de Viguerie & K.R. Shull, Anal. Chem. 96, 10551\u201310558 (2024) (http://dx.doi.org/10.1021/acs.analchem.4c00938). The dataset includes the following files and directories:\n- readme.md: Markdown file of this description. \n- dePolo et al. - 2024 - Using the Quartz Crystal Microbalance to Monitor t.pdf: the journal-formatted pdf. \n- supporting_info.pdf: Published supporting information. \n- python directory: python scripts used to generate figures in the paper. \n- data directory: raw data access by the pythons files\n- figures directory: figures generated by the python scripts and appearing in the main paper and supporting information. ", + "keywords": [ + "experiment", + "polymers", + "QCM" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acs.analchem.4c00938", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "35597120-2082-4d46-b054-16d625cf43b8", + "mdf_source_name": "35597120-2082-4d46-b054-16d625cf43b8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/35597120-2082-4d46-b054-16d625cf43b8/1.0/", + "version": "1.0", + "root_version": "35597120-2082-4d46-b054-16d625cf43b8", + "latest": true + } + }, + { + "source_id": "a049d762-5cb4-43e3-9f25-dd1c6a052e24", + "source_name": "a049d762-5cb4-43e3-9f25-dd1c6a052e24", + "version": "1.0", + "ingest_date": "2024-08-01T04:24:55.028378Z", + "doi": "10.18126/ts8g-1070", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a049d762-5cb4-43e3-9f25-dd1c6a052e24/1.0/", + "metadata": { + "title": "Disorder and demixing in bidisperse particle systems assembling bcc crystals", + "authors": [ + { + "name": "Kennard, Jasmin J.", + "given_name": "Jasmin J.", + "family_name": "Kennard", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Solano, H. Jonathan Zelaya", + "given_name": "H. Jonathan Zelaya", + "family_name": "Solano", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Biddulph, Caleb D.", + "given_name": "Caleb D.", + "family_name": "Biddulph", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Prager, Ryan C.", + "given_name": "Ryan C.", + "family_name": "Prager", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Dshemuchadse, Julia", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by J. J. Kennard, H. J. Zelaya Solano, R. C. Prager and J. Dshemuchadse, \u201cDisorder and demixing in bidisperse particle systems assembling bcc crystals\u201d J. Chem. Phys. 161, 054715 (2024).\n\nWe investigate the robustness of self-assembling bcc-type crystals via isotropic interaction potentials in binary systems with increasingly disparate particle sizes, by determining their terminal size ratio\u2014the most extreme size ratio at which a mixed, binary bcc bcrystal forms.\nOur findings show that two-well pair potentials produce bcc crystals that are more robust with respect to particle size ratio than one-well pair potentials. Additionally we document qualitative differences in the process of ordering and disordering: in bidisperse systems of particles interacting via one-well potentials, we observe a breakdown of order prior to demixing, while in systems interacting via two-well potentials demixing occurs first and bcc continues to form in parts of the droplet down to low size ratios.\n\nThis dataset includes 25,000 final simulation frames of the resulting crystal and amorphous droplets (.gsd) and the corresponding interaction potential and simulation parameters (.json) used in this study. A README.txt file is included for parsing the data.", + "publisher": "Materials Data Facility", + "publication_year": 2024, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1063/5.0219037", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "a049d762-5cb4-43e3-9f25-dd1c6a052e24", + "mdf_source_name": "a049d762-5cb4-43e3-9f25-dd1c6a052e24" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/a049d762-5cb4-43e3-9f25-dd1c6a052e24/1.0/", + "version": "1.0", + "root_version": "a049d762-5cb4-43e3-9f25-dd1c6a052e24", + "latest": true + } + }, + { + "source_id": "df637b81-b444-4c56-a2e5-45f56c94ac0a", + "source_name": "df637b81-b444-4c56-a2e5-45f56c94ac0a", + "version": "1.1", + "ingest_date": "2025-03-13T17:26:25.484844Z", + "doi": "10.18126/c4dc-5b89", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/df637b81-b444-4c56-a2e5-45f56c94ac0a/1.1/", + "metadata": { + "title": "Network Topology and Percolation in Model Covalent Adaptable Networks", + "authors": [ + { + "name": "Benjamin R. Hafner Broderick Lewis Kenneth R. Shull, Subhadeep Pal, Sinan Keten", + "given_name": "Sinan Keten", + "family_name": "Benjamin R. Hafner Broderick Lewis Kenneth R. Shull, Subhadeep Pal", + "affiliations": [ + "Northwestern University" + ] + } + ], + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\u201cNetwork Topology and Percolation in Model Covalent Adaptable Networks.\u201d B.R. Hafner, S. Pal, B. Lewis, S. Keten & K.R. Shull, ACS Macro Lett. 13, 1545\u20131550 (2024) (http://dx.doi.org/10.1021/acsmacrolett.4c00523).\n\nThe dataset includes the following files and directories: \n- readme.md: Markdown file of this description.\n- Hafner et al. - 2024 - Network Topology and Percolation in Model Covalent.pdf: the journal-formatted pdf.\n- SI.pdf: Published supporting information.\n- subfolders beginning with 'Fig': data and python files used to generate figures in the paper.\n- figures: figures generated by the python scripts. ", + "keywords": [ + "experiment", + "polymers", + "simulation", + "molecular structures" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "10.1021/acsmacrolett.4c00523", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "df637b81-b444-4c56-a2e5-45f56c94ac0a", + "mdf_source_name": "df637b81-b444-4c56-a2e5-45f56c94ac0a" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/df637b81-b444-4c56-a2e5-45f56c94ac0a/1.1/", + "version": "1.1", + "root_version": "df637b81-b444-4c56-a2e5-45f56c94ac0a", + "latest": true + } + }, + { + "source_id": "84e5df29-4dbc-455c-b790-89917d378796", + "source_name": "84e5df29-4dbc-455c-b790-89917d378796", + "version": "1.0", + "ingest_date": "2025-03-13T17:40:41.756711Z", + "doi": "10.18126/h3jv-yv88", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/84e5df29-4dbc-455c-b790-89917d378796/1.0/", + "metadata": { + "title": "BCC Fe Dislocation Atomic Energies from DFT+EDM and Classical Potentials", + "authors": [ + { + "name": "Dan, Yang", + "given_name": "Yang", + "family_name": "Dan", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Trinkle, Dallas R.", + "given_name": "Dallas R.", + "family_name": "Trinkle", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Core structures and energetics for BCC Fe dislocations: a[100] (010) edge, a[100] (011) edge, a/2[-1-11] (1-10) edge, and a/2[111] (1-10) 71 degree mixed dislocation. The structures are relaxed in density functional theory (DFT) using lattice Green function-based flexible boundary conditions (LGF FBC). The energetics of the dislocations are calculated using the energy density method (EDM) integrated over Bader volumes and charge-neutral volumes using the weight method ([doi:10.1063/1.3553716](https://doi.org/10.1063/1.3553716)). For comparison, we include the computation of the same dislocations using significantly larger cells with the Mendelev EAM potential and Zhang GAP potential for Fe, which had been earlier shown to perform well at predicting the core structures.", + "keywords": [ + "simulation", + "metals and alloys", + "defects", + "DFT" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "84e5df29-4dbc-455c-b790-89917d378796", + "mdf_source_name": "84e5df29-4dbc-455c-b790-89917d378796" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/84e5df29-4dbc-455c-b790-89917d378796/1.0/", + "version": "1.0", + "root_version": "84e5df29-4dbc-455c-b790-89917d378796", + "latest": true + } + }, + { + "source_id": "b5ef7e66-7a38-458f-be4a-4a2273914a9f", + "source_name": "b5ef7e66-7a38-458f-be4a-4a2273914a9f", + "version": "1.0", + "ingest_date": "2025-03-06T02:04:20.868102Z", + "doi": "10.18126/4gwk-2m53", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b5ef7e66-7a38-458f-be4a-4a2273914a9f/1.0/", + "metadata": { + "title": "Pressure tuning of competing interactions on a honeycomb lattice", + "authors": [ + { + "name": "Sakrikar, Piyush", + "given_name": "Piyush", + "family_name": "Sakrikar", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Shen, Bin", + "given_name": "Bin", + "family_name": "Shen", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Poldi, Eduardo H. T.", + "given_name": "Eduardo H. T.", + "family_name": "Poldi", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Bahrami, Faranak", + "given_name": "Faranak", + "family_name": "Bahrami", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Hu, Xiaodong", + "given_name": "Xiaodong", + "family_name": "Hu", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Kenney, Eric M.", + "given_name": "Eric M.", + "family_name": "Kenney", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Wang, Qiaochu", + "given_name": "Qiaochu", + "family_name": "Wang", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Fruhling, Kyle W.", + "given_name": "Kyle W.", + "family_name": "Fruhling", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Wang, Chennan", + "given_name": "Chennan", + "family_name": "Wang", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Gupta, Ritu", + "given_name": "Ritu", + "family_name": "Gupta", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Khasanov, Rustem", + "given_name": "Rustem", + "family_name": "Khasanov", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Luetkens, Hubertus", + "given_name": "Hubertus", + "family_name": "Luetkens", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Calder, Stuart A.", + "given_name": "Stuart A.", + "family_name": "Calder", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Aczel, Adam A.", + "given_name": "Adam A.", + "family_name": "Aczel", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Fabbris, Gilberto", + "given_name": "Gilberto", + "family_name": "Fabbris", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Hemley, Russell J.", + "given_name": "Russell J.", + "family_name": "Hemley", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Plumb, Kemp W.", + "given_name": "Kemp W.", + "family_name": "Plumb", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Ran, Ying", + "given_name": "Ying", + "family_name": "Ran", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Gegenwart, Philipp", + "given_name": "Philipp", + "family_name": "Gegenwart", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Tsirlin, Alexander A.", + "given_name": "Alexander A.", + "family_name": "Tsirlin", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Haskel, Daniel", + "given_name": "Daniel", + "family_name": "Haskel", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Graf, Michael J.", + "given_name": "Michael J.", + "family_name": "Graf", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "name": "Tafti, Fazel", + "given_name": "Fazel", + "family_name": "Tafti", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "b5ef7e66-7a38-458f-be4a-4a2273914a9f", + "mdf_source_name": "b5ef7e66-7a38-458f-be4a-4a2273914a9f" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/b5ef7e66-7a38-458f-be4a-4a2273914a9f/1.0/", + "version": "1.0", + "root_version": "b5ef7e66-7a38-458f-be4a-4a2273914a9f", + "latest": true + } + }, + { + "source_id": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "source_name": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "version": "1.1", + "ingest_date": "2025-03-09T22:56:09.655117Z", + "doi": "10.18126/b5s9-6r94", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8ac08d59-a322-4629-a40b-867c55cbd4a1/1.1/", + "metadata": { + "title": "Dataset for Actuating Superparamagnetic Nanoparticle Monolayers", + "authors": [ + { + "name": "Esposito, Edward P.", + "given_name": "Edward P.", + "family_name": "Esposito", + "affiliations": [ + "Northwestern University", + "University of Chicago" + ] + }, + { + "name": "Lopez Rios, Hector Manuel", + "given_name": "Hector Manuel", + "family_name": "Lopez Rios", + "affiliations": [ + "Northwestern University", + "University of Chicago" + ] + }, + { + "name": "Olvera de la Cruz, Monica", + "given_name": "Monica", + "family_name": "Olvera de la Cruz", + "affiliations": [ + "Northwestern University", + "University of Chicago" + ] + }, + { + "name": "Jaeger, Heinrich M.", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "Northwestern University", + "University of Chicago" + ] + } + ], + "description": "The dataset includes experimental and simulated data collected for the paper \"Actuating Superparamagnetic Nanoparticle Monolayers\". Data analysis was performed using in-house MATLAB code as described in the paper and available upon request. The set includes all data used to generate the figures in the paper.", + "keywords": [ + "simulation", + "experiment", + "superparamagnetism", + "magnetoelasticity", + "nanoparticles" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "mdf_source_name": "8ac08d59-a322-4629-a40b-867c55cbd4a1" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8ac08d59-a322-4629-a40b-867c55cbd4a1/1.1/", + "version": "1.1", + "previous_version": "8ac08d59-a322-4629-a40b-867c55cbd4a1_v1.0", + "root_version": "8ac08d59-a322-4629-a40b-867c55cbd4a1_v1.0", + "latest": true + } + }, + { + "source_id": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "source_name": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "version": "1.0", + "ingest_date": "2025-03-09T20:02:22.514564Z", + "doi": "10.18126/jcfh-wj57", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8ac08d59-a322-4629-a40b-867c55cbd4a1/1.0/", + "metadata": { + "title": "Dataset for Actuating Superparamagnetic Nanoparticle Monolayers", + "authors": [ + { + "name": "Esposito, Edward P.", + "given_name": "Edward P.", + "family_name": "Esposito", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "name": "Rios, Hector Manuel Lopez", + "given_name": "Hector Manuel Lopez", + "family_name": "Rios", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "name": "de la Cruz, Monica Olvera", + "given_name": "Monica Olvera", + "family_name": "de la Cruz", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "name": "Jaeger, Heinrich M.", + "given_name": "Heinrich M.", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + } + ], + "description": "The dataset includes experimental and simulated data collected for the paper \"Actuating Superparamagnetic Nanoparticle Monolayers\". Data analysis was performed using in-house MATLAB code as described in the paper and available upon request. 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+ "stockholm university" + ] + }, + { + "name": "Hicken, Thomas J.", + "given_name": "Thomas J.", + "family_name": "Hicken", + "affiliations": [ + "Boston College", + "National High Magnetic Field Laboratory", + "NIST", + "University of Maryland, College Park", + "Oak Ridge National Laboratory", + "laboratory of muon spin spectroscopy", + "PSI", + "stockholm university" + ] + }, + { + "name": "Luetkens, Hubertus", + "given_name": "Hubertus", + "family_name": "Luetkens", + "affiliations": [ + "Boston College", + "National High Magnetic Field Laboratory", + "NIST", + "University of Maryland, College Park", + "Oak Ridge National Laboratory", + "laboratory of muon spin spectroscopy", + "PSI", + "stockholm university" + ] + }, + { + "name": "Graf, Michael J.", + "given_name": "Michael J.", + "family_name": "Graf", + "affiliations": [ + "Boston College", + "National High Magnetic Field Laboratory", + "NIST", + "University of Maryland, College Park", + "Oak Ridge National Laboratory", + 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pumping\".", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "251e56f7-7f35-4edc-a159-0ef7ef45838a", + "mdf_source_name": "251e56f7-7f35-4edc-a159-0ef7ef45838a" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/251e56f7-7f35-4edc-a159-0ef7ef45838a/1.0/", + "version": "1.0", + "root_version": "251e56f7-7f35-4edc-a159-0ef7ef45838a", + "latest": true + } + }, + { + "source_id": "2ebc185f-99bb-4e19-b5ca-be12974e2d2d", + "source_name": "2ebc185f-99bb-4e19-b5ca-be12974e2d2d", + "version": "1.0", + "ingest_date": "2025-08-29T15:29:35.196355Z", + "doi": "10.18126/yvgn-w386", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/2ebc185f-99bb-4e19-b5ca-be12974e2d2d/1.0/", + "metadata": { + "title": "A ground truth dataset for anomaly detection on load-displacement nanoindentation curves for NiFe electroplated samples", + "authors": [ + { + "name": "Henriksen, Amelia", + "given_name": "Amelia", + "family_name": "Henriksen", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Zakariya, Peter", + "given_name": "Peter", + "family_name": "Zakariya", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Babuska, Tomas", + "given_name": "Tomas", + "family_name": "Babuska", + "affiliations": [ + "Sandia National Laboratory" + ] + }, + { + "name": "Mundt, Miranda", + "given_name": "Miranda", + "family_name": "Mundt", + "affiliations": [ + "Sandia National Laboratory" + ] + } + ], + "description": "This is a first-of-its-kind ground-truth dataset for anomaly detection and classification on nanoindentation load displacement curves. 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The software used to facilitate the consistency checks/reverification process for the second label set is openly available at https://github.com/sandialabs/nanoindentation-consistency-checker.\n\n*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy\u2019s National Nuclear Security Administration under contract DE-NA0003525.*", + "keywords": [ + "nanoindentation", + "metals and alloys", + "anomaly detection", + "machine learning" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1007/s40192-023-00315-5", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + }, + { + "identifier": "https://doi.org/10.18126/v84f-we14", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "2ebc185f-99bb-4e19-b5ca-be12974e2d2d", + "mdf_source_name": "2ebc185f-99bb-4e19-b5ca-be12974e2d2d" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/2ebc185f-99bb-4e19-b5ca-be12974e2d2d/1.0/", + "version": "1.0", + "root_version": "2ebc185f-99bb-4e19-b5ca-be12974e2d2d", + "latest": true + } + }, + { + "source_id": "b6d7a034-5483-4ae7-beec-ee2e5e1778b8", + "source_name": "b6d7a034-5483-4ae7-beec-ee2e5e1778b8", + "version": "1.0", + "ingest_date": "2025-05-23T14:46:08.285509Z", + "doi": "10.18126/2qxx-7p94", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b6d7a034-5483-4ae7-beec-ee2e5e1778b8/1.0/", + "metadata": { + "title": "Direct-Ink-Write Material and Process Data for In-Situ Rheology Models", + "authors": [ + { + "name": "Ruddock, Jennifer", + "given_name": "Jennifer", + "family_name": "Ruddock", + "affiliations": [ + "Air Force Research Laboratory", + "BlueHalo Inc", + "Harvard University" + ] + }, + { + "name": "Weeks, Robert", + "given_name": "Robert", + "family_name": "Weeks", + "affiliations": [ + "Air Force Research Laboratory", + "BlueHalo Inc", + "Harvard University" + ] + }, + { + "name": "Hardin, James", + "given_name": "James", + "family_name": "Hardin", + "affiliations": [ + "Air Force Research Laboratory", + "BlueHalo Inc", + "Harvard University" + ] + }, + { + "name": "Lewis, Jennifer", + "given_name": "Jennifer", + "family_name": "Lewis", + "affiliations": [ + "Air Force Research Laboratory", + "BlueHalo Inc", + "Harvard University" + ] + } + ], + "description": "This dataset was generated for an experiment to determine if a machine learning model can predict the rheological flow behavior of a material, using only a photo of a test print pattern and the relevant print process parameters. Details of the experiment and how the data was generated can be found in our in Advanced Intelligent Systems (https://doi.org/10.1002/aisy.202400293). This dataset contains Herschel-Bulkley rheological data on 15 materials used in Direct-Ink-Write 3D printing. Each material was printed in a test print pattern which was photographed, and has its own table of print data and links to the segmented pattern. The code used to process the data is also included. \n\nDISTRIBUTION STATEMENT A: Approved for public release: distribution unlimited. AFRL-2024-6234", + "keywords": [ + "experiment", + "machine learning", + "polymers" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "related_works": [ + { + "identifier": "https://doi.org/10.1002/aisy.202400293", + "identifier_type": "DOI", + "relation_type": "IsPartOf" + } + ], + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "b6d7a034-5483-4ae7-beec-ee2e5e1778b8", + "mdf_source_name": "b6d7a034-5483-4ae7-beec-ee2e5e1778b8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/b6d7a034-5483-4ae7-beec-ee2e5e1778b8/1.0/", + "version": "1.0", + "root_version": "b6d7a034-5483-4ae7-beec-ee2e5e1778b8", + "latest": true + } + }, + { + "source_id": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "source_name": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "version": "1.0", + "ingest_date": "2025-09-02T15:55:57.011434Z", + "doi": "10.18126/06wx-2589", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b2237392-8c20-40de-9ead-f91c9ee1840e/1.0/", + "metadata": { + "title": "Molar Mass Dependent Partitioning of Polyethylene in Nanopores of Model Catalyst Supports from Small Angle Neutron Scattering Data", + "authors": [ + { + "name": "Fawaz Motolani, 1", + "given_name": "1", + "family_name": "Fawaz Motolani", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Jones 2, Mason D.", + "given_name": "Mason D.", + "family_name": "Jones 2", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Gergely Nagy, 3", + "given_name": "3", + "family_name": "Gergely Nagy", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bert D. Chandler, 1", + "given_name": "1", + "family_name": "Bert D. Chandler", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Long 2, Brian K.", + "given_name": "Brian K.", + "family_name": "Long 2", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + }, + { + "name": "Bryan D. Vogt, 1", + "given_name": "1", + "family_name": "Bryan D. Vogt", + "affiliations": [ + "Pennsylvania State University", + "University of Tennessee", + "Oak Ridge National Laboratory" + ] + } + ], + "description": "This dataset supports a study on the molar mass-dependent partitioning behavior of perdeuterated polyethylene (dPE) within the nanopores of SBA-15, a model mesoporous silica catalyst support. Small Angle Neutron Scattering (SANS) was used in conjunction with complementary techniques to probe polymer confinement, adsorption, and contrast matching phenomena. Dataset contents include SBA-15 Characterization through TEM, BET, and TGA; Polymer characterization with HT-SEC; SANS Profiles of SBA-15, Contrast match determination vs temperature, and SANS profiles of dPE in SBA-15.", + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "mdf_source_name": "b2237392-8c20-40de-9ead-f91c9ee1840e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/b2237392-8c20-40de-9ead-f91c9ee1840e/1.0/", + "version": "1.0", + "root_version": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "latest": true + } + }, + { + "source_id": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab", + "source_name": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab", + "version": "1.0", + "ingest_date": "2025-09-08T19:45:06.551064Z", + "doi": "10.18126/wms3-v894", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6fad1e75-e902-49c0-88ca-4e72fbb6f7ab/1.0/", + "metadata": { + "title": "Data for Graphene-hBN interlayer interactions from quantum Monte Carlo", + "authors": [ + { + "name": "Kittithat Krongchon Tawfiqur Rakib, Daniel Palmer, Elif Ertekin, and Harley T. 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Huang, Rachael S. Skye, and Julia Dshemuchadse, \"Advancing the Self-Assembly of Binary Colloidal Crystals of Tetrahedra and Octahedra with Confinement,\" Molecular Systems Design & Engineering, in publication (2025). In this project, we used Monte Carlo simulations to study the assembly of binary systems of polyhedral colloidal nanoparticles at surfaces with spherical and flat-wall geometries and to examine the influence of confinement on the process and products of crystallization compared to the bulk. We found that crystals formed in confinement exhibit higher degrees of crystallinity and lower quantities of secondary-phase defects: the formation of well-ordered layers and shells appears to be promoted by flat walls and spherical container interfaces. This dataset includes all simulation trajectories used for analysis (in .gsd file format) and a README to assist with parsing the data. We hope that this dataset will be useful for future work studying the assembly of colloidal crystals and polyhedral particles.", + "keywords": [ + "simulation", + "colloids", + "Monte Carlo" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d", + "mdf_source_name": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/ebe16bf6-4c29-4f80-bbc8-0404c8fb015d/1.0/", + "version": "1.0", + "root_version": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d", + "latest": true + } + }, + { + "source_id": "ca0ec216-0ee8-4942-b303-fb648740d229", + "source_name": "ca0ec216-0ee8-4942-b303-fb648740d229", + "version": "1.0", + "ingest_date": "2025-11-13T22:12:47.085215Z", + "doi": "10.18126/0kyc-6g69", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ca0ec216-0ee8-4942-b303-fb648740d229/1.0/", + "metadata": { + "title": "Data for \"Softening of shear-thickening in suspensions by the addition of large particles\"", + "authors": [ + { + "name": "Pleosse, Alice", + "given_name": "Alice", + "family_name": "Pleosse", + "affiliations": [ + "University of Chicago" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "ca0ec216-0ee8-4942-b303-fb648740d229", + "mdf_source_name": "ca0ec216-0ee8-4942-b303-fb648740d229" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/ca0ec216-0ee8-4942-b303-fb648740d229/1.0/", + "version": "1.0", + "root_version": "ca0ec216-0ee8-4942-b303-fb648740d229", + "latest": true + } + }, + { + "source_id": "f3b5bdb0-ac14-462e-b287-6958d053af10", + "source_name": "f3b5bdb0-ac14-462e-b287-6958d053af10", + "version": "1.0", + "ingest_date": "2025-11-14T19:20:05.639114Z", + "doi": "10.18126/tc7d-1e15", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/f3b5bdb0-ac14-462e-b287-6958d053af10/1.0/", + "metadata": { + "title": "Dataset for \"Softening of shear-thickening in suspensions by the addition of large particles\" A. Pelosse", + "authors": [ + { + "name": "Pelosse, Alice", + "given_name": "Alice", + "family_name": "Pelosse", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "Dataset for the article \"Softening of shear-thickening in suspensions by the addition of large particles\" A. Pelosse and H. Jaeger", + "keywords": [ + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "f3b5bdb0-ac14-462e-b287-6958d053af10", + "mdf_source_name": "f3b5bdb0-ac14-462e-b287-6958d053af10" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/f3b5bdb0-ac14-462e-b287-6958d053af10/1.0/", + "version": "1.0", + "root_version": "f3b5bdb0-ac14-462e-b287-6958d053af10", + "latest": true + } + }, + { + "source_id": "cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3", + "source_name": "cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3", + "version": "1.0", + "ingest_date": "2025-11-18T09:48:01.601549Z", + "doi": "10.18126/hzd4-xv32", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3/1.0/", + "metadata": { + "title": "In-situ and quasi-static compression test data of irregular lattice materials", + "authors": [ + { + "name": "Gu, Zewen", + "given_name": "Zewen", + "family_name": "Gu", + "affiliations": [ + "China University of Petroleum Huadong" + ] + } + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3", + "mdf_source_name": "cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3/1.0/", + "version": "1.0", + "root_version": "cf8e442e-8a50-4462-ba8d-6bbf5a1bd2e3", + "latest": true + } + }, + { + "source_id": "f373ea19-9ebb-4d69-8834-ea33b456c0f4", + "source_name": "f373ea19-9ebb-4d69-8834-ea33b456c0f4", + "version": "1.0", + "ingest_date": "2025-11-21T19:51:43.446450Z", + "doi": "10.18126/81a7-b030", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/f373ea19-9ebb-4d69-8834-ea33b456c0f4/1.0/", + "metadata": { + "title": "Dataset for \"Fracture and failure of shear-jammed dense suspensions under impact\"", + "authors": [ + { + "name": "Malcolm Slutzky", + "given_name": "Malcolm", + "family_name": "Slutzky", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Alice Pelosse", + "given_name": "Alice", + "family_name": "Pelosse", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Michael van der Naald", + "given_name": "Michael", + "family_name": "van der Naald", + "affiliations": [ + "University of Chicago" + ] + }, + { + "name": "Heinrich Jaeger", + "given_name": "Heinrich", + "family_name": "Jaeger", + "affiliations": [ + "University of Chicago" + ] + } + ], + "description": "Dataset for \"Fracture and failure of shear-jammed dense suspensions under impact\"", + "keywords": [ + "experiment", + "experiment" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "f373ea19-9ebb-4d69-8834-ea33b456c0f4", + "mdf_source_name": "f373ea19-9ebb-4d69-8834-ea33b456c0f4" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/f373ea19-9ebb-4d69-8834-ea33b456c0f4/1.0/", + "version": "1.0", + "root_version": "f373ea19-9ebb-4d69-8834-ea33b456c0f4", + "latest": true + } + }, + { + "source_id": "36a0cbae-899b-4adb-84d9-28d72f786659", + "source_name": "36a0cbae-899b-4adb-84d9-28d72f786659", + "version": "1.0", + "ingest_date": "2026-01-27T17:08:24.250043Z", + "doi": "10.18126/faxs-ga32", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/36a0cbae-899b-4adb-84d9-28d72f786659/1.0/", + "metadata": { + "title": "Peeling adhesion of DGEBA-Tris polymer to mica", + "authors": [ + { + "name": "Zach Lamberty", + "given_name": "Zach", + "family_name": "Lamberty", + "affiliations": [ + "University of California", + "Berkeley" + ] + }, + { + "name": "Joelle Frechette", + "given_name": "Joelle", + "family_name": "Frechette", + "affiliations": [ + "University of California", + "Berkeley" + ] + }, + { + "name": "Ngon Tran", + "given_name": "Ngon", + "family_name": "Tran", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Daniel Knorr Jr.", + "given_name": "Daniel", + "family_name": "Knorr Jr.", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + } + ], + "description": "

Crack propagation data associated with the paper \" Stress-aided thermal activation of crack propagation in multidentate hydrogen bonding adhesives\" currently under review. Briefly, cured DGEBA-Tris polymers adhered to mica are peeled away by the sudden movement of a glass spacer, and the crack between the materials propagates, slowing down until it reaches equillibrium. By knowing the length of the crack and material properties one can calculate the strain energy release rate *G* at all times through the equation *G = (9 \u00b5 d^3 h^2)/(24 l^4)*, where *\u00b5* is the shear modulus of the material (25 GPa), *h* is the height of the spacer used to initate the crack, *d* is the thickness of the mica sheet, and *l* is the crack length. By analyzing the movement of the crack *G* can be correlated with the instantaneous crack velocity. Data was collected using a home-built microscope to record the position of the crack over time. Also included are the presented control curves for DGEBA and mica controls.

\n\n

Each CSV file is named with the temperature at which the measurement was performed and the identifier. It contains a header listing the height of the spacer used and the thickness of the mica sheet for each sample. The first two columns are the time and time stamp of the associated image. Then, the crack length is presented for each image, calculated from the known position of the spacer. The next four columns contain intermediate steps to obtain this crack length: first, the \"Mean Crack Edge\" records the position of the vertical mean of the crack in the image, calculated from the left edge of the frame. Then the \"Crack edge mean\" reports the error assocated with this mean crack edge, which largely comes from slight tilts to the crack front. The column \"Motor Position\" contains the position of the motor that moves the camera, allowing for absolute positioning beyond the frame edge. Larger values inidcate further to the right in the frame. Finally, the \"Absolute crack position\" column contains the sum of the crack edge and the motor moment positioning. The 8th column reports the calculated crack velocity, found as the derivative of crack position vs time after smoothing using a non-uniform savgol filter. The 9th column reports an alternative method of obtaining crack velocity through the differentiation of an interpolated function, which was not used in analysis. Finally, we have the calculated strain energy release rate and the associated error, which mostly comes from errors associated with determining the mica thickness.

", + "keywords": [ + "surface properties", + "polymers", + "adhesion", + "fuel cells", + "glasses", + "electrochemical testing", + "electron microscopy", + "high-entropy alloys", + "containerization", + "images", + "microscopy", + "simulation", + "carbides", + "surface properties", + "polymers", + "adhesion", + "fuel cells", + "glasses", + "electrochemical testing", + "electron microscopy", + "high-entropy alloys", + "containerization", + "images", + "microscopy", + "simulation", + "carbides" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "36a0cbae-899b-4adb-84d9-28d72f786659", + "mdf_source_name": "36a0cbae-899b-4adb-84d9-28d72f786659" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/36a0cbae-899b-4adb-84d9-28d72f786659/1.0/", + "version": "1.0", + "root_version": "36a0cbae-899b-4adb-84d9-28d72f786659", + "latest": true + } + }, + { + "source_id": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "source_name": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "version": "1.0", + "ingest_date": "2026-01-31T19:30:11.060765Z", + "doi": "10.18126/gm2r-kt69", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bc47f085-8265-4e7d-9bfc-fa981bd98cd8/1.0/", + "metadata": { + "title": "Peeling adhesion of DGEBA-Tris polymer to mica", + "authors": [ + { + "name": "Zach Lamberty", + "given_name": "Zach", + "family_name": "Lamberty", + "affiliations": [ + "University of California", + "Berkeley" + ] + }, + { + "name": "Ngon Tran", + "given_name": "Ngon", + "family_name": "Tran", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Daniel Knorr Jr.", + "given_name": "Daniel", + "family_name": "Knorr Jr.", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + }, + { + "name": "Joelle Frechette", + "given_name": "Joelle", + "family_name": "Frechette", + "affiliations": [ + "University of California", + "Berkeley" + ] + } + ], + "description": "Crack propagation data associated with the paper \" Stress-aided thermal activation of crack propagation in multidentate hydrogen bonding adhesives\" currently under review. Briefly, cured DGEBA-Tris polymers adhered to mica are peeled away by the sudden movement of a glass spacer, and the crack between the materials propagates, slowing down until it reaches equillibrium. By knowing the length of the crack and material properties one can calculate the strain energy release rate *G* at all times through the equation *G = (9 \u00b5 d^3 h^2)/(24 l^4)*, where *\u00b5* is the shear modulus of the material (25 GPa), *h* is the height of the spacer used to initate the crack, *d* is the thickness of the mica sheet, and *l* is the crack length. By analyzing the movement of the crack *G* can be correlated with the instantaneous crack velocity. Data was collected using a home-built microscope to record the position of the crack over time. Also included are the presented control curves for DGEBA and mica controls.\n\nEach CSV file is named with the temperature at which the measurement was performed and the identifier. It contains a header listing the height of the spacer used and the thickness of the mica sheet for each sample. The first two columns are the time and time stamp of the associated image. Then, the crack length is presented for each image, calculated from the known position of the spacer. The next four columns contain intermediate steps to obtain this crack length: first, the \"Mean Crack Edge\" records the position of the vertical mean of the crack in the image, calculated from the left edge of the frame. Then the \"Crack edge mean\" reports the error assocated with this mean crack edge, which largely comes from slight tilts to the crack front. The column \"Motor Position\" contains the position of the motor that moves the camera, allowing for absolute positioning beyond the frame edge. Larger values inidcate further to the right in the frame. Finally, the \"Absolute crack position\" column contains the sum of the crack edge and the motor moment positioning. The 8th column reports the calculated crack velocity, found as the derivative of crack position vs time after smoothing using a non-uniform savgol filter. The 9th column reports an alternative method of obtaining crack velocity through the differentiation of an interpolated function, which was not used in analysis. Finally, we have the calculated strain energy release rate and the associated error, which mostly comes from errors associated with determining the mica thickness.", + "keywords": [ + "surface properties", + "polymers", + "adhesion", + "fuel cells", + "glasses", + "electrochemical testing", + "electron microscopy", + "high-entropy alloys", + "containerization", + "images", + "microscopy", + "simulation", + "carbides", + "surface properties", + "polymers", + "adhesion", + "fuel cells", + "glasses", + "electrochemical testing", + "electron microscopy", + "high-entropy alloys", + "containerization", + "images", + "microscopy", + "simulation", + "carbides" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "mdf_source_name": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/bc47f085-8265-4e7d-9bfc-fa981bd98cd8/1.0/", + "version": "1.0", + "root_version": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "latest": true + } + }, + { + "source_id": "c54c9fa0-c601-4258-9314-3b531af104e2", + "source_name": "c54c9fa0-c601-4258-9314-3b531af104e2", + "version": "1.0", + "ingest_date": "2026-01-06T01:51:44.136819Z", + "doi": "10.18126/2hem-2r63", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c54c9fa0-c601-4258-9314-3b531af104e2/1.0/", + "metadata": { + "title": "Real-Time Nanoscale Visualization of Cholesterol Monohydrate Nucleation and Growth", + "authors": [ + { + "name": "Lioudmila V. Sorokina", + "given_name": "Lioudmila", + "family_name": "V. Sorokina" + }, + { + "name": "Azadeh Amiri", + "given_name": "Azadeh", + "family_name": "Amiri" + }, + { + "name": "Pavel L. Rehak", + "given_name": "Pavel", + "family_name": "L. Rehak" + }, + { + "name": " Alireza Ghorbani", + "family_name": "Alireza Ghorbani" + }, + { + "name": "Abhijit H. Phakatkar", + "given_name": "Abhijit", + "family_name": "H. Phakatkar" + }, + { + "name": "Petr Kr\u00e1l", + "given_name": "Petr", + "family_name": "Kr\u00e1l" + }, + { + "name": "Tolou Shokuhfar", + "given_name": "Tolou", + "family_name": "Shokuhfar" + }, + { + "name": " Reza Shahbazian \u2013 Yassar", + "family_name": "Reza Shahbazian \u2013 Yassar" + } + ], + "description": "This dataset accompanies the manuscript by Lioudmila V. Sorokina, Azadeh Amiri, Pavel L. Rehak, Alireza Ghorbani, Abhijit H. Phakatkar, Petr Kr\u00e1l, Tolou Shokuhfar, and Reza Shahbazian \u2013 Yassar, entitled \u201cReal-Time Nanoscale Visualization of Cholesterol Monohydrate Nucleation and Growth\u201d and published in Journal of Crystal Growth (2025).\n\nThis study utilized in-situ graphene liquid cell transmission electron microscopy to capture real-time nanoscale events of cholesterol monohydrate (ChM) nucleation and growth. The results revealed ChM triclinic forms through a combination of non-classical and classical modes, specifically, a modified Stranski \u2013 Krastanov mechanism. ChM triclinic nucleates from an amorphous precursor, which grows on triclinic surfaces as an epilayer. These epilayers coalesce into 2D layers formed along a preferred lattice plane, enabling 3D growth. Molecular dynamics simulations revealed that the amorphous to crystalline transition occurs via the self-assembly of small clusters, interconnected by filaments, which regrow into bilayers with exposed polar groups. These superstructures adsorb on the surfaces of crystalline cholesterol, form islands, which spread and form nuclei of a new bilayer. This study underscores the significance of homoepitaxy in ChM growth and may provide additional insights into biologically relevant processes, such as ChM nucleation on lipid droplets. Overall, this study lays the foundation for investigating the mechanisms of ChM growth from solution in real time and on the nanoscale.\n\nA README.txt file is included to assist with analyzing the data. This material is based upon work partially supported by National Science Foundation DMR-1710049 and DMR-2212123. \n\n", + "keywords": [ + "TEM", + "liquid cell TEM", + "graphene liquid cell", + "molecular dynamics", + "cholesterol", + "nucleation", + "stranski-krastanov", + "nanoscale", + "crystallization", + "cholesterol monohydrate", + "triclinic", + "TEM", + "liquid cell TEM", + "graphene liquid cell", + "molecular dynamics", + "cholesterol", + "nucleation", + "stranski-krastanov", + "nanoscale", + "crystallization", + "cholesterol monohydrate", + "triclinic" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "c54c9fa0-c601-4258-9314-3b531af104e2", + "mdf_source_name": "c54c9fa0-c601-4258-9314-3b531af104e2" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/c54c9fa0-c601-4258-9314-3b531af104e2/1.0/", + "version": "1.0", + "root_version": "c54c9fa0-c601-4258-9314-3b531af104e2", + "latest": true + } + }, + { + "source_id": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "source_name": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "version": "1.0", + "ingest_date": "2025-12-31T23:04:17.279446Z", + "doi": "10.18126/fy22-ry45", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8d34c45e-0b99-4f48-9d04-2890576e09c6/1.0/", + "metadata": { + "title": "Nickel as a Modifier of Calcium Oxalate: An In-Situ Liquid Cell TEM Investigation of Nucleation and Growth", + "authors": [ + { + "name": " Lioudmila V. Sorokina", + "family_name": "Lioudmila V. Sorokina", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "name": "Abhijit H. Phakatkar", + "given_name": "Abhijit", + "family_name": "H. Phakatkar", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "name": "Pavel Rehak", + "given_name": "Pavel", + "family_name": "Rehak", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "name": "Petr Kr\u00e1l", + "given_name": "Petr", + "family_name": "Kr\u00e1l", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "name": "Tolou Shokuhfar", + "given_name": "Tolou", + "family_name": "Shokuhfar", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "name": " Reza Shahbazian \u2013 Yassar", + "family_name": "Reza Shahbazian \u2013 Yassar", + "affiliations": [ + "University of Illinois Chicago" + ] + } + ], + "description": "This dataset accompanies the manuscript by Lioudmila V. Sorokina, Abhijit H. Phakatkar, Pavel Rehak, Petr Kr\u00e1l, Tolou Shokuhfar, and Reza Shahbazian \u2013 Yassar, entitled \u201cNickel as a Modifier of Calcium Oxalate: An In-Situ Liquid Cell TEM Investigation of Nucleation and Growth\u201d and published in Nanoscale (2024). \n\nIn this work, we leveraged in-situ graphene liquid cell transmission electron microscopy (GLC TEM) to observe real-time, nanoscale transformations of calcium oxalate crystals in the presence of nickel ions (Ni2+). The results showed that Ni2+ form Ni-water complexes, acting as a shape-directing species, generating a unique morphology and altering growth kinetics. Transient adsorption of Ni-water complexes resulted in a metastable phase formation of calcium oxalate trihydrate. Atomistic molecular dynamics simulations confirmed that Ni2+ acts as a weak inhibitor which slows down the CaOx crystallization, elucidating that Ni2+ impacts small-sized CaOx clusters by bringing more water into the clusters. This work highlighted the intricacies behind the effect of Ni2+ on CaOx biomineralization that were made possible to discern using in-situ GLC TEM.\n\nA README.txt file is included to assist with analyzing the data. This material is based upon work partially supported by a grant from the National Science Foundation (NSF-DMR Award No. 1710049). Additional support from NSF-DMR Award No. 1809439 for the microscopy efforts is acknowledged.", + "keywords": [ + "TEM", + "in situ TEM", + "liquid cell", + "graphene liquid cell", + "EDS", + "HAADF", + "calcium oxalate", + "biomineralization", + "nickel", + "TEM", + "in situ TEM", + "liquid cell", + "graphene liquid cell", + "EDS", + "HAADF", + "calcium oxalate", + "biomineralization", + "nickel" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "mdf_source_name": "8d34c45e-0b99-4f48-9d04-2890576e09c6" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/8d34c45e-0b99-4f48-9d04-2890576e09c6/1.0/", + "version": "1.0", + "root_version": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "latest": true + } + }, + { + "source_id": "79bf6888-a761-4366-987d-05cb836c2239", + "source_name": "79bf6888-a761-4366-987d-05cb836c2239", + "version": "1.0", + "ingest_date": "2025-12-09T23:14:39.730126Z", + "doi": "10.18126/46bb-sz19", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/79bf6888-a761-4366-987d-05cb836c2239/1.0/", + "metadata": { + "title": "Videos and front speeds of frontal ring-opening metathesis polymerization (FROMP) of DCPD/ENB with norbornene-functionalized PDMS comonomers", + "authors": [ + { + "name": "Clarke, Brandon R.", + "given_name": "Brandon R.", + "family_name": "Clarke", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "name": "Kim, Yun Seong", + "given_name": "Yun Seong", + "family_name": "Kim", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "name": "Galewsky, Benjamin", + "given_name": "Benjamin", + "family_name": "Galewsky", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "name": "Lewis, Jennifer A.", + "given_name": "Jennifer A.", + "family_name": "Lewis", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "name": "Tawfick, Sameh H.", + "given_name": "Sameh H.", + "family_name": "Tawfick", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + } + ], + "description": "This dataset contains videos and front speed measurements for 16 frontal ring-opening metastasis polymerization experiments of dicyclopentadiene (DCPD)/5-ethylidene-2-norbornene (ENB) resins and norbornene-functionalized polydimethylsiloxane (nor-PDMS) comonomers. Each run was carried out in a 10 mm diameter glass test tube and recorded to quantify front propagation behavior. Reported front speeds were extracted by video tracking and reported maximum front temperatures were measured with a thermocouple.\n", + "keywords": [ + "experiment", + "polymers", + "frontal ring-opening metastasis polymerization", + "DCPD", + "PDMS", + "ENB", + "chemistry", + "front speeds" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "79bf6888-a761-4366-987d-05cb836c2239", + "mdf_source_name": "79bf6888-a761-4366-987d-05cb836c2239" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/79bf6888-a761-4366-987d-05cb836c2239/1.0/", + "version": "1.0", + "root_version": "79bf6888-a761-4366-987d-05cb836c2239", + "latest": true + } + }, + { + "source_id": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "source_name": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "version": "1.0", + "ingest_date": "2025-12-11T16:42:15.791582Z", + "doi": "10.18126/wcfw-q906", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a48853a2-2ba0-433b-8eda-6c020240fd84/1.0/", + "metadata": { + "title": "Understanding the Structural Origin of Chirality in Magic-Size Semiconductor Nanoclusters through Self-Assembly Simulations", + "authors": [ + { + "name": "Hongjin Du", + "given_name": "Hongjin", + "family_name": "Du", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Ellery J. Hendrix", + "given_name": "Ellery", + "family_name": "J. Hendrix", + "affiliations": [ + "University of Michigan" + ] + }, + { + "name": "Richard D. Robinson", + "given_name": "Richard", + "family_name": "D. Robinson", + "affiliations": [ + "Cornell University" + ] + }, + { + "name": "Julia Dshemuchadse", + "given_name": "Julia", + "family_name": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "description": "This dataset accompanies the manuscript by H. Du, E. J. Hendrix, R. D. Robinson, and J. Dshemuchadse, \"Understanding the Structural Origin of Chirality in Magic-Size Semiconductor Nanoclusters through Self-Assembly Simulations'', Nano Letters, in publication (2025). In this work, we investigate the formation of magic-size clusters (MSCs) in zincblende-forming semiconductor systems using molecular dynamics simulations with a minimal, chemically agnostic pair potential. We simulate the self-assembly of binary clusters across a range of sizes and stoichiometries, reproducing key structural features observed in the experimentally synthesized MSCs. In particular, we examine the emergence of chirality in distorted icosahedral motifs. This dataset includes the final frames of simulation trajectories for systems with total particle numbers ranging from *N* = 4 to 110, provided in .xyz format. A README.txt file is included to assist with parsing the data. This material is based upon work supported by the National Science Foundation under Grant No. CHE-2003586.", + "keywords": [ + "magic-size clusters", + "chirality", + "molecular dynamics simulations", + "self-assembly", + "magic-size clusters", + "chirality", + "molecular dynamics simulations", + "self-assembly" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "mdf_source_name": "a48853a2-2ba0-433b-8eda-6c020240fd84" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/a48853a2-2ba0-433b-8eda-6c020240fd84/1.0/", + "version": "1.0", + "root_version": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "latest": true + } + }, + { + "source_id": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "source_name": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "version": "1.0", + "ingest_date": "2026-02-02T23:13:37.160497Z", + "doi": "10.18126/tg8t-mq17", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2/1.0/", + "metadata": { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M204/Tributyl Phosphite Resins", + "authors": [ + { + "name": "Cope, Reid", + "given_name": "Reid", + "family_name": "Cope", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Curran, Ethan M.", + "given_name": "Ethan M.", + "family_name": "Curran", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Cramblitt, Anna C.", + "given_name": "Anna C.", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Galewsky, Ben", + "given_name": "Ben", + "family_name": "Galewsky", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sottos, Nancy R.", + "given_name": "Nancy R.", + "family_name": "Sottos", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Moore, Jeffrey S.", + "given_name": "Jeffrey S.", + "family_name": "Moore", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Tawfick, Sameh", + "given_name": "Sameh", + "family_name": "Tawfick", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst 2nd Generation (M204), and Tributyl Phosphite in varying ratios.", + "keywords": [ + "FROMP", + "polymers", + "experiment", + "kinetics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "mdf_source_name": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2/1.0/", + "version": "1.0", + "root_version": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "latest": true + } + }, + { + "source_id": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "source_name": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "version": "1.0", + "ingest_date": "2025-12-05T19:16:50.579936Z", + "doi": "10.18126/28dg-wn78", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/70d169fd-7f83-43e7-b584-e13444d7fc9c/1.0/", + "metadata": { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M207/Tributyl Phosphite Resins", + "authors": [ + { + "name": "Curran, Ethan M.", + "given_name": "Ethan M.", + "family_name": "Curran", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Cramblitt, Anna C.", + "given_name": "Anna C.", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Galewsky, Ben", + "given_name": "Ben", + "family_name": "Galewsky", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sottos, Nancy R.", + "given_name": "Nancy R.", + "family_name": "Sottos", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst M207, and Tributyl Phosphite in varying ratios.", + "keywords": [ + "polymers", + "experiment", + "FROMP", + "kinetics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "mdf_source_name": "70d169fd-7f83-43e7-b584-e13444d7fc9c" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/70d169fd-7f83-43e7-b584-e13444d7fc9c/1.0/", + "version": "1.0", + "root_version": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "latest": true + } + }, + { + "source_id": "91e964ab-89ea-4995-9717-64d34053162f", + "source_name": "91e964ab-89ea-4995-9717-64d34053162f", + "version": "1.0", + "ingest_date": "2025-12-12T20:29:57.794376Z", + "doi": "10.18126/x0xf-9q27", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/91e964ab-89ea-4995-9717-64d34053162f/1.0/", + "metadata": { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M202/Tributyl Phosphite Resins", + "authors": [ + { + "name": "Curran, Ethan M.", + "given_name": "Ethan M.", + "family_name": "Curran", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Cramblitt, Anna C.", + "given_name": "Anna C.", + "family_name": "Cramblitt", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Arretche, Ignacio", + "given_name": "Ignacio", + "family_name": "Arretche", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Galewsky, Ben", + "given_name": "Ben", + "family_name": "Galewsky", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "name": "Sottos, Nancy R.", + "given_name": "Nancy R.", + "family_name": "Sottos", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst M202, and Tributyl Phosphite in varying ratios.\n\n", + "keywords": [ + "polymers", + "experiment", + "FROMP", + "kinetics" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "license": { + "name": "Creative Commons Attribution 4.0", + "url": null + }, + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "91e964ab-89ea-4995-9717-64d34053162f", + "mdf_source_name": "91e964ab-89ea-4995-9717-64d34053162f" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/91e964ab-89ea-4995-9717-64d34053162f/1.0/", + "version": "1.0", + "root_version": "91e964ab-89ea-4995-9717-64d34053162f", + "latest": true + } + }, + { + "source_id": "524a881b-f627-4c21-91cc-790399cef860", + "source_name": "524a881b-f627-4c21-91cc-790399cef860", + "version": "1.0", + "ingest_date": "2025-12-18T22:55:36.588844Z", + "doi": "10.18126/gc97-qa02", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/524a881b-f627-4c21-91cc-790399cef860/1.0/", + "metadata": { + "title": "Accurate atomic correlation and total energies for correlation consistent effective core potentials II: Rb-Xe elements", + "authors": [ + { + "name": "Aqsa Shaikh", + "given_name": "Aqsa", + "family_name": "Shaikh", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Omar Madany", + "given_name": "Omar", + "family_name": "Madany", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Benjamin Kincaid", + "given_name": "Benjamin", + "family_name": "Kincaid", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "name": "Lubos Mitas", + "given_name": "Lubos", + "family_name": "Mitas", + "affiliations": [ + "North Carolina State University" + ] + } + ], + "description": "A complete dataset of inputs and outputs for paper \"Accurate atomic correlation and total energies for correlation consistent effective core potentials II: Rb\u2212Xe elements\" by Aqsa Shaikh et al.", + "keywords": [ + "simulation", + "QMC", + "DMC", + "accurate energies", + "correlation", + "quantum chemistry", + "simulation", + "QMC", + "DMC", + "accurate energies", + "correlation", + "quantum chemistry" + ], + "publisher": "Materials Data Facility", + "publication_year": 2025, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "524a881b-f627-4c21-91cc-790399cef860", + "mdf_source_name": "524a881b-f627-4c21-91cc-790399cef860" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/524a881b-f627-4c21-91cc-790399cef860/1.0/", + "version": "1.0", + "root_version": "524a881b-f627-4c21-91cc-790399cef860", + "latest": true + } + }, + { + "source_id": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e", + "source_name": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e", + "version": "1.0", + "ingest_date": "2026-01-08T16:41:22.907238Z", + "doi": "10.18126/t0ca-5645", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e/1.0/", + "metadata": { + "title": "Original Data from \"Imaging Surface Topography with Coherent X-ray Reflectivity: Theory, Kinematics and Simulations\"", + "authors": [ + { + "name": "Irene Calvo Almazan", + "given_name": "Irene", + "family_name": "Calvo Almazan", + "affiliations": [ + "University of Zaragoza", + "Spain" + ] + }, + { + "name": "Anusheela Das", + "given_name": "Anusheela", + "family_name": "Das", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Ana Suzana", + "given_name": "Ana", + "family_name": "Suzana", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "name": "Fernando Bartolome", + "given_name": "Fernando", + "family_name": "Bartolome", + "affiliations": [ + "University of Zaragoza", + "Spain" + ] + }, + { + "name": "Paul Fenter", + "given_name": "Paul", + "family_name": "Fenter", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "description": "Archived Data are from a publication Physical Review B. See manuscript at [https://doi.org/10.1103/q75z-gk3l](https://doi.org/10.1103/q75z-gk3l)\n\nThe results are simulations of coherent X-ray reflectivity for a one-dimensional surface (along x) with a assumed topography, h(x), where x is in units of Angstroms and h(x), where h(x) clat is the surface height in angstroms (we use a layer spacing of clat = 3 Angstroms).\n\nSimulations assume a beam energy of 10 keV with a Gaussian beam shape having a width of 0.5 micrometers. Simulated scattering intensities are calculated for vertical momentum transfer centered at Q0 = 1.99 Ang^-1 = 0.95 QB, where QB = 2 * pi/clat, is the momentum transfer of the Bragg peak. The complex valued structure factor is calculated as a function of vertical and lateral momentum transfer (Qx, Qz), over a range of momentum transfer with full width of deltaQ = 0.01 QB in both the vertical and lateral directions. The effective density, rho_eff(x,z), is then obtained by inverse fourier transform.\n\nDescription of Archived data: Topography: this file contains the surface topography that is used in all calculations (shown in Figure 1, 5av, 5bv, 6ai, 6bi). This is in the form: x, h(x)\n\nFigure 2a: This includes documents the complex valued intrinsic effective density for an ideally terminated lattice calculated for each layer ,n, as a function of height, z. The figure shows the results for n = 1 to 20, over the range -200 Angstrom < z < 200 Angstrom. This archived data file includes the calculation for n = 1 to 500, and for 4500 Angstrom < z < 4500 Angstrom. This was calculated for the attenuation parameter beta = 0.99. Data is in the form: n, z, abs(rho_eff(n,z)), angle(rho_eff(n,z))\n\nFigure 2bc: This is effectve interfacial density as a function of height, which corresponds to the sum of intrinsic effective densities for all layers. Data is in the form: z, abs(rho_eff(z)), angle(rho_eff(z)).\n\nFigure 4abc_i: This documents the effective densities determined from the complex structure factors, a) using the orthogonal ROI, b) the experimental effective densites obtained from data that simulates the effect of the tilt of the Ewald sphere, and c) the density after unskewing experimental densities. Data are in the form: x, z, abs(rho_eff_orth(x,z)), abs(rho_eff_EXP(x,z)), abs(rho_eff_EXP_unskew(x,z)).\n\nFigure 4abii: This documents the structure factor magnitudes that contain a) the orthogonal ROI, b) the experimentally observed intensities that included the tilting the Ewald sphere. Data are in the form: qx, qz, I_tot(qx,qz), I_tot_exp(qx,qz). Here, qz is the reduced momentum transfer, qz = Qz - QB, and QB is the momentum transfer of the Bragg peak.\n\nFigure 4c_ii: This documents the structure factor magnitudes that contain c) the experimentally observed intensities recovered from the unskewed densities. Data are in the form: qx, qz, I_tot_exp_unskew(qx,qz). Here, qz is the reduced momentum transfer, qz = Qz - QB, and QB is the momentum transfer of the Bragg peak.\n\nFigure 5a_i_ii: This documents the unskewed effective densities determined from the complex structure factors, a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.95 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew(x,z)), abs(rho_eff_exp_unskew(x,z)).\n\nFigure 5b_i_ii: This documents the unskewed effective densities determined from the complex structure factors, a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.55 QB and deltaQ = 0.01 QB. Data are in the form: x,z,abs(rho_eff_exp_unskew(x,z)),abs(rho_eff_exp_unskew(x,z)).\n\nFigure 6a_ii_iii: This documents the unskewed effective densities determined from the complex structure factors after Fourier interpolation along the surface normal direction, including a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.95 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew_interp(x,z)), abs(rho_eff_exp_unskew_interp(x,z)).\n\nFigure 6b_ii_iii: This documents the unskewed effective densities determined from the complex structure factors after Fourier interpolation along the surface normal direction, including a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.55 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew_interp(x,z)), abs(rho_eff_exp_unskew_interp(x,z)).\n\n", + "keywords": [ + "simulation", + "microscopy", + "theory", + "Coherent X-ray Reflectivity", + "crystal structure", + "X-ray diffraction", + "DFT", + "semiconductors", + "experiment", + "fuel cells", + "simulation", + "microscopy", + "theory", + "Coherent X-ray Reflectivity", + "crystal structure", + "X-ray diffraction", + "DFT", + "semiconductors", + "experiment", + "fuel cells" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e", + "mdf_source_name": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e/1.0/", + "version": "1.0", + "root_version": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e", + "latest": true + } + }, + { + "source_id": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "source_name": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "version": "1.0", + "ingest_date": "2026-02-05T21:54:29.703601Z", + "doi": "10.18126/7hxx-t317", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bfecb497-b118-4332-ac7c-99de2430bcdd/1.0/", + "metadata": { + "title": "Adhesion Tape Loop Data", + "authors": [ + { + "name": "andrew b. croll", + "given_name": "andrew", + "family_name": "b. croll", + "affiliations": [ + "North Dakota State University" + ] + }, + { + "name": "Harmeet Singh", + "given_name": "Harmeet", + "family_name": "Singh", + "affiliations": [ + "Indian Institutes of Technology (IITs) Gandhinagar" + ] + }, + { + "name": "Krishnan Suryanarayanan", + "given_name": "Krishnan", + "family_name": "Suryanarayanan", + "affiliations": [ + "Indian Institutes of Technology (IITs) Gandhinagar" + ] + } + ], + "description": "Data used to compute state diagram for the Adhesive Tape Loop. Loops are rectangular prisms made of various modulus elastomer which are bent into a loop shape and adhered to itself. Loops may be stable, may open a small amount then reach equilibrium or may open completely. This spreadsheet contains all the necessary information from measurements and the steps in calculation to recreate the state diagram in our paper.", + "keywords": [ + "surface properties", + "polymers", + "adhesion", + "simulation", + "phase field", + "surface properties", + "polymers", + "adhesion", + "simulation", + "phase field" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "mdf_source_name": "bfecb497-b118-4332-ac7c-99de2430bcdd" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/bfecb497-b118-4332-ac7c-99de2430bcdd/1.0/", + "version": "1.0", + "root_version": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "latest": true + } + }, + { + "source_id": "058f62f4-9b99-4410-9211-7ab781326ab3", + "source_name": "058f62f4-9b99-4410-9211-7ab781326ab3", + "version": "1.0", + "ingest_date": "2026-02-06T23:44:08.381794Z", + "doi": "10.18126/ff0v-7304", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/058f62f4-9b99-4410-9211-7ab781326ab3/1.0/", + "metadata": { + "title": "Linear viscoelastic measurements of commerical therapy putties for design and intuition", + "authors": [ + { + "name": "Maxwell C. Marsh", + "given_name": "Maxwell", + "family_name": "C. Marsh", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "name": "Rebecca E. Corman", + "given_name": "Rebecca", + "family_name": "E. Corman", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "name": "Nabil Ramlawi", + "given_name": "Nabil", + "family_name": "Ramlawi", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "name": "Samya Sen", + "given_name": "Samya", + "family_name": "Sen", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "name": "Randy H. Ewoldt", + "given_name": "Randy", + "family_name": "H. Ewoldt", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + } + ], + "description": "This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation.\n\nAdditional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material.\n\nThe collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.", + "keywords": [ + "polymers", + "experiment", + "viscoelasticity", + "polymers", + "experiment", + "viscoelasticity" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "058f62f4-9b99-4410-9211-7ab781326ab3", + "mdf_source_name": "058f62f4-9b99-4410-9211-7ab781326ab3" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/058f62f4-9b99-4410-9211-7ab781326ab3/1.0/", + "version": "1.0", + "root_version": "058f62f4-9b99-4410-9211-7ab781326ab3", + "latest": true + } + }, + { + "source_id": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "source_name": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "version": "1.0", + "ingest_date": "2026-02-11T02:17:59.726353Z", + "doi": "10.18126/gfpd-mm33", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc/1.0/", + "metadata": { + "title": "Data for Thermodynamics and Kinetics of Li at the Ag-Li Battery Interface", + "authors": [ + { + "name": "Grace Lu", + "given_name": "Grace", + "family_name": "Lu", + "affiliations": [ + "University of California", + "Irvine" + ] + }, + { + "name": "Dallas R. Trinkle", + "given_name": "Dallas", + "family_name": "R. Trinkle", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + } + ], + "description": "This dataset complements the publication Thermodynamics and Kinetics of Li at the Ag-Li Battery Interface. In this project, we calculate thermodynamic properties for 6 different interfaces: KS, NW, Bain, Pitsch, FCC (111), and FCC (100). For all six interfaces, this dataset contains the per atom energies and vacancy formation energies for every atom calculated using the MACE-MP-0 pretrained potential. Also included are the MACE-MP-0 surface energies of each Ag and Li slab and MACE-MP-0 interfacial energies. For the two FCC surfaces, this dataset also contains DFT calculations for the surface and interfacial energies. Additionally, for the FCC surfaces, we include calculated migration barriers for vacancies across the Ag-Li interface using MACE-MP-0.", + "keywords": [ + "defects", + "machine learning potential", + "DFT", + "simulation", + "diffusion", + "lithium-silver", + "vacancy", + "defects", + "machine learning potential", + "DFT", + "simulation", + "diffusion", + "lithium-silver", + "vacancy" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "mdf_source_name": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc/1.0/", + "version": "1.0", + "root_version": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "latest": true + } + }, + { + "source_id": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "source_name": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "version": "1.0", + "ingest_date": "2026-02-16T20:17:49.163069Z", + "doi": "10.18126/xhm6-sx12", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd/1.0/", + "metadata": { + "title": "A Multi-Alloys and Multi-Modal Dataset for Spatial Intelligence Learning of Microstructure\u2013Plasticity\u2013Mechanical Property Relationships in Metals", + "authors": [ + { + "name": "Mathieu Calvat", + "given_name": "Mathieu", + "family_name": "Calvat", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Gregory Sparks", + "given_name": "Gregory", + "family_name": "Sparks", + "affiliations": [ + "US Air Force Research Laboratory" + ] + }, + { + "name": "Dhruv Anjaria", + "given_name": "Dhruv", + "family_name": "Anjaria", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Chris Bean", + "given_name": "Chris", + "family_name": "Bean", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "name": "Haoren Wang", + "given_name": "Haoren", + "family_name": "Wang", + "affiliations": [ + "University of California San Diego" + ] + }, + { + "name": "Maik Rajkowski", + "given_name": "Maik", + "family_name": "Rajkowski", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "name": "Aditya Srinivasan Tirunilai", + "given_name": "Aditya", + "family_name": "Srinivasan Tirunilai", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "name": "Guillaume Laplanche", + "given_name": "Guillaume", + "family_name": "Laplanche", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "name": "St\u00e9phane Forsik", + "given_name": "St\u00e9phane", + "family_name": "Forsik", + "affiliations": [ + "Carpenter Technology Corporation" + ] + }, + { + "name": "Kenneth Vecchio", + "given_name": "Kenneth", + "family_name": "Vecchio", + "affiliations": [ + "University of California San Diego" + ] + }, + { + "name": "J.C. Stinville", + "given_name": "J.C.", + "family_name": "Stinville", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "description": "This dataset provides a large-scale, multi-alloy, multimodal collection of spatially resolved microstructural states, local plastic deformation fields, and corresponding macroscopic mechanical properties for metallic materials. It is designed to enable material spatial intelligence, that is, data-driven learning of relationships between heterogeneous microstructure, plastic deformation resolved down to the nanometer scale over large fields of view, and bulk mechanical response.\nThe dataset combines high-resolution microstructure characterization, such as electron backscatter diffraction (EBSD) measurements, with full-field plasticity measurements obtained using high-resolution digital image correlation. These measurements are acquired over large fields of view and multiple deformation states and are systematically paired with macroscopic mechanical properties such as yield strength, hardening behavior, tensile strength, ductility, fatigue strength, and creep strength.\nMultiple alloys and processing conditions are included, capturing a wide range of chemistry, structure, and microstructural heterogeneity, deformation modes, and property envelopes. The dataset spans alloys with face-centered cubic, body-centered cubic, and hexagonal close-packed crystal structures, and includes both wrought and additively manufactured processing routes. Mechanical properties and deformation fields are captured at both room temperature and elevated temperature.\nBy explicitly linking local microstructural features, localized plastic deformation, and macroscopic mechanical behavior within a unified spatial framework, this dataset provides a foundational resource for developing, benchmarking, and validating machine-learning and physics-informed models aimed at microstructure-aware property prediction, mechanism discovery, and data-driven alloy design.", + "keywords": [ + "metals", + "mechanical properties", + "metals and alloys", + "experiment", + "defects", + "microscopy", + "spectroscopy", + "Multi-Alloys", + "Multi-Modal", + "Spatial Intelligence", + "Microstructure", + "EBSD", + "High-resolution digital image correlation", + "Plasticity localization", + "Fatigue", + "Creep", + "Invar", + "room temperature", + "microstructure", + "polymers", + "benchmark", + "crystal structure", + "software", + "containerization", + "images", + "continuum", + "metals", + "mechanical properties", + "metals and alloys", + "experiment", + "defects", + "microscopy", + "spectroscopy", + "Multi-Alloys", + "Multi-Modal", + "Spatial Intelligence", + "Microstructure", + "EBSD", + "High-resolution digital image correlation", + "Plasticity localization", + "Fatigue", + "Creep", + "Invar", + "room temperature", + "microstructure", + "polymers", + "benchmark", + "crystal structure", + "software", + "containerization", + "images", + "continuum" + ], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "organization": "MDF Open", + "acl": [ + "public" + ], + "extensions": { + "mdf_source_id": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "mdf_source_name": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd" + }, + "download_url": "https://data.materialsdatafacility.org//mdf_open/f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd/1.0/", + "version": "1.0", + "root_version": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "latest": true + } + } + ] +} \ No newline at end of file diff --git a/datasets.json b/datasets.json new file mode 100644 index 0000000..96e1096 --- /dev/null +++ b/datasets.json @@ -0,0 +1,120689 @@ +{ + "source_index": "1a57bbe5-5272-477f-9d31-343b8258b7a5", + "query": "mdf.resource_type:\"dataset\"", + "total_in_index": 904, + "fetched_count": 904, + "gmeta": [ + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "levine_abo2179_database_v2.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/levine_abo2179_database_v2.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/levine_abo2179_database_v2.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/levine_abo2179_database_v2.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2022-12-01T21:13:21.635945Z", + "source_id": "levine_abo2179_database_v2.1", + "scroll_id": 0, + "version": 2, + "source_name": "levine_abo2179_database" + }, + "dc": { + "identifier": { + "identifier": "10.18126/jx14-t0v8", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Levine", + "givenName": "David J.", + "creatorName": "Levine, David J.", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "familyName": "Iyer", + "givenName": "Gokulanand M.", + "creatorName": "Iyer, Gokulanand M.", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "familyName": "Turner", + "givenName": "Kevin T.", + "creatorName": "Turner, Kevin T.", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "familyName": "Pikul", + "givenName": "James H.", + "creatorName": "Pikul, James H.", + "affiliations": [ + "University of Pennsylvania" + ] + }, + { + "familyName": "Roosa", + "givenName": "R. Daelan", + "creatorName": "Roosa, R. Daelan", + "affiliations": [ + "University of Pennsylvania" + ] + } + ], + "subjects": [ + { + "subject": "electroadhesives" + }, + { + "subject": "materials" + }, + { + "subject": "soft robots" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-01-20", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "A mechanics-based approach to realize high-force capacity electroadhesives for robots" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1126/scirobotics.abo2179", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "djlev@seas.upenn.edu" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "All data in this file accompanies the manuscript 'A mechanics-based approach to high-force capacity electroadhesives for robots', D.J. Levine, G.M. Iyer, R.D. Roosa, K.T. Turner & J.H. Pikul, 2022. All authors are associated with the University of Pennsylvania, Department of Mechanical Engineering & Applied Mechanics, Philadelphia, PA. All data in this file are represented in Figures 3 & 6 in the manuscript. All electroadhesive force capacity tests were run with clutch designs with a Parylene-C dielectric layer of thickness 14 microns, with a dielectric constant of 3.1. For additional information, please consult the manuscript or Supplementary Materials." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "annaberdiyev_role_electron_methods_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/annaberdiyev_role_electron_methods_v1.1/", + "total_size": 1065711429, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/annaberdiyev_role_electron_methods_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/annaberdiyev_role_electron_methods_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-01-06T19:19:22.262949Z", + "source_id": "annaberdiyev_role_electron_methods_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "annaberdiyev_role_electron_methods" + }, + "dc": { + "identifier": { + "identifier": "10.18126/icm7-z0n8", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Annaberdiyev", + "givenName": "Abdulgani", + "creatorName": "Annaberdiyev, Abdulgani", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State University" + ] + }, + { + "familyName": "Mitas", + "givenName": "Lubos", + "creatorName": "Mitas, Lubos", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State University" + ] + }, + { + "familyName": "Krogel", + "givenName": "Jaron T.", + "creatorName": "Krogel, Jaron T.", + "affiliations": [ + "Oak Ridge National Laboratory", + "North Carolina State University" + ] + }, + { + "familyName": "Ganesh", + "givenName": "Panchapakesan", + "creatorName": "Ganesh, Panchapakesan", + 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"alternateIdentifierType": "NIST DSpace ID" + } + ], + "creators": [ + { + "givenName": "FC", + "familyName": "Campbell", + "creatorName": "Campbell, FC" + } + ], + "publisher": "NIST Materials Data Repository", + "publicationYear": "2013", + "titles": [ + { + "title": "Aluminum" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "surface_diffusion_bcc011_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/surface_diffusion_bcc011_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/surface_diffusion_bcc011_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/surface_diffusion_bcc011_v1/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2018-11-02T19:44:46.339540Z", + "source_id": "surface_diffusion_bcc011_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "surface_diffusion_bcc011" + }, + "dc": { + "creators": [ + { + "creatorName": "Ondrejcek, Michal", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ], + "givenName": "Michal", + "familyName": "Ondrejcek" + }, + { + "creatorName": "Rajappan, Mahesh", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ], + "givenName": "Mahesh", + "familyName": "Rajappan" + }, + { + "creatorName": "Swiech, Wacek", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ], + "givenName": "Wacek", + "familyName": "Swiech" + }, + { + "creatorName": "Swiech, Wacek", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ], + "givenName": "Wacek", + "familyName": "Swiech" + } + ], + "publisher": "University of Illinois at Urbana-Champaign", + "publicationYear": "2018", + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1103/PhysRevB.73.035418", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1063/1.2356108", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1080/14786430500398433", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1016/j.susc.2006.07.035", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1080/09500830310001646671", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Surface Mass Diffusion on bcc(011) Planes; V, Nb, Ta and Mo" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Surface mass diffusion on a smooth surfaces provide basic quantities, such as diffusion coefficients Ds relevant to surface kinetics. By step fluctuation experiments on ~300 nm thick films of four bcc(011) metals grown on (11\u201420) alpha-Al2O3, using Low Energy Electron Microscopy (LEEM), we determine the coefficient Ds for V, Nb, Ta and Mo in the temperature range above 0.5 Tm, with Tm being the melting temperature. The fluctuation amplitudes and step relaxation times are obtained as function of wave number q by calculating the Fourier components of the step edge displacement. The temperature dependence for Mo and Nb reveals both surface self-diffusion process and bulk vacancy diffusion also. The diffusion coefficients of V, Nb overlap well as functions of T/Tm but are an order of magnitude higher than that of Mo(011). The difference and a factor ~10+3 in prefactor probably arise from the native submonolayer oxidation of the column 5 metals." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mdr_item_558_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mrr": "This dataset was registered with the MRR.", + "citrine": "https://citrination.com/datasets/168824/", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/mdr_item_558_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_558_v1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "National Institute of Standards and Technology", + "U.S. Department of 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"affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ], + "givenName": "Rui", + "familyName": "Hu" + }, + { + "creatorName": "Li, Weibang", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ], + "givenName": "Weibang", + "familyName": "Li" + }, + { + "creatorName": "Tang, Bin", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ], + "givenName": "Bin", + "familyName": "Tang" + }, + { + "creatorName": "Zhou, Lian", + "affiliations": [ + "State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi\u2019an, China", + "Computational Alloy Design Group, IMDEA Materials Institute, Madrid, Spain" + ], + "givenName": "Lian", + "familyName": "Zhou" + } + ], + "subjects": [ + { + "subject": "Al-Cr-Ti" + } + ], + "publisher": "NIST Materials Data Repository", + "publicationYear": "2013", + "titles": [ + { + "title": "Al-Cr-Ti Assessment of diffusion mobility for the bcc phase" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "si_ti_oxidation_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/si_ti_oxidation_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/si_ti_oxidation_v1/" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MCPub", + "DOE", + "Department of Energy Software Innovation Center for Integrated Multi-Scale Modeling of Structural Metals", + "U.S. Department of Energy", + "Materials Commons", + "Center for Predictive Integrated Structural Materials Science", + "PRISMS" + ], + "ingest_date": "2018-10-30T21:44:27.860096Z", + "source_id": "si_ti_oxidation_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "si_ti_oxidation" + }, + "dc": { + "identifier": { + "identifier": "https://dx.doi.org/10.13011/M3B36G", + "identifierType": "DOI" + }, + "creators": [ + { + "givenName": "Kathleen", + "familyName": "Chou", + "creatorName": "Chou, Kathleen", + "affiliations": [ + "University of Michigan" + ] + }, + { + "givenName": "Peng-Wei", + "familyName": "Chu", + "creatorName": "Chu, Peng-Wei", + "affiliations": [ + "University of Michigan" + ] + }, + { + "givenName": "Emmanuelle", + "familyName": "Marquis", + "creatorName": "Marquis, Emmanuelle", + "affiliations": [ + "University of Michigan" + ] + } + ], + "subjects": [ + { + "subject": "Titanium" + }, + { + "subject": "Oxidation" + }, + { + "subject": "Silicon coatings" + }, + { + "subject": "Transmission electron microscopy" + } + ], + "publisher": "Materials Commons", + "publicationYear": "2018", + "titles": [ + { + "title": "Early oxidation behavior of Si-coated titanium" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset compiles images and data from oxidation studies of pure titanium coated with a pure Si coating at 800 degrees C that supplement submitted manuscript 'Early oxidation behavior of Si-coated titanium'. Please note, the meta-data provided may be updated over time. We reserve the right to update this data without notification. If you would like to be notified of changes, please email Kathleen Chou at kachou@umich.edu." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ta_melting_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/ta_melting_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ta_melting_v1/" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2018-11-06T16:46:32.187585Z", + "source_id": "ta_melting_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ta_melting" + }, + "dc": { + "rightsList": [ + { + "rightsURI": "http://creativecommons.org/licenses/by/3.0/us/", + "rights": "http://creativecommons.org/licenses/by/3.0/us/" + } + ], + "creators": [ + { + "givenName": "Qi-jun", + "familyName": "Hong", + "creatorName": "Hong, Qi-jun", + "affiliations": [ + "California Institute of Technology", + "Brown University" + ] + }, + { + "givenName": "Axel", + "familyName": "van de Walle", + "creatorName": "van de Walle, Axel", + "affiliations": [ + "California Institute of Technology", + "Brown University" + ] + } + ], + "publisher": "MDF (placeholder)", + "publicationYear": "2013", + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "http://dx.doi.org/10.1063/1.4819792", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Ta Melting Point Calculation by Small-cell Coexistence Method" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "We calculate the melting temperature of Tantalum, by employing the small-size coexistence solid-liquid coexistence method. Two pseudo-potentials are employed, namely Ta_v (5d^3 6s^2) and Ta_pv (5p^6 5d^3 6s^2). Results show the inner 5p electrons are necessary for melting properties calculations of Tantalum. 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Yarotski", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data for \"Tracking ultrafast photocurrents in the Weyl semimetal TaAs using THz emission spectroscopy\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Datafiles for figures in manuscript \"Tracking ultrafast photocurrents in the Weyl semimetal TaAs\r\nusing THz emission spectroscopy\"" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_31_abdulgani_new_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_31_abdulgani_new_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1139/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1139/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:24:24.425929Z", + "source_id": "pub_31_abdulgani_new_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_31_abdulgani_new" + }, + "custom": { + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M23W7S", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Abdulgani, Annaberdiyev", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Abdulgani", + "givenName": "Annaberdiyev" + }, + { + "creatorName": "Wang, Guangming", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Wang", + "givenName": "Guangming" + }, + { + "creatorName": "Melton, Cody A.", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Melton", + "givenName": "Cody A." + }, + { + "creatorName": "Shulenburger, Luke", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Shulenburger", + "givenName": "Luke" + }, + { + "creatorName": "Mitas, Lubos", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Mitas", + "givenName": "Lubos" + } + ], + "subjects": [ + { + "subject": "CPSFM" + }, + { + "subject": "simulation" + }, + { + "subject": "ECP" + }, + { + "subject": "Gaussian" + }, + { + "subject": "MolPro" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-05-23T16:01:51Z", + "dateType": "Accepted" + }, + { + "date": "2018-05-23T16:01:51Z", + "dateType": "Available" + }, + { + "date": "2018-05-22", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Cody Melton (camelto2@ncsu.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://github.com/camelto2/correlated-data", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for \"New Generation of Effective Core Potentials from Correlated Calculations: 3d Transition Metal Series\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for \"New generation of effective core potentials from correlated calculations: 3d transition metal series\"\r\n\r\nIncluded are various directories including atomic and molecular data for various ECPs. Gaussian and Molpro include input and output files for each code respectively. Each code directory is divided into atoms and molecules, containing individual directories for each species (e.g. Molpro/atoms/Fe or Gaussian/molecules/ZnO). Within each atom or molecule directory, we include data for the all-electron, uncorrelated core, and various ECPs considered in the paper. For the Gaussian molecular calculations, the molecular geometries are separated into individual directories, whereas for Molpro there is only one directory which contains one input file to calculate all geometries.\r\n\r\nAdditionally, there are two separate directories \"spectra\" and \"molecular_csv\" which contain the atomic spectra and molecular binding curves for a given basis set as csv files. Each directory also contains the py" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "vinckeviciutejulija_fundamental_insights_charge_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/vinckeviciutejulija_fundamental_insights_charge_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/vinckeviciutejulija_fundamental_insights_charge_v1.1/" + }, + "custom": { + "all_materials_included": "['Li', 'Na', 'Mg', 'Ni', 'Co', 'Mn', 'O']", + "funding_details": "['National Science Foundation, Grant DMR-1410242', 'Center for Scientific Computing from the CNSI, MRL, NSF MRSEC (DMR-1121053), and Hewlett-Packard', 'National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231', 'NorthEast Center for Chemical Energy Storage (NECCES), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0012583']" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "Density Functional Theory", + "DFT" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-07-05T14:00:34.297574Z", + "mdf_id": "5d1f5802bd8ab6322937dfbe", + "source_id": "vinckeviciutejulija_fundamental_insights_charge_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "vinckeviciutejulija_fundamental_insights_charge" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2ZW7J", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Vinckeviciute", + "givenName": "Julija", + "creatorName": "Vinckeviciute, Julija", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "familyName": "Radin", + "givenName": "Maxwell D.", + "creatorName": "Radin, Maxwell D.", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "familyName": "Faenza", + "givenName": "Nicholas V.", + "creatorName": "Faenza, Nicholas V.", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "familyName": "Amatucci", + "givenName": "Glenn G.", + "creatorName": "Amatucci, Glenn G.", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + }, + { + "familyName": "Van der Ven", + "givenName": "Anton", + "creatorName": "Van der Ven, Anton", + "affiliations": [ + "Materials Department, University of California, Santa Barbara", + "Energy Storage Research Group, Department of Materials Science and Engineering, Rutgers University, North Brunswick" + ] + } + ], + "subjects": [ + { + "subject": "cathode" + }, + { + "subject": "migration" + }, + { + "subject": "battery" + }, + { + "subject": "electrochemistry" + }, + { + "subject": "energy materials" + }, + { + "subject": "simulation" + }, + { + "subject": "transition-metal oxide" + }, + { + "subject": "layered" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-04-01T21:13:14Z", + "dateType": "Accepted" + }, + { + "date": "2019-04-01T21:13:14Z", + "dateType": "Available" + }, + { + "date": "2019-04-01", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Anton Van der Ven" + } + ], + "titles": [ + { + "title": "Fundamental insights about interlayer cation migration in Li-ion electrodes at high states of charge" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Calculated dataset to assess effects of composition on interlayer cation migration in common transition-metal oxides used in battery cathodes." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_24_antonius_orbital_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_24_antonius_orbital_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1128/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1128/" + }, + "mrr": { + "materialType": [ + "Layered material" + ], + "structuralFeature": [ + "Hexagonal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:11:11.474716Z", + "source_id": "pub_24_antonius_orbital_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_24_antonius_orbital" + }, + "custom": { + "funding_details": "['National Science Foundation under grant DMR-1508412', 'U.S. Department of Energy, Office of Basic Energy Sciences under contract no. DE-AC02- 05CH11231', 'Center for Computa- tional Study of Excited-State Phenomena in Energy Materials (C2SEPEM)']", + "all_materials_included": "['GaSe', 'GaTe']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2PS71", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Antonius, Gabriel", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Antonius", + "givenName": "Gabriel" + }, + { + "creatorName": "Qiu, Diana Y.", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Qiu", + "givenName": "Diana Y." + }, + { + "creatorName": "Louie, Steven G.", + "affiliations": [ + "University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Louie", + "givenName": "Steven G." + } + ], + "subjects": [ + { + "subject": "GaSe" + }, + { + "subject": "two-dimensional materials, monochalcogenides" + }, + { + "subject": "GaTe" + }, + { + "subject": "simulation" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-05-10T19:02:55Z", + "dateType": "Accepted" + }, + { + "date": "2018-05-10T19:02:55Z", + "dateType": "Available" + }, + { + "date": "2018-05-10", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Gabriel Antonius", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.nanolett.7b05286", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.1021/acs.nanolett.7b05286", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Orbital Symmetry and the Optical Response of Single-Layer MX Monochalcogenides" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Optical absorption calculations in the GW+BSE framework for single-layer GaSe and GaTe in the hexagonal phase." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_29_kramer_third_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_29_kramer_third_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_625/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_625/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:21:03.869352Z", + "source_id": "pub_29_kramer_third_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_29_kramer_third" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M26D20", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Kramer, Sharlotte", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Kramer", + "givenName": "Sharlotte" + }, + { + "creatorName": "Boyce, Brad", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Boyce", + "givenName": "Brad" + }, + { + "creatorName": "Jones, Amanda", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Jones", + "givenName": "Amanda" + }, + { + "creatorName": "Gearhart, Jhana", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Gearhart", + "givenName": "Jhana" + }, + { + "creatorName": "Salzbrenner, Brad", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Salzbrenner", + "givenName": "Brad" + } + ], + "subjects": [ + { + "subject": "" + }, + { + "subject": "EBSD" + }, + { + "subject": "machine learning" + }, + { + "subject": "images" + }, + { + "subject": "challenge" + }, + { + "subject": "experimental" + }, + { + "subject": "microstructure" + }, + { + "subject": "additive manufacturing" + }, + { + "subject": "fracture" + }, + { + "subject": "microscopy" + }, + { + "subject": "SFC3" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-01-22T02:09:24Z", + "dateType": "Accepted" + }, + { + "date": "2018-01-22T02:09:24Z", + "dateType": "Available" + }, + { + "date": "2018-01-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Sharlotte Kramer (slkrame@sandia.gov)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "The Third Sandia Fracture Challenge" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The Third Sandia Fracture Challenge (SFC3) is a collaborative effort for assessment of the state-of-the-art predictive capability in ductile failure. The Challenge is designed such that computational modelers are asked to predict ductile failure in an unfamiliar geometry using provided standard materials data. The SFC3, issued in December 2016, is centered on an additively manufactured 316L stainless steel geometry with through holes and internal cavities that could not be produced by conventional machining. The provided information to enable the predictions includes: base material tensile behavior for different material configurations and loading rates; notched base material tensile behavior; fabrication drawings of the base material specimens and the Challenge (unfamiliar) geometry; electron backscatter diffraction of the Challenge geometry mid-plane; micro-computed tomography data of all Challenge geometry specimens; scanning electron microscope images of the fracture surface of tensile and notched tensile base material specimens; macroscopic images of fractured base material specimens; descriptions of the boundary conditions for the base-material and Challenge-geometry tests; questions to be answered for the prediction (quantities of interest); and an overview presentation of the Challenge." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_28_shapera_databasedriven_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1135/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1135/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_28_shapera_databasedriven_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:18:10.556123Z", + "source_id": "pub_28_shapera_databasedriven_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_28_shapera_databasedriven" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M28K8M", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Shapera", + "givenName": "Ethan P.", + "creatorName": "Shapera, Ethan P.", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "experimental" + }, + { + "subject": "nanoparticles" + }, + { + "subject": "simulation" + }, + { + "subject": "semiconductor" + }, + { + "subject": "database" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-05-19T00:46:13Z", + "dateType": "Accepted" + }, + { + "date": "2018-05-19T00:46:13Z", + "dateType": "Available" + }, + { + "date": "2018-05-18", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Andr\u00e9 Schleife (schleife@illinois.edu)" + } + ], + "titles": [ + { + "title": "Database-driven Materials-selection Framework for Semiconductor Heterojunction Design" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "weilingfei_machine_learning_materials_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/weilingfei_machine_learning_materials_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/weilingfei_machine_learning_materials_v1.1/" + }, + "custom": { + "all_materials_included": "['zinc sulfide', 'copper sulfide']", + "funding_details": "Department of Energy" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "p-type conductor", + "Thin film" + ], + "characterizationMethod": [ + "Hall effect, UV-visible, AFM" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-08-29T17:06:39.897136Z", + "source_id": "weilingfei_machine_learning_materials_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "weilingfei_machine_learning_materials" + }, + "dc": { + "identifier": { + "identifier": "10.18126/d3gck322", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Wei", + "givenName": "Lingfei", + "creatorName": "Wei, Lingfei", + "affiliations": [ + "Lawrence Berkeley National" + ] + }, + { + "familyName": "Ager", + "givenName": "Joel", + "creatorName": "Ager, Joel", + "affiliations": [ + "Lawrence Berkeley National" + ] + } + ], + "subjects": [ + { + "subject": "chemical bath deposition" + }, + { + "subject": "machine learning" + }, + { + "subject": "thin film" + }, + { + "subject": "p-type transparent conductor" + }, + { + "subject": "electronic materials" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-05-20T21:56:41Z", + "dateType": "Accepted" + }, + { + "date": "2019-05-20T21:56:41Z", + "dateType": "Available" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "lwei@lbl.gov" + }, + { + "contributorType": "ContactPerson", + "contributorName": "jwager@lbl.gov" + } + ], + "titles": [ + { + "title": "Machine learning optimization of p-type transparent conducting materials" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "P-type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, ultraviolet photodetectors, displays, and flexible sensors. Cu-Zn-S (CZS) thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multi-dimensional parameter space such that optimization for a specific application is challenging and time consuming. Here, we show that strategic design of experiment (DOE) combined with machine learning (ML) allows for efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of 4 experimental CBD factors. The machine learning model is based on support vector regression (SVR) employing a radial basis function (RBF) as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_96_zhang_spatially_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_926/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_926/" + }, + "custom": { + "all_materials_included": "Pt" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_96_zhang_spatially_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "metallic glass", + "metallic glass" + ], + "characterizationMethod": [ + "electron microscopy" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:33:06.264522Z", + "source_id": "pub_96_zhang_spatially_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_96_zhang_spatially" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2GW5F", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Zhang", + "givenName": "Pei", + "creatorName": "Zhang, Pei", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Maldonis", + "givenName": "Jason", + "creatorName": "Maldonis, Jason", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Liu", + "givenName": "Ze", + "creatorName": "Liu, Ze", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Schroers", + "givenName": "Jan", + "creatorName": "Schroers, Jan", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul", + "creatorName": "Voyles, Paul", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + } + ], + "subjects": [ + { + "subject": "metallic glasses" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "electron correlation microscopy" + }, + { + "subject": "metallic glass" + }, + { + "subject": "glass transition" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "microscopy" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-01-09T16:58:26Z", + "dateType": "Accepted" + }, + { + "date": "2018-01-09T16:58:26Z", + "dateType": "Available" + }, + { + "date": "2018-01-09", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles (paul.voyles@wisc.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1038/s41467-018-03604-2", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Spatially Heterogeneous Dynamics in a Metallic Glass Forming Liquid Imaged by Electron Correlation Microscopy" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Supercooled liquids exhibit spatial heterogeneity in the dynamics of their fluctuating atomic arrangements. The length and time scales of the heterogeneous dynamics are central to the glass transition and influence nucleation and growth of crystals from the liquid. We report direct experimental visualization of the spatially heterogeneous dynamics as a function of temperature in the supercooled liquid state of a Pt- based metallic glass, using electron correlation microscopy with sub-nanometer resolution. An experimental four point space-time intensity correlation function demonstrates a growing dynamic correlation length, \u03be, upon cooling of the liquid toward the glass transition temperature. \u03be as a function of the relaxation time \u03c4 data are in the good agreement with the Adam-Gibbs, inhomogeneous mode coupling theory and random first order transition theory of the glass transition. The same experiments demonstrate the existence of a nanometer thickness near surface layer with order of magnitude shorter relaxation time than inside the bulk." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_100_li_detection_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_100_li_detection_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_997/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_997/" + }, + "mrr": { + "materialType": [ + "alloys", + "metal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:51:04.097983Z", + "source_id": "pub_100_li_detection_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_100_li_detection" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2692Z", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Li, Wei", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Li", + "givenName": "Wei" + }, + { + "creatorName": "Field, Kevin G.", + "affiliations": [ + "Oak Ridge National Laboratory" + ], + "familyName": "Field", + "givenName": "Kevin G." + }, + { + "creatorName": "Morgan, Dane", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Morgan", + "givenName": "Dane" + } + ], + "subjects": [ + { + "subject": "STEM" + }, + { + "subject": "machine learning" + }, + { + "subject": "experiment" + }, + { + "subject": "model" + }, + { + "subject": "deep learning" + }, + { + "subject": "defects" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "matlab" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-02-19T22:19:53Z", + "dateType": "Accepted" + }, + { + "date": "2018-02-19T22:19:53Z", + "dateType": "Available" + }, + { + "date": "2018-02-19", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Dane Morgan (ddmorgan@wisc.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1038/s41524-018-0093-8", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.1038/s41524-018-0093-8", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Detection of Open Loop Defects in STEM Images of Irradiation-Damaged Alloys \u2013 Source Code for Detection and Image Dataset" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The Dataset consists of the source code for defect detection and analysis, and the STEM images, along with the bounding boxes for open loop defects in the images. The source code is written with Matlab and was run with the Matlab r2017a\u200b version. Essential toolboxes required to run the code are: Image Processing Toolbox, Statistics and Machine Learning Toolbox, Computer Vision Toolbox. The project has been tested on macOS Sierra, Windows 7, Windows 10 and Ubuntu 17.10 platforms. Some parts of the code require GPU that supports CUDA to function correctly. \r\n\r\nThe project has three types of files: functions, scripts and data folders. Functions can be divided into several categories, such as data parser, screening method, etc. Scripts can be divided into several categories, such as image augmentation, train cascade object detector. \r\n\r\nTo run the code, move all the source code to the same folder where all image folders sits. The images folders are located in project folder. Image data folders can be divided into several categories, such as positive images, negative images., eg. \"aug_training_positive_images\u201d.\r\n\r\nFor more information, please refer to the documentation (\u201cREADME.pdf\u201d) in the dataset." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_70_shin_giwaxs_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1277/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1277/" + }, + "custom": { + "funding_details": "['National Science Foundation Engineering Research Center for Power Optimization of Electro-Thermal systems (POETS) with cooperative agreements EEC-1449548', 'Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_70_shin_giwaxs_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "polymer" + ], + "characterizationMethod": [ + "WAXS", + "SAXS", + "GIWAXS", + "GISAXS" + ], + "structuralFeature": [ + "side-chain azobenzene polymer" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:38:22.386183Z", + "source_id": "pub_70_shin_giwaxs_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_70_shin_giwaxs" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2VH2X", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Shin", + "givenName": "Jungwoo", + "creatorName": "Shin, Jungwoo", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Sung", + "givenName": "Jaeuk", + "creatorName": "Sung, Jaeuk", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Kang", + "givenName": "Minjee", + "creatorName": "Kang, Minjee", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Xie", + "givenName": "Xu", + "creatorName": "Xie, Xu", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Lee", + "givenName": "Byeongdu", + "creatorName": "Lee, Byeongdu", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Lee", + "givenName": "Kyung Min", + "creatorName": "Lee, Kyung Min", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "White", + "givenName": "Timothy", + "creatorName": "White, Timothy", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Leal", + "givenName": "Cecilia", + "creatorName": "Leal, Cecilia", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Sottos", + "givenName": "Nancy", + "creatorName": "Sottos, Nancy", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Braun", + "givenName": "Paul", + "creatorName": "Braun, Paul", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + }, + { + "familyName": "Cahill", + "givenName": "David", + "creatorName": "Cahill, David", + "affiliations": [ + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "X-ray Science Division, Argonne National Laboratory.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign.", + "Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign." + ] + } + ], + "subjects": [ + { + "subject": "GISAXS" + }, + { + "subject": "GIWAXS" + }, + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "SAXS" + }, + { + "subject": "energy materials" + }, + { + "subject": "WAXS" + }, + { + "subject": "Polymer" + }, + { + "subject": "Azobenzene" + }, + { + "subject": "thermodynamics" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-01-11T17:04:58Z", + "dateType": "Accepted" + }, + { + "date": "2019-01-11T17:04:58Z", + "dateType": "Available" + }, + { + "date": "2019-01-10", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Jungwoo Shin (jshin63@illinois.edu)" + } + ], + "titles": [ + { + "title": "GIWAXS, GISAXS, WAXS and SAXS data for publication: Light-triggered thermal conductivity switching in azobenzene polymers" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Raw 2-D Tif files collected from the APS 12-ID-B and 12-ID-C beamlines." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "maldonisjasonj_shortrange_order_glass_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/maldonisjasonj_shortrange_order_glass_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/maldonisjasonj_shortrange_order_glass_v1.1/" + }, + "mrr": { + "materialType": [ + "metal" + ], + "characterizationMethod": [ + "LAMMPS", + "point pattern matching", + "motif extraction" + ], + "structuralFeature": [ + "glass" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-08-29T16:16:28.933803Z", + "source_id": "maldonisjasonj_shortrange_order_glass_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "maldonisjasonj_shortrange_order_glass" + }, + "custom": { + "funding_details": "['NSF DMR-1332851', 'NSF DMR-1728933', 'AFOSR FA9550-17-1-0145']", + "all_materials_included": "Zr50Cu45Al5" + }, + "dc": { + "identifier": { + "identifier": "10.18126/1OVYIQOURCMLG", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Maldonis, Jason J.", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Maldonis", + "givenName": "Jason J." + }, + { + "creatorName": "Banadaki, Arash Dehghan", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Banadaki", + "givenName": "Arash Dehghan" + }, + { + "creatorName": "Patala, Srikanth", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Patala", + "givenName": "Srikanth" + }, + { + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Voyles", + "givenName": "Paul M." + } + ], + "subjects": [ + { + "subject": "metallic glasses" + }, + { + "subject": "machine learning" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "metallic glass, structure, motif, short-range order" + }, + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-06-10T15:57:23Z", + "dateType": "Accepted" + }, + { + "date": "2019-06-10T15:57:23Z", + "dateType": "Available" + } + ], + "contributors": [ + { + "contributorName": "\"Paul M. Voyles\" ", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.actamat.2019.05.002", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.1016/j.actamat.2019.05.002", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://tem.msae.wisc.edu", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/paul-voyles/motifextraction", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Short-Range Order Structure Motifs Learned from an Atomistic Model of a Zr50Cu45Al5 Metallic Glass" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Classical molecular dynamics trajectories for Zr50Cu45Al5 metallic glass as a function of temperature during cooling. Analysis of the atomic positions using motif extraction and point pattern matching. 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The orientation distribution functions were computed from pole figure data using OdfPf package developed by the Deformation Processes Laboratory at Cornell University." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "1-ID-C, Advanced Photon Source, Argonne National Laborator" + }, + { + "geoLocationPlace": "Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laborator" + }, + { + "geoLocationPlace": "1-ID-C" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_80_cheon_mining_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_80_cheon_mining_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_808/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_808/" + }, + "mrr": { + "materialType": [ + "weakly bonded solids", + "layered materials", + "weakly bonded solids" + ], + "characterizationMethod": [ + "data mining" + ], + "structuralFeature": [ + "one dimensional", + "two dimensional" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:58:04.880043Z", + "source_id": "pub_80_cheon_mining_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_80_cheon_mining" + }, + "custom": { + "funding_details": "This work was partially supported by Army Research Office Grant W911NF-15-1-0570, by Office of Naval Research Grant N00014-15-1-2697, by the U.S. Army Research Laboratory through the Army High Performance Computing Research Center, Cooperative Agreement W911NF-07-0027, by NSF Grant DMR-1455050 and EECS-1436626, and by the Stanford Graduate Fellowship program." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2DD03", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Cheon, Gowoon", + "affiliations": [ + "Stanford University" + ], + "familyName": "Cheon", + "givenName": "Gowoon" + }, + { + "creatorName": "Duerloo, Karel-Alexander N.", + "affiliations": [ + "Stanford University" + ], + "familyName": "Duerloo", + "givenName": "Karel-Alexander N." + }, + { + "creatorName": "Sendek, Austin D.", + "affiliations": [ + "Stanford University" + ], + "familyName": "Sendek", + "givenName": "Austin D." + }, + { + "creatorName": "Porter, Chase", + "affiliations": [ + "Stanford University" + ], + "familyName": "Porter", + "givenName": "Chase" + }, + { + "creatorName": "Chen, Yuan", + "affiliations": [ + "Stanford University" + ], + "familyName": "Chen", + "givenName": "Yuan" + }, + { + "creatorName": "Reed, Evan J.", + "affiliations": [ + "Stanford University" + ], + "familyName": "Reed", + "givenName": "Evan J." + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "van der Waals solids" + }, + { + "subject": "two-dimensional materials" + }, + { + "subject": "van der Waals hetereostructures" + }, + { + "subject": "data mining" + }, + { + "subject": "layered materials" + }, + { + "subject": "piezoelectricity" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-10-20T18:55:49Z", + "dateType": "Accepted" + }, + { + "date": "2017-10-20T18:55:49Z", + "dateType": "Available" + }, + { + "date": "2017-10", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Evan Reed (evanreed@stanford.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Gowoon Cheon (gcheon@stanford.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.nanolett.6b05229", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1021/acs.nanolett.6b05229", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This is the supporting information for the journal article Cheon, G. et al. Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures. Nano Lett. (2017). doi:10.1021/acs.nanolett.6b05229.\r\nMore information can be found on the journal article or Reed Group's website https://web.stanford.edu/group/evanreed/" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_75_sherman_s200_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1305/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1305/" + }, + "custom": { + "all_materials_included": "['SiC ceramic matrix reinforced by SiC fibers', 'SiC']", + "funding_details": "This work was funded in part by the Air Force Office of Scientific ResearchTask # 17RXCOR441 with Jamie Tiley as the program manager." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_75_sherman_s200_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "Ceramic Matrix Composite" + ], + "characterizationMethod": [ + "Serial Sectioning Microscope" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:47:42.320967Z", + "source_id": "pub_75_sherman_s200_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_75_sherman_s200" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2135G", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Sherman", + "givenName": "Samuel", + "creatorName": "Sherman, Samuel", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + }, + { + "familyName": "Simmons", + "givenName": "Jeff", + "creatorName": "Simmons, Jeff", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + }, + { + "familyName": "Przybyla", + "givenName": "Craig", + "creatorName": "Przybyla, Craig", + "affiliations": [ + "Air Force Life Cycle Management Center, Dayton, Oh 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Materials and Manufacturing Directorate, Air Force Research Laboratory, Dayton, OH 45433", + "Air Force Research Laboratories" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "ceramics" + }, + { + "subject": "semiconductors" + }, + { + "subject": "composites" + }, + { + "subject": "SiC" + }, + { + "subject": "fibers" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-02-12T20:45:03Z", + "dateType": "Accepted" + }, + { + "date": "2019-02-12T20:45:03Z", + "dateType": "Available" + }, + { + "date": "2019-02-12", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Sam Sherman (sherman.72@wright.edu)" + } + ], + "titles": [ + { + "title": "S200 SiC/SiC Seral Section Data and Fiber Chirality Codes with Tutorial" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The contents of this publication include a tutorial to approximate the chirality, or twist, of fibers in a fiber reinforced ceramic matrix composite. 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In the near future, the authors intend on publishing a paper about the derivation of the applied methods, which will be titled: \"Mesoscale Characterization of Continuous Fiber Reinforced Composites Through Machine Learning: Fiber Chirality\"." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_78_osborn_coherent_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_801/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_801/" + }, + "custom": { + "all_materials_included": "CePd3", + "funding_details": "Research supported by the Materials Sciences and Engineering Division, Office of Basic Energy Sciences, U.S. Department of Energy." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_78_osborn_coherent_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "metals and alloys", + "intermetallics" + ], + "characterizationMethod": [ + "scattering and diffraction", + "neutron [inelastic]" + ], + "structuralFeature": [ + "single crystal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:52:43.546754Z", + "source_id": "pub_78_osborn_coherent_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_78_osborn_coherent" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2C914", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Osborn", + "givenName": "Raymond", + "creatorName": "Osborn, Raymond", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "familyName": "Goremychkin", + "givenName": "Eugene", + "creatorName": "Goremychkin, Eugene", + "affiliations": [ + "Joint Institute for Nuclear Research" + ] + }, + { + "familyName": "Rosenkranz", + "givenName": "Stephan", + "creatorName": "Rosenkranz, Stephan", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "familyName": "Park", + "givenName": "Hyowon", + "creatorName": "Park, Hyowon", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "CePd3" + }, + { + "subject": "experiment" + }, + { + "subject": "strongly correlated electron systems" + }, + { + "subject": "superconductors" + }, + { + "subject": "quantum materials" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-11-17T19:25:02Z", + "dateType": "Accepted" + }, + { + "date": "2017-11-17T19:25:02Z", + "dateType": "Available" + }, + { + "date": "2017-11-17", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Raymond Osborn (rosborn@anl.gov)" + } + ], + "titles": [ + { + "title": "Coherent Band Excitations in CePd3" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "These files contain data sets used in the analysis of CePd3, reported in \u201cCoherent Band Excitations in CePd3: A Comparison of Neutron Scattering and ab initio Theory\u201d by Goremychkin et al. See the file \"Readme.txt\" for more details." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Spallation Neutron Source" + }, + { + "geoLocationPlace": "ISIS Pulsed Neutron Source" + }, + { + "geoLocationPlace": "SNS" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_45_lee_hotelectron_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_45_lee_hotelectron_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1161/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1161/" + }, + "mrr": { + "characterizationMethod": [ + "simulation" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:49:00.666504Z", + "source_id": "pub_45_lee_hotelectron_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_45_lee_hotelectron" + }, + "custom": { + "funding_details": "['Financial support from the Sandia National Laboratory-UIUC collaboration is ac- knowledged (SNL grant no. 1736375)', 'An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Exper- iment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.']", + "all_materials_included": "MgO" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2C35K", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Lee, Cheng-Wei", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Lee", + "givenName": "Cheng-Wei" + }, + { + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Schleife", + "givenName": "Andr\u00e9" + } + ], + "subjects": [ + { + "subject": "MgO" + }, + { + "subject": "simulation" + }, + { + "subject": "diffusion" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-07-01T03:42:13Z", + "dateType": "Accepted" + }, + { + "date": "2018-07-01T03:42:13Z", + "dateType": "Available" 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+ "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_69_maldonis_motif_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1268/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1268/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_69_maldonis_motif_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:36:26.293634Z", + "source_id": "pub_69_maldonis_motif_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_69_maldonis_motif" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2FD0D", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Maldonis", + "givenName": "Jason J.", + "creatorName": "Maldonis, Jason J.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Banadaki", + "givenName": "Arash Deghan", + "creatorName": "Banadaki, Arash Deghan", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Patala", + "givenName": "Srikanth", + "creatorName": "Patala, Srikanth", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "metallic glasses" + }, + { + "subject": "machine learning" + }, + { + "subject": "simulation" + }, + { + "subject": "Zr50Cu45Al5" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-12-21T05:12:09Z", + "dateType": "Accepted" + }, + { + "date": "2018-12-21T05:12:09Z", + "dateType": "Available" + }, + { + "date": "2018-12-20", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles (paul.voyles@wisc.edu)" + } + ], + "titles": [ + { + "title": "Data for Motif Extraction from Zr50Cu45Al5 Metallic Glass Models" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_91_zhang_nonequilibrium_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_903/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_903/" + }, + "custom": { + "all_materials_included": "['BN-ZnO', 'boron nitride (BN) phase of zinc oxide (ZnO)']", + "funding_details": "This material is based upon work supported by the National Science Foundation un- der Grant No. DMR-1555153. This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_91_zhang_nonequilibrium_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "semiconductor" + ], + "characterizationMethod": [ + "simulation", + "density functional theory" + ], + "structuralFeature": [ + "thin film" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:12:43.411855Z", + "source_id": "pub_91_zhang_nonequilibrium_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_91_zhang_nonequilibrium" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2SD1W", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Zhang", + "givenName": "Xiao", + "creatorName": "Zhang, Xiao", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "ZnO" + }, + { + "subject": "non-toxic" + }, + { + "subject": "thin film" + }, + { + "subject": "bandgap" + }, + { + "subject": "wurtzite" + }, + { + "subject": "simulation" + }, + { + "subject": "semiconductor" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-12-17T19:47:53Z", + "dateType": "Accepted" + }, + { + "date": "2017-12-17T19:47:53Z", + "dateType": "Available" + }, + { + "date": "2017-12-17", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Andr\u00e9 Schleife (schleife@illinois.edu)" + } + ], + "titles": [ + { + "title": "Dataset for Non-equilibrium BN-ZnO: Optical properties and excitonic effects from first principles" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This is the description file for the published data in the work [doi will be inserted once it\u2019s published]. The calculations are performed in Vienna Ab initio Simulation Package (VASP) thus the inputs and outputs files are in VASP format. Some part of the calculations use our own code that is not published. The data for publication is structured as follows: \r\n\r\n1. Data for structural relaxation: \r\n\r\nThis folder contains the relevant inputs (VASP INCAR, KPOINTS, POSCAR) and outputs (OUTCAR, OSZICAR, etc.) for the structural relaxation of both BN ZnO and WZ ZnO. These includes the INCAR, POSCAR, POTCAR, KPOINTS files used to run calculations for different volumes and OUTCARs and standard output for different volumes. The file names in BN relaxation are chosen such that the second number represents the k-points sampling and the third represents the plane wave cutoff. The file name of the subfolders within each k-point/plane wave cutoff combination denotes the a-lattice constant for that step. The WZ relaxation uses the converged value for these two parameters and has no additional test.\r\n\r\n2. Data for band structure calculations\r\n\r\nThis folder contains the relevant inputs (VASP INCAR, KPOINTS, POSCAR) and outputs (OUTCAR, OSZICAR, band structure, etc.) for the electronic structure calculations for WZ ZnO, BN ZnO in theoretical lattice constant and BN ZnO in experimental lattice constant. The GW result includes 1)G0W0, scGW+GGA, scGW+HSE for WZ ZnO 2)G0W0, scGW+GGA, scGW+HSE for theoretical BN lattice and 3)G0W0+HSE, G0W0+GGA and scGW+HSE for experimental BN lattice. In some cases, G0W0 results are not listed explicitly but the first step in the scGW. \r\n\r\n3. Dielectric constant\r\n\r\nThis folder contains the relevant inputs (VASP KPOINTS, POSCAR, bash script for INCARs) and output spectra files for the dielectric constants calculations, that is required for optical calculations. In each folder, necessary inputs and bash script to generate INCAR files for the optical spectra using different number of bands are provided, as well as the resulting spectra of the calculations. \r\n\r\n4. Optical \r\n\r\nThis folder contains the necessary inputs (VASP KPOINTS, POSCARs, INCARs) for the GGA optical calculations and BSE calculations and the output spectra of the GGA optical calculations and BSE calculations for BN and WZ ZnO. This folder also contains the transmission plot we generated using Maxwell\u2019s modeling. We use our own code for BSE calculations and Maxwell\u2019s modeling thus whoever want to reproduce the BSE calculations may need to contact the authors. This folder also contains the partial charge density files to generate the wave function plots in our paper.\r\n\r\n5. Exciton binding energy\r\n\r\nThis folder contains the necessary inputs (KPOINTS, INCARs, POSCARs) for calculating the exciton binding energy for different k-points sampling and BSE energy cutoff and the corresponding BSE output and exciton eigenvalues files for BN and WZ ZnO. The test of convergence of BSE energy cutoff is only done for BN ZnO. Again, to reproduce the BSE calculations, one may need to contact the authors." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "BlueWaters" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_95_paranjape_grain_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_95_paranjape_grain_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_414/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_414/" + }, + "mrr": { + "materialType": [ + "metal", + "alloy", + "shape memory alloy", + "SMA", + "metals" + ], + "characterizationMethod": [ + "HEDM", + "high energy diffraction microscopy" + ], + "structuralFeature": [ + "polycrystalline", + "austenite" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:31:25.882617Z", + "source_id": "pub_95_paranjape_grain_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_95_paranjape_grain" + }, + "custom": { + "funding_details": "HMP, PPP, and LCB acknowledge the financial support from Department of Energy, Basic Energy Sciences (grant no. de-sc0010594). APS acknowledges funding from NSF-Career award no. 1454668. Electron microscopy work reported in this article was performed at NUANCE and OMM Facilities (funded by NSF DMR-1121262) at Northwestern University. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Fa- cility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The ff-HEDM experiments were performed at 1-ID-E beamline at the Advanced Photon Source. This work used the Extreme Science and Engineering Discovery Environ- ment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. Technical support from Prof. B. Kappes (Colorado School of Mines) is acknowledged.", + "all_materials_included": "['NiTi', 'Nickel-Titanium', 'Ni = 50.9 atomic %. Ti = balance']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2NK5W", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Paranjape, Harshad M.", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Paranjape", + "givenName": "Harshad M." + }, + { + "creatorName": "Paul, Partha P.", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Paul", + "givenName": "Partha P." + }, + { + "creatorName": "Sharma, Hemant", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Sharma", + "givenName": "Hemant" + }, + { + "creatorName": "Kenesei, Peter", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Kenesei", + "givenName": "Peter" + }, + { + "creatorName": "Park, Jun-Sang", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Park", + "givenName": "Jun-Sang" + }, + { + "creatorName": "Duerig, T.W.", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Duerig", + "givenName": "T.W." + }, + { + "creatorName": "Brinson, L. Catherine", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Brinson", + "givenName": "L. Catherine" + }, + { + "creatorName": "Stebner, Aaron P.", + "affiliations": [ + "Colorado School of Mines", + "Northwestern University", + "Argonne National Laboratory", + "Confluent Medical Technologies" + ], + "familyName": "Stebner", + "givenName": "Aaron P." + } + ], + "subjects": [ + { + "subject": "electron backscattering diffraction" + }, + { + "subject": "EBSD" + }, + { + "subject": "lattice strain" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "HEDM" + }, + { + "subject": "shape memory alloy" + }, + { + "subject": "microstructure" + }, + { + "subject": "diffraction" + }, + { + "subject": "microstructures" + }, + { + "subject": "Nickel-Titanium" + }, + { + "subject": "SMA" + }, + { + "subject": "synchrotron diffraction" + }, + { + "subject": "microscopy" + }, + { + "subject": "NiTi" + }, + { + "subject": "high energy diffraction microscopy" + }, + { + "subject": "In situ X-ray diffraction" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-03-24T03:54:56Z", + "dateType": "Accepted" + }, + { + "date": "2017-03-24T03:54:56Z", + "dateType": "Available" + }, + { + "date": "2017-03-23", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Harshad Paranjape (hparanja@mines.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.jmps.2017.02.007", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1016/j.jmps.2017.02.007", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://arxiv.org/pdf/1610.08429.pdf", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Grain Structure, Grain-averaged Lattice Strains, and Macro-scale Strain Data for Superelastic Nickel-Titanium Shape Memory Alloy Polycrystal Loaded in Tension" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "A cylindrical dog bone specimen from NiTi shape memory alloy is loaded in tension at room temperature for eleven cycles. Maximum stress in each cycle is approximately 220 MPa. The experiment is performed in-situ at Sector 1, beam line 1ID-E of Advanced Photon Source, Argonne National Laboratory. Using far-field high-energy diffraction microscopy technique, grain centroids, grain-averaged lattice strain tensor, grain volume, and grain-averaged crystal orientation are obtained at the start of the first cycle and at nine stress values of the eleventh cycle." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_98_brajuskovic_realspace_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_432/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_432/" + }, + "custom": { + "all_materials_included": "['Permalloy (Ni=80%,Fe=20%)', 'Ni', 'Fe', 'nickel', 'iron']", + "funding_details": "['United States Department of Energy, Office of Science, Basic Energy Sciences, Materials Science Division', 'Use of the Center for Nanoscale Materials was supported by the United Sates Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract no. DE-AC02-06CH11357']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_98_brajuskovic_realspace_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "magnetization", + "magnetic induction", + "magnetic materials", + "metals", + "thin films" + ], + "characterizationMethod": [ + "Lorentz Transition Electron Microscopy (LTEM)", + "LTEM" + ], + "structuralFeature": [ + "polycrystalline", + "crystalline" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:39:27.799439Z", + "source_id": "pub_98_brajuskovic_realspace_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_98_brajuskovic_realspace" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M22P48", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Brajuskovic", + "givenName": "V.", + "creatorName": "Brajuskovic, V.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Barrows", + "givenName": "F.", + "creatorName": "Barrows, F.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Phatak", + "givenName": "C.D.", + "creatorName": "Phatak, C.D.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Petford-Long", + "givenName": "A.K.", + "creatorName": "Petford-Long, A.K.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "FIB" + }, + { + "subject": "LTEM" + }, + { + "subject": "magnetic materials" + }, + { + "subject": "artificial spin ice lattice" + }, + { + "subject": "focused ion beam milling" + }, + { + "subject": "sputter deposition" + }, + { + "subject": "quasicrystal" + }, + { + "subject": "Lorentz microscopy" + }, + { + "subject": "Permalloy" + }, + { + "subject": "thermal annealing" + }, + { + "subject": "magnetron sputtering" + }, + { + "subject": "transport of intensity equation" + }, + { + "subject": "microsopy" + }, + { + "subject": "quantum materials" + }, + { + "subject": "TEM" + }, + { + "subject": "microscopy" + }, + { + "subject": "thin films" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-05-05T19:16:19Z", + "dateType": "Accepted" + }, + { + "date": "2017-05-05T19:16:19Z", + "dateType": "Available" + }, + { + "date": "2017-04-07", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "C.D. Phatak (cd@anl.gov)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1038/srep34384", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.ultramic.2014.01.005", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1038/srep34384", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://doi.org/10.1016/j.ultramic.2014.01.005", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Real-space Observation of Magnetic Excitations and Avalanche Behavior in Artificial Quasicrystal Lattices" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Thin films of Permalloy were patterned into quasicrystal artificial spin ice lattices. Lorentz TEM was used to image the magnetic induction in the lattices in the as-deposited state, after a demagnetization protocol, and after thermal annealing in a sputter deposition chamber." + }, + { + "descriptionType": "Other", + "description": "The stacks contain two through-focus series images acquired from artificial quasicrystal lattices. The second stack is a through-focus series of images of the same pattern as in the first stack, except it has been rotated 180 degrees about the axis of the sample holder. In order to calculate the magnetic phase shift alone, the derivative of image intensity with respect to defocus at each pixel is computed for each of the two through-focus series. The two longitudinal derivatives are then subtracted from each other and divided by two. The magnetic phase shift is then be obtained by applying the transport of intensity method of phase reconstruction to the difference of the two derivatives. See the following for more details: Volkov, V.V., Zhu,Y., and De Graef, M., \"A new symmetrized solution for phase retrieval using the transport of intensity equation\", Micron 33 (2002) 411-416." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_94_yager_synthetic_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_406/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_406/" + }, + "custom": { + "all_materials_included": "synthetic", + "funding_details": "This research used resources of the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, at Brookhaven National Laboratory under Contract No. DE-SC0012704" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_94_yager_synthetic_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "scattering", + "synthetic", + "nanoparticle superlattices", + "meso-objects", + "crystalline materials" + ], + "characterizationMethod": [ + "synthetically-generated data" + ], + "structuralFeature": [ + "BCC", + "FCC", + "HCP", + "lamellar", + "polycrystalline", + "disordered" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:29:45.416714Z", + "source_id": "pub_94_yager_synthetic_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_94_yager_synthetic" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2Z30Z", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Yager", + "givenName": "Kevin G.", + "creatorName": "Yager, Kevin G.", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "familyName": "Lhermitte", + "givenName": "Julien", + "creatorName": "Lhermitte, Julien", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "familyName": "Yu", + "givenName": "Dantong", + "creatorName": "Yu, Dantong", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "familyName": "Wang", + "givenName": "Boyu", + "creatorName": "Wang, Boyu", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "familyName": "Guan", + "givenName": "Ziqiao", + "creatorName": "Guan, Ziqiao", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + }, + { + "familyName": "Liu", + "givenName": "Jiliang", + "creatorName": "Liu, Jiliang", + "affiliations": [ + "Brookhaven National Lab", + "Brookhaven National Laboratory", + "New Jersey Institute of Technology", + "Stony Brook University", + "Stony Brook University" + ] + } + ], + "subjects": [ + { + "subject": "x-ray" + }, + { + "subject": "xray" + }, + { + "subject": "machine learning" + }, + { + "subject": "CNN" + }, + { + "subject": "convolutional neural network" + }, + { + "subject": "simulation" + }, + { + "subject": "wide-angle x-ray scattering" + }, + { + "subject": "waxs" + }, + { + "subject": "saxs" + }, + { + "subject": "convolutional autoencoders" + }, + { + "subject": "small-angle x-ray scattering" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-03-22T02:52:07Z", + "dateType": "Accepted" + }, + { + "date": "2017-03-22T02:52:07Z", + "dateType": "Available" + }, + { + "date": "2017-03-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Kevin Yager (kyager@bnl.gov)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1109/WACV.2014.6836004", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1109/NYSDS.2016.7747813", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://arxiv.org/abs/1611.03313", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset of Synthetic X-ray Scattering Images for Classification Using Deep Learning" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset contains a large number of example x-ray scattering images; each image is tagged with a variety of attributes describing the data features appearing in the image ('rings', 'anisotropic', etc.) or describing the underlying material ('BCC', 'FCC', etc.). The main purpose of this dataset is as a training set for machine-learning methods. The images were generated synthetically, using a combination of ad hoc methods (e.g. superimposing features such as rings and halos) and simple simulations (e.g. generating realspace arrangements of nanoparticles, and then computing the far-field scattering pattern). The presented code iterates across a wide variety of input conditions, such that the output images cover a wide range of expected x-ray scattering image types. Experimentally-realistic artifacts, including masks, parasitic streaks, and Poisson noise, are also included." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Brookhaven National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_87_senabulya_on_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_87_senabulya_on_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_857/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_857/" + }, + "mrr": { + "materialType": [ + "aperiodic", + "thermodynamic", + "kinetic", + "metal", + "quasicrystalline" + ], + "characterizationMethod": [ + "X-ray computed tomography" + ], + "structuralFeature": [ + "liquid", + "quasicrystalline" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T17:18:07.020522Z", + "source_id": "pub_87_senabulya_on_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_87_senabulya_on" + }, + "custom": { + "all_materials_included": "['Al-Pd-Mn', 'Al', 'Pd']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2J053", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Senabulya, Nancy", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Senabulya", + "givenName": "Nancy" + }, + { + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Xiao", + "givenName": "Xianghui" + }, + { + "creatorName": "Han, Insung", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Han", + "givenName": "Insung" + }, + { + "creatorName": "Shahani, Ashwin J.", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Shahani", + "givenName": "Ashwin J." + } + ], + "subjects": [ + { + "subject": "synchrotron" + }, + { + "subject": "gravity-driven convection" + }, + { + "subject": "Al-Pd-Mn alloys" + }, + { + "subject": "4D x-ray tomography" + }, + { + "subject": "icosahedral quasicrystal" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "anisotropic velocity" + }, + { + "subject": "kinetic shape" + }, + { + "subject": "experiment" + }, + { + "subject": "supersaturation" + }, + { + "subject": "mass transport" + }, + { + "subject": "growth" + }, + { + "subject": "continuous cooling" + }, + { + "subject": "microscopy" + }, + { + "subject": "microstructures" + }, + { + "subject": "in situ" + }, + { + "subject": "equilibrium shape" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-11-08T17:18:59Z", + "dateType": "Accepted" + }, + { + "date": "2017-11-08T17:18:59Z", + "dateType": "Available" + }, + { + "date": "2017-11-08", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Ashwin J. Shahani (shahani@umich.edu )", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Nancy Senabulya (nsenab@umich.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "On the kinetic and equilibrium shapes of icosahedral Al71Pd19Mn10 quasicrystals" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study the growth and relaxation dynamics of an icosahedral quasicrystal from a liquid. The composition of the imaged QC is Al-19at%Pd-10at%Mn, and the growth and relaxation dynamics of an icosahedral quasicrystal were observed under slow cooling conditions. \r\nThe quasicrystalline phase shows weaker projection intensity because of more X-ray absorption resulting from its heavy elemental (Pd and Mn) composition. \r\nThis contrast difference allowed us to segment the quasicrystal from the liquid phase and visualize the 3D volume of the icosahedral quasicrystal. \r\nDuring the growth, a pentagonal dodecahedral shape with 6 five-fold facets was clearly visible growing from the oxide skin (Al2O3) into the liquid. Facet velocities are noticeably anisotropic with facets perpendicular to the gravitational field growing faster than facets further away from gravity. \r\nGrowth is clearly bulk transport limited. Near equilibrium, we observe a morphological transformation to a truncated dodecahedral shape characterized by the truncation of five-fold vertices in the growth shape (pentagonal dodecahedron) into triangular facets. \r\nThis signifies a relaxation mechanism to a lower energy equilibrium shape in this icosahedral quasicrystal thus the growth and equilibrium shapes in icosahedral QCs are not identical. \r\nFor details on the resulting 3D reconstruction, visualization, and kinetic analyses, the readers are encouraged to look up the following publication." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Beamline 2-BM at Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + }, + { + "geoLocationPlace": "ANL" + }, + { + "geoLocationPlace": "APS" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_12_narayan_characterizing_v2.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_12_narayan_characterizing_v2.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_590/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_590/" + }, + "mrr": { + "materialType": [ + "total energy", + "spin density", + "charge density", + "charge spin susceptibility", + "superconductors", + "magnetic materials" + ], + "characterizationMethod": [ + "Diffusion Monte Carlo", + "simulation", + "Monte Carlo" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-07T22:40:08.473449Z", + "source_id": "pub_12_narayan_characterizing_v2.1", + "scroll_id": 0, + "version": 1, + "source_name": "pub_12_narayan_characterizing" + }, + "custom": { + "funding_details": "Center for Emergent Superconductivity, United States Department of Energy, Office of Science, Basic Energy Sciences under award number DEAC0298CH1088", + "all_materials_included": "['BaCr2As2', 'BaMn2As2', 'BaFe2As2', 'BaCo2As2', 'BaNi2As2', 'BaCo2As2', 'FeSe', 'FeTe', 'Ca2CuCl2O2']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2905X", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Narayan, Awadhesh", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Narayan", + "givenName": "Awadhesh" + }, + { + "creatorName": "Busemeyer, Brian", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Busemeyer", + "givenName": "Brian" + }, + { + "creatorName": "Wagner, Lucas K.", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Wagner", + "givenName": "Lucas K." + } + ], + "subjects": [ + { + "subject": "high temperature superconductivity" + }, + { + "subject": "QMC" + }, + { + "subject": "machine learning" + }, + { + "subject": "iron based superconductors" + }, + { + "subject": "spin charge coupling" + }, + { + "subject": "QWalk" + }, + { + "subject": "simulation" + }, + { + "subject": "cuprates" + }, + { + "subject": "superconductors" + }, + { + "subject": "PBE0" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-06-29T14:47:29Z", + "dateType": "Accepted" + }, + { + "date": "2017-06-29T14:47:29Z", + "dateType": "Available" + }, + { + "date": "2017-06-29", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Lucas Wagner (lkwagner@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Characterizing the Unifying Thread in High Temperature Superconductors Using Realistic Simulations" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This is a dataset collecting high accuracy quantum Monte Carlo (QMC) results for pnictide and cuprate compounds, some of which are high temperature superconductors. Our trial wavefunction consists of a Slater determinant augmented by a Jastrow factor. We construct the Slater determinant using orbitals from density functional theory (DFT). Our DFT calculations are done using the CRYSTAL package, with the PBE0 functional. We use Burkatzki-Filippi-Dolg pseudopotentials to replace the core electrons. We then use the open-source QWalk package to optimize the trial wavefunction and perform diffusion Monte Carlo calculations. We control finite size errors by constructing 2x2x1 supercells and averaging over 8 twist boundary conditions." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "University of Illinois at Urbana-Champaign" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_66_kim_mechanical_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_232/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_232/" + }, + "custom": { + "all_materials_included": "['XB (X=Ti/Fe/Mo/Nb/V)', '(X^1_{0.5}X^2_{0.5})B', '(X^1_{0.25}X^2_{0.75})B', '(X^1_{0.75}X^2_{0.25})B', '(X^1_{1/24}X^2_{23/24})B', '(X^1_{23/24}X^2_{1/24})B']", + "funding_details": "['National Science Foundation (NSF/DMREF Grant 1435545)', 'Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number (ACI-1053575)']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_66_kim_mechanical_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "structural", + "mechanical", + "phase stability", + "intermetallics", + "ceramics", + "metals" + ], + "characterizationMethod": [ + "density functional theory calculation", + "DFT" + ], + "structuralFeature": [ + "B27 structure", + "Bf structure", + "special quasirandom structure (SQS)" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:30:24.781504Z", + "source_id": "pub_66_kim_mechanical_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_66_kim_mechanical" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M24S3J", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Kim", + "givenName": "Hyojung", + "creatorName": "Kim, Hyojung", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Trinkle", + "givenName": "Dallas R.", + "creatorName": "Trinkle, Dallas R.", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "solubility limit" + }, + { + "subject": "Bf structure" + }, + { + "subject": "machine learning" + }, + { + "subject": "monoboride" + }, + { + "subject": "B27 structure" + }, + { + "subject": "phases" + }, + { + "subject": "polycrystalline mechanical properties" + }, + { + "subject": "ab-initio" + }, + { + "subject": "simulation" + }, + { + "subject": "Vegard's law" + }, + { + "subject": "stacking fault energy" + }, + { + "subject": "special quasirandom structure" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-01-04T16:24:46Z", + "dateType": "Accepted" + }, + { + "date": "2017-01-04T16:24:46Z", + "dateType": "Available" + }, + { + "date": "2017-01-04", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Dallas Trinkle (dtrinkle@illinois.edu)" + } + ], + "titles": [ + { + "title": "Mechanical Properties and Phase Stability of Monoborides using Density Functional Theory Calculations" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data demonstrates the Ti-monoborides with improved polycrystalline elastic properties such as Young's modulus and Pugh's ratio, and stacking fault energies. The lattice parameters, total energies and elastic constants of monoborides are computed using density functional theory" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "University of Illinois at Urbana-Champaign" + }, + { + "geoLocationPlace": "Extreme Science and Engineering Discovery Environment (XSEDE)" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_90_obrien_silicon_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_90_obrien_silicon_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_374/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_374/" + }, + "mrr": { + "materialType": [ + "semiconductor", + "dielectric", + "Silicon Si(111)", + "Sillicon Nitride (SiNx)", + "Sillicon Dioxide (SiO2)" + ], + "characterizationMethod": [ + "Scanning Electron Microscopy", + "SEM" + ], + "structuralFeature": [ + "Si(111) single crystal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T17:32:36.759425Z", + "source_id": "pub_90_obrien_silicon_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_90_obrien_silicon" + }, + "custom": { + "funding_details": "National Science Foundation NSF ACI 1443013" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2C88F", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "O'Brien Jr., Thomas Robert", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ], + "familyName": "O'Brien Jr.", + "givenName": "Thomas Robert" + }, + { + "creatorName": "Chow, Edmond", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ], + "familyName": "Chow", + "givenName": "Edmond" + }, + { + "creatorName": "Nahrstedt, Klara", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ], + "familyName": "Nahrstedt", + "givenName": "Klara" + }, + { + "creatorName": "Lewis, Bruce", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ], + "familyName": "Lewis", + "givenName": "Bruce" + }, + { + "creatorName": "Dallesasse, John Michael", + "affiliations": [ + "University of Illinois at Urbana-Champaign, Urbana, Illinois, USA", + "Electrical and Computer Engineering, University of Illinois at Urbana-Champaign" + ], + "familyName": "Dallesasse", + "givenName": "John Michael" + } + ], + "subjects": [ + { + "subject": "Si" + }, + { + "subject": "dielectric etch" + }, + { + "subject": "dry-etching" + }, + { + "subject": "semiconductors" + }, + { + "subject": "plasma-etching" + }, + { + "subject": "Si(111)" + }, + { + "subject": "silicon etch" + }, + { + "subject": "microscopy" + }, + { + "subject": "Scanning Electron Microscopy (SEM)" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-02-09T20:30:48Z", + "dateType": "Accepted" + }, + { + "date": "2017-02-09T20:30:48Z", + "dateType": "Available" + }, + { + "date": "2017-02-09", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Thomas R. O'Brien, Jr. (tobrien3@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Silicon and Dielectric Dry-Etch Recipes" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This work contains a series of SEM images and descriptions describing various etches used commonly for dielectrics (SiNx, SiO2) and Si. Some are failures, some are successes. 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Krogel", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Dataset for Compton profile of VO2 across the metal-insulator transition: evidence of a non-Fermi liquid metal" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for \"Compton profile of VO2 across the metal-insulator transition: evidence of a non-Fermi\r\nliquid metal\", Ilkka Kylanpaa, Ye Luo , Olle Heinonen, Paul R. C. Kent, and Jaron T. Krogel (2019)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_88_ren_atom_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_88_ren_atom_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_371/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_371/" + }, + "mrr": { + "materialType": [ + "topological insulator", + "superconductor", + "2D materials", + "Ag doped (PbSe)5(Bi2Se3)3" + ], + "characterizationMethod": [ + "atom probe tomography", + "APT", + "DFT", + "density functional theory" + ], + "structuralFeature": [ + 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"givenName": "Mercouri G." + }, + { + "creatorName": "Tavazza, Francesca", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials Science and Engineering Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States", + "Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, United States", + "Materials Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States", + "Northwestern University, Department of Materials Science and Engineering, Lauhon Group" + ], + "familyName": "Tavazza", + "givenName": "Francesca" + }, + { + "creatorName": "Davydov, Albert V.", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University, 2220 Campus Drive, Evanston, Illinois 60208, United States", + "Materials 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DOI: 10.1021/acs.jpcc.6b02073" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_42_sharma_nanoscale_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1150/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1150/" + }, + "custom": { + "all_materials_included": "VO2", + "funding_details": "This work was supported by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (synthesis) and as part of the Computational Materials Sciences Program (characterization and theory). 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This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of DOE under Contract DE-AC02-05CH11231." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_42_sharma_nanoscale_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "XRD", + "KPFM", + "QMC", + "DFT", + "CPD", + "ToF-SIMS" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-03-08T15:44:00.192915Z", + "source_id": "pub_42_sharma_nanoscale_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_42_sharma_nanoscale" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2TD16", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Sharma", + "givenName": "Yogesh", + "creatorName": "Sharma, Yogesh", + "affiliations": [ + "Materials Science and Technology 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"Ovchinnikova", + "givenName": "Olga S.", + "creatorName": "Ovchinnikova, Olga S.", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Kalinin", + "givenName": "Sergei V.", + "creatorName": "Kalinin, Sergei V.", + "affiliations": [ + "Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Heinonen", + "givenName": "Olle", + "creatorName": "Heinonen, Olle", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory, Lemont, IL" + ] + }, + { + "familyName": "Lee", + "givenName": "Ho Nyung", + "creatorName": "Lee, Ho Nyung", + "affiliations": [ + "Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN" + ] + } + ], + "subjects": [ + { + "subject": "density functional theory" + }, + { + "subject": "DMC" + }, + { + "subject": "VASP" + }, + { + "subject": "Quantum Espresso" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "oxygen vacancy" + }, + { + "subject": "metal-insulator transition" + }, + { + "subject": "scanning probe microscopy" + }, + { + "subject": "vanadium dioxide" + }, + { + "subject": "QMCPACK" + }, + { + "subject": "diffusion Monte Carlo" + }, + { + "subject": "simulation" + }, + { + "subject": "quantum Monte Carlo" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-06-21T19:39:54Z", + "dateType": "Accepted" + }, + { + "date": "2018-06-21T19:39:54Z", + "dateType": "Available" + }, + { + "date": "2018-06-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Ho Nyung Lee (hnlee@ornl.gov)" + } + ], + "titles": [ + { + "title": "Dataset for Nanoscale Control of Oxygen Defects and Metal\u2212Insulator Transition in Epitaxial Vanadium Dioxides" + } + ], 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Krogel, Panchapakesan Ganesh, Liam Collins, Anton V. Ievlev, Qian Li, Xiang Gao, Nina Balke, Olga S. Ovchinnikova, Sergei V. Kalinin, Olle Heinonen, and Ho Nyung Lee (2018)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_27_jang_anisotropic_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_111/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_111/" + }, + "custom": { + "all_materials_included": "phosphorus", + "funding_details": "['NSF EFRI-1433467', 'NSF DMR-1121262', 'ONR N00014-14-1-066', 'ANSER DOE DE-SC0001059']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_27_jang_anisotropic_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "thermal", + "elastic", + "semiconductor", + "nanomaterials" + ], + "characterizationMethod": [ + "Time-domain Thermoreflectance", + "TDTR" + ], + "structuralFeature": [ + "crystalline", + "2D", + "film" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:16:11.862633Z", + "source_id": "pub_27_jang_anisotropic_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_27_jang_anisotropic" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2BC7S", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Jang", + "givenName": "Hyejin", + "creatorName": "Jang, Hyejin", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "familyName": "Wood", + "givenName": "Joshua D.", + "creatorName": "Wood, Joshua D.", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "familyName": "Ryder", + "givenName": "Christopher R.", + "creatorName": "Ryder, Christopher R.", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + }, + { + "familyName": "Cahill", + "givenName": "David G.", + "creatorName": "Cahill, David G.", + "affiliations": [ + "University of Illinois at Urbana-Champaign", + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "time-domain thermoreflectance" + }, + { + "subject": "speed of sound" + }, + { + "subject": "experiment" + }, + { + "subject": "TDTR" + }, + { + "subject": "2D materials" + }, + { + "subject": "black phosphorus" + }, + { + "subject": "thermal conductivity" + }, + { + "subject": "elastic constants" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-05-27T19:43:10Z", + "dateType": "Accepted" + }, + { + "date": "2016-05-27T19:43:10Z", + "dateType": "Available" + }, + { + "date": "2016-05-27", + "dateType": "Issued" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1002/adma.201503466", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1002/adma.201503466", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Anisotropic Thermal Conductivity of Exfoliated Black Phosphorus" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "These are the raw data of Time-domain Thermoreflectance (TDTR) measurements on exfoliated black phosphorus (BP) flakes. The flakes are mechanically exfoliated onto Si substrates and have the thicknesses ranging from 138 to 552 nm. They are encapsulated with ALD-grown ~3 nm alumina layer to prevent possible sample degradation. For TDTR measurements, the samples are further coated with ~70 nm NbV transducer. The through-plane thermal conductivity of the BP flakes along with their two interfactial thermal conductances (at the top and bottom of the flakes) are determined by using conventional TDTR method. We use two different modulation frequencies to adjust the laser thermal penetration depth." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Frederick Seitz Materials Research Laboratory at the University of Illinois at Urbana-Champaign" + }, + { + "geoLocationPlace": "Materials Research Science and Engineering Center at Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_16_dolejsi_gisaxs_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_16_dolejsi_gisaxs_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1112/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1112/" + }, + "mrr": { + "materialType": [ + "polymer" + ], + "characterizationMethod": [ + "grazing incidence small-angle X-ray scattering", + "GISAXS" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-07T22:47:02.471210Z", + "source_id": "pub_16_dolejsi_gisaxs_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_16_dolejsi_gisaxs" + }, + "custom": { + "funding_details": "The submitted manuscript was created by UChicago Argonne, LLC, the Operator of Argonne National Laboratory (`Argonne'). Argonne, a US Department of Energy (DOE) Office of Science Laboratory, is operated under contract No. DE-AC02-06CH11357. The US Government retains for itself, and others acting on its behalf, a paid-up nonexclusive irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Goverment. This research used resources of the Advanced Photon Source and the Center for Nanoscale Materials, a US DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under contract No. DE-AC02-06CH11357." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M25630", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Dolejsi, Moshe", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Dolejsi", + "givenName": "Moshe" + }, + { + "creatorName": "Bowen, Alec", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Bowen", + "givenName": "Alec" + }, + { + "creatorName": "de Pablo, Juan", + "affiliations": [ + "University of Chicago" + ], + "familyName": "de Pablo", + "givenName": "Juan" + }, + { + "creatorName": "Nealey, Paul", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Nealey", + "givenName": "Paul" + } + ], + "subjects": [ + { + "subject": "GISAXS" + }, + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "grazing-incidence small-angle X-ray scattering" + }, + { + "subject": "polymer gratings" + }, + { + "subject": "DSA" + }, + { + "subject": "scattering" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-04-13T17:58:23Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-13T17:58:23Z", + "dateType": "Available" + }, + { + "date": "2018-04-13", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Paul Nealey (nealey@uchicago.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1107/S1600576716004453", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.1107/S1600576716004453", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "GISAXS Data for Polymer Thin Films and Gratings" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data set contains the raw GISAXS profiles for a number of polymer grating samples and DSA samples. Data is organized by the sample name and the date taken." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Sector 8-ID-E at the Advanced Photon Source, Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_93_shin_nature_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_914/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_914/" + }, + "custom": { + "all_materials_included": "C", + "funding_details": "['This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials.', 'This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology.']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_93_shin_nature_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "simulation" + ], + "structuralFeature": [ + "Graphyne", + "Graphene" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-03-08T19:16:03.540054Z", + "source_id": "pub_93_shin_nature_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_93_shin_nature" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2NK9D", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Shin", + "givenName": "Hyeondeok", + "creatorName": "Shin, Hyeondeok", + "affiliations": [ + "Leadership Computing Facility, Argonne National Laboratory, Argonne, 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Seoul 05029, Korea" + ] + } + ], + "subjects": [ + { + "subject": "QMC" + }, + { + "subject": "DMC" + }, + { + "subject": "Graphyne" + }, + { + "subject": "2D materials" + }, + { + "subject": "Monte Carlo" + }, + { + "subject": "QMCPACK" + }, + { + "subject": "simulation" + }, + { + "subject": "Quantum Monte Carlo" + }, + { + "subject": "Graphene" + }, + { + "subject": "Diffusion Monte Carlo" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-12-22T21:47:22Z", + "dateType": "Accepted" + }, + { + "date": "2017-12-22T21:47:22Z", + "dateType": "Available" + }, + { + "date": "2017-09-25", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "hshin@anl.gov" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.jctc.7b00747", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + 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R.", + "creatorName": "Shea, Jacqueline A. 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Shifler. N.M.L. was supported by a Holbrook Foundation Graduate Fellowship through the UCSB IEE; a UCSB Chancellor's Graduate Fellowship; a Doctoral Fellowship from the Advanced Light Source (ALS), a Division of Lawrence Berkeley National Laboratory (LBNL), with funding provided by the U.S. Department of Energy; and a National Science Foundation (NSF) Graduate Research Fellowship under Grant No. 1144085. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The study was also supported by Beamline 8.3.2 at the ALS, a Division of LBNL, and the Molecular Foundry at LBNL. The ALS and the Molecular Foundry are supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. DOE under Contract No. DE-AC02-05CH11231. The MRL Shared Experimental Facilities at UCSB are supported by the MRSEC Program of the NSF under Award No. DMR 1121053, a member of the NSF-funded Materials Research Facilities Network. The SiC fibers used in this study were kindly provided by Pratt and Whitney. Finally, the authors gratefully acknowledge John Williams for fruitful discussions, Dr. John Shaw for help running the XCT experiment, Dr. Ilir Beta for running the TGMS experiments, as well as Drs. 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"services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/leveilleejoshua_freeelectron_effects_principles_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/leveilleejoshua_freeelectron_effects_principles_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-07-05T13:56:32.626182Z", + "mdf_id": "5d1f5710bd8ab6323037dfbe", + "source_id": "leveilleejoshua_freeelectron_effects_principles_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "leveilleejoshua_freeelectron_effects_principles" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2D64J", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Leveillee, Joshua", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ], + "familyName": "Leveillee", + "givenName": "Joshua" + }, + { + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ], + "familyName": "Schleife", + "givenName": "Andr\u00e9" + } + ], + "subjects": [ + { + "subject": "spectroscopy" + }, + { + "subject": "MAPbI3" + }, + { + "subject": "experiment" + }, + { + "subject": "electrochemistry" + }, + { + "subject": "semiconductors" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-03-22T18:55:48Z", + "dateType": "Accepted" + }, + { + "date": "2019-03-22T18:55:48Z", + "dateType": "Available" + }, + { + "date": "2019-03-22", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Andr\u00e9 Schleife (schleife@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Dataset for \"Free-electron effects on optical absorption of hybrid perovskite CH3NH3PbI3 from first principles\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The calculations are performed in Vienna Ab initio Simulation Package (VASP) thus the inputs and outputs files are in VASP format. Parts of the calculations concerning the Bethe-Salpeter equation use our own code that is not published. The data for publication is structured as follows: \r\n\r\nThere are two main directories: (1) 1_Data_Sets_In_Paper contains the figures and grace files (which contain explicit data sets). The grace files can be opened using the xmgrace software, and arrays of data sets can be exported to txt files using xmgrace export functionality. (2) 2_VASP_and_BSE_IO contains the inputs for the VASP calculations performed in this work. Details will be discussed in the following section.\r\n\r\nDISCLAIMER: For the energy ranges discussed in the paper and presented in figures, optical spectra have been carefully calculated and converged. Outside of these energy ranges, however, there is no guarantee of the physicality of the results. Please be mindful of this when downloading data from the xmgrace files, and consult with the figures in the paper for the relevant energy range where results are physically meaningful. For further questions, please contact the authors. \r\n\r\nVASP_and_BSE_IO: Contains the explicit files related to VASP and the BSE code used in this study. POSCAR is the atomic structure, INCAR are setting for electronic structure calculations, and KPOINTS define the kmesh use to integrate over the Brillouin zone. Transmatrix files contain the transition dipole matrix elements between bands as a function of k-point. Details on file format can be found in the VASP manual at: https://cms f.mpi.univie.ac.at/vasp/vasp/vasp.html.\r\n\r\n(1): 1_Atomic_Structure contains, for each phase, the calculated ground state electronic structure needed to reproduce the results in this study. The DATA_X files (X indicates the phase) contain the energy v.s. volume curves used to confirm the stable ground state volume.\r\n\r\n(2): 2_Band_Structures contains the relevant files (VASP POSCAR, KPOINTS, INCAR) and band data to reproduce the band structures for each phase in this study. \r\n\r\n(3): 3_Density_of_States contains relevant files (VASP POSCAR, KPOINTS, INCAR) to reproduce the DOS for each phase in this study.\r\n\r\n(4): 4_GW0 contains the relevant files (VASP POSCAR, KPOINTS, INCAR) and QP eigenvalues to reproduce the prediction of the GW0 QP band gap for each phase found in this study.\r\n\r\n(5): 5_Optical_Spectra_and_BSE: This directory contains all relevant files to reproduce the optical response for each phase in this study. The two sub directories are detailed below. For others interested in reproducing the BSE results found herin, you will need to contact the authors for details in the code and implementation.\r\n\r\n(5a): shifted_kmesh: Coarse k-point meshes with random shifts are used to calculate optical spectra over a wide energy range. This directory contains three sub directories (C_SPEC_IO, T_SPEC_IO, and O_SPEC_IO, where C=cubic, T=tetragonal, and O=orthorhombic). The T and O directories only contains the SOC spectra IO, without BSE. The C_SPEC_IO contains SOC, EL, and FC directories. The SOC directory contains files to calculate the PBE+dGW0+SOC spectra without exciton effects on the shifted kmesh. The EL directory contains the IO for BSE+dGW0+SOC containing exciton effects and with interband electronic screening of the electron-hole Coulomb interaction only. The SPEC_OPP and SPEC_PAR files contain the spin conserving and spin-flip transitions spectra under SOC, respectively. The BSE folders contain IO for the BSE code used in this study. The FC directory contains the IO for BSE+dGW0+SOC containing exciton effects and with interband electronic screening PLUS Thomas-Fermi screening by additional free-electrons in the CB of the electron-hole Coulomb interaction. Similarly, it contains BSE run files (in the Wpar, Wopp, Vpar, Vopp, and CSC directories), and spectra output in the SPEC directories. Free electron concentrations are indicated by the presence of an FCX string in the file name, where X gives the free electron concentration in the CBM in cm^-3.\r\n\r\n(5b): hybrid_kmesh contains calculations using the hybrid k-point mesh technique to carefully sample the Brillouin zone around the band extrema. From hybrid mesh calculations we predict the converged exciton binding energies and the carefully sampled optical spectra around optical onset. Burstein-Moss shifts and band gap renormalization are also calculated on these grids. Similar to 5a, it contains C, T, and O subdirectories, where the O and T directories only include PBE+dGW0+SOC calculations. The C_IO directory contains both PBE+dGW0+SOC and BSE+dGW0+SOC calculations, and are arranged in the same structure as discussed in 5a." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_25_agarwal_solute_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_25_agarwal_solute_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_620/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_620/" + }, + "mrr": { + "materialType": [ + "kinetics", + "diffusivity", + "metals" + ], + "characterizationMethod": [ + "density functional theory calculation", + "DFT" + ], + "structuralFeature": [ + "hexagonal close packed" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:12:50.706594Z", + "source_id": "pub_25_agarwal_solute_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_25_agarwal_solute" + }, + "custom": { + "funding_details": "['National Science Foundation (NSF grant 1411106)', 'Office of Naval Research (ONR grant N000141210752)']", + "all_materials_included": "['Mg-X', 'Mg']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M20G83", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Agarwal, Ravi", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Agarwal", + "givenName": "Ravi" + }, + { + "creatorName": "Trinkle, Dallas R.", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Trinkle", + "givenName": "Dallas R." + } + ], + "subjects": [ + { + "subject": "solute diffusivity" + }, + { + "subject": "diffusion" + }, + { + "subject": "binding energy" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "Arrhenius parameters" + }, + { + "subject": "13-frequency" + }, + { + "subject": "migration barrier" + }, + { + "subject": "database" + }, + { + "subject": "Green function" + }, + { + "subject": "simulation" + }, + { + "subject": "drag ratio" + }, + { + "subject": "8-frequency" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-08-23T19:31:27Z", + "dateType": "Accepted" + }, + { + "date": "2017-08-23T19:31:27Z", + "dateType": "Available" + }, + { + "date": "2017-08-23", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Dallas Trinke (dtrinkle@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.5281/zenodo.494987", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://doi.org/10.5281/zenodo.494987", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/DallasTrinkle/Onsager", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://pypi.python.org/pypi/onsager", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dallastrinkle.github.io/Onsager", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Solute transport database in Mg using ab initio and exact diffusion theory" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset demonstrate the improved solute transport in hcp Mg through exact diffusion theory:Green function approach. We report the database of 61 solutes consisting of solute diffusivity and solute drag ratio. We also report the results from previously used approximate models of diffusion namely 8-frequency and 13-frequency models. The parameters needed for the diffusion models i.e. binding energy, migration barrier and attempt frequency are obtained through density functional theory calculations" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "University of Illinois at Urbana-Champaign" + }, + { + "geoLocationPlace": "Extreme Science and Engineering Discovery Environment (XSEDE)\"" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_53_de_emsoft_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_160/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_160/" + }, + "custom": { + "all_materials_included": "['iron', 'Fe']", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_53_de_emsoft_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "non-specific", + "metal" + ], + "characterizationMethod": [ + "numerical simulation" + ], + "structuralFeature": [ + "crystalline", + "crystal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:02:33.629273Z", + "source_id": "pub_53_de_emsoft_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_53_de_emsoft" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2SG6G", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "De Graef", + "givenName": "Marc", + "creatorName": "De Graef, Marc", + "affiliations": [ + "Carnegie Mellon University" + ] + } + ], + "subjects": [ + { + "subject": "EBSD" + }, + { + "subject": "Fe" + }, + { + "subject": "pattern dictionary" + }, + { + "subject": "machine learning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "backscattering" + }, + { + "subject": "master pattern" + }, + { + "subject": "EMSoft" + }, + { + "subject": "simulation" + }, + { + "subject": "diffraction" + }, + { + "subject": "electron backscattered diffraction" + }, + { + "subject": "microscopy" + }, + { + "subject": "dictionary" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-10-20T15:13:55Z", + "dateType": "Accepted" + }, + { + "date": "2016-10-20T15:13:55Z", + "dateType": "Available" + }, + { + "date": "2016-10-20", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Marc De Graef (degraef@cmu.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1017/S1431927613001840", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/marcdegraef/EMsoft", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1017/S1431927613001840", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Iron (Fe)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for bcc-Fe for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Carnegie Mellon University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_57_wu_highthroughput_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_164/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_164/" + }, + "custom": { + "all_materials_included": "['Al', 'Au', 'Ca', 'Cu', 'Fe', 'Ir', 'Mg', 'Mo', 'Ni', 'Pb', 'Pt', 'W']", + "funding_details": "['NSF SI2-SSI 1148011', 'NSF DGE-0718123', 'UW-Madison GERS']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_57_wu_highthroughput_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "diffusion", + "metal", + "alloy" + ], + "characterizationMethod": [ + "density functional theory", + "computational", + "DFT" + ], + "structuralFeature": [ + "bulk", + "crystalline", + "HCP", + "FCC" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:09:20.557396Z", + "source_id": "pub_57_wu_highthroughput_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_57_wu_highthroughput" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2X59R", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Wu", + "givenName": "Henry", + "creatorName": "Wu, Henry", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Mayeshiba", + "givenName": "Tam", + "creatorName": "Mayeshiba, Tam", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Morgan", + "givenName": "Dane", + "creatorName": "Morgan, Dane", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "iridium" + }, + { + "subject": "calcium" + }, + { + "subject": "DFT" + }, + { + "subject": "high-throughput" + }, + { + "subject": "aluminum" + }, + { + "subject": "lead" + }, + { + "subject": "copper" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "nickel" + }, + { + "subject": "platinum" + }, + { + "subject": "experiment" + }, + { + "subject": "magnesium" + }, + { + "subject": "simulation" + }, + { + "subject": "solute" + }, + { + "subject": "gold" + }, + { + "subject": "molybdenum" + }, + { + "subject": "palladium" + }, + { + "subject": "diffusion" + }, + { + "subject": "dilute" + }, + { + "subject": "database" + }, + { + "subject": "tungsten" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-10-12T22:09:02Z", + "dateType": "Accepted" + }, + { + "date": "2016-10-12T22:09:02Z", + "dateType": "Available" + }, + { + "date": "2016-10", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Dane Morgan (ddmorgan@wisc.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1038/sdata.2016.54", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1038/sdata.2016.54", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.6084/m9.figshare.1546772", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for High-throughput Ab-initio Dilute Solute Diffusion Database" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. 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Fifteen experimental runs were performed at different scan speeds, power levels, and numbers of passes to investigate porosity formation in real time and correlate it to the IR signature of the top surface. Details of the experiment are provided in the accompanying publication by the same authors [1]. The raw IR data is not provided for runs 12 and 15 because it is being used in an ongoing study.\n\n###### Reference\n[1] Paulson, N.H., Gould, B., Wolff, S.J., Stan, M., Greco, A.C., Correlations between thermal history and keyhole porosity in laser powder bed fusion, Additive Manufacturing (2020), https://doi.org/10.1016/j.addma.2020.101213\n\n###### Acknowledgements\nThis work is supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. The authors also acknowledge the staff at beamline 32-ID of the Advanced Photon Source for their help in data collection, particularly Tao Sun, Niranjan Parab, Cang Zhao, and Kamel Fezzaa." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ting_comparing_zwitterionic_micelles_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ting_comparing_zwitterionic_micelles_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ting_comparing_zwitterionic_micelles_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/ting_comparing_zwitterionic_micelles_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-05-26T15:24:03.712619Z", + "source_id": "ting_comparing_zwitterionic_micelles_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ting_comparing_zwitterionic_micelles" + }, + "dc": { + "identifier": { + "identifier": "10.18126/n4un-6usf", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Ting", + "givenName": "Jeffrey", + "creatorName": "Ting, Jeffrey", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Marras", + "givenName": "Alexander", + "creatorName": "Marras, Alexander", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Mitchell", + "givenName": "Joseph", + "creatorName": "Mitchell, Joseph", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Campagna", + "givenName": "Trinity", + "creatorName": "Campagna, Trinity", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Tirrell", + "givenName": "Matthew", + "creatorName": "Tirrell, Matthew", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "CHiMaD" + }, + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "polyelectrolyte" + }, + { + "subject": "SAXS" + }, + { + "subject": "DLS" + }, + { + "subject": "dynamic light scattering" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-05-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Matthew Tirrell " + }, + { + "contributorType": "ContactPerson", + "contributorName": "Jeffrey Ting " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.26434/chemrxiv.12237020.v1", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Dataset for Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "(1) background subtracted small angle X-ray scattering (SAXS) data from the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357, and the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Contract No. DE-AC02-76SF00515. Data is organized by polyelectrolyte complex micelles at investigated chain lengths.\n\n(2) autocorrelation functions from the dynamic light scattering (DLS) data in Figures 5 and S-9. Data is organized by scattering angle (Figure 5) or salt condition (Figure S-9) for each polyelectrolyte complex micelle system." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "polar_racemates_nw_hc_fordham_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/polar_racemates_nw_hc_fordham_v1.2/", + "total_size": 25485050, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/polar_racemates_nw_hc_fordham_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/polar_racemates_nw_hc_fordham_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-03-24T18:30:45.950106Z", + "source_id": "polar_racemates_nw_hc_fordham_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "polar_racemates_nw_hc_fordham" + }, + "dc": { + "identifier": { + "identifier": "10.18126/vnhj-jaz5", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Nisbet", + "givenName": "Matthew L.", + "creatorName": "Nisbet, Matthew L.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Pendleton", + "givenName": "Ian M.", + "creatorName": "Pendleton, Ian M.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Nolis", + "givenName": "Gene M.", + "creatorName": "Nolis, Gene M.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Griffith", + "givenName": "Kent J.", + "creatorName": "Griffith, Kent J.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Schrier", + "givenName": "Joshua", + "creatorName": "Schrier, Joshua", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Cabana", + "givenName": "Jordi", + "creatorName": "Cabana, Jordi", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Norquist", + "givenName": "Alexander J.", + "creatorName": "Norquist, Alexander J.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + }, + { + "familyName": "Poeppelmeier", + "givenName": "Kenneth R.", + "creatorName": "Poeppelmeier, Kenneth R.", + "affiliations": [ + "Northwestern University", + "Haverford College", + "University of Illinois at Chicago", + "Fordham University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "ferroelectric" + }, + { + "subject": "piezoelectric" + }, + { + "subject": "nonlinear optics" + }, + { + "subject": "composition space" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-03-24", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "krp@northwestern.edu" + } + ], + "titles": [ + { + "title": "Machine-learning-assisted Synthesis of Polar Racemates" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset used here consists of 51 experiments transcribed from laboratory notebook records, using the ESCALATE \"entity, materials, actions, observations\" ontology. The composition spaces of the (CuO, MO2)/bpy/HF(aq) (M = Ti, Zr, Hf) systems were explored by varying the amounts of bpy and HF(aq) used in each reaction, while the amounts of CuO and MO2 (M = Ti, Zr, Hf) were held constant. In addition to this raw experimental data, additional calculated stoichiometric properties and computed electronic structure properties were added. Stoichiometric features, such as molar amounts and molar ratios, were calculated directly from the experimental observations. Electronic structure calculations were performed on the [TiF6]2\u2212, [ZrF6]2\u2212, and [HfF6]2\u2212 anionic building units to provide data on geometry, energetics, and charges using Gaussian 09, The B3LYP/LANL2DZ model chemistry was used as it provides good estimations (+-10 pm) for bond lengths of transition metal oxides and halides. Atomic charges were assessed for the optimized geometries using Mulliken, Hirshfeld, CM5, Natural Bond Orbital (NBO), and electrostatic potential fitting (Merz\u2013Singh\u2013Kollman using UFF radii, MKUFF) methods. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mobility_5d_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/mobility_5d_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mobility_5d_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/mobility_5d_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-06-08T22:34:14.062425Z", + "source_id": "mobility_5d_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "mobility_5d" + }, + "dc": { + "identifier": { + "identifier": "10.18126/qh91-00tw", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Zhang", + "givenName": "Jin", + "creatorName": "Zhang, Jin", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Ludwig", + "givenName": "Wolfgang", + "creatorName": "Ludwig, Wolfgang", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Zhang", + "givenName": "Yubin", + "creatorName": "Zhang, Yubin", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "S\u00f8rensen", + "givenName": "Hans Henrik B.", + "creatorName": "S\u00f8rensen, Hans Henrik B.", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Rowenhorst", + "givenName": "David J.", + "creatorName": "Rowenhorst, David J.", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Yamanaka", + "givenName": "Akinori", + "creatorName": "Yamanaka, Akinori", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter W.", + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + }, + { + "familyName": "Poulsen", + "givenName": "Henning F.", + "creatorName": "Poulsen, Henning F.", + "affiliations": [ + "Northwestern University", + "Technical University of Denmark", + "European Synchrotron Radiation Facility", + "The US Naval Research Laboratory", + "Tokyo University of Agriculture and Technology" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "grain boundary mobilities" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-09", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Jin Zhang (jzhang@northwestern.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1016/j.actamat.2020.03.044", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Reduced grain boundary mobilities in pure iron" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Reduced grain boundary mobilities of 344 boundaries in pure iron measured by a fitting method described in *Grain boundary mobilities in polycrystals*, *Acta Materialia* 191 (2020) 211-220. The first three columns are components of the Rodrigues vector (in the fundamental zone of the 3D Rodrigues space) of the misorientation of the boundary. Columns 4 to 6 are components of the inclination (grain boundary normal in crystal coordinates) in the crystal coordinates of the first grain. Columns 7 to 9 are components of the inclination in the crystal coordinates of the second grain. Column 10 is the reduced grain boundary mobility in the unit of \u03bcm2/s. This is the data used to plot Fig. 4 in the publication." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "cdx_impurity_levels_dft_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/cdx_impurity_levels_dft_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/cdx_impurity_levels_dft_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cdx_impurity_levels_dft_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-06-03T22:35:39.505834Z", + "source_id": "cdx_impurity_levels_dft_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "cdx_impurity_levels_dft" + }, + "dc": { + "identifier": { + "identifier": "10.18126/oq93-9asv", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Mannodi-Kanakkithodi, Arun", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Mannodi-Kanakkithodi", + "givenName": "Arun" + }, + { + "creatorName": "Toriyama, Michael Y.", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Toriyama", + "givenName": "Michael Y." + }, + { + "creatorName": "Sen, Fatih G.", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Sen", + "givenName": "Fatih G." + }, + { + "creatorName": "Davis, Michael J.", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Davis", + "givenName": "Michael J." + }, + { + "creatorName": "Klie, Robert F.", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Klie", + "givenName": "Robert F." + }, + { + "creatorName": "Chan, Maria K. Y.", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ], + "familyName": "Chan", + "givenName": "Maria K. Y." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "semiconductors" + }, + { + "subject": "defects" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Arun Mannodi-Kanakkithodi", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Maria K.Y. 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Measurements were extracted from the table and figures of the original publications. Corrections have been made for historical temperature scales and Zr contamination where necessary. Reported errors have been converted to 1-sigma normal errors according to the GUM standard and estimated when not available." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kim_ml_qh_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/kim_ml_qh_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kim_ml_qh_v1.1/", + "total_size": 3845065, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kim_ml_qh_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-03-23T21:33:33.033083Z", + "source_id": "kim_ml_qh_v1.1", 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], + "titles": [ + { + "title": "Dataset for the paper \"Local structure of potassium doped nickel oxide: a combined experimental-theoretical study\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for the paper \"Local structure of potassium doped nickel oxide: a combined experimental-theoretical study\", Friederike Wrobel, Hyeondeok Shin, George E. Sterbinsky, Haw-Wen Hsiao, Jian-Min Zuo, Panchapakesan Ganesh, Jaron T. Krogel, Anouar Benali, Paul R.C. Kent, Olle Heinonen, and Anand Bhattacharya, Physical Review M (2019)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "charge_ice_prappl_2020_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/charge_ice_prappl_2020_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/charge_ice_prappl_2020_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/charge_ice_prappl_2020_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-03-26T15:23:18.595763Z", + "source_id": "charge_ice_prappl_2020_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "charge_ice_prappl_2020" + }, + "dc": { + "identifier": { + "identifier": "10.18126/6ndo-w7yk", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Iacocca, Ezio", + "affiliations": [ + "Northumbria University" + ], + "familyName": "Iacocca", + "givenName": "Ezio" + }, + { + "creatorName": "Gliga, Sebastian", + "affiliations": [ + "Paul Scherrer Institute" + ], + "familyName": "Gliga", + "givenName": "Sebastian" + }, + { + "creatorName": "Heinonen, Olle G.", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Heinonen", + "givenName": "Olle G." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "artificial spin ice" + }, + { + "subject": "magnonic lattices" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-03-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Olle Heinonen (heinonen@anl.gov)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data for \u201cTailoring spin wave channels in a reconfigurable artificial spin ice\u201d " + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for \u201cTailoring spin wave channels in a reconfigurable artificial spin ice\u201d, by Ezio Iacocca, Sebastian Gliga, and Olle G. Heinonen, to be published in Physical Review Applied in 2020, also at URL: https://arxiv.org/abs/1911.05354. Data content and organization are described in top level README file\n " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "guo_gapml_model_hexrd_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/guo_gapml_model_hexrd_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/guo_gapml_model_hexrd_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/guo_gapml_model_hexrd_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-06-27T15:08:46.229482Z", + "source_id": "guo_gapml_model_hexrd_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "guo_gapml_model_hexrd" + }, + "dc": { + "identifier": { + "identifier": "10.18126/66pj-gpnr", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Guo", + "givenName": "Jicheng", + "creatorName": "Guo, Jicheng", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Ward", + "givenName": "Logan", + "creatorName": "Ward, Logan", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Babuji", + "givenName": "Yadu", + "creatorName": "Babuji, Yadu", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Hoyt", + "givenName": "Nathaniel", + "creatorName": "Hoyt, Nathaniel", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Williamson", + "givenName": "Mark", + "creatorName": "Williamson, Mark", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Foster", + "givenName": "Ian", + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Jackson", + "givenName": "Nicholas", + "creatorName": "Jackson, Nicholas", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Benmore", + "givenName": "Chris", + "creatorName": "Benmore, Chris", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Sivaraman", + "givenName": "Ganesh", + "creatorName": "Sivaraman, Ganesh", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "DFT" + }, + { + "subject": "energy materials" + }, + { + "subject": "SCAN" + }, + { + "subject": "HEXRD" + }, + { + "subject": "MOLTEN SALTS" + }, + { + "subject": "GAP" + }, + { + "subject": "forcefield" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-06-27", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "cet.ganesh@gmail.com" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://chemrxiv.org/engage/chemrxiv/article-details/61fb07df32e10e139bd27770", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.26434/chemrxiv-2022-8w9ft", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "GAP-ML model, training and MD dataset for \"A Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by HEXRD\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "## Usage Notes \n\n### ML training dataset and potential \n\n1) \"POT/\"\n\n### MD\n1. The full MD trajectory for the thermal conducitivity calculations can be found in \"MD_WAVE_METHOD/\"\n\n2. All the MD trajectories used for structure factor / CN estimation at multiple composition / temperatures can be foudn in \"MD/\". The \".tar.gz\" file with '.extxyz' trajectory is according to the LiCl-KCl molar fraction followed by the temperature in K units.\n\n3. The \"MD/\" folder also the log files for two additional compositions (i.e. 30-70, & 80-20) and can be used to verify the density from volume relaxation. \n\n\n### Misc. Info\n\nMolten LiCl as a system on its own is reported in https://pubs.acs.org/doi/abs/10.1021/acs.jpclett.1c00901 \n\nThe potential provided in this data deposit does not include any pure LiCl melt training data. Hence should be used with caution at ultra low concentrations of LiCl." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "krogel_surrogate_hessian_relax_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/krogel_surrogate_hessian_relax_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/krogel_surrogate_hessian_relax_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/krogel_surrogate_hessian_relax_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-10T18:30:00.559946Z", + "source_id": "krogel_surrogate_hessian_relax_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "krogel_surrogate_hessian_relax" + }, + "dc": { + "identifier": { + "identifier": "10.18126/082j-dtbv", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Tiihonen, Juha", + "affiliations": [ + "Oak Ridge National Laboratory" + ], + "familyName": "Tiihonen", + "givenName": "Juha" + }, + { + "creatorName": "Kent, Paul R. C.", + "affiliations": [ + "Oak Ridge National Laboratory" + ], + "familyName": "Kent", + "givenName": "Paul R. C." + }, + { + "creatorName": "Krogel, Jaron T.", + "affiliations": [ + "Oak Ridge National Laboratory" + ], + "familyName": "Krogel", + "givenName": "Jaron T." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "molecular structures" + }, + { + "subject": "DFT" + }, + { + "subject": "QMCPack" + }, + { + "subject": "QMC" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-10", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "krogeljt@ornl.gov", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1063/5.0079046", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Surrogate Hessian Accelerated Structural Optimization for Stochastic Electronic Structure Theories" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The data in this repository comprise the original runs, including input and output data, for the diffusion Monte Carlo structural relaxation examples given in the publication for the benzene, coronene, and ovalene molecules.\n\nThis repository also includes the original code implementation of the Surrogate Hessian Accelerated Structural Optimization Method that was used to relax these molecules.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "cencer_cyclic_alkenes_fromp_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/cencer_cyclic_alkenes_fromp_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/cencer_cyclic_alkenes_fromp_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cencer_cyclic_alkenes_fromp_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-12-16T20:43:15.181748Z", + "source_id": "cencer_cyclic_alkenes_fromp_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "cencer_cyclic_alkenes_fromp" + }, + "dc": { + "identifier": { + "identifier": "10.18126/bpmg-0a78", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Cencer, Morgan M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Cencer", + "givenName": "Morgan M." + }, + { + "creatorName": "Moore, Jeffrey S.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Moore", + "givenName": "Jeffrey S." + } + ], + "subjects": [ + { + "subject": "molecular structures" + }, + { + "subject": "DFT" + }, + { + "subject": "polymers" + }, + { + "subject": "cyclic alkenes" + }, + { + "subject": "FROMP" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-12-17", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "morgan.cencer@gmail.com", + "contributorType": "ContactPerson" + }, + { + "contributorName": "jsmoore@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Cyclic Alkenes as Candidates for FROMP" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset consists of data about known FROMP monomers and a variety of candidate monomers, which are all cyclic alkenes. While this data was focused on FROMP monomers, we believe that the list of candidate cyclic alkenes (>11 million SMILES strings) and the ~1800 high theory level (M06/def2tzvp) DFT files may prove useful for other projects.\n\nThe data and code in this collection was created by Morgan Cencer (except where otherwise noted) during doctoral research with Prof. Jeff Moore. More information can be found in chapter 5 of Cencer, M. M. (2022) Computational Tools for Materials Design: Applications in Dynamic \nCovalent Chemistry, Polymers, and Electrochemical Systems (Ph.D. Dissertation) University of Illinois, Urbana-Champaign." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "abx3_perovs_alloys_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/abx3_perovs_alloys_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/abx3_perovs_alloys_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/abx3_perovs_alloys_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-26T23:34:35.637009Z", + "source_id": "abx3_perovs_alloys_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "abx3_perovs_alloys" + }, + "dc": { + "identifier": { + "identifier": "10.18126/3jn1-o5nk", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Mannodi-Kanakkithodi", + "givenName": "Arun", + "creatorName": "Mannodi-Kanakkithodi, Arun", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Chan", + "givenName": "Maria K.Y.", + "creatorName": "Chan, Maria K.Y.", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Yang", + "givenName": "Jiaqi", + "creatorName": "Yang, Jiaqi", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Manganaris", + "givenName": "Panayotis", + "creatorName": "Manganaris, Panayotis", + "affiliations": [ + "Purdue University", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "semiconductors" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-27", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Arun Mannodi-Kanakkithodi" + } + ], + "titles": [ + { + "title": "High-Throughput DFT Dataset of Halide Perovskite Alloys" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset contains DFT computations on 550 halide perovskite compounds, 90 of which are pure ABX3 compositions and the remaining involve mixing/alloying at the A, B or X sites. Computation folders include geometry optimization using GGA-PBE and HSE06, optical absorption calculations, vacancy defect calculations, and dielectric constant calculations. Machine learning models were trained using this data to accelerate the prediction of stability and optoelectronic properties of a combinatorial perovskite dataset. One publication is currently under review: https://arxiv.org/abs/2109.10798, and two others are in preparation." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kirill_marcus_screening2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/kirill_marcus_screening2_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kirill_marcus_screening2_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kirill_marcus_screening2_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-18T19:00:59.453286Z", + "source_id": "kirill_marcus_screening2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kirill_marcus_screening2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/ef9q-qzod", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Shmilovich, Kirill", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Shmilovich", + "givenName": "Kirill" + }, + { + "creatorName": "Yao, Yifan", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Yao", + "givenName": "Yifan" + }, + { + "creatorName": "Tovar, John D.", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Tovar", + "givenName": "John D." + }, + { + "creatorName": "Katz, Howard E.", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Katz", + "givenName": "Howard E." + }, + { + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Schleife", + "givenName": "Andr\u00e9" + }, + { + "creatorName": "Ferguson, Andrew L.", + "affiliations": [ + "University of Chicago", + "Johns Hopkins University", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Ferguson", + "givenName": "Andrew L." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "DFT" + }, + { + "subject": "semiconductors" + }, + { + "subject": "high-throughput" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-18", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "kirills@uchicago.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "yifany6@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Supplemental data for \"Computational discovery of high charge mobility self-assembling \u03c0-conjugated peptides\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset contains supplemental data related to the molecular dynamics simulations and electronic structure calculations in \"Computational discovery of high charge mobility self-assembling \u03c0-conjugated peptides\" by Kirill Shmilovich, Yifan Yao, John D. Tovar, Howard E. Katz, Andr\u00e9 Schleife, and Andrew L. Ferguson." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "staros_cri3_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/staros_cri3_v1.1/", + "total_size": 991880242, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/staros_cri3_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/staros_cri3_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-12-20T23:23:23.114793Z", + "source_id": "staros_cri3_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "staros_cri3" + }, + "dc": { + "identifier": { + "identifier": "10.18126/51af-op9h", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Staros", + "givenName": "Daniel", + "creatorName": "Staros, Daniel", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Hu", + "givenName": "Guoxiang", + "creatorName": "Hu, Guoxiang", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Tiihonen", + "givenName": "Juha", + "creatorName": "Tiihonen, Juha", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Nanguneri", + "givenName": "Ravindra", + "creatorName": "Nanguneri, Ravindra", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Krogel", + "givenName": "Jaron T.", + "creatorName": "Krogel, Jaron T.", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Bennett", + "givenName": "M. Chandler", + "creatorName": "Bennett, M. Chandler", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Heinonen", + "givenName": "Olle G.", + "creatorName": "Heinonen, Olle G.", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Ganesh", + "givenName": "Panchapakesan", + "creatorName": "Ganesh, Panchapakesan", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Rubenstein", + "givenName": "Brenda", + "creatorName": "Rubenstein, Brenda", + "affiliations": [ + "Brown University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "2D materials" + }, + { + "subject": "magnetism" + }, + { + "subject": "spin-phonon coupling" + }, + { + "subject": "Diffusion Monte Carlo" + }, + { + "subject": "Density Functional Theory" + }, + { + "subject": "Center for Predictive Simulation of Functional Materials" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-12-20", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "daniel_staros@brown.edu" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1063/5.0074848", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "A Combined First Principles Study of the Structural, Magnetic, and Phonon Properties of Monolayer CrI3" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "sinha_correlative_microscopy_steel_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/sinha_correlative_microscopy_steel_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/sinha_correlative_microscopy_steel_v1.1", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/sinha_correlative_microscopy_steel_v1.1" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2019-08-07T14:43:17.599920Z", + "source_id": "sinha_correlative_microscopy_steel_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "sinha_correlative_microscopy_steel" + }, + "dc": { + "identifier": { + "identifier": "10.18126/iv89-3293", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Sinha, V.", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ], + "familyName": "Sinha", + "givenName": "V." + }, + { + "creatorName": "Gonzales, M.", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ], + "familyName": "Gonzales", + "givenName": "M." + }, + { + "creatorName": "Payton, E.J.", + "affiliations": [ + "Air Force Research Laboratory", + "UES, Inc." + ], + "familyName": "Payton", + "givenName": "E.J." + } + ], + "subjects": [ + { + "subject": "EBSD" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metals" + }, + { + "subject": "microstructures" + }, + { + "subject": "composites" + }, + { + "subject": "microscopy" + } + ], + "publicationYear": 2019, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-08-07", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Correlative Microscopy Data for Quantification of Prior Austenite Grain Size in AF9628 Steel" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The files in this dataset are analyzed, discussed, and referenced in these two publications: \n\n1. V. Sinha, M. Gonzales, R.A. Abrahams, B.S. Song, and E.J. Payton, \u201cCorrelative microscopy for quantification of prior austenite grain size in AF9628 steel\u201d, Materials Characterization (Accepted).\n\n2. V. Sinha, M. Gonzales, and E.J. Payton, \u201cDatasets acquired with correlative microscopy method for delineation of prior austenite grain boundaries and characterization of prior austenite grain size in a low-alloy high-performance steel\u201d, Data in Brief (Submitted).\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "myh20220118snse_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/myh20220118snse_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/myh20220118snse_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/myh20220118snse_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-18T15:09:51.256722Z", + "source_id": "myh20220118snse_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "myh20220118snse" + }, + "dc": { + "identifier": { + "identifier": "10.18126/s0qs-fkgf", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Hu, Michael Y.", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Hu", + "givenName": "Michael Y." + } + ], + "subjects": [ + { + "subject": "experiment" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-21", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "myhu@anl.gov", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1103/PhysRevB.104.184303", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "SnSe NRIXS part 1" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "NRIXS data and analysis of SnSe lattice dynamics at various temperatures" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mapbx3_defect_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/mapbx3_defect_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mapbx3_defect_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/mapbx3_defect_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-26T20:09:23.958525Z", + "source_id": "mapbx3_defect_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "mapbx3_defect" + }, + "dc": { + "identifier": { + "identifier": "10.18126/vncu-w2so", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Mannodi-Kanakkithodi", + "givenName": "Arun", + "creatorName": "Mannodi-Kanakkithodi, Arun", + "affiliations": [ + "Purdue University" + ] + }, + { + "familyName": "Chan", + "givenName": "Maria K.Y.", + "creatorName": "Chan, Maria K.Y.", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "semiconductors" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + }, + { + "subject": "defects" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Arun Mannodi-Kanakkithodi" + }, + { + "contributorType": "ContactPerson", + "contributorName": " " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1021/acs.chemmater.8b04017", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": " https://doi.org/10.1021/acs.jpcc.0c02486", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "High-Throughput Density Functional Theory Dataset of Pb-site Impurities in Hybrid Perovskites" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ravi_braggnn_training_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/ravi_braggnn_training_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ravi_braggnn_training_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ravi_braggnn_training_v1.2/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-06-25T02:06:38.483041Z", + "source_id": "ravi_braggnn_training_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "ravi_braggnn_training" + }, + "dc": { + "identifier": { + "identifier": "10.18126/iftp-twz1", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Ravi, Nikil", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Ravi", + "givenName": "Nikil" + }, + { + "creatorName": "Liu, Zhengchun", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Liu", + "givenName": "Zhengchun" + }, + { + "creatorName": "Sharma, Hemant", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Sharma", + "givenName": "Hemant" + }, + { + "creatorName": "Chaturvedi, Pranshu", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Chaturvedi", + "givenName": "Pranshu" + }, + { + "creatorName": "Huerta, E.A.", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Huerta", + "givenName": "E.A." + }, + { + "creatorName": "Scourtas, Aristana", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Scourtas", + "givenName": "Aristana" + }, + { + "creatorName": "KJ, Schmidt", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "KJ", + "givenName": "Schmidt" + }, + { + "creatorName": "Chard, Ryan", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Chard", + "givenName": "Ryan" + }, + { + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Blaiszik", + "givenName": "Ben" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "microstructures" + }, + { + "subject": "experiment" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-06-25", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Eliu Huerta ", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Zhengchun Liu ", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1107/S2052252521011258", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "BraggNN: Training Dataset" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "# BraggNN Training Dataset\n\n## Data\nThere are two HDF5 files in the dataset \n\n* The `frames-exp4train.hdf5` contains diffraction frames, stored as a 3D array (dataset name must be \"frames\"). The first dimension is the frame ID starting with 0, i.e., the series of frames at different scanning angle. The second and third dimensions are the height and width of the area detector.\n\n* The file `peaks-exp4train-psz11.hdf5` contains the peak position information, generated using conventional methods (e.g., using MIDAS: https://github.com/marinerhemant/MIDAS). In our work, we used the peak position that we got using 2D psuedo Voigt fitting. This file stores three 1D array with each record / index represent different information of a peak. The first 1D array, must be named as `peak_fidx` represents the index of the frame (in the frames.h5) that the peak sits on; the second array, `peak_row` is the vertical distance, in pixel and can be floating point number, from the peak center to the top edge of the frame. Similarly, the `peak_col` denotes horizental distance, in pixel and can be floating point number, from peak center to left edge of the frame. \n\n* By default, this implementation will use 80% of the samples for training, the rest 20% for online model validation.\n\n## Code\n** Important ** \nTo run the DLHub versions of these models using GPU resources, users must first request access to the following Globus group: https://app.globus.org/groups/d0b13474-c265-11ec-9444-51db4d10f5bd/about\n\nNotebooks are provided in the `code-examples` folder to showcase how to load the datasets and run the PyTorch, TRT, and SambaNova models." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ravi_braggnn_validation_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ravi_braggnn_validation_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ravi_braggnn_validation_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/ravi_braggnn_validation_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-06-24T21:08:28.417449Z", + "source_id": "ravi_braggnn_validation_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ravi_braggnn_validation" + }, + "dc": { + "identifier": { + "identifier": "10.18126/hvny-s7ax", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Ravi", + "givenName": "Nikil", + "creatorName": "Ravi, Nikil", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Liu", + "givenName": "Zhengchun", + "creatorName": "Liu, Zhengchun", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Sharma", + "givenName": "Hemant", + "creatorName": "Sharma, Hemant", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Chaturvedi", + "givenName": "Pranshu", + "creatorName": "Chaturvedi, Pranshu", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Huerta", + "givenName": "E.A.", + "creatorName": "Huerta, E.A.", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Scourtas", + "givenName": "Aristana", + "creatorName": "Scourtas, Aristana", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "KJ", + "givenName": "Schmidt", + "creatorName": "KJ, Schmidt", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Chard", + "givenName": "Ryan", + "creatorName": "Chard, Ryan", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Blaiszik", + "givenName": "Ben", + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "microstructures" + }, + { + "subject": "experiment" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-06-25", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Eliu Huerta " + }, + { + "contributorType": "ContactPerson", + "contributorName": "Zhengchun Liu " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1107/S2052252521011258", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "BraggNN: Validation Dataset" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "# BraggNN Validation Dataset\n\nThis dataset contains `13799` samples from the training dataset,\nand is used to evaluate, and provide metrics for, the models\nthat were trained on the larger training dataset. \n\n## Data\nThe data are located in the `/data` folder, and store the collected peaks and frames.\n\nThere are two datasets in the hdf5 file:\n- `Patch`: input to the BraggNN model. It is a 3D array where its first dimension is the index of samples, i.e., 13799. The second and third dimension are the height and width of the patch/peak respectively.\n- `ploc`: the peak location for each of the 13799 peaks, i.e., label of the dataset.\n\n## Code\n\n** Important ** \nTo run the DLHub versions of these models using GPU resources, users must first request access to the following Globus group: https://app.globus.org/groups/d0b13474-c265-11ec-9444-51db4d10f5bd/about\n\nNotebooks are provided in the `code-examples` folder to showcase how to load the datasets and run the PyTorch, TRT, and SambaNova models." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "butera_sem_images_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/butera_sem_images_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/butera_sem_images_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/butera_sem_images_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-08-24T18:58:42.781529Z", + "source_id": "butera_sem_images_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "butera_sem_images" + }, + "dc": { + "identifier": { + "identifier": "10.18126/d1bg-nwtg", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Butera, Tony", + "affiliations": [ + "University of Kentucky" + ], + "familyName": "Butera", + "givenName": "Tony" + }, + { + "creatorName": "Waldman, Laura J.", + "affiliations": [ + "University of Kentucky" + ], + "familyName": "Waldman", + "givenName": "Laura J." + }, + { + "creatorName": "Grady, Martha E.", + "affiliations": [ + "University of Kentucky" + ], + "familyName": "Grady", + "givenName": "Martha E." + } + ], + "subjects": [ + { + "subject": "biomaterials" + }, + { + "subject": "biofilms" + }, + { + "subject": "laser spallation" + }, + { + "subject": "scanning electron microscopy" + }, + { + "subject": "adhesion" + }, + { + "subject": "dental implants" + }, + { + "subject": "streptococcus mutans" + }, + { + "subject": "sucrose" + }, + { + "subject": "titanium" + }, + { + "subject": "experiment" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-08-24", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "m.grady@uky.edu", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data: Sucrose-mediated formation and adhesion strength of Streptococcus mutans biofilms on titanium" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Bacterial biofilms associated with implants remain a significant source of infections in dental, implant, and other healthcare industries due to challenges in biofilm removal. Biofilms consist of bacterial cells surrounded by a matrix of extracellular polymeric substance (EPS), which protects the colony from many countermeasures, including antibiotic treatments. Biofilm EPS composition is also affected by environmental factors. In the oral cavity, the presence of sucrose affects the growth of Streptococcus mutans that produce acids, eroding enamel and forming dental caries. Biofilm formation on dental implants commonly leads to severe infections and failure of the implant. This work determines the effect of sucrose concentration on biofilm EPS formation and adhesion of Streptococcus mutans, a common oral colonizer. Bacterial biofilms are grown with varying concentrations of sucrose on titanium substrates simulating dental implant material. Strategies for measuring adhesion for films such as peel tests are inadequate for biofilms, which have low cohesive strength and will fall apart when tensile loading is applied directly. The laser spallation technique is used to apply a stress wave loading to the biofilm, causing the biofilm to delaminate at a critical tensile stress threshold. Biofilm formation and EPS structures are visualized at high magnification with scanning electron microscopy (SEM). Sucrose enhanced the EPS production of S. mutans biofilms and increased the adhesion strength to titanium, the most prevalent dental implant material. However, there exists a wide range of sucrose concentrations that are conducive for robust formation and adhesion of S. mutans biofilms on implant surfaces." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "tekawade_realtime_porosity_tomography_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/tekawade_realtime_porosity_tomography_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/tekawade_realtime_porosity_tomography_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tekawade_realtime_porosity_tomography_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-08-30T19:57:46.270231Z", + "source_id": "tekawade_realtime_porosity_tomography_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "tekawade_realtime_porosity_tomography" + }, + "dc": { + "identifier": { + "identifier": "10.18126/c54t-4ceh", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Tekawade, Aniket", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Tekawade", + "givenName": "Aniket" + }, + { + "creatorName": "Nikitin, Viktor", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Nikitin", + "givenName": "Viktor" + }, + { + "creatorName": "Satapathy, Yashas", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Satapathy", + "givenName": "Yashas" + }, + { + "creatorName": "Liu, Zhengchun", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Liu", + "givenName": "Zhengchun" + }, + { + "creatorName": "Zhang, Xuan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Zhang", + "givenName": "Xuan" + }, + { + "creatorName": "Kenesei, Peter", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Kenesei", + "givenName": "Peter" + }, + { + "creatorName": "De Carlo, Francesco", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "De Carlo", + "givenName": "Francesco" + }, + { + "creatorName": "Kettimuthu, Rajkumar", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Kettimuthu", + "givenName": "Rajkumar" + }, + { + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Foster", + "givenName": "Ian" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "microstructures" + }, + { + "subject": "microscopy" + }, + { + "subject": "tomography" + }, + { + "subject": "CT" + }, + { + "subject": "convolutional neural networks" + }, + { + "subject": "real-time analysis" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-08-30", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Ian Foster ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Real-time Porosity Mapping and Visualization for Synchrotron Tomography" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data is of a 3d-printed steel cylindrical specimen (nominal diameter 6-millimeter) scanned under monochromatic hard X-ray. The mosaic comprised of 3 horizontal and 6 vertical positions (total 18 scans). The field-of-view for each scan was approximately 2.2 mm wide and 1.4 mm tall with 1.17 micrometer voxel size. With an exposure time of 0.13 milliseconds for 3000 projections, scanning this mosaic took ~2 hours not counting instrument-specific overheads in moving motors to reposition the sample between scans. This resulted in 3000 projections over 180 degrees sample rotation with raw image size 4096 x 4096 (100 gigabytes of raw data at 16-bit precision) that would reconstruct to a 3D volume with 4096^3 voxels. Two versions of the raw-data are shared. The original data (referred to as {4k} volume) with 1.17 micrometer pixel size (3000 projections) and a binned version ({2k} volume) with 2.34 micrometer pixel size (1500 projections)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kononov_firstprinciples_simulation_graphene_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kononov_firstprinciples_simulation_graphene_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kononov_firstprinciples_simulation_graphene_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/kononov_firstprinciples_simulation_graphene_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-08-23T18:45:59.776918Z", + "source_id": "kononov_firstprinciples_simulation_graphene_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kononov_firstprinciples_simulation_graphene" + }, + "dc": { + "identifier": { + "identifier": "10.18126/kkid-wa9u", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Kononov", + "givenName": "Alina", + "creatorName": "Kononov, Alina", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Olmstead", + "givenName": "Alexandra", + "creatorName": "Olmstead, Alexandra", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Baczewski", + "givenName": "Andrew D.", + "creatorName": "Baczewski, Andrew D.", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "Sandia National Laboratories", + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "microscopy" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-08-23", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "schleife@illinois.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "akonono@sandia.gov" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.18126/xnxt-wn6i", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "First-principles simulation of light-ion microscopy of graphene" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "TDDFT simulations of light-ion irradiated graphene performed with Qbox/Qb@ll (https://github.com/LLNL/qball) " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "gladier_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/gladier_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/gladier_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gladier_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-09-02T15:57:54.653159Z", + "source_id": "gladier_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "gladier" + }, + "dc": { + "identifier": { + "identifier": "10.18126/17yw-eeht", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Vescovi, Rafael", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Vescovi", + "givenName": "Rafael" + }, + { + "creatorName": "Chard, Ryan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Chard", + "givenName": "Ryan" + }, + { + "creatorName": "Saint, Nickolaus", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Saint", + "givenName": "Nickolaus" + }, + { + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Blaiszik", + "givenName": "Ben" + }, + { + "creatorName": "Pruyne, Jim", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Pruyne", + "givenName": "Jim" + }, + { + "creatorName": "Bicer, Tekin", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Bicer", + "givenName": "Tekin" + }, + { + "creatorName": "Lavens, Alex", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Lavens", + "givenName": "Alex" + }, + { + "creatorName": "Liu, Zhengchun", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Liu", + "givenName": "Zhengchun" + }, + { + "creatorName": "Papka, Michael E.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Papka", + "givenName": "Michael E." + }, + { + "creatorName": "Narayanan, Suresh", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Narayanan", + "givenName": "Suresh" + }, + { + "creatorName": "Schwarz, Nicholas", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Schwarz", + "givenName": "Nicholas" + }, + { + "creatorName": "Chard, Kyle", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Chard", + "givenName": "Kyle" + }, + { + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Globus" + ], + "familyName": "Foster", + "givenName": "Ian" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "high-throughput" + }, + { + "subject": "automation" + }, + { + "subject": "beamline" + }, + { + "subject": "facility" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-09-02", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Ian Foster ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Gladier - Linking Scientific Instruments and Computation - Example Data" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset includes test data to reproduce and build upon the results in the paper \"Linking Scientific Instruments and Computation:\nPatterns, Technologies, Experiences\" to appear. The dataset includes example datasets from four use cases High Energy Diffraction Microscopy (HEDM), BraggNN, Ptychography, and Serial X-ray Crystallography. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "shark_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/shark_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/shark_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shark_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-09-02T19:18:55.416738Z", + "source_id": "shark_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "shark" + }, + "dc": { + "identifier": { + "identifier": "10.18126/snb5-r5pf", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Park, Jun-Sang", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ], + "familyName": "Park", + "givenName": "Jun-Sang" + }, + { + "creatorName": "Almer, Jonathan", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ], + "familyName": "Almer", + "givenName": "Jonathan" + }, + { + "creatorName": "James, Kelsey C.", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ], + "familyName": "James", + "givenName": "Kelsey C." + }, + { + "creatorName": "Natanson, Lisa J.", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ], + "familyName": "Natanson", + "givenName": "Lisa J." + }, + { + "creatorName": "Stock, Stuart", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University" + ], + "familyName": "Stock", + "givenName": "Stuart" + } + ], + "subjects": [ + { + "subject": "biomaterials" + }, + { + "subject": "microstructures" + }, + { + "subject": "SAXS" + }, + { + "subject": "WAXS" + }, + { + "subject": "experiment" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-09-02", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Stuart Stock ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Dataset for \"Bioapatite in shark centra studied by wide angle and by small angle x-ray scattering\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This is the raw WAXS and SAXS patterns from the Advanced Photon Source 1-ID beamline for the paper titled \"Bioapatite in shark centra studied by wide angle and by small angle x-ray scattering\" published in Journal of the Royal Society Interface. The WAXS patterns were acquired using the HYDRA array with only GE2 panel active. The SAXS patterns were acquired using the Pixirad detector. The instrument parameters are available as GSAS-2 imctrl file. Also a set of Matlab m-files used for data processing is included." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "yager_pathway_priming_sem_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/yager_pathway_priming_sem_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/yager_pathway_priming_sem_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/yager_pathway_priming_sem_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-08-26T17:45:57.671893Z", + "source_id": "yager_pathway_priming_sem_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "yager_pathway_priming_sem" + }, + "dc": { + "identifier": { + "identifier": "10.18126/hx77-iub1", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Yager", + "givenName": "Kevin G.", + "creatorName": "Yager, Kevin G.", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Russell", + "givenName": "Sebastian T.", + "creatorName": "Russell, Sebastian T.", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "microscopy" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-08-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Kevin G. Yager" + } + ], + "titles": [ + { + "title": "Scanning Electron Microscopy (SEM) images of Pathway-primed Layered Block Copolymer Thin Films" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Scanning electron microscopy (SEM) images (top-view and perspective view) of a variety of block copolymer (BCP) thin films. The films were formed by layering different BCP types and thermally annealing (at various temperatures and annealing times). 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defect", + "units": "electrons", + "type": "input", + "key": [ + "charge_to" + ] + }, + { + "description": "Experimental bandgap of the host material", + "units": "eV", + "type": "input", + "key": [ + "host bandgap_[eV]" + ] + }, + { + "description": "Lattice constant of the host material", + "units": "Angstroms", + "type": "input", + "key": [ + "host lattice constant_[Ang.]" + ] + }, + { + "description": "Dielectric constant of the host material", + "units": "", + "type": "input", + "key": [ + "host_epsilon" + ] + }, + { + "description": "Band alignment correction shift", + "units": "eV", + "type": "input", + "key": [ + "ba_shift" + ] + }, + { + "description": "DFT-PBE calculated defect charge state transition levels", + "units": "eV", + "type": "target", + "key": [ + "pbe defect level (relative to VBM)_[eV]" + ] + }, + { + "description": "Modified band alignment correction shift", + "units": "eV", + "type": "input", + "key": [ + "mba_pbe" + ] + }, + { + "description": "Modified band alignment correction shift, given as fraction of the bandgap", + "units": "", + "type": "input", + "key": [ + "mba_pbe_gapfrac" + ] + }, + { + "description": "DFT-HSE calculated defect charge state transition levels", + "units": "eV", + "type": "target", + "key": [ + "hse defect level (relative to VBM)_[eV]" + ] + } + ], + "domain": [ + "materials science" + ], + "data_type": "tabular", + "n_items": 2910, + "short_name": "Dataset_semiconductor_defectlevels", + "task_type": [ + "supervised" + ] + } + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/semiconductor_defectlevels_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/semiconductor_defectlevels_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/semiconductor_defectlevels_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": 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More details about our work can be found in https://arxiv.org/abs/2106.08783." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ge_nanoparticles_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/ge_nanoparticles_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ge_nanoparticles_v1/" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2018-11-06T17:25:23.949096Z", + "source_id": "ge_nanoparticles_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ge_nanoparticles" + }, + "dc": { + "rightsList": [ + { + "rightsURI": "https://creativecommons.org/publicdomain/zero/1.0/", + "rights": "https://creativecommons.org/publicdomain/zero/1.0/" + } + ], + "creators": [ + { + "givenName": "Niccolo", + "familyName": "Corsini", + "creatorName": "Corsini, Niccolo", + "affiliations": [ + "Imperial College London" + ] + } + ], + "subjects": [ + { + "subject": "amorphization" + }, + { + "subject": "density functional theory calculations" + }, + { + "subject": "Ge nanoparticles" + }, + { + "subject": "high pressure" + }, + { + "subject": "phase transformation" + }, + { + "subject": "Raman" + }, + { + "subject": "X-ray absorption" + }, + { + "subject": "zip" + } + ], + "publisher": "MDF (placeholder)", + "publicationYear": "2015", + "dates": [ + { + "date": "2017-08-09T19:44:38.397091Z", + "dateType": "Collected" + } + ], + "contributors": [ + { + "givenName": "Niccolo", + "familyName": "Corsini", + "affiliations": [ + "Imperial College London" + ], + "contributorType": "ContactPerson", + "contributorName": "Corsini, Niccolo" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "http://pubs.acs.org/doi/abs/10.1021/acs.nanolett.5b02627", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Pressure-induced amorphisation and a new high density amorphous metallic phase in matrix-free Ge nanoparticles: simulation data" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Over the last two decades, it has been demonstrated that size effects have significant consequences for the atomic arrangements and phase behavior of matter under extreme pressure. 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"g4mp2_enthalpy" + ] + }, + { + "description": "G4MP2 Free eergy at 0K", + "units": "Ha", + "type": "target", + "key": [ + "g4mp2_free" + ] + }, + { + "description": "G4MP2 atomization energy at 0K", + "units": "Ha", + "type": "target", + "key": [ + "g4mp2_atom" + ] + }, + { + "description": "Solvation energy, acetone", + "units": "kcal/mol", + "type": "target", + "key": [ + "sol_acetone" + ] + }, + { + "description": "Solvation energy, acetonitrile", + "units": "kcal/mol", + "type": "target", + "key": [ + "sol_acn" + ] + }, + { + "description": "Solvation energy, dimethyl sulfoxide", + "units": "kcal/mol", + "type": "target", + "key": [ + "sol_dmso" + ] + }, + { + "description": "Solvation energy, ethanol", + "units": "kcal/mol", + "type": "target", + "key": [ + "sol_ethanol" + ] + }, + { + "description": "Solvation energy, water", + "units": "kcal/mol", + "type": "target", + "key": [ + "sol_water" + ] + } + ], + "domain": [ + "materials science", + "chemistry" + ], + "data_type": "tabular", + "n_items": 130258, + "short_name": "g4mp2_solvation", + "task_type": [ + "supervised" + ] + } + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/foundry_g4mp2_solvation_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/foundry_g4mp2_solvation_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/foundry_g4mp2_solvation_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "Foundry" + ], + "ingest_date": "2022-01-24T17:46:06.787200Z", + "source_id": "foundry_g4mp2_solvation_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "foundry_g4mp2_solvation" + }, + "dc": { + "identifier": { + "identifier": "10.18126/jos5-wj65", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Ward", + "givenName": "Logan", + "creatorName": "Ward, Logan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Dandu", + "givenName": "Naveen", + "creatorName": "Dandu, Naveen", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Blaiszik", + "givenName": "Ben", + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Narayanan", + "givenName": "Badri", + "creatorName": "Narayanan, Badri", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Assary", + "givenName": "Rajeev S.", + "creatorName": "Assary, Rajeev S.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Redfern", + "givenName": "Paul C.", + "creatorName": "Redfern, Paul C.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Foster", + "givenName": "Ian", + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + }, + { + "familyName": "Curtiss", + "givenName": "Larry A.", + "creatorName": "Curtiss, Larry A.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "University of Louisville" + ] + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "foundry" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "DFT Estimates of Solvation Energy in Multiple Solvents" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "tb166_sr_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/tb166_sr_v1.1/", + "total_size": 1440437198, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tb166_sr_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/tb166_sr_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-01-19T21:49:28.568146Z", + "source_id": "tb166_sr_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "tb166_sr" + }, + "dc": { + "identifier": { + "identifier": "10.18126/vwae-pa0m", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "McQueeney", + "givenName": "Robert J", + "creatorName": "McQueeney, Robert J", + "affiliations": [ + "Ames Laboratory", + "Iowa State University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-01-20", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Orbital character of the spin-reorientation transition in TbMn6Sn6" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "mcqueeney@ameslab.gov" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "dense_flow_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/dense_flow_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/dense_flow_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/dense_flow_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-02-08T15:18:14.647502Z", + "source_id": "dense_flow_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "dense_flow" + }, + "dc": { + "identifier": { + "identifier": "10.18126/xpcw-kgpv", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "van der Naald, Michael", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "van der Naald", + "givenName": "Michael" + }, + { + "creatorName": "Singh, Abhi", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "Singh", + "givenName": "Abhi" + }, + { + "creatorName": "Eid, Toka", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "Eid", + "givenName": "Toka" + }, + { + "creatorName": "Tang, Kenan", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "Tang", + "givenName": "Kenan" + }, + { + "creatorName": "de Pablo, Juan J.", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "de Pablo", + "givenName": "Juan J." + }, + { + "creatorName": "Jaeger, Heinrich M.", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory", + "Case Western Reserve University" + ], + "familyName": "Jaeger", + "givenName": "Heinrich M." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "suspension" + }, + { + "subject": "flow" + }, + { + "subject": "rheology" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-02-08", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for Minimally Rigid Clusters in Dense Suspension Flow" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.21203/rs.3.rs-2468688/v1", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "\"Mike van der Naald\" ", + "contributorType": "ContactPerson" + }, + { + "contributorName": "\"Abhi Singh\" ", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "# Rigid Clusters in Dense Suspension Materials Database\n\nThe database includes simulated rheology for two dimensional shear thickening suspensions with various sliding friction values and number of particles, initially generated and analyzed for the paper \u201cMinimally Rigid Clusters in Dense Suspension Flow\u201d (https://doi.org/10.21203/rs.3.rs-2468688/v1). All data was generated using LF_DEM (https://github.com/ryseto/LF_DEM), which is a state of the art tool for simulating dense suspension flow in both two and three dimensions. All analysis was done using in-house code that can be found here https://github.com/mikevandernaald/rigidCluster_LFDEM. For this paper we focused on suspensions in two dimensions with hydrodynamic forces, normal repulsive force, and a frictional contact force. This repository contains all of the data needed to reproduce all figures from the paper. In addition to this \u201cREADME\u201d file there is a \u201csampleCode.py\u201d that shows how some of the data can be read and manipulated.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bannigan_machine_learning_injectables_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bannigan_machine_learning_injectables_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bannigan_machine_learning_injectables_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/bannigan_machine_learning_injectables_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T14:25:37.175587Z", + "source_id": "bannigan_machine_learning_injectables_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "bannigan_machine_learning_injectables" + }, + "dc": { + "identifier": { + "identifier": "10.18126/4hfb-s3y1", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Bannigan", + "givenName": "Pauric", + "creatorName": "Bannigan, Pauric" + }, + { + "familyName": "Bao", + "givenName": "Zeqing", + "creatorName": "Bao, Zeqing" + }, + { + "familyName": "Hickman", + "givenName": "Riley John", + "creatorName": "Hickman, Riley John" + }, + { + "familyName": "Aldeghi", + "givenName": "Matteo", + "creatorName": "Aldeghi, Matteo" + }, + { + "familyName": "H\u00e4se", + "givenName": "Florian", + "creatorName": "H\u00e4se, Florian" + }, + { + "familyName": "Aspuru-Guzik", + "givenName": "Al\u00e1n", + "creatorName": "Aspuru-Guzik, Al\u00e1n" + }, + { + "familyName": "Allen", + "givenName": "Christine", + "creatorName": "Allen, Christine" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "materials science" + }, + { + "subject": "therapeutic" + }, + { + "subject": "polymer" + }, + { + "subject": "LAI" + }, + { + "subject": "long acting injectable" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7309021", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nLong-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. Our study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles using this dataset.\n\nThe dataset was constructed from previously published studies by our research group and other research groups.The studies performed by our group include spherical and cylinder shaped polymeric LAIs. Data from external sources was identified using the Web of Science search engine and the keyword combination \u201cpolymeric microparticle\u201d and \u201cdrug delivery\u201d. Information related to the preparation, final composition, and release kinetics of drug from LAIs was collected. The latter was primarily extracted from figures of in vitro drug release profiles using the \u201cGetData Graph Digitizer\u201d application. The final dataset contained 181 drug release profiles for 43 unique drug-polymer combinations. In total this comprised 3783 individual fractional release measurements. The initially collected dataset was composed of a table of drug and polymer names, as well as physicochemical properties of the formulation, and fractional drug release values at various timepoints. In order to use this data to construct and train ML models it is necessary to describe various elements using machine-readable descriptors which were generated using RDkit. The polymers and LAI formulations were described exclusively using information reported in the relevant published articles, these included; polymer molecular weight (Polymer_MW), lactide-to-glycolide ratio (LA/GA; for non-PLGA systems this was set as zero), molecular crosslinking ratio of polymers (CL_Ratio; for non-cross-linked systems this was set as zero), initial drug-to-polymer ratio (Initial D/M ratio), drug loading capacity (DLC), surface area-to-volume (SA-V) ratio for the LAI system, fractional drug release at 6 h (T=0.25), fractional drug release at 12 h (T=0.5), fractional drug release at 24 h (T=1.0), and the precent of surfactant present in the release media (SE; where no surfactant was present in the release media, this was set as zero). With the exception of SA-V, T=0.25, T=0.5, and T=1.0, the 17 input features were either extracted from original publications or calculated using the RDkit package. SA-V was constructed and implemented for this study as it confers information that is related to the size and shape of the LAI system. This enables the inclusion of both spherical and cylindrical shaped LAIs in one model. For initial fractional drug release timepoints (i.e., T=0.25, T=0.5, and T=1.0), where these values were not available from the previously published studies, they were imputed using best fit polynomial curves that range from T = 0 to T = 2 days.\n\nThe code and results that support the findings of our study are available at the Aspuru-Guzik Group\u2019s GitHub page (https://github.com/aspuru-guzik-group/long-acting-injectables) and in the preprint of the related manuscript available on ChemRxiv (https://doi.org/10.26434/chemrxiv-2021-mxrxw-v2).\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kotaro_figure_reproduction_estimation_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/kotaro_figure_reproduction_estimation_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kotaro_figure_reproduction_estimation_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kotaro_figure_reproduction_estimation_v1.2/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T15:07:45.481524Z", + "source_id": "kotaro_figure_reproduction_estimation_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "kotaro_figure_reproduction_estimation" + }, + "dc": { + "identifier": { + "identifier": "10.18126/j8hi-6987", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Kotaro, Saito", + "familyName": "Kotaro", + "givenName": "Saito" + } + ], + "subjects": [ + { + "subject": "small angle neutron scattering" + }, + { + "subject": "small angle x-ray scattering" + }, + { + "subject": "materials science" + }, + { + "subject": "high throughput measurement" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Figure reproduction for 'Accelerating small angle scattering experiments on anisotropic samples using kernel density estimation'" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.2547757", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThese datasets and a Jupyter notebook reproduce figures in a publication by Saito et al in Scientific Reports. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version.\n\nThis work is supported by the Elements Strategy Initiative Center for Magnetic Materials (ESICMM) under the outsourcing project of the Ministry of Education, Culture, Sports, Science, Technology (MEXT) and the Magnetic Materials for High- Efficient Motors (MagHEM) project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). K.S. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 701647. H.H. is partly supported by JST CREST grant number JPMJCR1761.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "brinkhaus_decimer_handdrawn_images_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/brinkhaus_decimer_handdrawn_images_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/brinkhaus_decimer_handdrawn_images_v1.1/", + "total_size": 80630174, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/brinkhaus_decimer_handdrawn_images_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T21:33:44.171527Z", + "source_id": "brinkhaus_decimer_handdrawn_images_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "brinkhaus_decimer_handdrawn_images" + }, + "dc": { + "identifier": { + "identifier": "10.18126/k0pm-4alm", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Brinkhaus, Henning Otto", + "familyName": "Brinkhaus", + "givenName": "Henning Otto" + }, + { + "creatorName": "Zielesny, Achim", + "familyName": "Zielesny", + "givenName": "Achim" + }, + { + "creatorName": "Steinbeck, Christoph", + "familyName": "Steinbeck", + "givenName": "Christoph" + }, + { + "creatorName": "Rajan, Kohulan", + "familyName": "Rajan", + "givenName": "Kohulan" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "image dataset" + }, + { + "subject": "hand drawn" + }, + { + "subject": "OCR" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "DECIMER - Hand-drawn molecule images dataset" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7617107", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThe translation of images of chemical structures into machine-readable representations of the depicted molecules is known as optical chemical structure recognition (OCSR). There has been a lot of progress over the last three decades in this field, but the development of systems for the recognition of complex hand-drawn structure depictions is still at the beginning. Currently, there is no data for the systematic evaluation of OCSR methods on hand-drawn structures available.\n\nHere we present DECIMER - Hand-drawn molecule images, a standardised, openly available benchmark dataset of 5088 hand-drawn depictions of diversely picked chemical structures. Every structure depiction in the dataset is mapped to a machine-readable representation of the underlying molecule. The dataset is openly available and published under the CC-BY 4.0 licence which applies very few limitations. We hope that it will contribute to the further development of the field.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "cannelli_atomiclevel_description_publication_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/cannelli_atomiclevel_description_publication_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/cannelli_atomiclevel_description_publication_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cannelli_atomiclevel_description_publication_v1.2/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T14:58:20.281075Z", + "source_id": "cannelli_atomiclevel_description_publication_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "cannelli_atomiclevel_description_publication" + }, + "dc": { + "identifier": { + "identifier": "10.18126/aa9d-7ngv", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Cannelli, Oliviero", + "familyName": "Cannelli", + "givenName": "Oliviero" + }, + { + "creatorName": "Wiktor, Julia", + "familyName": "Wiktor", + "givenName": "Julia" + }, + { + "creatorName": "Colonna, Nicola", + "familyName": "Colonna", + "givenName": "Nicola" + }, + { + "creatorName": "Leroy, Ludmila M. D.", + "familyName": "Leroy", + "givenName": "Ludmila M. D." + }, + { + "creatorName": "Puppin, Michele", + "familyName": "Puppin", + "givenName": "Michele" + }, + { + "creatorName": "Bacellar, Camila", + "familyName": "Bacellar", + "givenName": "Camila" + }, + { + "creatorName": "Sadykov, Ilia", + "familyName": "Sadykov", + "givenName": "Ilia" + }, + { + "creatorName": "Krieg, Franziska", + "familyName": "Krieg", + "givenName": "Franziska" + }, + { + "creatorName": "Smolentsev, Grigory", + "familyName": "Smolentsev", + "givenName": "Grigory" + }, + { + "creatorName": "Kovalenko, Maksym V.", + "familyName": "Kovalenko", + "givenName": "Maksym V." + }, + { + "creatorName": "Pasquarello, Alfredo", + "familyName": "Pasquarello", + "givenName": "Alfredo" + }, + { + "creatorName": "Chergui, Majed", + "familyName": "Chergui", + "givenName": "Majed" + }, + { + "creatorName": "Mancini, Giulia F.", + "familyName": "Mancini", + "givenName": "Giulia F." + } + ], + "subjects": [ + { + "subject": "lead halide perovskites" + }, + { + "subject": "CsPbBr3" + }, + { + "subject": "X-ray absorption spectroscopy" + }, + { + "subject": "X-ray diffraction" + }, + { + "subject": "thermal disorder" + }, + { + "subject": "phonon anharmonicity" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "soft mode model" + }, + { + "subject": "displacive phase transition" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset of 'Atomic-level description of thermal fluctuations in inorganic lead halide perovskites' publication" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.6394065", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThe \"Zenodo_22-02-2022.zip\" file contains a \"files\" folder and a jupyter notebook to plot the figures reported in the publication. The \"files\" folder contains several subfolders with the txt files required to plot the figures.\n\nThe \"XAS_simulations.zip\" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.\n\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "manojlovi\u0107_titanium_alloys_applications_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/manojlovi\u0107_titanium_alloys_applications_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/manojlovi%C4%87_titanium_alloys_applications_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manojlovi\u0107_titanium_alloys_applications_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T01:57:23.775925Z", + "source_id": "manojlovi\u0107_titanium_alloys_applications_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "manojlovi\u0107_titanium_alloys_applications" + }, + "dc": { + "identifier": { + "identifier": "10.18126/rwuh-osea", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Manojlovi\u0107, Vaso D", + "familyName": "Manojlovi\u0107", + "givenName": "Vaso D" + }, + { + "creatorName": "Markovi\u0107, Gordana", + "familyName": "Markovi\u0107", + "givenName": "Gordana" + } + ], + "subjects": [ + { + "subject": "materials" + }, + { + "subject": "titanium" + }, + { + "subject": "medical" + }, + { + "subject": "biocompatibility" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Titanium Alloys Database for Medical Applications" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7802694", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nIn this database, 238 titanium alloys were collected, almost entirely of biocompatible alloying elements. The primary motivation behind creating such a database is to establish a foundation for designing new alloys using machine learning methods. The database can assist researchers, engineers, and biomedical professionals in developing titanium alloys for various medical applications, thereby improving health outcomes and driving advancements in biomaterials and biomedical engineering.\n\nFor more information read the paper at: https://doi.org/10.30544/MMD5\n\nNOTE: To avoid misunderstandings, please cite both the database and the published article when citing this database.\n\nWe invite other authors to contribute to the updating of this database (send at least 20 new alloys to appear as co-author)\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "avramova_retrotransformdb_transforms_reactions_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/avramova_retrotransformdb_transforms_reactions_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/avramova_retrotransformdb_transforms_reactions_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/avramova_retrotransformdb_transforms_reactions_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T21:42:18.120892Z", + "source_id": "avramova_retrotransformdb_transforms_reactions_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "avramova_retrotransformdb_transforms_reactions" + }, + "dc": { + "identifier": { + "identifier": "10.18126/kqww-lkdy", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Avramova, Svetlana", + "familyName": "Avramova", + "givenName": "Svetlana" + }, + { + "creatorName": "Kochev, Nikolay", + "familyName": "Kochev", + "givenName": "Nikolay" + }, + { + "creatorName": "Angelov, Plamen", + "familyName": "Angelov", + "givenName": "Plamen" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "synthesis" + }, + { + "subject": "retrosynthesis" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "RetroTransformDB - a dataset of transforms (retrosynthetic reactions)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.1209313", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nHere we present a dataset of transforms compiled and coded in SMIRKS line notation by us. The collection is comprised of more than 100 records each including id, reactions name, SMIRKS linear notation, target functional group and transform type. All SMIRKS transforms were tested syntactically, semantically and from chemical point of view in different software platforms. SMIRKS notations are written with explicit H atoms, therefore it is expected that the used software will apply the SMIRKS transforms against molecules with explicit H atoms.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kellerrudek_mpimainz_uvvis_molecules_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/kellerrudek_mpimainz_uvvis_molecules_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kellerrudek_mpimainz_uvvis_molecules_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kellerrudek_mpimainz_uvvis_molecules_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T21:55:44.760265Z", + "source_id": "kellerrudek_mpimainz_uvvis_molecules_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kellerrudek_mpimainz_uvvis_molecules" + }, + "dc": { + "identifier": { + "identifier": "10.18126/x2vw-dqx6", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Keller-Rudek, Hannelore", + "familyName": "Keller-Rudek", + "givenName": "Hannelore" + }, + { + "creatorName": "Moortgat, Geert K.", + "familyName": "Moortgat", + "givenName": "Geert K." + }, + { + "creatorName": "Sander, Rolf", + "familyName": "Sander", + "givenName": "Rolf" + }, + { + "creatorName": "S\u00f6rensen, R\u00fcdiger", + "familyName": "S\u00f6rensen", + "givenName": "R\u00fcdiger" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "gaseous" + }, + { + "subject": "molecules" + }, + { + "subject": "quantum yield" + }, + { + "subject": "uv-vis" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "The MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.6951", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThis archive contains a frozen snapshot of all cross section and quantum yield data files from the MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules. To view the data, open the files cross_sections.html and quantum_yields.html in your browser. The up-to-date version of the Spectral Atlas is available at: http://www.uv-vis-spectral-atlas-mainz.org\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "li_reproducible_attributable_workflows_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/li_reproducible_attributable_workflows_v1.1/", + "total_size": 1526593, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/li_reproducible_attributable_workflows_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/li_reproducible_attributable_workflows_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T15:10:48.645623Z", + "source_id": "li_reproducible_attributable_workflows_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "li_reproducible_attributable_workflows" + }, + "dc": { + "identifier": { + "identifier": "10.18126/s783-fa4g", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Li", + "givenName": "Ye", + "creatorName": "Li, Ye" + }, + { + "familyName": "Wilson", + "givenName": "Sara", + "creatorName": "Wilson, Sara" + }, + { + "familyName": "Altman", + "givenName": "Micah", + "creatorName": "Altman, Micah" + } + ], + "subjects": [ + { + "subject": "materials science" + }, + { + "subject": "reproduciblity" + }, + { + "subject": "research workflow" + }, + { + "subject": "data management" + }, + { + "subject": "data sharing" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Reproducible and Attributable Materials Science Workflows" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7158715", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThis set includes the deidentified data, reproducible analysis and research report of the project on Reproducible and Attributable Materials Science Workflows.\nSupplement to\nhttps://github.com/YeLibrarian/MatSciWorkflow/tree/v01_pre_PeerReview\n\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "hoja_qm7x_comprehensive_molecules_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/hoja_qm7x_comprehensive_molecules_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/hoja_qm7x_comprehensive_molecules_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hoja_qm7x_comprehensive_molecules_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T15:54:27.591037Z", + "source_id": "hoja_qm7x_comprehensive_molecules_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "hoja_qm7x_comprehensive_molecules" + }, + "dc": { + "identifier": { + "identifier": "10.18126/5y39-v72p", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Hoja, Johannes", + "familyName": "Hoja", + "givenName": "Johannes" + }, + { + "creatorName": "Medrano Sandonas, Leonardo", + "familyName": "Medrano Sandonas", + "givenName": "Leonardo" + }, + { + "creatorName": "Ernst, Brian", + "familyName": "Ernst", + "givenName": "Brian" + }, + { + "creatorName": "Vazquez-Mayagoitia, Alvaro", + "familyName": "Vazquez-Mayagoitia", + "givenName": "Alvaro" + }, + { + "creatorName": "DiStasio Jr., Robert A.", + "familyName": "DiStasio Jr.", + "givenName": "Robert A." + }, + { + "creatorName": "Tkatchenko, Alexandre", + "familyName": "Tkatchenko", + "givenName": "Alexandre" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "qm7" + }, + { + "subject": "molecular property" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.4288677", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nHere, we introduce QM7-X, a comprehensive dataset of > 40 physicochemical properties for ~4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures---comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-trans-and conformational isomers)---as well as 100 non-equilibrium structural variations thereof to reach a total of ~4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly converged dataset of quantum-mechanically computed physical and chemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "metallic_glass_database_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "projects": { + "foundry": { + "data_type": "tabular", + "short_name": "metallic glass", + "splits": [ + { + "type": "train", + "path": "train.csv", + "label": "train" + } + ], + "task_type": [ + "supervised" + ], + "keys": [ + { + "description": "The rheological glass transition temperature, Tg (K) defined as the temperature at which the equilibrium liquid viscosity is 10^12 Pa-s", + "units": "K", + "type": "input", + "key": [ + "Tg_[K]" + ] + }, + { + "description": "The alloy liquidus temperature, TL (K) defined as the temperature above which the equilibrium alloy is fully liquid (contains no solid phases)", + "units": "K", + "type": "input", + "key": [ + "Tl_[K]" + ] + }, + { + "description": "The dimensionless Angell Fragility Parameter", + "units": "", + "type": "input", + "key": [ + "m" + ] + }, + { + "description": "Log of the square of the experimentally observed glass forming ability of the alloy expressed in terms of the maximum reported diameter of a rod, dmax, (in mm) for which the liquid alloy can be quenched to a glass with no detectable crystallinity", + "units": "mm", + "type": "target", + "key": [ + "log10(Dmax_sq)" + ] + } + ], + "domain": [ + "materials science" + ] + } + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/metallic_glass_database_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/metallic_glass_database_v1.2/", + "total_size": 3146842, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/metallic_glass_database_v1.2/" + }, + "mdf": { + "organizations": [ + "Foundry" + ], + "resource_type": "dataset", + "ingest_date": "2023-05-10T20:20:27.532913Z", + "source_id": "metallic_glass_database_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "metallic_glass_database" + }, + "dc": { + "identifier": { + "identifier": "10.18126/5svl-7ruk", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "W.L. Johnson, J.H. Na & M.D. Demetriou1", + "familyName": "W.L. Johnson", + "givenName": "J.H. Na & M.D. Demetriou1" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "foundry" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-05-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Quantifying the Origin of Metallic Glass Formation" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "casting_thickness_database_v1.4", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "projects": { + "foundry": { + "data_type": "tabular", + "short_name": "casting thickness", + "splits": [ + { + "type": "train", + "path": "Lmax.csv", + "label": "train" + } + ], + "task_type": [ + "supervised" + ], + "keys": [ + { + "description": "The reported composition for the glass. In atomic percentage", + "units": "", + "type": "input", + "key": [ + "Composition" + ] + }, + { + "description": "The combination of Dmax and Zmax", + "units": "mm", + "type": "target", + "key": [ + "Lmax_[mm]" + ] + } + ], + "domain": [ + "materials science" + ] + } + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/casting_thickness_database_v1.4" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/casting_thickness_database_v1.4/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/casting_thickness_database_v1.4/" + }, + "mdf": { + "organizations": [ + "Foundry" + ], + "resource_type": "dataset", + "ingest_date": "2023-05-10T22:09:38.105587Z", + "source_id": "casting_thickness_database_v1.4", + "scroll_id": 0, + "version": 1, + "source_name": "casting_thickness_database" + }, + "dc": { + "identifier": { + "identifier": "10.18126/uxu4-kvnt", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Zhang, Shixin", + "familyName": "Zhang", + "givenName": "Shixin" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "foundry" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-05-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Metallic Glass Critical Thicknesses for Atomistic Feature Model" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "uml_dependency_chain_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/uml_dependency_chain_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/uml_dependency_chain_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/uml_dependency_chain_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-26T15:40:00.256716Z", + "source_id": "uml_dependency_chain_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "uml_dependency_chain" + }, + "dc": { + "identifier": { + "identifier": "10.18126/fu80-s9bt", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Colon, Austin R.", + "affiliations": [ + "University of Massachusetts Lowell" + ], + "familyName": "Colon", + "givenName": "Austin R." + }, + { + "creatorName": "Kazmer, David O.", + "affiliations": [ + "University of Massachusetts Lowell" + ], + "familyName": "Kazmer", + "givenName": "David O." + }, + { + "creatorName": "Peterson, Amy M.", + "affiliations": [ + "University of Massachusetts Lowell" + ], + "familyName": "Peterson", + "givenName": "Amy M." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-26", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Transient torque, pressure, and temperature data for material extrusion additive manufacturing of acrylonitrile butadiene styrene" + } + ], + "contributors": [ + { + "contributorName": "Amy Peterson (amy_peterson@uml.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Austin Colon (Austincolon97@gmail.com)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset includes transient torque, infeed pressure, melt pressure, and melt temperature that were acquired by an instrumented hot end for material extrusion additive manufacturing of acrylonitrile butadiene styrene (ABS). Data were collected according to a design of experiments wherein the volumetric flow rate, temperature setpoint, and the nozzle orifice diameter were varied one factor at a time." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "george_corresponding_publication_rules_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/george_corresponding_publication_rules_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/george_corresponding_publication_rules_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/george_corresponding_publication_rules_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-25T16:47:47.399912Z", + "source_id": "george_corresponding_publication_rules_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "george_corresponding_publication_rules" + }, + "dc": { + "identifier": { + "identifier": "10.18126/9yh2-ywlf", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "George, Janine", + "familyName": "George", + "givenName": "Janine" + }, + { + "creatorName": "Waroquiers, David", + "familyName": "Waroquiers", + "givenName": "David" + }, + { + "creatorName": "Di Stefano, Davide", + "familyName": "Di Stefano", + "givenName": "Davide" + }, + { + "creatorName": "Petretto, Guido", + "familyName": "Petretto", + "givenName": "Guido" + }, + { + "creatorName": "Rignanese, Gian-Marco", + "familyName": "Rignanese", + "givenName": "Gian-Marco" + }, + { + "creatorName": "Hautier, Geoffroy", + "familyName": "Hautier", + "givenName": "Geoffroy" + } + ], + "subjects": [ + { + "subject": "materials science" + }, + { + "subject": "oxides" + }, + { + "subject": "Pauling rules" + }, + { + "subject": "coordination environments" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Data corresponding to the publication 'The limited predictive power of the Pauling Rules'" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.3654989", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThis is the data set corrresponding to the publication \"The limited predictive power of the Pauling rules\" (see https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202000829). This data can be reproduced with the following code: https://doi.org/10.5281/zenodo.3654428.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "stevens_effect_charged_cores_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/stevens_effect_charged_cores_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/stevens_effect_charged_cores_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/stevens_effect_charged_cores_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-05-09T21:22:10.366808Z", + "source_id": "stevens_effect_charged_cores_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "stevens_effect_charged_cores" + }, + "dc": { + "identifier": { + "identifier": "10.18126/m15g-20yj", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Stevens", + "givenName": "Kaden C.", + "creatorName": "Stevens, Kaden C.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Marras", + "givenName": "Alexander E.", + "creatorName": "Marras, Alexander E.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Campagna", + "givenName": "Trinity R.", + "creatorName": "Campagna, Trinity R.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Ting", + "givenName": "Jeffrey M.", + "creatorName": "Ting, Jeffrey M.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Tirrell", + "givenName": "Matthew V.", + "creatorName": "Tirrell, Matthew V.", + "affiliations": [ + "University of Chicago" + ] + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "polyelectrolytes" + }, + { + "subject": "micelles" + }, + { + "subject": "SAXS" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-05-09", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Data for Effect of Charged Block Length Mismatch on Double Diblock Polyelectrolyte Complex Micelle Cores" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "kadenstevens@uchicago.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "mtirrell@uchicago.edu" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Raw SAXS data." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pacbedcnn_thickness_mistilt_training_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "projects": { + "foundry": { + "splits": [ + { + "type": "train", + "path": "data.h5", + "label": "train" + } + ], + "keys": [ + { + "type": "input", + "description": "all simulated pacbed images from three different material, different conv angle, and different electron energy", + "key": [ + "pacbed" + ] + }, + { + "type": "target", + "description": "groundtruth thickness and mistilt for each pacbed together with other metadata", + "key": [ + "thickness_mistilt_plus_metadata" + ] + } + ], + "domain": [ + "materials science" + ], + "data_type": "hdf5", + "n_items": 384030, + "short_name": "PACBEDCNN_thickness_mistilt_training", + "task_type": [ + "supervised" + ] + } + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pacbedcnn_thickness_mistilt_training_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/pacbedcnn_thickness_mistilt_training_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/foundry/pacbedcnn_thickness_mistilt_training_v1.1/" + }, + "mdf": { + "organizations": [ + "Foundry" + ], + "resource_type": "dataset", + "ingest_date": "2023-06-07T20:16:05.784118Z", + "source_id": "pacbedcnn_thickness_mistilt_training_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "pacbedcnn_thickness_mistilt_training" + }, + "dc": { + "identifier": { + "identifier": "10.18126/2q0n-ce0d", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Oberaigner, Michael", + "familyName": "Oberaigner", + "givenName": "Michael" + }, + { + "creatorName": "Clausen, Alexander", + "familyName": "Clausen", + "givenName": "Alexander" + }, + { + "creatorName": "Weber, Dieter", + "familyName": "Weber", + "givenName": "Dieter" + }, + { + "creatorName": "Kothleitner, Gerald", + "familyName": "Kothleitner", + "givenName": "Gerald" + }, + { + "creatorName": "E Dunin-Borkowski, Rafal", + "familyName": "E Dunin-Borkowski", + "givenName": "Rafal" + }, + { + "creatorName": "Knez, Daniel", + "familyName": "Knez", + "givenName": "Daniel" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "foundry" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-06-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Training Dataset of PACBED-CNN for Infering Specimen Thickness and Mistilt" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "melting_acoustics_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/melting_acoustics_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/melting_acoustics_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/melting_acoustics_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-06-28T16:43:08.188468Z", + "source_id": "melting_acoustics_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "melting_acoustics" + }, + "dc": { + "identifier": { + "identifier": "10.18126/fjhl-cy15", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Wu", + "givenName": "Brady", + "creatorName": "Wu, Brady", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "VanSaders", + "givenName": "Bryan", + "creatorName": "VanSaders, Bryan", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Lim", + "givenName": "Melody X.", + "creatorName": "Lim, Melody X.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Jaeger", + "givenName": "Heinrich M.", + "creatorName": "Jaeger, Heinrich M.", + "affiliations": [ + "University of Chicago" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-06-28", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for Hydrodynamic Coupling Melts Acoustically Levitated Crystalline Rafts" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://arxiv.org/abs/2211.02750", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Brady Wu (bwu34@uchicago.edu)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The database includes the raw data file for acoustically levitated granular raft with particle diameter 30-60 micrometers and various acoustic pressure, conducted and analyzed for the paper \u201cHydrodynamic Coupling Melts Acoustically Levitated Crystalline Rafts\u201d (https://arxiv.org/abs/2211.02750). This repository contains the necessary data needed to reproduce all figures from the paper. The \u201cREADME\u201d file describes the structure of the data and any relevant experimental conditions and simulation parameters. 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In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\n In theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\n The data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "TOMCAT beamline at the Swiss Light Source, Paul Scherrer Institut" + }, + { + "geoLocationPlace": "SLS" + }, + { + "geoLocationPlace": "PSI" + }, + { + "geoLocationPlace": "Paul Scherrer Institut" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_32_shahani_ostwald_v2.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_32_shahani_ostwald_v2.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_115/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_115/" + }, + "mrr": { + "materialType": [ + "diffusion", + "thermodynamic", + "kinetic", + "metal", + "semiconductor" + ], + "characterizationMethod": [ + "X-ray computed tomography" + ], + "structuralFeature": [ + "bulk", + "crystalline", + "fluid", + "interphase" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:26:09.095488Z", + "source_id": "pub_32_shahani_ostwald_v2.1", + "scroll_id": 0, + "version": 1, + "source_name": "pub_32_shahani_ostwald" + }, + "custom": { + "experiment_nominal_alloy_composition": "['Al (53 wt%); Si (32 wt%), Cu (15 wt%)', 'Al 53 wt%', 'Si 32 wt%', 'Cu 15 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "Gridrec algorithm", + "processing_segmentation_method": "anisotropic diffusion filter followed by thresholding", + "all_materials_included": "Al-Si-Cu", + "experiment_holding_temperature": "['isothermal at 650 C', '650 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)']", + "experiment_xray_energy": "pink beam" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2Z592", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Shahani, Ashwin J.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ], + "familyName": "Shahani", + "givenName": "Ashwin J." + }, + { + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ], + "familyName": "Xiao", + "givenName": "Xianghui" + }, + { + "creatorName": "Skinner, Kwan", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ], + "familyName": "Skinner", + "givenName": "Kwan" + }, + { + "creatorName": "Peters, Matthew", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ], + "familyName": "Peters", + "givenName": "Matthew" + }, + { + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Dow Corning Corporation" + ], + "familyName": "Voorhees", + "givenName": "Peter W." + } + ], + "subjects": [ + { + "subject": "" + }, + { + "subject": "synchrotron" + }, + { + "subject": "anisotropy" + }, + { + "subject": "experiment" + }, + { + "subject": "coarsening" + }, + { + "subject": "4D materials science" + }, + { + "subject": "X-ray computed tomography" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "simulation" + }, + { + "subject": "microstructures" + }, + { + "subject": "Al-Si-Cu alloys" + }, + { + "subject": "facets" + }, + { + "subject": "in situ" + }, + { + "subject": "tomography" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "ostwald ripening" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-06-15T20:15:23Z", + "dateType": "Accepted" + }, + { + "date": "2016-06-15T20:15:23Z", + "dateType": "Available" + }, + { + "date": "2016-06-15", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Ashwin J. Shahani (shahani@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Ostwald Ripening of Faceted Si Particles in an Al-Si-Cu Melt" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study the isothermal coarsening of faceted Si particles in an Al-Si-Cu liquid. The as-cast, hyper-eutectic sample consisted of primary Si particles in a eutectic matrix. Upon heating to above the eutectic temperature (here, 650 C), the eutectic constituents melted and the Si particles were in contact with a featureless liquid. Tracking the evolution of the solid-liquid interfaces under isothermal conditions was the focus of this work. The raw data were obtained in the following sequence: (i) for the first 60 time-steps (i.e., 3D reconstructions), projections were collected continuously using 1000 projections per 180 degree revolution and a rotation rate of 9 degrees per s; (ii) for the next 20 time-steps, scans were spaced 1 min apart, using 3000 projections and a rotation rate of 3 degrees per s; (iii) for the following 20 time-steps, scans were spaced 5 min apart using the same imaging parameters; (iv) for the last 5 time-steps, the interval between scans increased to 11 minutes, using the same imaging parameters as in (ii)-(iii). Thus, a total of 105 reconstructions were collected over the course of 5 hours, where each reconstruction captured the average microstructure in a 20 s window. The motivation for collecting the X-ray projections in this manner was that the system-average length-scale increased logarithmically with time during coarsening. Therefore, this data collection scheme adequately captured the interfacial dynamics. The data here includes the raw projections. Note that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the data processing and quantitative analysis, the reader is pointed to the following publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu)." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Beamline 2-BM at the Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + }, + { + "geoLocationPlace": "APS" + }, + { + "geoLocationPlace": "ANL" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_37_shahani_mechanism_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_112/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_112/" + }, + "custom": { + "experiment_nominal_alloy_composition": "['Al (51.6 wt%); Ge (48.4 wt%)', 'Al 51.6 wt%', 'Ge 48.4 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "TIMBIR Algorithm", + "processing_segmentation_method": "median filtering followed by Otsu thresholding", + "all_materials_included": "['Al-Ge', 'Al', 'Ge']", + "experiment_holding_temperature": "['Sample was allowed to equilibrate immediately above the eutectic temperature (we use 420 C); then, the sample temperature was lowered to 417 C, which is in the eutectic regime. The sample was held at this temperature isothermally while X-ray projections were recorded continuously.', '417 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)', 'DOE (grant DE-FG02-99ER45782)']", + "experiment_xray_energy": "pink beam" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_37_shahani_mechanism_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "diffusion", + "thermodynamic", + "kinetic", + "metal", + "semiconductor" + ], + "characterizationMethod": [ + "X-ray computed tomography" + ], + "structuralFeature": [ + "bulk", + "crystalline", + "fluid", + "interphase" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:34:38.547988Z", + "source_id": "pub_37_shahani_mechanism_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_37_shahani_mechanism" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M26P4H", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Shahani", + "givenName": "Ashwin J.", + "creatorName": "Shahani, Ashwin J.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Xiao", + "givenName": "Xianghui", + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter W.", + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "defects" + }, + { + "subject": "irregular eutectic" + }, + { + "subject": "anisotropy" + }, + { + "subject": "patterns" + }, + { + "subject": "synchrotron" + }, + { + "subject": "experiment" + }, + { + "subject": "eutectic" + }, + { + "subject": "solidification" + }, + { + "subject": "4D materials science" + }, + { + "subject": "X-ray computed tomography" + }, + { + "subject": "growth" + }, + { + "subject": "facets" + }, + { + "subject": "Al-Ge alloys" + }, + { + "subject": "in situ" + }, + { + "subject": "twinning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "twins" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-06-15T20:06:06Z", + "dateType": "Accepted" + }, + { + "date": "2016-06-15T20:06:06Z", + "dateType": "Available" + }, + { + "date": "2016-06-15", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Ashwin J. Shahani (shahani@u.northwestern.edu)" + } + ], + "titles": [ + { + "title": "The Mechanism of Eutectic Growth in Highly Anisotropic Materials" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study the growth process of an irregular eutectic from a featureless liquid. An irregular eutectic (such as Al-Ge in this work) consists of a faceted constituent (e.g., Si or Ge) and a nonfaceted one (e.g., Al or Ag) which may grow in a coupled manner. The growth process of an irregular eutectic alloy remains poorly understood due to the lack of 4D (i.e., 3D space plus time) resolved data. Thus, the collected data aims to fill-in-the-gaps in our understanding of how irregular eutectics solidify. To achieve the necessary temporal and spatial resolution necessary to resolve the eutectic growth process we use the TIMBIR reconstruction algorithm, described elsewhere. In this technique, we use the following acquisition parameters: 4 sub-frames and 2700 projections per frame. Due to the small penetration depth through the heavy element Ge, the large number of projections and the high exposure time guaranteed high quality images. Included is the raw data collected in the form of the projections. The TIMBIR algorithm is open-source and can be found here: Note also that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the resulting 3D reconstruction visualization, the reader is pointed to the associated journal publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu)." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Beamline 2-BM at the Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + }, + { + "geoLocationPlace": "APS" + }, + { + "geoLocationPlace": "ANL" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_85_cool_cslm4_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_842/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_842/" + }, + "custom": { + "all_materials_included": "Pb-Sn (30 volume percent dendrites)", + "funding_details": "NASA contract NNX14AB73G" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_85_cool_cslm4_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "metal", + "metals" + ], + "characterizationMethod": [ + "Serial sectioning" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T17:14:46.770017Z", + "source_id": "pub_85_cool_cslm4_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_85_cool_cslm4" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2JW52", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Cool", + "givenName": "Thomas", + "creatorName": "Cool, Thomas", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter", + "creatorName": "Voorhees, Peter", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "serial sectioning" + }, + { + "subject": "experiment" + }, + { + "subject": "dendrites" + }, + { + "subject": "coarsening" + }, + { + "subject": "dendrite fragmentation" + }, + { + "subject": "isothermal coarsening" + }, + { + "subject": "reconstruction" + }, + { + "subject": "dendritic coarsening" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "tomography" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-12-01T21:15:12Z", + "dateType": "Accepted" + }, + { + "date": "2017-12-01T21:15:12Z", + "dateType": "Available" + }, + { + "date": "2017-12-01", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Zach Thompson (zacharythompson2021@u.northwestern.edu)" + }, + { + "contributorType": "ContactPerson", + "contributorName": "Peter Voorhees (p-voorhees@northwestern.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.actamat.2017.01.029", + 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B. Cohen X-ray Diffraction Facility and the Keck-II, EPIC, and SPID facilities of the NUANCE Center at Northwestern University, which have received support from the Soft and Hybrid Nanotechnology Experimental (SHyNE) Resource (NSF NNCI-1542205); the MRSEC program (NSF DMR-1121262) at the Materials Research Center; the International Institute for Nanotechnology (IIN); the Keck Foundation; and the State of Illinois, through the IIN.', 'M.J.M. gratefully acknowledges support from the Ryan Fellowship and the Northwestern University International Institute for Nanotechnology.', 'A.H. acknowledges the support of a Research Fellowship from the Deutsche Forschungsgemeinschaft (Grant HE 7999/1-1).', 'H.B. acknowledges support from the NSERC Postgraduate Scholarship-Doctoral Program and National Science Foundation Graduate Research Fellowship.']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_64_moody_atomic_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "2D Materials", + "MoS2", + "MoOx" + ], + "characterizationMethod": [ + "Raman", + "XPS", + "XRD", + "Three-terminal electrical measurements", + "AFM" + ], + "structuralFeature": [ + "Amorphous MoOx", + "Crystalline MoS2" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T16:23:00.801411Z", + "source_id": "pub_64_moody_atomic_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_64_moody_atomic" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M26351", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Moody", + "givenName": "Michael J.", + "creatorName": "Moody, Michael J.", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Henning", + "givenName": "Alex", + "creatorName": "Henning, Alex", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Jurca", + "givenName": "Titel", + "creatorName": "Jurca, Titel", + "affiliations": [ + "Department of Chemistry, Northwestern University" + ] + }, + { + "familyName": "Shang", + "givenName": "Ju Ying", + "creatorName": "Shang, Ju Ying", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Bergeron", + "givenName": "Hadallia", + "creatorName": "Bergeron, Hadallia", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Balla", + "givenName": "Itamar", + "creatorName": "Balla, Itamar", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Olding", + "givenName": "Jack N.", + "creatorName": "Olding, Jack N.", + "affiliations": [ + "Graduate Program in Applied Physics, Northwestern University" + ] + }, + { + "familyName": "Weiss", + "givenName": "Emily A.", + "creatorName": "Weiss, Emily A.", + "affiliations": [ + "Department of Chemistry and Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Department of Materials Science and Engineering, Department of Chemistry, Department of Medicine, Department of Electrical Engineering and Computer Science, Northwestern University" + ] + }, + { + "familyName": "Lohr", + "givenName": "Tracy L.", + "creatorName": "Lohr, Tracy L.", + "affiliations": [ + "Department of Chemistry, Northwestern University" + ] + }, + { + "familyName": "Marks", + "givenName": "Tobin J.", + "creatorName": "Marks, Tobin J.", + "affiliations": [ + "Department of Chemistry and Department of Materials Science and Engineering, Northwestern University" + ] + }, + { + "familyName": "Lauhon", + "givenName": "Lincoln J.", + "creatorName": "Lauhon, Lincoln J.", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "MoS2" + }, + { + "subject": "2D materials" + }, + { + "subject": "semiconductors" + }, + { + "subject": "ALD" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-11-21T20:19:24Z", + "dateType": "Accepted" + }, + { + "date": "2018-11-21T20:19:24Z", + "dateType": "Available" + }, + { + "date": "2018-11-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Lincoln J. Lauhon (lauhon@northwestern.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.chemmater.8b01171", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://pubs.acs.org/doi/10.1021/acs.chemmater.8b01171", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for Atomic Layer Deposition of Molybdenum Oxides with Tunable Stoichiometry Enables Controllable Doping of MoS2" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "parate_histaminesensing6_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam_protected/parate_histaminesensing6_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam_protected/parate_histaminesensing6_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/parate_histaminesensing6_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "Hersam Group" + ], + "ingest_date": "2020-09-26T01:03:48.763987Z", + "source_id": "parate_histaminesensing6_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "parate_histaminesensing6" + }, + "dc": { + "identifier": { + "identifier": "10.18126/htxv-4ioz", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Parate", + "givenName": "Kshama", + "creatorName": "Parate, Kshama", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Pola", + "givenName": "C\u00edcero C", + "creatorName": "Pola, C\u00edcero C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Rangnekar", + "givenName": "Sonal V", + "creatorName": "Rangnekar, Sonal V", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Mendivelso-Perez", + "givenName": "Deyny L", + "creatorName": "Mendivelso-Perez, Deyny L", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Smith", + "givenName": "Emily A", + "creatorName": "Smith, Emily A", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C", + "creatorName": "Hersam, Mark C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Gomes", + "givenName": "Carmen L", + "creatorName": "Gomes, Carmen L", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + }, + { + "familyName": "Claussen", + "givenName": "Jonathan C", + "creatorName": "Claussen, Jonathan C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ] + } + ], + "subjects": [ + { + "subject": "graphene" + }, + { + "subject": "biosensors" + }, + { + "subject": "experiment" + }, + { + "subject": "histamine" + }, + { + "subject": "aerosol jet printing" + }, + { + "subject": "ink" + }, + { + "subject": "electrochemical sensing" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2020, + "dates": [ + { + "date": "2020-09-26", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Aerosol-jet-printed graphene electrochemical histamine sensors for food safety monitoring" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1088/2053-1583/ab8919", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "m-hersam@northwestern.edu" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Carbon nanomaterials such as graphene exhibit unique material properties including high electrical conductivity, surface area, and biocompatibility that have the potential to significantly improve the performance of electrochemical sensors. Since in-field electrochemical sensors are typically disposable, they require materials that are amenable to low-cost, high-throughput, and scalable manufacturing. Conventional graphene devices based on low-yield chemical vapor deposition techniques are too expensive for such applications, while low-cost alternatives such as screen and inkjet printing do not possess sufficient control over electrode geometry to achieve favorable electrochemical sensor performance. In this work, aerosol jet printing (AJP) is used to create high-resolution (~40 \u03bcm line width) interdigitated electrodes (IDEs) on flexible substrates, which are then converted into histamine sensors by covalently linking monoclonal antibodies to oxygen moieties created on the graphene surface through a CO2 thermal annealing process. The resulting electrochemical sensors exhibit a wide histamine sensing range of 6.25\u2013200 ppm (56.25 \u03bcM\u20131.8 mM) and a low detection limit of 3.41 ppm (30.7 \u03bcM) within actual tuna broth samples. These sensor metrics are significant since histamine levels over 50 ppm in fish induce adverse health effects including severe allergic reactions (e.g. Scombroid food poisoning). Beyond the histamine case study presented here, the AJP and functionalization process can likely be generalized to a diverse range of sensing applications including environmental toxin detection, foodborne pathogen detection, wearable health monitoring, and health diagnostics." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "moraes_chargetransportgrec2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/moraes_chargetransportgrec2_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam_protected/moraes_chargetransportgrec2_v1.1/", + "total_size": 1231050, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam_protected/moraes_chargetransportgrec2_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "Hersam Group" + ], + "ingest_date": "2020-09-26T01:04:28.818425Z", + "source_id": "moraes_chargetransportgrec2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "moraes_chargetransportgrec2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/wxl1-ih5d", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "de Moraes, Ana C. M.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "de Moraes", + "givenName": "Ana C. M." + }, + { + "creatorName": "Obrzut, Jan", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Obrzut", + "givenName": "Jan" + }, + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Downing, Julia R.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Downing", + "givenName": "Julia R." + }, + { + "creatorName": "Chaney, Lindsay E.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Chaney", + "givenName": "Lindsay E." + }, + { + "creatorName": "Patel, Dinesh K.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Patel", + "givenName": "Dinesh K." + }, + { + "creatorName": "Elmquist, Randolph E.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Elmquist", + "givenName": "Randolph E." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "National Institute of Standards and Technology" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "graphene" + }, + { + "subject": "semiconductors" + }, + { + "subject": "2D materials" + }, + { + "subject": "inks" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2020, + "dates": [ + { + "date": "2020-09-26", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Elucidating charge transport mechanisms in cellulose-stabilized graphene inks" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1039/D0TC03309J", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "m-hersam@northwestern.edu", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Solution-processed graphene inks that use ethyl cellulose as a polymer stabilizer are blade-coated into large-area thin films. Following blade-coating, the graphene thin films are cured to pyrolyze the cellulosic polymer, leaving behind an sp2-rich amorphous carbon residue that serves as a binder in addition to facilitating charge transport between graphene flakes. Systematic charge transport measurements, including temperature-dependent Hall effect and non-contact microwave resonant cavity characterization, reveal that the resulting electrically percolating graphene thin films possess high mobility (\u2248160 cm2 V\u22121 s\u22121), low energy gap, and thermally activated charge transport, which develop weak localization behavior at cryogenic temperatures." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mdr_item_545_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mrr": "This dataset was registered with the MRR.", + "citrine": "https://citrination.com/datasets/168810/", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": 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Institute of Standards and Technology", + "U.S. Department of Commerce", + "DOC", + "MDR", + "NIST", + "NIST Materials Data Repository", + "NIST MDR" + ], + "ingest_date": "2018-11-15T20:03:33.438586Z", + "source_id": "mdr_item_1045_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "mdr_item_1045" + }, + "dc": { + "rightsList": [ + { + "rightsURI": "https://creativecommons.org/licenses/by/2.0/", + "rights": "CC0 2.0 Attribution" + } + ], + "alternateIdentifiers": [ + { + "alternateIdentifier": "http://hdl.handle.net/11256/618", + "alternateIdentifierType": "Handle" + }, + { + "alternateIdentifier": "1045", + "alternateIdentifierType": "NIST DSpace ID" + } + ], + "creators": [ + { + "givenName": "I", + "familyName": "Ansara", + "creatorName": "Ansara, I" + }, + { + "givenName": "AT", + "familyName": "Dinsdale", + "creatorName": "Dinsdale, AT" + }, + { + "givenName": "MH", + "familyName": "Rand", + "creatorName": "Rand, MH" + } + ], + "subjects": [ + { + "subject": "Light metal alloys" + } + ], + "publisher": "NIST Materials Data Repository", + "publicationYear": "2016", + "titles": [ + { + "title": "Al-Mg COST 507 Thermochemical database for light metal alloys" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_18_xie_high_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_18_xie_high_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1119/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1119/" + }, + "mrr": { + "materialType": [ + "Amorphous macromolecules: 5 polymers, 9 polymer salts, 4 caged molecules" + ], + "characterizationMethod": [ + "Time domain thermoreflectence (TDTR)" + ], + "structuralFeature": [ + "Polymer" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-07T22:50:24.149106Z", + "source_id": "pub_18_xie_high_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_18_xie_high" + }, + "custom": { + "funding_details": "Air Force Office of Scientific Research (AFOSR); Multidisciplinary Research Program of the University Research Initiative (MURI) (Grant No. FA9550-12-1-0002);", + "all_materials_included": "['PALi: Poly(acrylic acid lithium salt)', 'PANa: Poly(acrylic acid sodium salt)', 'PACa: Poly(acrylic acid calcium salt)', 'PAFe: Poly(acrylic acid iron salt)', 'PACu: Poly(acrylic acid copper salt)', 'PVPA: Poly(vinylphosphonic acid)', 'PVPLi: Poly(vinylphosphonic acid lithium salt)', 'PVPMg: Poly(vinylphosphonic acid magnesium salt)', 'PVPCa: Poly(vinylphosphonic acid calcium salt)', 'PVSNa: Poly(vinylsulfonic acid sodium salt)', 'PDDA: Poly(diallyldimethylammonium chloride)', 'PAH: Poly(allylamine hydrochloride)', 'PMPC: Poly (2-methacryloyloxyethyl phosphorylcholine)', 'PS: Polystyrene', \"ADP: 4,4' -(1,3-adamantanediyl)diphenol\", 'DSQ: Dodecaphenyl silsesquioxanes', 'GHSQ: (3-glycidyl)propoxyheptaisobutyl silsesquioxanes', 'PC71BM: [6,6]-phenyl C71 butyric acid methyl ester']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M28S79", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Xie, Xu", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Xie", + "givenName": "Xu" + }, + { + "creatorName": "Yang, Kexin", + "affiliations": [ + "Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Yang", + "givenName": "Kexin" + }, + { + "creatorName": "Li, Dongyao", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Li", + "givenName": "Dongyao" + }, + { + "creatorName": "Tsai, Tsung-Han", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Tsai", + "givenName": "Tsung-Han" + }, + { + "creatorName": "Shin, Jungwoo", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Shin", + "givenName": "Jungwoo" + }, + { + "creatorName": "Braun, Paul V.", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Braun", + "givenName": "Paul V." + }, + { + "creatorName": "Cahill, David G.", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ], + "familyName": "Cahill", + "givenName": "David G." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "thermal conductivity" + }, + { + "subject": "macromolecules" + }, + { + "subject": "polymers" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-04-27T14:49:47Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-27T14:49:47Z", + "dateType": "Available" + }, + { + "date": "2018-04-27", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "xuxie1@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1103/PhysRevB.95.035406", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://dx.doi.org/10.1103/PhysRevB.95.035406", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "High and low thermal conductivity of amorphous macromolecules" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Prior to the TDTR measurement, a thin layer of Al optical transducer (\u224890 nm) was deposited (via magnetron sputtering) onto the sample. The water contents in the sample after Al coating were expected to be negligible, due to the long-time pumping in vacuum before deposition (90 \u00b0C baking for 2 h at 1\u00d7 10^-4 Torr, followed by pumping at <10^\u22127 Torr overnight). The Al coating prevents the water vapor from diffusing into the sample. To check these assumptions, AFM was used to verify the thicknesses of several hydrophilic samples (PANa, PALi, PVPA, PMCP, PDDA). A portion of the polymers was scratched off the substrate by a razor blade before Al deposition to create a step edge that facilitates the AFM measurement.\r\nThe height of the step edges matched with the thicknesses measured by ellipsometry at 90 \u00b0C to within the combined experimental uncertainties of <8 nm. In a TDTR measurement, the ratio of in-phase\r\n(Vin) and out-of-phase (Vout) signals of the reflected probe beam recorded by an radio frequency (RF) lock-in amplifier with the reference frequency set at f is measured. Changing the modulation\r\nfrequency f between 1 and 9 MHz allowed the modification of the sensitivity of Vin/Vout with respect to thermal conductivity and heat capacity for thin films with thicknesses of \u2248100 \u223c 200 nm. We typically\r\nacquire data for Vin/Vout at three modulation frequencies, 1.1, 5.1, and 9.1 MHz, as a function of pump-probe delay time from \u221225 to 3600 ps. To extract the thermal conductivity and heat capacity from the TDTR data, we used a heat diffusion model to fit the three Vin/Vout curves simultaneously. The model consists of three layers: the Al transducer, the thin film for investigation, and the Si substrate. All\r\nthe geometric parameters and thermal properties of the Al transducer and Si substrate are measured separately or adopted from literature values; the only fitting variables are and C of\r\nthe thin film. The effects of the interfacial thermal conductance for the Al/polymer and polymer/Si interfaces are small since the Kapitza lengths of the interfaces are on the order of a\r\nfew nanometers. Therefore both of the interfacial conductances is set to a nominal value of 100 MW m^\u22122 K^\u22121 in the modeling. The TDTR measurements is validated by testing a reference\r\nsample of PMMA. The extracted and C are 0.19 \u00b1 0.02 W m^\u22121 K^\u22121 and 1.60 \u00b1 0.15 J cm^\u22123 K^\u22121, respectively, which is consistent with prior studies." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Frederick Seitz Materials Research Laboratory Central Research Facilities, University of Illinois." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_67_marras_polyelectrolyte_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_67_marras_polyelectrolyte_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1264/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1264/" + }, + "mrr": { + "materialType": [ + "polymer", + "polymer" + ], + "characterizationMethod": [ + "X-ray scattering (SAXS)", + "SAXS" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:32:04.022671Z", + "source_id": "pub_67_marras_polyelectrolyte_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_67_marras_polyelectrolyte" + }, + "custom": { + "funding_details": "This work was supported by the U.S. Department of Energy Office of Science, Program in Basic Energy Sciences, Materials Sciences and Engineering Division and used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. J.M.T. acknowledges support from the NIST-CHiMaD Postdoctoral Fellowship, supported by the U.S. Department of Commerce, National Institute of Standards and Technology (NIST) through the Center for Hierarchical Materials Design (CHiMaD) under financial assistance award 70NANB14H012.", + "all_materials_included": "['poly-l-lysine-PEG', 'poly-vinylbenzyltrimethyl-ammonium', 'nucleic acid']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2QW8R", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Marras, Alexander E.", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Marras", + "givenName": "Alexander E." + }, + { + "creatorName": "Vieregg, Jeffrey R.", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Vieregg", + "givenName": "Jeffrey R." + }, + { + "creatorName": "Ting, Jeffrey M.", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Ting", + "givenName": "Jeffrey M." + }, + { + "creatorName": "Rubien, Jack D.", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Rubien", + "givenName": "Jack D." + }, + { + "creatorName": "Tirrell, Matthew V.", + "affiliations": [ + "University of Chicago" + ], + "familyName": "Tirrell", + "givenName": "Matthew V." + } + ], + "subjects": [ + { + "subject": "spectroscopy" + }, + { + "subject": "experiment" + }, + { + "subject": "electrochemistry" + }, + { + "subject": "polyelectrolytes" + }, + { + "subject": "phase separation" + }, + { + "subject": "phases" + }, + { + "subject": "oligonucleotides" + }, + { + "subject": "nanoparticles" + }, + { + "subject": "complex coacervation" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-12-17T20:18:26Z", + "dateType": "Accepted" + }, + { + "date": "2018-12-17T20:18:26Z", + "dateType": "Available" + }, + { + "date": "2018-12-17", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Alex Marras (marras@uchicago.edu )", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.26434/chemrxiv.7456322.v1", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://www.dx.doi.org/10.26434/chemrxiv.7456322.v1", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for Polyelectrolyte Complexation of Oligonucleotides by Charged Hydrophobic \u2013 Neutral Hydrophilic Block Polymers" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\u201cPolyelectrolyte Complexation of Oligonucleotides by Charged Hydrophobic \u2013 Neutral Hydrophilic Block Polymers\u201c\r\n\r\nAlexander E. Marras, Jeffrey R. Vieregg, Jeffrey M. Ting, Jack D. Rubien, Matthew V. Tirrell\r\n\r\nInstitute for Molecular Engineering, University of Chicago, Chicago, IL 60637\r\n\r\ncontact via twitter @AEMarras, @J_Ting1 or jvieregg@uchicago.edu\r\n\r\npreprint DOI: 10.26434/chemrxiv.7456322.v1\r\n\r\nFiles include background subtracted small angle X-ray scattering (SAXS) data from the Advanced Photon Source at Argonne National Laboratory. Data is organized by cation type (poly-l-lysine-PEG vs. poly-vinylbenzyltrimethyl-ammonium) and nucleic acid hybridization (single-stranded vs. double-stranded)." + }, + { + "descriptionType": "Other", + "description": "Polyelectrolyte complex micelles (PCMs, core-shell nanoparticles formed by complexation of a polyelectrolyte with a polyelectrolyte-hydrophilic neutral block polymer) offer an attractive solution to the critical problem of delivering therapeutic nucleic acids, but few structure-property studies have been carried out to date. We present data comparing oligonucleotide PCMs formed with poly(vinylbenzyl trimethylammonium) as the cationic block to those using poly(lysine), which is more commonly used. Despite its higher charge density, increased hydrophobicity, and permanent charge, pVBTMA appears to complex DNA more weakly than does poly(lysine). Using small angle X-ray scattering and electron microscopy, we find that, at physiological ionic strength, PCMs formed from both cationic blocks exhibit very similar structure-property relationships, with PCM radius determined by the cationic block size and shape controlled by the hybridization state of the oligonucleotides. These observations narrow the design space for optimizing therapeutic PCMs and provide new insights into the rich polymer physics of polyelectrolyte self-assembly." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_17_jain_vacancy_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1113/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1113/" + }, + "custom": { + "all_materials_included": "Zr" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_17_jain_vacancy_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-07T22:48:44.109038Z", + "source_id": "pub_17_jain_vacancy_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_17_jain_vacancy" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2R04K", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Jain", + "givenName": "Abhinav C. P.", + "creatorName": "Jain, Abhinav C. P.", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + }, + { + "familyName": "Burr", + "givenName": "Patrick A.", + "creatorName": "Burr, Patrick A.", + "affiliations": [ + "The University of New South Wales" + ] + }, + { + "familyName": "Trinkle", + "givenName": "Dallas R.", + "creatorName": "Trinkle, Dallas R.", + "affiliations": [ + "Imperial College London" + ] + } + ], + "subjects": [ + { + "subject": "DFT" + }, + { + "subject": "simulation" + }, + { + "subject": "diffusion" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-04-18T20:24:49Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-18T20:24:49Z", + "dateType": "Available" + }, + { + "date": "2018-04-18", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Dallas Trinkle (dtrinkle@illinois.edu)" + } + ], + "titles": [ + { + "title": "Vacancy Mediated and Interstitial Solute Transport in Zr from Density Functional Theory Calculations" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The Interstitial folder contains the DFT dataset for the interstitial site and migration energies of all solutes, scripts required to extract the data and compute diffusivity. The Vacancy_Mediated folder contains the DFT dataset for the solute-vacancy binding and vacancy migration energies, scripts required to extract the data and compute diffusivity. The scripts/ directory in site Interstitial and Vacancy_Mediated folders contains the python and bash scripts, which extracts the input data for diffusion model from DFT dataset. The extraction of data can be done by running ExtractDFT.sh scripts for Interstitial or Vacancy_Mediated data for each solute located inside the /scripts directory of either data set. The vacancy mediated extracted data for each solute contains solute-vacancy binding energies and migration barriers in one file called solute_elist.yaml, and frequencies for sites and transitions in another file called solute_flist.yaml. Similarly, the interstitial data is separated into two yaml files for each solute. For the convenience, these files are already generated. The Diffusion folders inside Vacancy_Mediated and Interstitial directories each have subdirectories for the solutes. These solute sub-directories have scripts that read the extracted data and compute diffusivity. The scripts/ directories of each data set also consist of a master script \"TransportCalculation.sh\" which computes the diffusion coefficients of each solute instead of executing them manually. The results of diffusion calculations and arrhenius fits are saved under corresponding solute folders within the Diffusion directory. The \"POTCARS.txt\" file details the POTCAR used for Zr and solutes. Note: Before running \"ExtractDFT.sh\", make sure the pythonpath is correctly set in FrequencyCalculation.sh and AttemptFrequency.sh which are present in scripts/ directory. The python 3.4 and above with modules yaml, numpy, argparse, os, scipy, h5py and Onsager is required. The Onsager module can be obtained from [ONSAGER,http://dallastrinkle.github.io/Onsager]." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ab_initio_solute_database_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/ab_initio_solute_database_v1-2/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/ab_initio_solute_database_v1-2/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2018-11-24T08:12:11.852893Z", + "source_id": "ab_initio_solute_database_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "ab_initio_solute_database" + }, + "dc": { + "creators": [ + { + "creatorName": "Morgan, Dane", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Morgan", + "givenName": "Dane" + }, + { + "creatorName": "Mayeshiba, Tam", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Mayeshiba", + "givenName": "Tam" + }, + { + "creatorName": "Henry, Wu", + "affiliations": [ + "University of Wisconsin-Madison" + ], + "familyName": "Henry", + "givenName": "Wu" + } + ], + "subjects": [ + { + "subject": "dilute" + }, + { + "subject": "solute" + }, + { + "subject": "DFT" + }, + { + "subject": "diffusion" + }, + { + "subject": "dataset" + }, + { + "subject": "database" + }, + { + "subject": "high-throughput" + } + ], + "publicationYear": "2016", + "publisher": "MDF (placeholder)", + "dates": [ + { + "date": "2017-08-07T16:07:32.938812Z", + "dateType": "Collected" + } + ], + "contributors": [ + { + "affiliations": [ + "University of Wisconsin-Madison" + ], + "contributorType": "ContactPerson", + "contributorName": "Morgan, Dane", + "familyName": "Morgan", + "givenName": "Dane" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "http://dx.doi.org/10.1038/sdata.2016.54", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "High-throughput Ab-initio Dilute Solute Diffusion Database" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "We demonstrate automated generation of diffusion databases from high-throughput density functional theory (DFT) calculations. A total of more than 230 dilute solute diffusion systems in Mg, Al, Cu, Ni, Pd, and Pt host lattices have been determined using multi-frequency diffusion models. 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"mdr_item_633_v2.2", + "scroll_id": 0, + "version": 2, + "source_name": "mdr_item_633" + }, + "dc": { + "alternateIdentifiers": [ + { + "alternateIdentifier": "http://hdl.handle.net/11115/177", + "alternateIdentifierType": "Handle" + }, + { + "alternateIdentifier": "633", + "alternateIdentifierType": "NIST DSpace ID" + } + ], + "creators": [ + { + "familyName": "Liu", + "givenName": "Alan F.", + "creatorName": "Liu, Alan F." + } + ], + "publicationYear": "2013", + "publisher": "NIST Materials Data Repository", + "titles": [ + { + "title": "Mechanical Properties Data for Selected Aluminum Alloys," + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mdr_item_795_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/mdr_item_795_v1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/mdr_item_795_v1/" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mrr": "This dataset was registered with the MRR." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "National Institute of Standards and Technology", + "U.S. Department of Commerce", + "DOC", + "MDR", + "NIST", + "NIST Materials Data Repository", + "NIST MDR" + ], + "ingest_date": "2018-11-15T19:30:18.805150Z", + "source_id": "mdr_item_795_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "mdr_item_795" + }, + "dc": { + "alternateIdentifiers": [ + { + "alternateIdentifier": "http://hdl.handle.net/11115/191", + "alternateIdentifierType": "Handle" + }, + { + "alternateIdentifier": "795", + "alternateIdentifierType": "NIST DSpace ID" + } + ], + "creators": [ + { + "familyName": "Hatch", + "givenName": "J E", + "creatorName": "Hatch, J E" + } + ], + "publicationYear": "2013", + "publisher": "NIST Materials Data Repository", + "titles": [ + { + "title": "Microstructure of Alloys" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_34_jokisaari_simulated_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_34_jokisaari_simulated_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_630/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_630/" + }, + "mrr": { + "materialType": [ + "metal", + "cobalt superalloy" + ], + "characterizationMethod": [ + "simulation", + "phase field modeling", + "DFT" + ], + "structuralFeature": [ + "fcc gamma phase", + "L1_2 gamma' phase", + "FCC" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:29:37.791640Z", + "source_id": "pub_34_jokisaari_simulated_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_34_jokisaari_simulated" + }, + "custom": { + "funding_details": "Financial assistance award: 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design", + "all_materials_included": "['Co-Al-W', 'Co', 'Al', 'W']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2Q33X", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Jokisaari, Andrea M.", + "affiliations": [ + "Center for Hierarchical Materials Design, Northwestern University and Physical Sciences and Engineering Directorate, Argonne National Laboratory" + ], + "familyName": "Jokisaari", + "givenName": "Andrea M." + }, + { + "creatorName": "Naghavi, Shahab", + "affiliations": [ + "Department of Materials Science and Engineering, Northwestern University" + ], + "familyName": "Naghavi", + "givenName": "Shahab" + }, + { + "creatorName": "Wolverton, Chris", + "affiliations": [ + "Department of Materials Science and Engineering and Center for Hierarchical Materials Design, Northwestern University" + ], + "familyName": "Wolverton", + "givenName": "Chris" + }, + { + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Department of Materials Science and Engineering and Center for Hierarchical Materials Design, Northwestern University" + ], + "familyName": "Voorhees", + "givenName": "Peter W." + }, + { + "creatorName": "Heinonen, Olle G.", + "affiliations": [ + "Materials Science Division, Argonne National Laboratory and Northwestern-Argonne Institute for Science and Engineering" + ], + "familyName": "Heinonen", + "givenName": "Olle G." + } + ], + "subjects": [ + { + "subject": "superalloy" + }, + { + "subject": "Co" + }, + { + "subject": "Al" + }, + { + "subject": "experimental" + }, + { + "subject": "cobalt" + }, + { + "subject": "W" + }, + { + "subject": "microstructure" + }, + { + "subject": "simulation" + }, + { + "subject": "microstructures" + }, + { + "subject": "CHiMaD" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-09-07T18:13:57Z", + "dateType": "Accepted" + }, + { + "date": "2017-09-07T18:13:57Z", + "dateType": "Available" + }, + { + "date": "2017-09-06", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Andrea M. Jokisaari", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Olle G. Heinonen", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Simulated microstructures of gamma' precipitates in cobalt-based superalloys" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data set is that generated by the authors to create the publication below for the study on the equilibrium shapes of gamma' precipitates in novel Co-based superalloys.\r\n\r\nPlease see the paper for details:\r\n\r\nA. M. Jokisaari, S. S. Naghavi, C. Wolverton, P. W. Voorhees, O. G. Heinonen, Predicting the morphologies of gamma prime precipitates in cobalt-based superalloys, submitted as a preprint to arXiv and accepted to Acta Materialia.\r\n\r\nPlease contact O. G. Heinonen (heinonen@anl.gov) or A. M. Jokisaari (andrea.jokisaari@northwestern.edu) with questions, etc.\r\n\r\nThe mesh data may be visualized with Paraview.\r\n\r\nThere are three main directories: \r\n1) phase field code, which contains the .h and .C files for the MOOSE-based phase field simulations.\r\n2) phase field data, which contains the outputs of the phase field simulations. \r\n3) DFT data, which contains the data used for the DFT portion of the work.\r\n\r\nEach phase field data set contains the solutions (meshes) in .e files, postprocessor data in a .csv file, and the input in a .i file.\r\n\r\nThe phase field data was generated by a MOOSE-based application, including the phase_field module (see http://mooseframework.org). To run this code, you will need to generate your own MOOSE-based application, add the code to the appropriate directories and register the new code in the src/base/MyApp.C file (see http://mooseframework.com/wiki/MooseSystems/Kernels/ for an example). Because this code is in the Materials Data Facility, it is not being kept up-to-date - you may need to update it for API changes within MOOSE or the phase_field module." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Laboratory Computing Resource Center, Argonne National Laboratory" + }, + { + "geoLocationPlace": "High Performance Computing Center, Idaho National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_59_miao_loadpartitioning_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_59_miao_loadpartitioning_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_174/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_174/" + }, + "mrr": { + "materialType": [ + "ODS 310" + ], + "characterizationMethod": [ + "Synchrotron WAXS" + ], + "structuralFeature": [ + "FCC" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:12:39.374730Z", + "source_id": "pub_59_miao_loadpartitioning_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_59_miao_loadpartitioning" + }, + "custom": { + "funding_details": "This work was supported by 973 DOE INL120293. The TEM experiments were carried out in part at the Frederick Seitz Materials Research Laboratory Central Facilities, University of Illinois, which is partially supported by the U.S. Department of Energy (DOE) under Grants DEFG02-07ER46453 and DE-FG02-07ER46471. The author gratefully acknowledge the support of the International Institute for Carbon Neutral Energy Research (WPI-I2CNER), sponsored by the World Premier International Research Center Initiative (WPI), Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The efforts involving Argonne National Laboratory were sponsored under Contract no. DE-AC02-06CH11357 between UChicago Argonne, LLC and the U.S. Department of Energy.", + "all_materials_included": "Fe-24.0Cr-18.3Ni-1,9Mo-0.3Ti-0.35Y2O3" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M28G6T", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Miao, Yinbin", + "familyName": "Miao", + "givenName": "Yinbin" + }, + { + "creatorName": "Mo, Kun", + "familyName": "Mo", + "givenName": "Kun" + }, + { + "creatorName": "Zhou, Zhangjian", + "familyName": "Zhou", + "givenName": "Zhangjian" + }, + { + "creatorName": "Liu, Xiang", + "familyName": "Liu", + "givenName": "Xiang" + }, + { + "creatorName": "Lan, Kuan-Che", + "familyName": "Lan", + "givenName": "Kuan-Che" + }, + { + "creatorName": "Zhang, Guangming", + "familyName": "Zhang", + "givenName": "Guangming" + }, + { + "creatorName": "Park, Jun-Sang", + "familyName": "Park", + "givenName": "Jun-Sang" + }, + { + "creatorName": "Almer, Jonathan", + "familyName": "Almer", + "givenName": "Jonathan" + }, + { + "creatorName": "Stubbins, James", + "familyName": "Stubbins", + "givenName": "James" + } + ], + "subjects": [ + { + "subject": "oxides" + }, + { + "subject": "experiment" + }, + { + "subject": "HEDM" + }, + { + "subject": "microscopy" + }, + { + "subject": "microstructures" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "steel" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-11-13T18:44:10Z", + "dateType": "Accepted" + }, + { + "date": "2016-11-13T18:44:10Z", + "dateType": "Available" + }, + { + "date": "2016-11-13", + "dateType": "Issued" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1016/j.matdes.2016.09.015", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://www.sciencedirect.com/science/article/pii/S0264127516311728", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for \"Load-partitioning in an Oxide Dispersion-strengthened 310 Steel at Elevated Temperatures\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Here the high temperature tensile performance of an oxide dispersion-strengthened (ODS) 310 steel is reported upon. The microstructure of the steel was examined through both transmission electron microscopy (TEM) and synchrotron scattering. In situ synchrotron X-ray tensile investigation was performed at a variety of temperatures, from room temperature up to 800 \u25e6C. Pyrochlore structure yttrium titanate and sodium chloride structure titanium nitride phases were identified in the steel along with an austenite matrix and marginal residual a -martensite. The inclusion phases strengthen the steel by taking extra load through particle-dislocation interaction during plastic deformation or dislocation creep procedures. As temperature rises, lattice strain measurement implies that the load partitioning effect of conventional precipitate phases starts to diminish, whereas those ultra-fine oxygen-enriched nanoparticles continue to maintain a considerable amount of extra lattice strain. Introduction of oxygen-enriched nanoparticles in austenitic steel is shown to improve the high temperature performance, making austenitic ODS steels promising for advanced nuclear applications." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Advanced Photon Source Sector 1" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_109_sangwan_selfaligned_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_109_sangwan_selfaligned_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam/published/publication_1109/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_1109/" + }, + "mrr": { + "materialType": [ + "electronic", + "optical", + "semiconductor", + "nanomaterial", + "van der Waals" + ], + "characterizationMethod": [ + "atomic force microscopy", + "x-ray photoelectron spectroscopy", + "photoluminescence spectroscopy", + "Raman spectroscopy", + "I-V electrical measurements", + "Sentaurus TCAD" + ], + "structuralFeature": [ + "crystalline", + "2D" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T20:20:25.627162Z", + "source_id": "pub_109_sangwan_selfaligned_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_109_sangwan_selfaligned" + }, + "custom": { + "funding_details": "['NSF DMR-1720139', 'NSF EFRI-1433510', 'NIST CHiMaD 70NANB14H012', 'ONR N00014-16-1-3179']", + "all_materials_included": "['MoS2', 'black phosphorus']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2FK9J", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Beck, Megan E.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Beck", + "givenName": "Megan E." + }, + { + "creatorName": "Henning, Alex", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Henning", + "givenName": "Alex" + }, + { + "creatorName": "Luo, Jiajia", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Luo", + "givenName": "Jiajia" + }, + { + "creatorName": "Bergeron, Hadallia", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Bergeron", + "givenName": "Hadallia" + }, + { + "creatorName": "Kang, Junmo", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Kang", + "givenName": "Junmo" + }, + { + "creatorName": "Balla, Itamar", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Balla", + "givenName": "Itamar" + }, + { + "creatorName": "Inbar, Hadass", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Inbar", + "givenName": "Hadass" + }, + { + "creatorName": "Lauhon, Lincoln J.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Lauhon", + "givenName": "Lincoln J." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "van der Waals heterojunction" + }, + { + "subject": "self-aligned" + }, + { + "subject": "short-channel" + }, + { + "subject": "source-gated" + }, + { + "subject": "current saturation" + }, + { + "subject": "p-n diode" + }, + { + "subject": "anti-ambipolar" + }, + { + "subject": "mixed dimensional" + }, + { + "subject": "MoS2" + }, + { + "subject": "black phosophorus" + }, + { + "subject": "drift-diffusion model" + }, + { + "subject": "screening" + }, + { + "subject": "mobility" + } + ], + "publicationYear": "2018", + "publisher": "Nano Letters", + "dates": [ + { + "date": "2018-04-10T20:26:23Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-10T20:26:23Z", + "dateType": "Available" + }, + { + "date": "2018-01-31", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Mark C. Hersam", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.nanolett.7b05177", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://pubs.acs.org/doi/abs/10.1021/acs.nanolett.7b05177", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Self-Aligned van der Waals Heterojunction Diodes and Transistors" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data corresponds to the demonstration of a novel self-aligned method to fabricate transistors and van der Waals heterojunctions based on 2D materials. Monolayer MoS2 is grown by chemical vapor deposition, and few-layer black phosphorus is mechanically exfoliated and transferred on top of MoS2. For self-aligned transistors, a dielectric extension is obtained by using natural undercut in photoresist and e-beam resist profiles. The channel length of ~150 nm is obtained, and resulting devices showed better current saturation than the literature reports on conventional short-channel transistors. The observed behavior is due to a source-gating effect, as verified by commercial device simulator Sentaurus TCAD. Similarly, BP-MoS2 heterojunction diode showed significantly better electrostatic control than lateral or vertical heterojunction between these two semiconductors. This behavior, as verified from device simulator, also originates from the novel self-aligned structure. The approach is generalized to mixed dimensional heterojunction between pentacene-MoS2." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_110_sangwan_multiterminal_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_110_sangwan_multiterminal_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam/published/publication_1110/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_1110/" + }, + "mrr": { + "materialType": [ + "electronic", + "van der Waals", + "semiconductor", + "memristor", + "nanomaterial" + ], + "characterizationMethod": [ + "atomic force microscopy", + "lateral force microscopy", + "electrostatic force microscopy", + "x-ray photoelectron spectroscopy", + "photoluminescence spectroscopy", + "raman spectroscopy", + "I-V electrical measurements", + "variable temperature I-V measurements", + "pulsed I-V measurements" + ], + "structuralFeature": [ + "crystalline", + "2D" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T20:22:05.021577Z", + "source_id": "pub_110_sangwan_multiterminal_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_110_sangwan_multiterminal" + }, + "custom": { + "funding_details": "['NSF DMR-1720139', 'NSF EFRI-1433510', 'NIST CHiMaD 70NANB14H012', 'ONR N00014-16-1-3179', 'NSF ECCS-1542205']", + "all_materials_included": "MoS2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M29W68", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Lee, Hong-Sub", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Lee", + "givenName": "Hong-Sub" + }, + { + "creatorName": "Bergeron, Hadallia", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Bergeron", + "givenName": "Hadallia" + }, + { + "creatorName": "Balla, Itamar", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Balla", + "givenName": "Itamar" + }, + { + "creatorName": "Beck, Megan", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Beck", + "givenName": "Megan" + }, + { + "creatorName": "Chen, Kan-Sheng", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Chen", + "givenName": "Kan-Sheng" + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "MoS2" + }, + { + "subject": "memristor" + }, + { + "subject": "memtransistor" + }, + { + "subject": "gate-tunable" + }, + { + "subject": "long-term potentiation" + }, + { + "subject": "spike-timing-dependenct plasticity" + }, + { + "subject": "heterosynaptic plasticity" + }, + { + "subject": "defects" + } + ], + "publicationYear": "2018", + "publisher": "Nature", + "dates": [ + { + "date": "2018-04-10T20:49:44Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-10T20:49:44Z", + "dateType": "Available" + }, + { + "date": "2018-02-22", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Mark C. Hersam", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1038/nature25747", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://www.nature.com/articles/nature25747", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data corresponds to the demonstration of the first memtransistor on monolayer MoS2. Polycrystalline monolayer MoS2 was grown by chemical vapor deposition with grain sizes of 3-5 microns. Memtransistor devices were fabricated on Si substrates coated with 300 nm thermal oxide by following custom-made photolithography and reactive ion etching recipes. Characterization of the devices using atomic force microscopy, electrostatic force microscopy, and cryogenic measurement revealed switching mechanism governed by a dynamically tunable Schottky barrier at contact. Schottky barrier tuning, as modeled by a device physics model, arises from the migration of defects near metal edge possibly assisted by grain boundaries. The devices show large resistive switching ratios, large gate-tunability of resistive switching ratio, retention of distinct states for at least 24 hours, the endurance of >400 cycles, and some device to device variability. The device is compatible with multi-terminal architecture thus mimicking biological neurons." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_104_homan_ultrafast_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam/published/publication_810/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_810/" + }, + "custom": { + "all_materials_included": "['MoS2', 'pentacene', 'quartz']", + "funding_details": "['Materials Research Science and Engineering Center (MRSEC) of Northwestern University (NSF DMR-1121262)', '2-DARE program (NSF EFRI- 1433510)', 'National Institute of Standards and Technology (NIST CHiMaD 70NANB14H012)', 'Argonne\u2212Northwestern Solar Energy Research (ANSER) Energy Frontier Research Center (DOE DE-SC0001059)']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_104_homan_ultrafast_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "electronic", + "nanomaterial", + "semiconductor", + "organic molecule" + ], + "characterizationMethod": [ + "absorbance spectroscopy", + "photoluminescence spectroscopy", + "atomic force microscopy", + "transient absorption spectroscopy", + "x-ray photoelectron spectroscopy", + "raman spectroscopy" + ], + "structuralFeature": [ + "2D", + "crystalline", + "aromatic hydrocarbon", + "interfacial" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T20:02:51.312747Z", + "source_id": "pub_104_homan_ultrafast_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_104_homan_ultrafast" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2405C", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Homan", + "givenName": "Stephanie B.", + "creatorName": "Homan, Stephanie B.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Sangwan", + "givenName": "Vinod K.", + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Balla", + "givenName": "Itamar", + "creatorName": "Balla, Itamar", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Bergeron", + "givenName": "Hadallia", + "creatorName": "Bergeron, Hadallia", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Weiss", + "givenName": "Emily A.", + "creatorName": "Weiss, Emily A.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "organic" + }, + { + "subject": "transition metal dichalcogenide" + }, + { + "subject": "transient absorption spectroscopy" + }, + { + "subject": "ultrafast" + }, + { + "subject": "charge transfer" + } + ], + "publicationYear": "2016", + "publisher": "Nano Letters", + "dates": [ + { + "date": "2017-10-24T02:02:17Z", + "dateType": "Accepted" + }, + { + "date": "2017-10-24T02:02:17Z", + "dateType": "Available" + }, + { + "date": "2016-12-05", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Mark C. Hersam" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.nanolett.6b03704", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://pubs.acs.org/doi/abs/10.1021/acs.nanolett.6b03704", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Ultrafast Exciton Dissociation and Long-Lived Charge Separation in a Photovoltaic Pentacene\u2212MoS2 van der Waals Heterojunction" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data corresponds to the study of the dynamics of charge transfer in an ultrathin p-n heterojunction of the organic molecule of pentacene and monolayer MoS2. The transient absorption spectroscopy measurements show that exciton dissociation occurs on an ultrafast timescale and that the charge separation is long-lived with respect to recombination and trapping mechanisms. DOI: 10.1021/acs.nanolett.6b03704" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_103_shastry_mutual_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_103_shastry_mutual_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam/published/publication_809/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam/published/publication_809/" + }, + "mrr": { + "materialType": [ + "electronic", + "semiconductor", + "nanomaterials", + "polymer", + "solar" + ], + "characterizationMethod": [ + "atomic force microscopy", + "Raman spectroscopy", + "absorbance spectroscopy", + "photoluminescence spectroscopy", + "I-V electrical measurements" + ], + "structuralFeature": [ + "2D", + "crystalline", + "amorphous", + "interfacial" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:59:11.318551Z", + "source_id": "pub_103_shastry_mutual_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_103_shastry_mutual" + }, + "custom": { + "funding_details": "['Argonne-Northwestern Solar Energy Research (ANSER) Center (DE-SC0001059)', 'National Institute of Standards and Technology (NIST CHiMaD 70NANB14H012)', 'n']", + "all_materials_included": "['MoS2', 'PTB7', 'ITO']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M27P7Z", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Shastry, Tejas A.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Shastry", + "givenName": "Tejas A." + }, + { + "creatorName": "Balla, Itamar", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Balla", + "givenName": "Itamar" + }, + { + "creatorName": "Bergeron, Hadallia", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Bergeron", + "givenName": "Hadallia" + }, + { + "creatorName": "Amsterdam, Samuel H.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Amsterdam", + "givenName": "Samuel H." + }, + { + "creatorName": "Marks, Tobin J.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Marks", + "givenName": "Tobin J." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "two-dimensional materials" + }, + { + "subject": "transition metal dichalcogenides" + }, + { + "subject": "photovoltaic effect" + }, + { + "subject": "heterojunction solar cell" + }, + { + "subject": "photoluminescence" + } + ], + "publicationYear": "2016", + "publisher": "ACS Nano", + "dates": [ + { + "date": "2017-10-24T01:33:02Z", + "dateType": "Accepted" + }, + { + "date": "2017-10-24T01:33:02Z", + "dateType": "Available" + }, + { + "date": "2016-10-26", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Mark C. Hersam", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acsnano.6b06592", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://pubs.acs.org/doi/pdf/10.1021/acsnano.6b06592", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Mutual Photoluminescence Quenching and Photovoltaic Effect in Large-Area Single-Layer MoS2\u2013Polymer Heterojunctions" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data corresponds to the demonstration of the photovoltaic effect in an ultrathin heterojunction of monolayer MoS2 and poly[[4,8-bis[(2- ethylhexyl)oxy]benzo[1,2-b:4,5-b\u2032]dithiophene-2,6-diyl][3-flu-oro-2[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]] (PTB7). Tuning of the PTB7 thickness enables complete quenching of the MoS2 photoluminescence. Solar cells made from the heterojunction on transparent indium tin oxide demonstrate exceptional current density per absorbing thickness. DOI: 10.1021/acsnano.6b06592" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "wanglinlin_single_pair_semimetal_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/wanglinlin_single_pair_semimetal_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/wanglinlin_single_pair_semimetal_v1.1/" + }, + "custom": { + "all_materials_included": "EuCd2As2", + "funding_details": "['Center for the Advancement of Topological Semimetals , an Energy Frontier Research Center funded by the U.S. Department of Energy Office of Science, Office of Basic Energy Sciences through the Ames Laboratory under its Contract No. DE - AC02 - 07CH11358', 'CATS EFRC']" + }, + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "Weyl semimetal" + ], + "characterizationMethod": [ + "First-principle calculations (VASP), Wannier90, WannierTools, VESTA" + ], + "structuralFeature": [ + "CaAl2Si2-type structure symmetry group P \u03043m1" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-07-05T13:56:32.554896Z", + "mdf_id": "5d1f5710bd8ab6322e37dfbe", + "source_id": "wanglinlin_single_pair_semimetal_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "wanglinlin_single_pair_semimetal" + }, + "dc": { + "identifier": { + "identifier": "10.18126/I0UOPQF3J3SW", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Wang", + "givenName": "Lin-lin", + "creatorName": "Wang, Lin-lin", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "Jo", + "givenName": "Na Hyun", + "creatorName": "Jo, Na Hyun", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "Kuthanazhi", + "givenName": "Brinda", + "creatorName": "Kuthanazhi, Brinda", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "Wu", + "givenName": "Yun", + "creatorName": "Wu, Yun", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "McQueeney", + "givenName": "Robert J.", + "creatorName": "McQueeney, Robert J.", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "Kaminski", + "givenName": "Adam", + "creatorName": "Kaminski, Adam", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + }, + { + "familyName": "Canfield", + "givenName": "Paul C.", + "creatorName": "Canfield, Paul C.", + "affiliations": [ + "Ames Laboratory", + "Department of Physics and Astronomy, Iowa State University" + ] + } + ], + "subjects": [ + { + "subject": "Weyl semimetal" + }, + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "EuCd2As2" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-06-14T16:55:15Z", + "dateType": "Accepted" + }, + { + "date": "2019-06-14T16:55:15Z", + "dateType": "Available" + }, + { + "date": "2019-04-01", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Lin-lin Wang" + } + ], + "titles": [ + { + "title": "A Single Pair of Weyl Fermions in Half-metallic EuCd 2 As 2 Semimetal" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "arXiv:1901.08234\r\n\r\nTitle: A Single Pair of Weyl Fermions in Half-metallic EuCd2As2 Semimetal\r\nAuthors: Lin-Lin Wang, Na Hyun Jo, Brinda Kuthanazhi, Yun Wu, Robert J. McQueeney, Adam Kaminski and Paul C. Canfield\r\n\r\nllw@ameslab.gov\r\n\r\nBand structures in Fig.1, 3 and 5 have been calculated in VASP.5.2.12 with PBE+U+SOC and other settings for different magnetic configurations.\r\n\r\nCrystal structure in Fig.1b has been plotted in Vesta-3.4.0.\r\n\r\nBerrycurvature in Fig.1f and Fermi arcs in Fig.2 have been calculated in WannierTools-2.0 using tight-binding model constructed with maximally localized Wannier functions in Wannier90-2.1.0.\r\n\r\nBand structures in Fig.4 have been plotted for the 4-band low-energy Hamiltionian with parameters listed in the main text." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_86_kylanpaa_accuracy_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_851/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_851/" + }, + "custom": { + "all_materials_included": "VO2", + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_86_kylanpaa_accuracy_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "simulation" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T17:16:28.490701Z", + "source_id": "pub_86_kylanpaa_accuracy_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_86_kylanpaa_accuracy" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2NS7Q", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Kylanpaa", + "givenName": "Ilkka", + "creatorName": "Kylanpaa, Ilkka", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Balachandran", + "givenName": "Janakiraman", + "creatorName": "Balachandran, Janakiraman", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Ganesh", + "givenName": "Panchapakesan", + "creatorName": "Ganesh, Panchapakesan", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Heinonen", + "givenName": "Olle", + "creatorName": "Heinonen, Olle", + "affiliations": [ + "Argonne National Laboratory, Argonne, IL" + ] + }, + { + "familyName": "Kent", + "givenName": "Paul R. C.", + "creatorName": "Kent, Paul R. C.", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + }, + { + "familyName": "Krogel", + "givenName": "Jaron T.", + "creatorName": "Krogel, Jaron T.", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, TN" + ] + } + ], + "subjects": [ + { + "subject": "QMCPACK" + }, + { + "subject": "Monte Carlo" + }, + { + "subject": "Diffusion Monte Carlo" + }, + { + "subject": "DMC" + }, + { + "subject": "Variational Monte Carlo" + }, + { + "subject": "VMC" + }, + { + "subject": "Quantum Monte Carlo" + }, + { + "subject": "Vanadium dioxide" + }, + { + "subject": "Electron density" + } + ], + "publicationYear": "2017", + "publisher": "Oak Ridge National Laboratory, Oak Ridge, TN", + "dates": [ + { + "date": "2017-11-06T20:51:27Z", + "dateType": "Accepted" + }, + { + "date": "2017-11-06T20:51:27Z", + "dateType": "Available" + }, + { + "date": "2017-11-03", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Jaron T. Krogel" + } + ], + "titles": [ + { + "title": "Dataset for Accuracy of ab initio electron correlation and electron densities in vanadium dioxide" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for \"Accuracy of ab initio electron correlation and electron densities in vanadium dioxide\" Ilkka Kylanpaa , Janakiraman Balachandran, Panchapakesan Ganesh , Olle Heinonen, Paul R. C. Kent, and Jaron T. Krogel (2017)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_97_da_berkeleygw_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_97_da_berkeleygw_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_937/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_937/" + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T19:34:45.960368Z", + "source_id": "pub_97_da_berkeleygw_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_97_da_berkeleygw" + }, + "custom": { + "funding_details": "DE-AC02-05CH11231" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2X06W", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "da Jornada, Felipe H.", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "da Jornada", + "givenName": "Felipe H." + }, + { + "creatorName": "Deslippe, Jack", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Deslippe", + "givenName": "Jack" + }, + { + "creatorName": "Strubbe, David A.", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Strubbe", + "givenName": "David A." + }, + { + "creatorName": "Vigil-Fowler, Derek", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Vigil-Fowler", + "givenName": "Derek" + }, + { + "creatorName": "Qiu, Diana Y.", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Qiu", + "givenName": "Diana Y." + }, + { + "creatorName": "Antonius, Gabriel", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Antonius", + "givenName": "Gabriel" + }, + { + "creatorName": "del Ben, Mauro", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "del Ben", + "givenName": "Mauro" + }, + { + "creatorName": "Louie, Steven G.", + "affiliations": [ + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory", + "University of California, Merced", + "National Renewable Energy Laboratory", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory and University of California, Berkeley", + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Louie", + "givenName": "Steven G." + } + ], + "subjects": [ + { + "subject": "BerkeleyGW" + }, + { + "subject": "workshop" + }, + { + "subject": "tutorial" + }, + { + "subject": "examples" + }, + { + "subject": "2018" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-01-26T15:18:11Z", + "dateType": "Accepted" + }, + { + "date": "2018-01-26T15:18:11Z", + "dateType": "Available" + }, + { + "date": "2018-01-25", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "jornada@berkeley.edu", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "sites.google.com/site/berkeleygw2018/", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "BerkeleyGW 2018 Workshop Examples" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Tutorial files for the BerkeleyGW 2018 Workshop. See: sites.google.com/site/berkeleygw2018/" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_10_gibbs_liquidsolid_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_47/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_47/" + }, + "custom": { + "experiment_nominal_alloy_composition": "['Al (73 wt%); Cu (27 wt%)', 'Al:73 wt%', 'Cu:27 wt%']", + "experiment_pixel_size": "['1.79 \\\\u00b5m', '1.79 um']", + "experiment_time_between_scans": "44 s", + "processing_reconstruction_method": "filtered back projection", + "processing_segmentation_method": "piecewise constant iterative method; see www.gibbsium.org/thesis or doi: 10.1186/2193-9772-3-6", + "all_materials_included": "Al-Cu", + "experiment_total_duration": "75 min", + "experiment_holding_temperature": "['558 C', '5 C above the eutectic', '558 C (5 C above the eutectic)']", + "funding_details": "['DOE Grant DE-FG02-99ER45782/A012', 'DOE NNSA Stewardship Science Graduate Fellowship (grant DE-FC52-08NA28752)']", + "experiment_xray_energy": "polychromatic" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_10_gibbs_liquidsolid_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "diffusion", + "thermodynamic", + "metal" + ], + "characterizationMethod": [ + "X-ray computed tomography" + ], + "structuralFeature": [ + "bulk", + "crystalline", + "fluid", + "interphase" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-07T22:36:52.451268Z", + "source_id": "pub_10_gibbs_liquidsolid_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_10_gibbs_liquidsolid" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2059C", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Gibbs", + "givenName": "John W.", + "creatorName": "Gibbs, John W.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter W.", + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Fife", + "givenName": "Julie L.", + "creatorName": "Fife, Julie L.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "registration" + }, + { + "subject": "4D data" + }, + { + "subject": "coarsening" + }, + { + "subject": "experimentation" + }, + { + "subject": "computed tomography" + }, + { + "subject": "in situ" + }, + { + "subject": "Al-Cu" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "tomography" + }, + { + "subject": "liquid-solid mixture" + }, + { + "subject": "segmentation" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-03-20T04:04:29Z", + "dateType": "Accepted" + }, + { + "date": "2016-03-20T04:04:29Z", + "dateType": "Available" + }, + { + "date": "2016-03-19", + "dateType": "Issued" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1186/2193-9772-3-6", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.actamat.2014.01.024", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1186/2193-9772-3-6", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1016/j.actamat.2014.01.024", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://www.gibbsium.org/thesis", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Liquid-solid Metallic Mixture Coarsening Data - 35% Solid" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study isothermal coarsening of a liquid-solid metallic mixture. In these experiments, an Al-Cu alloy was heated to 5K above the eutectic temperature, forming a liquid-solid mixture with a constant amount of the two phases. The initial microstructure on heating is a dendritic array within a eutectic matrix; once the temperature exceeds the eutectic temperature, the eutectic matrix melts leaving the dendrites surrounded by liquid. In this state, the interfaces between liquid and solid can rapidly coarsen to decrease the total interfacial area and energy of the system. Observing this process of coarsening was the original goal of collecting these datasets. \\r\\nIn theses experiments, the liquid-solid mixture is held for a period of 2 to 15 hours with 3D x-ray tomographic scans taking place every 50 to 250 seconds. The tomographic scans were performed at the TOMCAT beamline at the Swiss Light Source. Both monochromatic and polychromatic beams were used during this experimental campaign to achieve either higher data acquisition rates (polychromatic beam) or images with fewer artifacts (monochromatic beam). These details are specified in the metadata of each dataset. \\r\\nThe data here is includes the projection data, reconstructed tomograms, segmented tomograms, and processed data. The reconstructions were done with the filtered back projection algorithm. Segmentation was done using a 4D implementation of the method described in [1]. The processed data includes fields for the interfacial area per voxel (A), principal curvatures (k1, k2), and interfacial velocity (V). \\r\\n 1: J.W. Gibbs, P.W. Voorhees, \\\"Segmentation of four-dimensional, X-ray computed tomography data\\\" IMMI (2014). doi: 10.1186/2193-9772-3-6\"" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "TOMCAT beamline at the Swiss Light Source, Paul Scherrer Institut" + }, + { + "geoLocationPlace": "SLS" + }, + { + "geoLocationPlace": "PSI" + }, + { + "geoLocationPlace": "Swiss Light Source" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_36_xie_thermal_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1144/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1144/" + }, + "custom": { + "all_materials_included": "['poly(vinyl alcohol) (PVA)', 'poly(acrylic acid) (PAA)', 'polyacrylamide (PAM)', 'poly(vinylpyrrolidone) (PVP)', 'methyl cellulose (MC)', 'poly(4-styrenesulfonic acid) (PSS)', 'poly(N-acryloylpiperidine) (PAP)', 'poly(methyl methacrylate) (PMMA)', 'polymer blend of PVA/PAA']", + "funding_details": "['Air Force Office of Scientific Research (AFOSR) Multidisciplinary Research Program of the University Research Initiative (MURI) (Grant No. FA9550-12-1-0002)', 'Frederick Seitz Materials Research Laboratory Central Research Facilities, University of Illinois', 'International Institute for Carbon Neutral Energy Research (WPI-I2CNER, sponsored by the World Premier International Research Center Initiative (WPI), MEXT, Japan)']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_36_xie_thermal_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "Water soluble polymers and polymer blends" + ], + "characterizationMethod": [ + "Time domain thermoreflectence (TDTR)" + ], + "structuralFeature": [ + "Polymer" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:32:58.589979Z", + "source_id": "pub_36_xie_thermal_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_36_xie_thermal" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2GP9W", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Xie", + "givenName": "Xu", + "creatorName": "Xie, Xu", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "familyName": "Li", + "givenName": "Dongyao", + "creatorName": "Li, Dongyao", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA; International Institute for Carbon Neutral Energy Research, Kyushu University, Fukuoka 819-0395, Japan" + ] + }, + { + "familyName": "Tsai", + "givenName": "Tsung-Han", + "creatorName": "Tsai, Tsung-Han", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "familyName": "Liu", + "givenName": "Jun", + "creatorName": "Liu, Jun", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "familyName": "Braun", + "givenName": "Paul V.", + "creatorName": "Braun, Paul V.", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA" + ] + }, + { + "familyName": "Cahill", + "givenName": "David G.", + "creatorName": "Cahill, David G.", + "affiliations": [ + "Department of Materials Science and Engineering, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA ; International Institute for Carbon Neutral Energy Research, Kyushu University, Fukuoka 819-0395, Japan" + ] + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "experiment" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-08-16T15:22:29Z", + "dateType": "Accepted" + }, + { + "date": "2018-08-16T15:22:29Z", + "dateType": "Available" + }, + { + "date": "2018-08-16", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Xu Xie (xuxie1@illinois.edu)" + }, + { + "contributorType": "ContactPerson", + "contributorName": "David G. Cahill (d-cahill@illinois.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.macromol.5b02477", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1021/acs.macromol.5b02477", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Thermal Conductivity, Heat Capacity, and Elastic Constants of Water-Soluble Polymers and Polymer Blends" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "In the present work a laser-based pump\u2212probe technique, time-domain thermoreflectance (TDTR), is applied to measure the thermal conductivity and heat capacity of several common water-soluble polymers and one of their blends." + }, + { + "descriptionType": "Other", + "description": "The thin film of polymer is spin-cast on Si substrates followed by solvent evaporation. The polymer concentration, solvent, and spin-coating speeds are adjusted to optimize the morphology and thickness for different measurements. Dispersion of each water-soluble polymer (PVA 5 wt %, PAA 3 wt %,\r\nPAM 3 wt %, PVP 6 wt %, MC \u22482 wt %, PSS 6 wt %) in DI water followed by stirring at \u224860 \u00b0C for several hours ensures full dissolution. PAP (3 wt %) is dissolved in toluene at \u224880 \u00b0C. To form polymer blends of PAP and PAA, and blends of PVA and PAA, polymer solutions of the individual polymers are prepared by\r\ndissolving the polymer in dimethylformamide (DMF) at \u2248150 \u00b0C for 10 min while stirring, followed by cooling to room temperature. Solutions for spin-coating are prepared by mixing PAP (1 wt %) with PAA (1 wt %) or PVA (3 wt %) with PAA (3 wt %) at different ratios. The blends are then heated to \u2248150 \u00b0C for 5 min.\r\nPrior to spin-coating, the Si substrates is prepared by rinsing sequentially with acetone (sonication for 5 min), IPA (sonication for 5 min), water, and IPA, followed by drying with nitrogen gas. Finally, the Si substrates are exposed to a UV ozone treatment for 20 min that produces a hydrophilic surface. Spin-coating with speeds ranging from 3000 to 5000 rpm for 60 s results in polymer films with thicknesses\r\nbetween 80 and 150 nm. The thin films are initially annealed in air at 90 \u00b0C (for polymers dissolved in water and toluene) or 110 \u00b0C (for PVA/PAA blends dissolved in DMF) for 30 min and then baked at 60\r\n\u00b0C in a vacuum chamber at a pressure of <1 \u00d7 10\u22124 Torr for >16 h. To obtain cross-linked PAA, the spin-coated PAA film is baked at 200 \u00b0C (30 min) followed by the same vacuum annealing. The cross-linked\r\nfilm becomes insoluble in water due to anhydride formation. Thin films of PAA, PVP, PAM, PSS, MC, PMMA, and PAP prepared by spin-casting and annealing are generally considered amorphous, and\r\nPVA film is partially crystalline. All polymer layers are assumed isotropic for all the measurements. The surface roughness of the as-prepared polymers is <2 nm as measured by atomic force microscopy (AFM) in areas >25 \u03bcm2\r\n." + }, + { + "descriptionType": "Other", + "description": "Thermal conductivity (\u039b) and volumetric heat capacity (C) of polymer thin films are derived from TDTR data acquired at multiple modulation frequencies." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Frederick Seitz Materials Research Laboratory Central Research Facilities, University of Illinois." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_35_hanson_crystallographic_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_35_hanson_crystallographic_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1142/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1142/" + }, + "mrr": { + "materialType": [ + "metal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:31:19.513218Z", + "source_id": "pub_35_hanson_crystallographic_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_35_hanson_crystallographic" + }, + "custom": { + "all_materials_included": "Ni" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2063F", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Hanson, John P.", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Hanson", + "givenName": "John P." + }, + { + "creatorName": "Bagri, Akbar", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Bagri", + "givenName": "Akbar" + }, + { + "creatorName": "Lind, Jonathan", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Lind", + "givenName": "Jonathan" + }, + { + "creatorName": "Kenesei, Peter", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Kenesei", + "givenName": "Peter" + }, + { + "creatorName": "Suter, Robert M.", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Suter", + "givenName": "Robert M." + }, + { + "creatorName": "Grade\u010dak, Silvija", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Grade\u010dak", + "givenName": "Silvija" + }, + { + "creatorName": "Demkowicz, Michael J.", + "affiliations": [ + "Massachusetts Institute of Technology", + "Johns Hopkins University", + "Carnegie Mellon University", + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Texas A&M University" + ], + "familyName": "Demkowicz", + "givenName": "Michael J." + } + ], + "subjects": [ + { + "subject": "superalloy" + }, + { + "subject": "grain boundaries" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "microstructure" + }, + { + "subject": "hydrogen embrittlement" + }, + { + "subject": "fracture" + }, + { + "subject": "microstructures" + }, + { + "subject": "failure" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-05-24T02:49:06Z", + "dateType": "Accepted" + }, + { + "date": "2018-05-24T02:49:06Z", + "dateType": "Available" + }, + { + "date": "2018-05-23", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Peter Kenesei (kenesei@anl.gov)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1007/s11661-016-3831-x", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1038/s41467-018-05549-y", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Crystallographic character of grain boundaries resistant to hydrogen-assisted fracture in Ni-base alloy 725" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Hydrogen embrittlement (HE) causes sudden, costly failures of metal components across a wide range of industries. Yet, despite over a century of research, the physical mechanisms of HE are too poorly understood to predict HE-induced failures with confidence. We use non-destructive, synchrotron-based techniques to investigate the relationship between the crystallographic character of grain boundaries and their susceptibility to hydrogen-assisted fracture in a nickel superalloy. Our data leads us to identify a class of grain boundaries with striking resistance to hydrogen-assisted crack propagation: boundaries with low-index planes (BLIPs). BLIPs are boundaries where at least one of the neighboring grains has a low Miller index facet\u2014{001}, {011}, or {111}\u2014along the grain boundary plane. These boundaries deflect propagating cracks, toughening the material and improving its HE-resistance. Our finding paves the way to improved predictions of HE based on the density and distribution of BLIPs in metal microstructures.\r\n\r\nSee the associated publications:\r\n\r\nJohn P. Hanson, Akbar Bagri, Jonathan Lind, Peter Kenesei, Robert M. Suter, Silvija Gradecak, Michael J. Demkowicz, \"Crystallographic character of grain boundaries resistant to hydrogen-assisted fracture in Ni-base alloy 725\", Nature Communications 9:338 (2018). DOI: 10.1038/s41467-018-05549-y\r\n\r\nAkbar Bagri, John P. Hanson, Jonathan Lind, Peter Kenesei, Robert M. Suter, Silvija Gradecak, Michael J. Demkowicz, \"Measuring grain boundary character distributions in Ni-base alloy 725 using high-energy diffraction microscopy\", Metallurgical and Materials Transactions A 48, 354-361 (2017). DOI: 10.1007/s11661-016-3831-x" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_40_han_probing_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_40_han_probing_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_634/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_634/" + }, + "mrr": { + "materialType": [ + "aperiodic", + "thermodynamic", + "kinetic", + "metal", + "quasicrystal" + ], + "characterizationMethod": [ + "X-ray computed tomography", + "CT" + ], + "structuralFeature": [ + "liquid", + "quasicrystalline" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:39:39.656944Z", + "source_id": "pub_40_han_probing_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_40_han_probing" + }, + "custom": { + "all_materials_included": "['Al-Ni-Co', 'Al', 'Ni', 'Co']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2K910", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Han, Insung", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Han", + "givenName": "Insung" + }, + { + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Xiao", + "givenName": "Xianghui" + }, + { + "creatorName": "Shahani, Ashwin J.", + "affiliations": [ + "University of Michigan - Ann Arbor", + "Argonne National Laboratory" + ], + "familyName": "Shahani", + "givenName": "Ashwin J." + } + ], + "subjects": [ + { + "subject": "density" + }, + { + "subject": "interface kinetics" + }, + { + "subject": "melting" + }, + { + "subject": "anisotropy" + }, + { + "subject": "synchrotron" + }, + { + "subject": "gravity-driven convection" + }, + { + "subject": "isotropy" + }, + { + "subject": "4D x-ray tomography" + }, + { + "subject": "experiment" + }, + { + "subject": "tomography" + }, + { + "subject": "supersaturation" + }, + { + "subject": "growth" + }, + { + "subject": "decagonal quasicrystal" + }, + { + "subject": "continuous cooling" + }, + { + "subject": "composition analysis" + }, + { + "subject": "in situ" + }, + { + "subject": "undercooling" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "Al-Ni-Co alloys" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-09-11T16:29:35Z", + "dateType": "Accepted" + }, + { + "date": "2017-09-11T16:29:35Z", + "dateType": "Available" + }, + { + "date": "2017-09-11", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Ashwin J. Shahani (shahani@umich.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Insung Han (insungh@umich.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Probing the growth and melting pathways of a decagonal quasicrystal in real-time" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study the principles of the growth and melting of a decagonal quasicrystal from a liquid. The composition of imaged sample is Al-9.55at%Ni-9.55at%Co, and the growth and melting of a decagonal quasicrystal were observed during the continuous slow cooling. The quasicrystalline phase shows weaker projection intensity because of more X-ray absorption resulting from its heavy elemental (Ni and Co) composition. This contrast difference allowed us to segment the quasicrystal from the liquid phase and visualize 3D volume of a decagonal quasicrystal. During the growth, the ten facets of the quasicrystal were clearly seen. On the other hand, marked interface curvature was observed during the melting, such that the quasicrystal eventually lost its ten habit planes. In addition, the growth rate is nearly constant for every facet; however, the melting rate is shown to depend on the physical orientation. Therefore, the growth and melting processes do not have time-reversal symmetry and different mechanisms are applied to explain this behavior. To determine the underlying mechanisms of the microstructural evolution, we analyzed the projection intensity and correlated it to the phase diagram. The 3D visualization and projection intensity analysis proved that growth is governed by first-order kinetics due to interfacial attachments and the melting is affected more strongly by gravity-driven convection. For details on the resulting 3D reconstruction, visualization, and kinetic analyses, the readers are encouraged to look up the following publication." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Beamline 2-BM at the Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + }, + { + "geoLocationPlace": "ANL" + }, + { + "geoLocationPlace": "APS" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_73_gabriel_temperatureinduced_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_786/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_786/" + }, + "custom": { + "all_materials_included": "['BiThS2', 'BiThSe2']", + "funding_details": "['National Science Foundation under Grant No. DMR- 1508412', 'Center for Computational Study of Excited- State Phenomena in Energy Materials, funded by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-AC02-05CH11231', 'National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE- AC02-05CH11231', 'Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. 787 ACI-1053575']" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_73_gabriel_temperatureinduced_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "crystalline insulator", + "topological insulator" + ], + "characterizationMethod": [ + "simulation" + ], + "structuralFeature": [ + "rhombrohedral" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:44:23.238092Z", + "source_id": "pub_73_gabriel_temperatureinduced_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_73_gabriel_temperatureinduced" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2TP77", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Gabriel", + "givenName": "Antonius", + "creatorName": "Gabriel, Antonius", + "affiliations": [ + "University of California, Berkeley" + ] + }, + { + "familyName": "Steven", + "givenName": "Louie", + "creatorName": "Steven, Louie", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "chalcogenides" + }, + { + "subject": "first-principles calculations" + }, + { + "subject": "transition temperature" + }, + { + "subject": "electron-phonon coupling" + }, + { + "subject": "topological insulators" + }, + { + "subject": "phases" + }, + { + "subject": "superconductors" + }, + { + "subject": "phonons" + }, + { + "subject": "quantum materials" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-10-16T21:08:37Z", + "dateType": "Accepted" + }, + { + "date": "2017-10-16T21:08:37Z", + "dateType": "Available" + }, + { + "date": "2017-10-16", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Gabriel Antonius (gabriel.antonius@lbl.gov)" + }, + { + "contributorType": "ContactPerson", + "contributorName": "gabriel.antonius@lbl.gov" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1103/PhysRevLett.117.246401", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://link.aps.org/doi/10.1103/PhysRevLett.117.246401", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Temperature-Induced Topological Phase Transitions" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Relaxed structures and pseudopotentials." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Lawrence Berkeley National Lab" + }, + { + "geoLocationPlace": "LBNL" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_68_kaufman_naxcoo2_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_68_kaufman_naxcoo2_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1265/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1265/" + }, + "mrr": { + "materialType": [ + "energy storage", + "battery cathodes", + "Na intercalation compounds" + ], + "characterizationMethod": [ + "density functional theory calculations" + ], + "structuralFeature": [ + "layered" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:34:44.486805Z", + "source_id": "pub_68_kaufman_naxcoo2_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_68_kaufman_naxcoo2" + }, + "custom": { + "funding_details": "['United States Department of Energy through the Computational Science Graduate Fellowship (DOE CSGF) under grant number: DE-FG02-97ER25308', 'The Center for Synthetic Control Across Length-scales for Advancing Rechargeables (SCALAR), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award\\xa0# DE-SC0019381', 'Center for Scientific Computing from the CNSI, MRL: an NSF MRSEC (DMR-1720256)', 'National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231']", + "all_materials_included": "NaxCoO2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2505P", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Kaufman, Jonas L.", + "affiliations": [ + "Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States" + ], + "familyName": "Kaufman", + "givenName": "Jonas L." + }, + { + "creatorName": "Van der Ven, Anton", + "affiliations": [ + "Materials Department, University of California, Santa Barbara, Santa Barbara, California 93106, United States" + ], + "familyName": "Van der Ven", + "givenName": "Anton" + } + ], + "subjects": [ + { + "subject": "density functional theory" + }, + { + "subject": "oxides" + }, + { + "subject": "VASP" + }, + { + "subject": "layered intercalation compounds" + }, + { + "subject": "CASM" + }, + { + "subject": "Na-ion batteries" + }, + { + "subject": "simulation" + }, + { + "subject": "DFT" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-12-31T15:50:09Z", + "dateType": "Accepted" + }, + { + "date": "2018-12-31T15:50:09Z", + "dateType": "Available" + }, + { + "date": "2018-12-31", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Jonas L. Kaufman (jlk@ucsb.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Anton Van der Ven (avdv@ucsb.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/prisms-center/CASMcode", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "NaxCoO2 phase stability and hierarchical orderings in the O3/P3 structure family" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset contains VASP calculations for Na-vacancy orderings in various host structures of NaxCoO2 enumerated using the CASM code (https://github.com/prisms-center/CASMcode)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_54_de_emsoft_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_161/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_161/" + }, + "custom": { + "all_materials_included": "['magnesium', 'Mg']", + "funding_details": "Air Force Office of Scientific Research (AFOSR) MURI program (contract FA9550-12-1-0458)" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_54_de_emsoft_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "non-specific", + "metal" + ], + "characterizationMethod": [ + "numerical simulation" + ], + "structuralFeature": [ + "crystaline", + "crystal" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T16:04:13.987402Z", + "source_id": "pub_54_de_emsoft_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_54_de_emsoft" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2NP4J", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "De Graef", + "givenName": "Marc", + "creatorName": "De Graef, Marc", + "affiliations": [ + "Caregie Mellon University" + ] + } + ], + "subjects": [ + { + "subject": "EBSD" + }, + { + "subject": "machine learning" + }, + { + "subject": "magnesium" + }, + { + "subject": "backscattering" + }, + { + "subject": "master pattern" + }, + { + "subject": "EMSoft" + }, + { + "subject": "simulation" + }, + { + "subject": "Mg" + }, + { + "subject": "diffraction" + }, + { + "subject": "electron backscattered diffraction" + }, + { + "subject": "microscopy" + }, + { + "subject": "dictionary" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-10-20T15:22:27Z", + "dateType": "Accepted" + }, + { + "date": "2016-10-20T15:22:27Z", + "dateType": "Available" + }, + { + "date": "2016-10-20", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Marc De Graef (degraef@cmu.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1017/S1431927613001840", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.18126/M2SG6G", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/marcdegraef/EMsoft", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1017/S1431927613001840", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "EMsoft Electron Backscattered Diffraction (EBSD) Master Pattern Dataset for Magnesium (Mg)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This set of data files provides the master patterns for the simulation of EBSD patterns for hexagonal Mg for a number of different microscope accelerating voltages. These files can be used to simulate actual patterns and to generate a pattern dictionary for dictionary-based indexing of experimental EBSD patterns. Information on the pattern simulation can be found in P.G. Callahan and M. De Graef, Dynamical EBSD Patterns Part I: Pattern Simulations, Microscopy and Microanalysis, vol. 19, pp. 1255-1265 (2013) - http://dx.doi.org/10.1017/S1431927613001840. The EMSoft software is located at https://github.com/marcdegraef/EMsoft. EMSoft version 3.1 beta was used for these calculations." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Carnegie Mellon University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_26_qiu_environmental_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_26_qiu_environmental_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1134/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1134/" + }, + "mrr": { + "materialType": [ + "few-layer black phosphorus" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:14:32.884894Z", + "source_id": "pub_26_qiu_environmental_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_26_qiu_environmental" + }, + "custom": { + "funding_details": "This work was supported by the Center for Computational Study of Excited-State Phenomena in Energy Materials at the Lawrence Berkeley National Laboratory, which is funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05CH11231, as part of the Computational Materials Sciences Program. This research used resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575.", + "all_materials_included": "phosphorus" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2DD1G", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Qiu, Diana Y.", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ], + "familyName": "Qiu", + "givenName": "Diana Y." + }, + { + "creatorName": "da Jornada, Felipe H.", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ], + "familyName": "da Jornada", + "givenName": "Felipe H." + }, + { + "creatorName": "Louie, Steven G.", + "affiliations": [ + "Lawrence Berkeley National Lab", + "University of California - Berkeley" + ], + "familyName": "Louie", + "givenName": "Steven G." + } + ], + "subjects": [ + { + "subject": "2D materials" + }, + { + "subject": "semiconductors" + }, + { + "subject": "black phosphorus" + }, + { + "subject": "layered materials" + }, + { + "subject": "black phosphorus, layered materials, substrates, encapsulation" + }, + { + "subject": "bandgap" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-05-17T00:30:36Z", + "dateType": "Accepted" + }, + { + "date": "2018-05-17T00:30:36Z", + "dateType": "Available" + }, + { + "date": "2018-05-15", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Diana Qiu (diana.qiu@berkeley.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.nanolett.7b01365", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1021/acs.nanolett.7b01365", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Environmental Screening Effects in 2D Materials: Renormalization of the Bandgap, Electronic Structure, and Optical Spectra of Few-Layer Black Phosphorus" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Quantum Espresso input and output file and pseudopotentials associated with calculations published in \"Environmental Screening Effects in 2D Materials: Renormalization of the Bandgap, Electronic Structure, and Optical Spectra of Few-Layer Black Phosphorus,\" Nano Lett. 17, 4706-4712 (2017)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_20_wang_far_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1122/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1122/" + }, + "custom": { + "all_materials_included": "['Mg', 'Y']", + "funding_details": "['National Natural Science Foundation of China (Nos. 51671127 and 51631006)', 'Alexander von Humboldt Foundation and Shanghai Pujiang Program (No 16PJ1404600)', 'United States Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357']", + "reduction_method": "MIDAS at APS" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_20_wang_far_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "structural alloy", + "engineering alloy", + "Magnesium", + "Magnesium alloy", + "Mg-Y", + "Mg", + "structural alloy" + ], + "characterizationMethod": [ + "far field high energy diffraction microscopy", + "FF-HEDM", + "HEDM" + ], + "structuralFeature": [ + "hexagonal close packed", + "hcp" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:03:30.306935Z", + "source_id": "pub_20_wang_far_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_20_wang_far" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2Z05T", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Wang", + "givenName": "Leyun", + "creatorName": "Wang, Leyun" + }, + { + "familyName": "Huang", + "givenName": "Zhonghe", + "creatorName": "Huang, Zhonghe" + }, + { + "familyName": "Wang", + "givenName": "Huamiao", + "creatorName": "Wang, Huamiao" + }, + { + "familyName": "Maldar", + "givenName": "Alireza", + "creatorName": "Maldar, Alireza" + }, + { + "familyName": "Yi", + "givenName": "Sangbong", + "creatorName": "Yi, Sangbong" + }, + { + "familyName": "Park", + "givenName": "Jun-Sang", + "creatorName": "Park, Jun-Sang" + }, + { + "familyName": "Kenesei", + "givenName": "Peter", + "creatorName": "Kenesei, Peter" + }, + { + "familyName": "Lilleodden", + "givenName": "Erica", + "creatorName": "Lilleodden, Erica" + }, + { + "familyName": "Zeng", + "givenName": "Xiaoqin", + "creatorName": "Zeng, Xiaoqin" + } + ], + "subjects": [ + { + "subject": "Mg alloy" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "HEDM" + }, + { + "subject": "Synchrotron radiation" + }, + { + "subject": "microstructures" + }, + { + "subject": "EVPSC" + }, + { + "subject": "microscopy" + }, + { + "subject": "Solute strengthening" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-06-25T16:20:56Z", + "dateType": "Accepted" + }, + { + "date": "2018-06-25T16:20:56Z", + "dateType": "Available" + }, + { + "date": "2018-06-25", + "dateType": "Issued" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.1016/j.actamat.2018.05.065", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Far field high energy diffraction data for \"Study of slip activity in a Mg-Y alloy by in situ high energy X-ray diffraction microscopy and elastic viscoplastic self-consistent modeling\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "FF-HEDM data set (including the raw data) and reduced grain-by-grain information for several points along the stress-strain curve. These are GE image files." + }, + { + "descriptionType": "Other", + "description": "Calibration files from CeO2." + }, + { + "descriptionType": "Other", + "description": "Metadata indicating associated load and crosshead displacement for each FF-HEDM image set." + }, + { + "descriptionType": "Other", + "description": "Digital image correlation images for macroscopic stress data" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_30_shahani_twinmediated_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_30_shahani_twinmediated_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_113/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_113/" + }, + "mrr": { + "materialType": [ + "diffusion", + "thermodynamic", + "kinetic", + "metal", + "semiconductor" + ], + "characterizationMethod": [ + "X-ray computed tomography" + ], + "structuralFeature": [ + "bulk", + "crystalline", + "fluid", + "interphase" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:22:43.272879Z", + "source_id": "pub_30_shahani_twinmediated_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_30_shahani_twinmediated" + }, + "custom": { + "experiment_nominal_alloy_composition": "['Al (53 wt%); Si (32 wt%), Cu (15 wt%)', 'Al 53 wt%', 'Si 32 wt%', 'Cu 15 wt%']", + "experiment_pixel_size": "['0.65 micrometers per voxel', '0.65 um']", + "processing_reconstruction_method": "Gridrec algorithm", + "processing_segmentation_method": "median filtering and morphological operations", + "all_materials_included": "Al-Si-Cu", + "experiment_holding_temperature": "['Sample was held at 910 C prior to data collection. This is above the liquidus temperature for Si for the alloy composition of interest. Then, the sample temperature was lowered at a rate of 1 C per minute while tomographic projections were recorded. Note that the temperature corresponding to a given 3D reconstruction can be calculated using this information and the time-stamp of each HDF file.', '910 C']", + "funding_details": "['Multidisciplinary University Research Initiative (grant AFOSR FA9550-12-1-0458)', 'NSF Graduate Research Fellowship Program (grant DGE-1324585)', 'DOE (grant DE-FG02-99ER45782)']", + "experiment_xray_energy": "pink beam" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2301J", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Shahani, Ashwin", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ], + "familyName": "Shahani", + "givenName": "Ashwin" + }, + { + "creatorName": "Gulsoy, E. Begum", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ], + "familyName": "Gulsoy", + "givenName": "E. Begum" + }, + { + "creatorName": "Poulsen, Stefan O.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ], + "familyName": "Poulsen", + "givenName": "Stefan O." + }, + { + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ], + "familyName": "Xiao", + "givenName": "Xianghui" + }, + { + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ], + "familyName": "Voorhees", + "givenName": "Peter W." + } + ], + "subjects": [ + { + "subject": "" + }, + { + "subject": "synchrotron" + }, + { + "subject": "anisotropy" + }, + { + "subject": "solidification" + }, + { + "subject": "4D materials science" + }, + { + "subject": "X-ray computed tomography" + }, + { + "subject": "growth" + }, + { + "subject": "microscopy" + }, + { + "subject": "microstructures" + }, + { + "subject": "Al-Si-Cu alloys" + }, + { + "subject": "facets" + }, + { + "subject": "defects" + }, + { + "subject": "in situ" + }, + { + "subject": "twinning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "twins" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-06-15T20:11:50Z", + "dateType": "Accepted" + }, + { + "date": "2016-06-15T20:11:50Z", + "dateType": "Available" + }, + { + "date": "2016-06-15", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Ashwin J. Shahani (shahani@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Twin-mediated Crystal Growth: an Enigma Resolved" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data was collected to study the growth behavior of faceted Si particles in an Al-Si-Cu liquid, upon continuous cooling. The as-cast, hyper-eutectic sample consisted of primary Si particles in a eutectic matrix. Upon heating to above the liquidus temperature for Si (here, we heat to 910 C), the Si particles melted, leaving behind a featureless liquid. Then, the sample temperature was lowered at a rate of 1 C per minute while projections were recorded. After a brief incubation period, the Si particles grew from the oxide skin of the sample into the liquid. The weight fraction of Si particles at temperature was consistent with predictions from equilibrium, indicating that the growth of solid Si can keep up with the quench rate. Nevertheless, the morphologies of the Si particles were not given by the equilibrium shape of Si due to the prevalence of defects during the growth process. Identifying the crystallography and dynamics of these defects (including twin boundaries that intersect the solid-liquid interfaces) was central to this work. The raw data were obtained in the following sequence: for the first 20 min, projections were collected continuously, providing a temporal resolution of 30 s between subsequent 3D reconstructions; for the next 120 minutes, 40 more tomographic scans with the same parameters were spaced 150 s apart. Thus, 80 scans were collected over the course of over 2 hours. The motivation for collecting the X-ray projections in this manner was that the system-average length-scale increased logarithmically with time during bulk diffusion-limited growth. Therefore, this data collection scheme adequately captured the interfacial dynamics. The data here includes the raw projections. Note that dark field and flat field measurements, which are used to normalize the data, are in a separate file. For details on the data processing and quantitative analysis, the reader is pointed to the following publication. Correspondence should be directed to Ashwin J. Shahani (shahani@u.northwestern.edu)." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Beamline 2-BM at Advanced Photon Source" + }, + { + "geoLocationPlace": "Argonne National Laboratory" + }, + { + "geoLocationPlace": "APS" + }, + { + "geoLocationPlace": "ANL" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_89_ondrejcek_fluctuation_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_372/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_372/" + }, + "custom": { + "all_materials_included": "Pt single crystal", + "funding_details": "This research was supported in part by the Department of Energy, Division of Materials Research, under contract DEFG02-91ER45439 and DEFG02-02ER46011, which includes the Center for Microanalysis of Materials in which the LEEM is maintained." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_89_ondrejcek_fluctuation_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "metals", + "platinum" + ], + "characterizationMethod": [ + "Low-energy electron microscopy (LEEM)" + ], + "structuralFeature": [ + "Pt(111) surface" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T17:29:55.636526Z", + "source_id": "pub_89_ondrejcek_fluctuation_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_89_ondrejcek_fluctuation" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2H018", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Ondrejcek", + "givenName": "Michal", + "creatorName": "Ondrejcek, Michal", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "familyName": "Swiech", + "givenName": "Wacek", + "creatorName": "Swiech, Wacek", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "familyName": "Rajappan", + "givenName": "Mahesh", + "creatorName": "Rajappan, Mahesh", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "familyName": "Yang", + "givenName": "G.", + "creatorName": "Yang, G.", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + }, + { + "familyName": "Flynn", + "givenName": "C. Peter", + "creatorName": "Flynn, C. Peter", + "affiliations": [ + "Department of Physics and Materials Research Laboratory, University of Illinois at Urbana-Champaign, 1110 W. Green Street, Urbana, IL 61801, USA" + ] + } + ], + "subjects": [ + { + "subject": "diffusion" + }, + { + "subject": "spectroscopy" + }, + { + "subject": "experiment" + }, + { + "subject": "low-energy electron microscopy (LEEM)" + }, + { + "subject": "surface diffusion" + }, + { + "subject": "bulk diffusion" + }, + { + "subject": "step fluctuation spectroscopy" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "platinum" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-02-03T22:15:04Z", + "dateType": "Accepted" + }, + { + "date": "2017-02-03T22:15:04Z", + "dateType": "Available" + }, + { + "date": "2017-02-03", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Michal Ondrejcek, PhD (ondrejce@illinois.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/S0009-2614(03)01243-0", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1103/PhysRevB.72.085422", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1080/09500830310001646671", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1080/09500830410001731774", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Fluctuation spectroscopy of step edges on Pt(111); Crossover from bulk to surface diffusion" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "By step fluctuation spectroscopy, using low-energy electron microscopy LEEM, we investigate step energies and relaxation on clean Pt(111) surface at temperatures above half the melting temperature Tm (range 1190K" + }, + { + "contributorType": "ContactPerson", + "contributorName": "\"Jeffrey M. Ting\" " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.26434/chemrxiv.7905275.v1", + "relatedIdentifierType": "URL" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://doi.org/10.26434/chemrxiv.7905275.v1", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Interparticle Interactions in Dilute Solutions of Polyelectrolyte Complex Micelles" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Files include background subtracted small angle X-ray scattering (SAXS) data from the Stanford Synchrotron Radiation Lightsource at SLAC National Accelerator Laboratory. Data is organized by PEO-PVBTMA/PAA polyelectrolyte complex micelles at investigated concentrations." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_43_bennett_new_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_43_bennett_new_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1152/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1152/" + }, + "mrr": { + "characterizationMethod": [ + "simulation" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-03-08T15:45:40.650517Z", + "source_id": "pub_43_bennett_new_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_43_bennett_new" + }, + "custom": { + "all_materials_included": "['Al', 'Ar', 'Cl', 'Mg', 'Na', 'P', 'S', 'Si']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M23S7R", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Bennett, Chandler", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Bennett", + "givenName": "Chandler" + }, + { + "creatorName": "Wang, Guangming", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Wang", + "givenName": "Guangming" + }, + { + "creatorName": "Annaberdiyev, Abdulgani", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Annaberdiyev", + "givenName": "Abdulgani" + }, + { + "creatorName": "Melton, Cody A.", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Melton", + "givenName": "Cody A." + }, + { + "creatorName": "Shulenburger, Luke", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Shulenburger", + "givenName": "Luke" + }, + { + "creatorName": "Mitas, Lubos", + "affiliations": [ + "North Carolina State University", + "Sandia National Laboratories" + ], + "familyName": "Mitas", + "givenName": "Lubos" + } + ], + "subjects": [ + { + "subject": "binding energy" + }, + { + "subject": "atomic total energy calculations" + }, + { + "subject": "atomic spectra" + }, + { + "subject": "simulation" + }, + { + "subject": "electron affinity" + }, + { + "subject": "ionization potential" + }, + { + "subject": "molecular spectra" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-06-22T15:18:30Z", + "dateType": "Accepted" + }, + { + "date": "2018-06-22T15:18:30Z", + "dateType": "Available" + }, + { + "date": "2018-06-22", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Chandler Bennett (mcbennet@ncsu.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://github.com/mcbennet/2nd_Row_Data", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "New generation of effective core potentials from correlated calculations: 2nd row elements" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Contained in the 'Molpro' directory is the full set of Molpro input (filename.com) and output (filename.{out,xml}) files used in the study including any ancillary files such as basis set inputs, etc.. All molecular, atomic spectra and atomic total energy calculations are represented in these files. The two directories 'Spectra' and 'Molecules' contain the compiled CCSD(T) energies for all core approximations. Within the 'Spectra' directory, all electron affinities and ionization potentials are given for each atom and each core approximation. Within the 'Molecules' directory, all binding energies are given for each molecule and core approximation." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "zhaoluning_variational_excitations_theory_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/zhaoluning_variational_excitations_theory_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/zhaoluning_variational_excitations_theory_v1.1/" + }, + "mrr": { + "materialType": [ + "sp-insulators and semi-conductors, transition metal oxide", + "transition metal oxide" + ], + "characterizationMethod": [ + "Quantum Espresso", + "QMCPACK" + ], + "structuralFeature": [ + "rocksalt, silicon" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-08-29T16:24:12.026855Z", + "source_id": "zhaoluning_variational_excitations_theory_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "zhaoluning_variational_excitations_theory" + }, + "custom": { + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.", + "all_materials_included": "['Lithium Hydride', 'Silicon', 'SiC', 'LiF', 'ZnO', 'diamond', 'zinc oxide']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/1QJG8XUOESBLA", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Zhao, Luning", + "affiliations": [ + "University of California, Berkeley" + ], + "familyName": "Zhao", + "givenName": "Luning" + }, + { + "creatorName": "Neuscamman, Eric", + "affiliations": [ + "Lawrence Berkeley National Laboratory" + ], + "familyName": "Neuscamman", + "givenName": "Eric" + } + ], + "subjects": [ + { + "subject": "QMC" + }, + { + "subject": "many-body perturbation theory" + }, + { + "subject": "optical gaps" + }, + { + "subject": "Monte Carlo" + }, + { + "subject": "variational principle" + }, + { + "subject": "simulation" + }, + { + "subject": "Quantum Monte Carlo" + } + ], + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-06-26T18:40:14Z", + "dateType": "Accepted" + }, + { + "date": "2019-06-26T18:40:14Z", + "dateType": "Available" + } + ], + "contributors": [ + { + "contributorName": "\"Eric Neuscamman\" ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Dataset for Variational Excitations in Real Solids: Optical Gaps and Insights into Many-Body Perturbation Theory" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This is the dataset for paper \"Variational Excitations in Real Solids: Optical Gaps and Insights into Many-Body Perturbation Theory\", accepted by Phys. Rev. Lett. on June 13th, 2019." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Berkeley High Performance Computing Savio Cluster" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_49_gunda_firstprinciples_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_49_gunda_firstprinciples_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1168/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1168/" + }, + "mrr": { + "materialType": [ + "light weight metal alloys", + "Titanium interstitial alloys" + ], + "characterizationMethod": [ + "First-principles calculations" + ], + "structuralFeature": [ + "hcp and rocksalt crystal systems" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-08T15:55:51.190981Z", + "source_id": "pub_49_gunda_firstprinciples_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_49_gunda_firstprinciples" + }, + "custom": { + "funding_details": "['NSF DMREF grant: DMR1436154 \u201cDMREF: Integrated Computational Framework for Designing Dynamically Controlled Alloy -Oxide Heterostructures\u201d', 'Center for Scientific Computing, CNSI and MRL at UCSB under NSF MRSEC (DMR- 1121053)', 'National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231']", + "all_materials_included": "['Ti', 'N', 'C']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M22K94", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Gunda, Naga Sri Harsha", + "affiliations": [ + "Materials Department, University of California Santa Barbara" + ], + "familyName": "Gunda", + "givenName": "Naga Sri Harsha" + }, + { + "creatorName": "Van der Ven, Anton", + "affiliations": [ + "Materials Department, University of California Santa Barbara" + ], + "familyName": "Van der Ven", + "givenName": "Anton" + } + ], + "subjects": [ + { + "subject": "First-principles calculations" + }, + { + "subject": "VASP" + }, + { + "subject": "Ti" + }, + { + "subject": "simulation" + }, + { + "subject": "Titanium Interstitial alloys" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-07-10T14:04:14Z", + "dateType": "Accepted" + }, + { + "date": "2018-07-10T14:04:14Z", + "dateType": "Available" + }, + { + "date": "2018-07-10", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Anton Van der Ven (avdv@ucsb.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Naga Sri Harsha Gunda (gunda@ucsb.edu)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "First-principles calculations of hcp and rocksalt TiX (X=C,N) systems" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset contains VASP calculations for various configurations enumerated using CASM code (https://github.com/prisms-center/CASMcode) in hcp and rocksalt TiX (X=C,N) alloys." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_13_lee_electronic_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1103/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1103/" + }, + "custom": { + "all_materials_included": "['InP', 'GaP', 'In0.5Ga0.5P']", + "funding_details": "C.-W. L. acknowledges support from the Government Scholarship to Study Abroad from the Taiwan Ministry of Education. Financial support from Sandia National Laboratories through the Sandia-UIUC collaboration is gratefully acknowledged. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_13_lee_electronic_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "semiconductor", + "semiconductor" + ], + "structuralFeature": [ + "phosphide-based III-V semiconductors" + ] + }, + "mdf": { + "resource_type": "dataset", + "ingest_date": "2019-03-07T22:41:49.764095Z", + "source_id": "pub_13_lee_electronic_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_13_lee_electronic" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2R337", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Lee", + "givenName": "Cheng-Wei", + "creatorName": "Lee, Cheng-Wei", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaigna-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "stopping power" + }, + { + "subject": "radiation damage" + }, + { + "subject": "stopping power, simulation, radiation damage" + }, + { + "subject": "simulations" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-03-29T21:59:30Z", + "dateType": "Accepted" + }, + { + "date": "2018-03-29T21:59:30Z", + "dateType": "Available" + }, + { + "date": "2018-03-29", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Andr\u00e9 Schleife (schleife@illinois.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://arxiv.org/abs/1803.10182", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Electronic Stopping and Proton Dynamics in InP, GaP, and In0.5Ga0.5P from First Principles" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_62_ondrejcek_surface_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1209/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1209/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_62_ondrejcek_surface_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T16:19:40.493019Z", + "source_id": "pub_62_ondrejcek_surface_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_62_ondrejcek_surface" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2M932", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Ondrejcek", + "givenName": "Michal", + "creatorName": "Ondrejcek, Michal", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Rajappan", + "givenName": "Mahesh", + "creatorName": "Rajappan, Mahesh", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Swiech", + "givenName": "Wacek", + "creatorName": "Swiech, Wacek", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "metals and alloys" + }, + { + "subject": "microscopy" + }, + { + "subject": "diffusion" + }, + { + "subject": "experiment" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-11-08T18:13:42Z", + "dateType": "Accepted" + }, + { + "date": "2018-11-08T18:13:42Z", + "dateType": "Available" + }, + { + "date": "2018-11-08", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Michal Ondrejcek (ondrejce@illinois.edu)" + } + ], + "titles": [ + { + "title": "Surface Mass Diffusion on bcc(011) Planes; V, Nb, Ta and Mo" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_46_ondrejcek_surface_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_46_ondrejcek_surface_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_139/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_139/" + }, + "mrr": { + "materialType": [ + "metal", + "metal" + ], + "characterizationMethod": [ + "Low-energy electron microscopy", + "LEEM", + "MBE" + ], + "structuralFeature": [ + "single crystal" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:50:50.166796Z", + "source_id": "pub_46_ondrejcek_surface_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_46_ondrejcek_surface" + }, + "custom": { + "funding_details": "This research was supported in part by the Department of Energy, Division of Materials Research, under contract DEFG02- 91ER45439, which includes the Center for Microanalysis of Materials in which the LEEM is maintained.", + "all_materials_included": "['Molybdenum(011)', 'Sapphire (11-20)']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2PP4V", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Ondrejcek, Michal", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Ondrejcek", + "givenName": "Michal" + }, + { + "creatorName": "Swiech, Wacek", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Swiech", + "givenName": "Wacek" + }, + { + "creatorName": "Durfee, Curtis S.", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Durfee", + "givenName": "Curtis S." + }, + { + "creatorName": "Flynn, C. Peter", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ], + "familyName": "Flynn", + "givenName": "C. Peter" + } + ], + "subjects": [ + { + "subject": "diffusion" + }, + { + "subject": "Sapphire" + }, + { + "subject": "Thin film" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "Molybdenum" + }, + { + "subject": "Surface diffusion" + }, + { + "subject": "Low-energy electron microscopy" + }, + { + "subject": "microscopy" + }, + { + "subject": "Molecular beam epitaxy" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-08-26T16:04:17Z", + "dateType": "Accepted" + }, + { + "date": "2016-08-26T16:04:17Z", + "dateType": "Available" + }, + { + "date": "2016-08-24", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Michal Ondrejcek (ondrejce@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/S0039-6028(03)00745-3", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1016/j.susc.2006.07.035", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Surface Mass Diffusion and Step Stiffness on an Anisotropic Surface; Mo(011)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Step fluctuations have been studied on Mo(011) thin single crystal films with various orientations of miscut, in order to determine the step stiffnesses. Measurements of fluctuation amplitudes and relaxation times were made in the temperature range 1080\u20131680 K. \r\nSurface diffusion Ds is documented at T/Tm~0.5, while the crossover to relaxation driven by bulk vacancy diffusion is inferred for T/Tm~0.6. Here, Tm is the melting temperature Tm = 2896 K. We obtain Ds = 4x10-4 exp(-1.13 eV/kBT) cm2/s for the temperature interval above. Possible indications of diffusion along step edges appear for T/Tm~0.4. \r\nAdditionally, the results show an anisotropic stiffness of about 0.36 eV/nm along [0-11] and about 0.15 eV/nm along [100]. No temperature dependence of the stiffness was detected. The step free energies derived from the stiffnesses average about 0.27 eV/nm and are less anisotropic by about a factor 3. It is shown that three independent procedures yield mutually consistent step stiffness anisotropies. These are (1) step fluctuation amplitudes; (2) step relaxation rate anisotropies; and (3) the observed anisotropies of islands in equilibrium on the Mo(011) surface. The magnitude of the step stiffness obtained from step edge relaxation is consistent with earlier measurements that determine diffusion from grain boundary grooving." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Materials Research Laboratory" + }, + { + "geoLocationPlace": "Department of Physics" + }, + { + "geoLocationPlace": "University of Illinois at Urbana-Champaign" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_60_jokisaari_phase_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_177/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_177/" + }, + "custom": { + "funding_details": "Grant number 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_60_jokisaari_phase_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "phase field modeling and simulation" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T16:16:20.769895Z", + "source_id": "pub_60_jokisaari_phase_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_60_jokisaari_phase" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2101X", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Jokisaari", + "givenName": "Andrea M.", + "creatorName": "Jokisaari, Andrea M.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter W.", + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Guyer", + "givenName": "Jonathan E.", + "creatorName": "Guyer, Jonathan E.", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "familyName": "Warren", + "givenName": "James A.", + "creatorName": "Warren, James A.", + "affiliations": [ + "National Institute of Standards and Technology" + ] + }, + { + "familyName": "Heinonen", + "givenName": "Olle", + "creatorName": "Heinonen, Olle", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "benchmark" + }, + { + "subject": "benchmark problem" + }, + { + "subject": "phases" + }, + { + "subject": "simulation" + }, + { + "subject": "spinodal decomposition" + }, + { + "subject": "Ostwald ripening" + }, + { + "subject": "phase field" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-01-04T18:08:07Z", + "dateType": "Accepted" + }, + { + "date": "2017-01-04T18:08:07Z", + "dateType": "Available" + }, + { + "date": "2017-01-04", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Olle Heinonen (heinonen@anl.gov)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://dx.doi.org/10.1016/j.commatsci.2016.09.022", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "https://pages.nist.gov/chimad-phase-field/", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Phase Field Benchmark I Dataset" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This data set is that generated by the authors to create the publication below for the first set of phase field benchmark problems covering spinodal decomposition and Ostwald ripening. Please see the paper for details: A. M. Jokisaari, P. W. Voorhees, J. E. Guyer, J. Warren, O. G. Heinonen, Computational Materials Science 126 (2017) 139-151 (http://dx.doi.org/10.1016/j.commatsci.2016.09.022). You may also refer to the website, https://pages.nist.gov/chimad-phase-field/. Please contact O. G. Heinonen (heinonen@anl.gov) with questions, etc." + }, + { + "descriptionType": "Other", + "description": "The mesh data may be visualized with Paraview. There are four directories: 1) code, which contains the .h and .C files written specifically for these problems. 2) meshes, which contains the spherical and T-shaped meshes. 3) ostwald_ripening, which contains benchmark problem 2. 4) spinodal_decomposition, which contains benchmark problem 1. Each data set contains the solutions (meshes) in .e files, postprocessor data + post-calculated (1/V)(dF/dt) values in a .csv file, and the input in a .i file. NB: (1/V)(dF/dt) calculated by backward differencing on the postprocessor-supplied data." + }, + { + "descriptionType": "Other", + "description": "This data was generated by a MOOSE-based application, including the phase_field module (see http://mooseframework.org). To run this code, you will need to generate your own MOOSE-based application, add the code to the appropriate directories and register the new code in the src/base/MyApp.C file (see http://mooseframework.com/wiki/MooseSystems/Kernels/ for an example). Because this code is in the Materials Data Facility, it is not being kept up-to-date - you may need to update it for API changes within MOOSE or the phase_field module." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Laboratory Computing Resource Center" + }, + { + "geoLocationPlace": "Argonne National Laboratory and High Performance Computing Center" + }, + { + "geoLocationPlace": "Idaho National Laboratory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_48_secor_gravure_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_48_secor_gravure_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_142/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_142/" + }, + "mrr": { + "materialType": [ + "electronic", + "thermal", + "mechanical", + "nanomaterial", + "colloid", + "conductor" + ], + "characterizationMethod": [ + "optical microscopy", + "rheometry", + "atomic force microscopy", + "scanning electron microscopy", + "profilometry", + "I-V electrical measurements", + "AFM", + "SEM" + ], + "structuralFeature": [ + "colloidal ink", + "2D" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:54:11.657857Z", + "source_id": "pub_48_secor_gravure_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_48_secor_gravure" + }, + "custom": { + "funding_details": "['Office of Naval Research MURI Program (N00014-11-1-0690)', 'NSF-MRSEC (DMR-1121262)']", + "all_materials_included": "['graphene', 'carbon']" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2F59D", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Secor, Ethan B.", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Secor", + "givenName": "Ethan B." + }, + { + "creatorName": "Lim, Sooman", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Lim", + "givenName": "Sooman" + }, + { + "creatorName": "Zhang, Heng", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Zhang", + "givenName": "Heng" + }, + { + "creatorName": "Frisbie, Daniel C.", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Frisbie", + "givenName": "Daniel C." + }, + { + "creatorName": "Francis, Lorraine F.", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Francis", + "givenName": "Lorraine F." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "University of Minnesota" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "flexible electronics" + }, + { + "subject": "graphene" + }, + { + "subject": "experiment" + }, + { + "subject": "2D materials" + }, + { + "subject": "nanomaterial" + }, + { + "subject": "printed electronics" + }, + { + "subject": "gravure printing" + }, + { + "subject": "electronic materials" + }, + { + "subject": "conductive ink" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-08-26T18:17:41Z", + "dateType": "Accepted" + }, + { + "date": "2016-08-26T18:17:41Z", + "dateType": "Available" + }, + { + "date": "2016-08-26", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Mark Hersam (m-hersam@northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/adma.201401052", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://onlinelibrary.wiley.com/doi/10.1002/adma.201401052/abstract", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Gravure Printing of Graphene for Large-Area Flexible Electronics" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The data corresponds to the demonstration of gravure-printed graphene patterns using polymer ethyl cellulose as an effective ink stabilizer. 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DOI:10.1021/adma.201401052" + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Northwestern University" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_47_secor_inkjet_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_141/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_141/" + }, + "custom": { + "all_materials_included": "['graphene', 'carbon']", + "funding_details": "Office of Naval Research MURI Program (N00014-11-1-0690)" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_47_secor_inkjet_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "electronic", + "thermal", + "structural", + "mechanical", + "nanomaterial", + "colloid", + "conductor" + ], + "characterizationMethod": [ + "ultraviolet-visible spectroscopy", + "atomic force microscopy", + "thermal gravimetric analysis", + "scanning electron microscopy", + "uv-vis", + "TGA", + "SEM", + "AFM" + ], + "structuralFeature": [ + "colloidal ink", + "2D" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T15:52:31.718938Z", + "source_id": "pub_47_secor_inkjet_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_47_secor_inkjet" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2K01W", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Secor", + "givenName": "Ethan B.", + "creatorName": "Secor, Ethan B.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Prabhumirashi", + "givenName": "Pradyumna L.", + "creatorName": "Prabhumirashi, Pradyumna L.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Puntambekar", + "givenName": "Kanan", + "creatorName": "Puntambekar, Kanan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Geier", + "givenName": "Michael L.", + "creatorName": "Geier, Michael L.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "flexible electronics" + }, + { + "subject": "graphene" + }, + { + "subject": "experiment" + }, + { + "subject": "inkjet printing" + }, + { + "subject": "2D materials" + }, + { + "subject": "nanomaterial" + }, + { + "subject": "electrode" + }, + { + "subject": "carbon" + }, + { + "subject": "printed electronics" + }, + { + "subject": "colloid" + }, + { + "subject": "ink" + }, + { + "subject": "exfoliation" + }, + { + "subject": "electronic materials" + } + ], + "publicationYear": "2016", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2016-08-26T18:04:48Z", + "dateType": "Accepted" + }, + { + "date": "2016-08-26T18:04:48Z", + "dateType": "Available" + }, + { + "date": "2016-08-26", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Mark Hersam (m-hersam@northwestern.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/jz400644c", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://pubs.acs.org/doi/abs/10.1021/jz400644c", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Inkjet Printing of High Conductivity, Flexible Graphene Patterns" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The data corresponds to the demonstration of inkjet-printed graphene patterns using polymer ethyl cellulose as an effective ink stabilizer. 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In addition, this work used allocations from the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575. EFD was supported by the U.S. Department of Energy Office of Science, Advanced Scientific Computing Research (ASCR). YWL and computing resources were supported by the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05- 00OR22725. PK was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M24328", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "McDaniel, Tyler", + "affiliations": [ + "University of Tennessee, Knoxville, Tennessee" + ], + "familyName": "McDaniel", + "givenName": "Tyler" + }, + { + "creatorName": "D'Azevedo, Ed F.", + "affiliations": [ + "Oak Ridge National Laboratory, Oak Ridge, Tennessee" + ], + "familyName": "D'Azevedo", + "givenName": "Ed F." + }, + { + "creatorName": "Li, Ying Wai", + "affiliations": [ + "University of Tennessee, Knoxville, Tennessee" + ], + "familyName": "Li", + "givenName": "Ying Wai" + }, + { + "creatorName": "Wong, Kwai", + "affiliations": [ + "University of Tennessee, Knoxville, Tennessee" + ], + "familyName": "Wong", + "givenName": "Kwai" + }, + { + "creatorName": "Kent, Paul R. 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{ + "date": "2018-06-21T19:47:49Z", + "dateType": "Accepted" + }, + { + "date": "2018-06-21T19:47:49Z", + "dateType": "Available" + }, + { + "date": "2018-06-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Olle Heinonen (heinonen@anl.gov)" + } + ], + "titles": [ + { + "title": "Dataset for Zirconia and hafnia polymorphs -- ground state structural properties from diffusion Monte Carlo" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for \"Zirconia and hafnia polymorphs -- ground state structural properties from diffusion Monte Carlo\" Hyeondeok Shin, Anouar Benali, Ye Luo, Emily Crabb, Alejandro Lopez-Bezanilla, Laura E. 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"resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_14_ward_machine_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1106/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1106/" + }, + "custom": { + "funding_details": "Financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD)" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_14_ward_machine_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "Metal alloy", + "Metallic Glass" + ], + "structuralFeature": [ + "Amorphous" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-07T22:43:41.694426Z", + "source_id": "pub_14_ward_machine_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_14_ward_machine" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2662X", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Ward", + "givenName": "Logan", + "creatorName": "Ward, Logan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "O\u2019Keeffe", + "givenName": "Stephanie C.", + "creatorName": "O\u2019Keeffe, Stephanie C.", + "affiliations": [ + "Liquidmetal Technologies" + ] + }, + { + "familyName": "Stevick", + "givenName": "Joesph", + "creatorName": "Stevick, Joesph", + "affiliations": [ + "Liquidmetal Technologies" + ] + }, + { + "familyName": "Jelbert", + "givenName": "Glenton R.", + "creatorName": "Jelbert, Glenton R.", + "affiliations": [ + "Liquidmetal Technologies" + ] + }, + { + "familyName": "Aykol", + "givenName": "Muratahan", + "creatorName": "Aykol, Muratahan", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Wolverton", + "givenName": "Chris", + "creatorName": "Wolverton, Chris", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "metalic glasses" + }, + { + "subject": "machine learning" + }, + { + "subject": "Bulk Metallic Glasses" + }, + { + "subject": "Machine learning" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-04-11T15:23:18Z", + "dateType": "Accepted" + }, + { + "date": "2018-04-11T15:23:18Z", + "dateType": "Available" + }, + { + "date": "2018-04-09", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Logan Ward" + } + ], + "titles": [ + { + "title": "A Machine 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"globus://61177e74-e1ce-11e7-8031-0a208f818180/published/publication_1062/" + }, + "mrr": { + "characterizationMethod": [ + "Atom probe tomography" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-07T22:30:06.370275Z", + "source_id": "pub_6_sauza_influence_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_6_sauza_influence" + }, + "custom": { + "funding_details": "This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD)." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2CS77", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Sauza, Daniel", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Sauza", + "givenName": "Daniel" + }, + { + "creatorName": "Bocchini, Peter", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Bocchini", + "givenName": "Peter" + }, + { + "creatorName": "Chung, Ding-Wen", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Chung", + "givenName": "Ding-Wen" + }, + { + "creatorName": "Dunand, David", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Dunand", + "givenName": "David" + }, + { + "creatorName": "Seidman, David", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Seidman", + "givenName": "David" + } + ], + "subjects": [ + { + "subject": "metals and alloys" + }, + { + "subject": "microstructures" + }, + { + "subject": "microscopy" + }, + { + "subject": "experiment" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-03-09T18:57:24Z", + "dateType": "Accepted" + }, + { + "date": "2018-03-09T18:57:24Z", + "dateType": "Available" + }, + { + "date": "2018-03-09", + "dateType": "Issued" + } + ], + "titles": [ + { + "title": "Influence of ruthenium in a model Co-Al-W superalloy" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The effect of 2 at.% Ru addition on a base Co-8.8Al-7.3W at.% superalloy, consisting of a gamma-(fcc) matrix with gamma\u2019-(L12 structure) precipitates is studied\r\nusing atom-probe tomography. Ru is found to partition to the gamma' precipitate after aging at 900C for 16h." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "NUCAPT" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "groebermichael_afrl_am_package2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search." + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/afrl-challenge-data/published/publication_1151/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/afrl-challenge-data/published/publication_1151/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "AFRL Additive Manufacturing Challenge" + ], + "ingest_date": "2019-08-29T16:55:53.269086Z", + "source_id": "groebermichael_afrl_am_package2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "groebermichael_afrl_am_package2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M27H1Z", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Groeber, Michael", + "familyName": "Groeber", + "givenName": "Michael" + }, + { + "creatorName": "Schwalbach, Edwin", + "familyName": "Schwalbach", + "givenName": "Edwin" + }, + { + "creatorName": "Donegan, Sean", + "familyName": "Donegan", + "givenName": "Sean" + }, + { + "creatorName": "Uchic, Michael", + "familyName": "Uchic", + "givenName": "Michael" + }, + { + "creatorName": "Chapman, Michael", + "familyName": "Chapman", + "givenName": "Michael" + }, + { + "creatorName": "Shade, Paul", + "familyName": "Shade", + "givenName": "Paul" + }, + { + "creatorName": "Musinski, William", + "familyName": "Musinski", + "givenName": "William" + }, + { + "creatorName": "Miller, Jonathan", + "familyName": "Miller", + "givenName": "Jonathan" + }, + { + "creatorName": "Turner, Todd", + "familyName": "Turner", + "givenName": "Todd" + }, + { + "creatorName": "Sparkman, Daniel", + "familyName": "Sparkman", + "givenName": "Daniel" + }, + { + "creatorName": "Cox, Marie", + "familyName": "Cox", + "givenName": "Marie" + } + ], + "subjects": [ + { + "subject": "additive manufacturing" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "experiment" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-06-22T14:48:37Z", + "dateType": "Accepted" + }, + { + "date": "2018-06-22T14:48:37Z", + "dateType": "Available" + }, + { + "date": "2018-06-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Marie Cox (marie.cox@us.af.mil)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "AFRL AM Modeling Challenge Series: Challenge 2 Data Package" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Calibration, Input and Supplementary Data for the Second Challenge Problem of the AFRL AM Modeling Challenge Series. This Challenge Problem Focuses on the Prediction of Melt Pool Geometries as a Function of Processing Conditions." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_21_zhao_blocked_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_614/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_614/" + }, + "custom": { + "all_materials_included": "['H2O', 'N2', 'H16', 'H16 hydrogen ring', 'C2']", + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_21_zhao_blocked_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "materialType": [ + "molecules" + ], + "characterizationMethod": [ + "Monte Carlo methods" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-03-08T15:06:09.828358Z", + "source_id": "pub_21_zhao_blocked_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_21_zhao_blocked" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2S59H", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Zhao", + "givenName": "Luning", + "creatorName": "Zhao, Luning", + "affiliations": [ + "University of California, Berkeley" + ] + }, + { + "familyName": "Neuscamman", + "givenName": "Eric", + "creatorName": "Neuscamman, Eric", + "affiliations": [ + "University of California, Berkeley" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "Monte Carlo" + }, + { + "subject": "QMC" + } + ], + "publicationYear": "2017", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2017-05-26T14:28:40Z", + "dateType": "Accepted" + }, + { + "date": "2017-05-26T14:28:40Z", + "dateType": "Available" + }, + { + "date": "2017-05-26", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Eric Neuscamman (eneuscamman@berkeley.edu)" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "10.1021/acs.jctc.7b00119", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsSupplementTo", + "relatedIdentifier": "http://www.dx.doi.org/10.1021/acs.jctc.7b00119", + "relatedIdentifierType": "URL" + } + ], + "titles": [ + { + "title": "Dataset for A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "We present a modification to variational Monte Carlo\u2019s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thou- sands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much bet- ter fit for modern supercomputer architectures in which data communication and per- process memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involving both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multi-Slater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we test a systematically convergent, non-perturbative approach to excitation energies on the example of a Mott-insulating hydrogen ring." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "Berkeley Research Computing Savio cluster" + }, + { + "geoLocationPlace": "University of California, Berkeley" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_63_malone_overcoming_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_63_malone_overcoming_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/published/publication_1214/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/published/publication_1214/" + }, + "mrr": { + "materialType": [ + "Carbon" + ], + "structuralFeature": [ + "Diamond" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CPSFM" + ], + "ingest_date": "2019-03-08T16:21:21.517608Z", + "source_id": "pub_63_malone_overcoming_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_63_malone_overcoming" + }, + "custom": { + "funding_details": "This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M29S67", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Malone, Fionn D.", + "affiliations": [ + "Lawrence Livermore National Laboratory" + ], + "familyName": "Malone", + "givenName": "Fionn D." + }, + { + "creatorName": "Shuai, Zhang", + "affiliations": [ + "Lawrence Livermore National Laboratory" + ], + "familyName": "Shuai", + "givenName": "Zhang" + }, + { + "creatorName": "Miguel, Morales. A", + "affiliations": [ + "Lawrence Livermore National Laboratory" + ], + "familyName": "Miguel", + "givenName": "Morales. A" + } + ], + "subjects": [ + { + "subject": "QMC" + }, + { + "subject": "ISDF" + }, + { + "subject": "Monte Carlo" + }, + { + "subject": "QMCPACK" + }, + { + "subject": "simulation" + }, + { + "subject": "AFQMC" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-11-21T20:11:07Z", + "dateType": "Accepted" + }, + { + "date": "2018-11-21T20:11:07Z", + "dateType": "Available" + }, + { + "date": "2018-11-21", + "dateType": "Issued" + } + ], + "contributors": [ + { + "contributorName": "Fionn Malone (malone14@llnl.gov)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data for Overcoming the Overcoming the Memory Bottleneck in Auxiliary Field Quantum Monte Carlo Simulations with Interpolative Separable Density Fitting" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for \"Overcoming the Memory Bottleneck in Auxiliary Field Quantum Monte Carlo\r\nSimulations with Interpolative Separable Density Fitting.\"\r\n=====================\r\n\r\nIn this dataset you will find:\r\n1. The analysed data and plotting scripts necessary to reproduce the figures in the above\r\npaper.\r\n2. The raw data produced from qmcpack calculations:\r\n - convergence/ (with respect to ISDF rank parameter c):\r\n - 2x2x2/:\r\n - DZ/\r\n - TZ/\r\n - 3x3x3/\r\n - DZ/\r\n - TZ/\r\n - cold_curve/ (AFQMC results for cold curve)\r\n - sparse/\r\n - thc/\r\n - cohesive_energy/\r\n - solid/\r\n - 2x2x2\r\n - 3x3x3\r\n - 4x4x4\r\n - atom/\r\n - cc-pvdz\r\n - cc-pvtz\r\n - cc-pvqz\r\n\r\nThe general procedure for running qmcpack using ISDF for the integrals is as follows:\r\n1. Run a mean field calculation to generate the MO matrix. In All directories you should\r\nfind the checkpoint file generated by pyscf (*.dump) which will contain the necessary data.\r\nThe script to generate this data will be called kpoints.py.\r\n2. Generate the supercell MOs and trial wavefunction from mean field dump file. This is\r\ngenerated using the script dump_wfn.py which will be distributed with qmcpack with the\r\nthc++ code in the future.\r\n3. Run thc++ to generate the ISDF factorization. Input files for this step are located in\r\nthe calculation subdirectories (typically called input.json).\r\n4. Run qmcpack using the xml input file (also located in the subdirectories).\r\n5. Analyse the output (*.scalar.dat) using analysis script." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_5_reyes_microstructures_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_5_reyes_microstructures_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=61177e74-e1ce-11e7-8031-0a208f818180&origin_path=/published/publication_1061/", + "endpoint_path": "globus://61177e74-e1ce-11e7-8031-0a208f818180/published/publication_1061/" + }, + "mrr": { + "characterizationMethod": [ + "Atom probe tomography" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-08T20:46:58.711413Z", + "source_id": "pub_5_reyes_microstructures_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "pub_5_reyes_microstructures" + }, + "custom": { + "funding_details": "This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD)." + }, + "dc": { + "identifier": { + "identifier": "10.18126/M21M0R", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Tirado, Fernando Reyes", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Tirado", + "givenName": "Fernando Reyes" + }, + { + "creatorName": "Toinin, Jacques Perrin", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Toinin", + "givenName": "Jacques Perrin" + }, + { + "creatorName": "Dunand, David", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Dunand", + "givenName": "David" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals" + }, + { + "subject": "microstructure" + }, + { + "subject": "NUCAPT" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-03-09T18:50:40Z", + "dateType": "Accepted" + }, + { + "date": "2018-03-09T18:50:40Z", + "dateType": "Available" + }, + { + "date": "2018-03-09", + "dateType": "Issued" + } + ], + "titles": [ + { + "title": "\u03b3+\u03b3\u2019 Microstructures in the Co-Nb-V Ternary System" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The Co-Nb-V ternary systems are investigated in a search for L12-ordered \u03b3\u2019 precipitation. Alloys are arc-melted, homogenized at 1250 \u00b0C, and aged at 900 \u00b0C for 2 h. The novel system displays metastable \u03b3\u2019 precipitates." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "NUCAPT" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_1_bocchini_atom_v2.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=61177e74-e1ce-11e7-8031-0a208f818180&origin_path=/published/publication_1058/", + "endpoint_path": "globus://61177e74-e1ce-11e7-8031-0a208f818180/published/publication_1058/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/pub_1_bocchini_atom_v2.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mrr": { + "characterizationMethod": [ + "Atom probe tomography" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-07T22:16:44.786494Z", + "source_id": "pub_1_bocchini_atom_v2.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_1_bocchini_atom" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2S34X", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Bocchini", + "givenName": "Peter", + "creatorName": "Bocchini, Peter", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Chung", + "givenName": "Ding-Wen", + "creatorName": "Chung, Ding-Wen", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Dunand", + "givenName": "David", + "creatorName": "Dunand, David", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Seidman", + "givenName": "David", + "creatorName": "Seidman, David", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-03-09T15:41:43Z", + "dateType": "Accepted" + }, + { + "date": "2018-03-09T15:41:43Z", + "dateType": "Available" + }, + { + "date": "2018-03-09", + "dateType": "Issued" + } + ], + "titles": [ + { + "title": "Atom Probe Tomography Reconstruction and Analysis for the Temporal Evolution of Co-Al-W Superalloys at 650\u02daC" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Series of APT study for different aging time at 650C for Co-8.9Al-7.3W. Data were generated between 2012 to 2014.\r\n\r\nThe temporal evolution of a \u03b3(f.c.c.)/\u03b3\u2019(L12) Co-8.8Al-7.3W superalloy aged at 650 \u00b0C (10 min to 4096 h) is studied utilizing atom-probe tomography (APT). Results allows characterization of nucleation and coarsening kinetics of Co superalloys." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "NUCAPT" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pub_2_bocchini_atom_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pub_2_bocchini_atom_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=61177e74-e1ce-11e7-8031-0a208f818180&origin_path=/published/publication_1059/", + "endpoint_path": "globus://61177e74-e1ce-11e7-8031-0a208f818180/published/publication_1059/" + }, + "mrr": { + "characterizationMethod": [ + "Atom probe tomography" + ] + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-03-07T22:21:25.678460Z", + "source_id": "pub_2_bocchini_atom_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "pub_2_bocchini_atom" + }, + "dc": { + "identifier": { + "identifier": "10.18126/M2WS7W", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Bocchini, Peter", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Bocchini", + "givenName": "Peter" + }, + { + "creatorName": "Chung, Ding-Wen", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Chung", + "givenName": "Ding-Wen" + }, + { + "creatorName": "Dunand, David", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Dunand", + "givenName": "David" + }, + { + "creatorName": "Seidman, David", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Seidman", + "givenName": "David" + } + ], + "publicationYear": "2018", + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2018-03-09T18:53:35Z", + "dateType": "Accepted" + }, + { + "date": "2018-03-09T18:53:35Z", + "dateType": "Available" + }, + { + "date": "2018-03-09", + "dateType": "Issued" + } + ], + "titles": [ + { + "title": "Atom Probe Tomography Reconstruction and Analysis for the Temporal Evolution of Co-Al-W Superalloys at 750\u02daC" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The temporal evolution of a \u03b3(f.c.c.)/\u03b3\u2019(L12) Co-8.8Al-7.3W superalloy aged at 750 \u00b0C (10 min to 256 h) is studied utilizing atom-probe tomography (APT). Results allows characterization of nucleation and coarsening kinetics of Co superalloys." + } + ], + "geoLocations": [ + { + "geoLocationPlace": "NUCAPT" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mrr": "This dataset was registered with the MRR.", + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations_v1-1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/legacy/dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations_v1-1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "CHiMaD" + ], + "ingest_date": "2019-04-01T22:31:07.868875Z", + "source_id": "dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "dataset_for_machine_learning_prediction_accurate_atomization_energies_organic_molecules_from_lowfidelity_quantum_chemical_calculations" + }, + "dc": { + "publicationYear": "2019", + "publisher": "Materials Data Facility", + "titles": [ + { + "title": "Dataset for Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations" + } + ], + "creators": [ + { + "creatorName": "Ward, Logan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Ward", + "givenName": "Logan" + }, + { + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Blaiszik", + "givenName": "Ben" + }, + { + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Foster", + "givenName": "Ian" + }, + { + "creatorName": "Assary, Rajeev", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Assary", + "givenName": "Rajeev" + }, + { + "creatorName": "Narayanan, Badri", + "affiliations": [ + "Argonne National Laboratory", + "University of Louisville" + ], + "familyName": "Narayanan", + "givenName": "Badri" + }, + { + "creatorName": "Curtiss, Larry", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Curtiss", + "givenName": "Larry" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "DFT" + }, + { + "subject": "high-throughput" + }, + { + "subject": "database" + }, + { + "subject": "energy storage" + }, + { + "subject": "metals" + }, + { + "subject": "machine learning" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "raman_analysis_nanohub_gresq_id_49_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/legacy/raman_analysis_nanohub_gresq_id_49_v1.1/", + 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"affiliations": [ + "University of California, Santa Barbara" + ], + "familyName": "Kaufman", + "givenName": "Jonas L." + }, + { + "creatorName": "Van der Ven, Anton", + "affiliations": [ + "University of California, Santa Barbara" + ], + "familyName": "Van der Ven", + "givenName": "Anton" + } + ], + "subjects": [ + { + "subject": "density functional theory" + }, + { + "subject": "oxides" + }, + { + "subject": "VASP" + }, + { + "subject": "layered intercalation compounds" + }, + { + "subject": "CASM" + }, + { + "subject": "K-ion batteries" + }, + { + "subject": "simulation" + }, + { + "subject": "DFT" + }, + { + "subject": "energy materials" + }, + { + "subject": "crystal structure" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2020, + "dates": [ + { + "date": "2020-07-07", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Ordering and structural transformations in layered KxCrO2 for K-ion batteries" + } + ], + "contributors": [ + { + "contributorName": "Jonas L. Kaufman (jlk@ucsb.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Anton Van der Ven (avdv@ucsb.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset contains VASP calculations for K-vacancy orderings in layered host structures of KxCrO2 enumerated using the CASM code (https://github.com/prisms-center/CASMcode), as well as some cluster expansion predictions and calculations of magnetic orderings." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "printed_hbnionogel_transistors_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/hersam_protected/printed_hbnionogel_transistors_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/hersam_protected/printed_hbnionogel_transistors_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/printed_hbnionogel_transistors_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "Hersam Group" + ], + "ingest_date": "2020-01-21T21:40:44.282612Z", + "source_id": "printed_hbnionogel_transistors_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "printed_hbnionogel_transistors" + }, + "dc": { + "identifier": { + "identifier": "10.18126/9pqj-hc6f", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Hyun", + "givenName": "Woo Jin", + "creatorName": "Hyun, Woo Jin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Chaney", + "givenName": "Lindsay E", + "creatorName": "Chaney, Lindsay E", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Downing", + "givenName": "Julia R.", + "creatorName": "Downing, Julia R.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "de Moraes", + "givenName": "Ana C. M.", + "creatorName": "de Moraes, Ana C. M.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "dielectric" + }, + { + "subject": "2D materials" + }, + { + "subject": "boron nitride" + }, + { + "subject": "hBN" + }, + { + "subject": "ionic liquid" + }, + { + "subject": "ionogel" + }, + { + "subject": "printed" + }, + { + "subject": "transistor" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2020, + "dates": [ + { + "date": "2020-01-21", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Printable Hexagonal Boron Nitride Ionogels" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1039/C9FD00113A", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Sonal Rangnekar (sonal@u.northwestern.edu)" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kearns_biofilm_rupture_location_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kearns_biofilm_rupture_location_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kearns_biofilm_rupture_location_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/kearns_biofilm_rupture_location_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2019-10-28T16:18:39.877641Z", + "source_id": "kearns_biofilm_rupture_location_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kearns_biofilm_rupture_location" + }, + "dc": { + "identifier": { + "identifier": "10.18126/5ly6-orul", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Kearns", + "givenName": "Kaitlyn L.", + "creatorName": "Kearns, Kaitlyn L.", + "affiliations": [ + "University of Kentucky" + ] + }, + { + "familyName": "Boyd", + "givenName": "James D.", + "creatorName": "Boyd, James D.", + "affiliations": [ + "University of Kentucky" + ] + }, + { + "familyName": "Grady", + "givenName": "Martha E.", + "creatorName": "Grady, Martha E.", + "affiliations": [ + "University of Kentucky" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "biomaterials" + }, + { + "subject": "microscopy" + }, + { + "subject": "laser spallation" + }, + { + "subject": "adhesion" + } + ], + "publicationYear": 2019, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-10-28", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Martha Grady " + } + ], + "titles": [ + { + "title": "Biofilm rupture by laser-induced stress waves increases with loading amplitude, independent of location" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Integral to the production of safe and biocompatible medical devices is to determine the interfacial properties that effect or control strong biofilm adhesion. The laser spallation technique has recently emerged as an advantageous technique to quantify biofilm adhesion across candidate biomedical surfaces. However, there is a possibility that membrane tension is a factor that contributes to the stress required to separate biofilm and substrate. In that case, the stress amplitude, controlled by laser fluence, that initiates biofilm rupture would vary systematically with location on the biofilm. Film rupture, also known as spallation, occurs when film material is ejected during stress wave loading. In order to determine effects of membrane tension, we present a protocol that measures spall size with increasing laser fluence (variable fluence) and with respect to distance from the biofilm centroid (iso-fluence). Streptococcus mutans biofilms on titanium substrates serves as our model system. A total of 185 biofilm loading locations are analyzed in this study. We demonstrate that biofilm spall size increases monotonically with laser fluence and apply our procedure to failure of non-biological films. In iso-fluence experiments, no correlation is found between biofilm spall size and loading location, thus providing evidence that membrane tension does not play a dominant role in biofilm adhesion measurements. We recommend our procedure as a straightforward method to determine membrane effects in the measurement of adhesion of biological films on substrate surfaces via the laser spallation technique." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ma_datasets_optical_paper_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ma_datasets_optical_paper_v1.1/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ma_datasets_optical_paper_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/ma_datasets_optical_paper_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2019-11-20T18:25:33.355838Z", + "source_id": "ma_datasets_optical_paper_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ma_datasets_optical_paper" + }, + "dc": { + "identifier": { + "identifier": "10.18126/r4sn-m7sa", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Ma", + "givenName": "Su-Yang", + "creatorName": "Ma, Su-Yang", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Ma", + "givenName": "Qiong", + "creatorName": "Ma, Qiong", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Gao", + "givenName": "Yang", + "creatorName": "Gao, Yang", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Kogar", + "givenName": "Anshul", + "creatorName": "Kogar, Anshul", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Zong", + "givenName": "Guo Alfred", + "creatorName": "Zong, Guo Alfred", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Mier Valdivia", + "givenName": "Andres M.", + "creatorName": "Mier Valdivia, Andres M.", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Dinh", + "givenName": "Thao H.", + "creatorName": "Dinh, Thao H.", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Huang", + "givenName": "Shin-Ming", + "creatorName": "Huang, Shin-Ming", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Singh", + "givenName": "Bahadur", + "creatorName": "Singh, Bahadur", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Hsu", + "givenName": "Chuang-Han", + "creatorName": "Hsu, Chuang-Han", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Chang", + "givenName": "Tay-Rong", + "creatorName": "Chang, Tay-Rong", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Ruff", + "givenName": "Jacob P.C.", + "creatorName": "Ruff, Jacob P.C.", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Watanabe", + "givenName": "Kenji", + "creatorName": "Watanabe, Kenji", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Taniguchi", + "givenName": "Takashi", + "creatorName": "Taniguchi, Takashi", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Lin", + "givenName": "Hsin", + "creatorName": "Lin, Hsin", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Karapetrov", + "givenName": "Goran", + "creatorName": "Karapetrov, Goran", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Xiao", + "givenName": "Di", + "creatorName": "Xiao, Di", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Jarillo-Herrero", + "givenName": "Pablo", + "creatorName": "Jarillo-Herrero, Pablo", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + }, + { + "familyName": "Gedik", + "givenName": "Nuh", + "creatorName": "Gedik, Nuh", + "affiliations": [ + "Massachusetts Institute of Technology", + "National Sun Yat-sen University", + "Shenzhen University", + "Northeastern University", + "National University of Singapore", + "National Cheng Kung University", + "National Institute for Materials Science Japan", + "Academica Sinica", + "Drexel University", + "Carnegie Mellon University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2D Material" + }, + { + "subject": "Chiral photogalvanic current" + }, + { + "subject": "Chiral electronic phases" + } + ], + "publicationYear": 2019, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-11-20", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "qiongm@mit.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "heinonen@anl.gov" + } + ], + "titles": [ + { + "title": "Datasets for \"Optical detection and manipulation of spontaneous gyrotropic 1 electronic order in a transition-metal dichalcogenide semimetal\" Nature paper" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Experimental data accompanying publication (Nature) \"Optical detection and manipulation of spontaneous gyrotropic electronic order in a transition-metal dichalcogenide semimetal\"\nThe experimental data were collected from four samples, grouped into their individual folders. *.ibw is Igor binary file and .pxp is Igor Packed Experiment file that contains Igor waves. They need to be opened with Igor Pro. Simple description of the data can be found from the name of the file and folder." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "srivastava_structure_morphology_polyelectrolytes_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/srivastava_structure_morphology_polyelectrolytes_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/srivastava_structure_morphology_polyelectrolytes_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/srivastava_structure_morphology_polyelectrolytes_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-10-31T19:26:55.141503Z", + "source_id": "srivastava_structure_morphology_polyelectrolytes_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "srivastava_structure_morphology_polyelectrolytes" + }, + "dc": { + "identifier": { + "identifier": "10.18126/eicu-wshs", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Srivastava, Samanvaya", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "UCLA " + ], + "familyName": "Srivastava", + "givenName": "Samanvaya" + }, + { + "creatorName": "Levi, Adam E.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "UCLA " + ], + "familyName": "Levi", + "givenName": "Adam E." + }, + { + "creatorName": "Goldfeld, David J.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "UCLA " + ], + "familyName": "Goldfeld", + "givenName": "David J." + }, + { + "creatorName": "Tirrell, Matthew V.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "UCLA " + ], + "familyName": "Tirrell", + "givenName": "Matthew V." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "self-assembly" + }, + { + "subject": "hydrogels" + }, + { + "subject": "polyelectrolytes" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-10-31", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "samsri@ucla.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "mtirrell@uchicago.edu", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acs.macromol.0c00847", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Dataset for Structure, Morphology, and Rheology of Polyelectrolyte Complex Hydrogels Formed by Self-Assembly of Oppositely Charged Triblock Polyelectrolytes" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset includes small angle X-ray scattering (SAXS) data from the Advanced Photon Source at Argonne National Laboratory. Data is organized by polymer length and polymer concentration in the hydrogels. See the accompanying excel file for details.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "atomagined_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/atomagined_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/atomagined_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/atomagined_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-03-09T17:06:55.354964Z", + "source_id": "atomagined_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "atomagined" + }, + "dc": { + "identifier": { + "identifier": "10.18126/szeq-yde5", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Schwenker", + "givenName": "Eric", + "creatorName": "Schwenker, Eric", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "familyName": "Sen", + "givenName": "Fatih", + "creatorName": "Sen, Fatih", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "familyName": "Wolverton", + "givenName": "Chris", + "creatorName": "Wolverton, Chris", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "familyName": "Ophus", + "givenName": "Colin", + "creatorName": "Ophus, Colin", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + }, + { + "familyName": "Chan", + "givenName": "Maria K.Y", + "creatorName": "Chan, Maria K.Y", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "Lawrence Berkeley National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "high-throughput" + }, + { + "subject": "microscopy" + }, + { + "subject": "STEM" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-03-09", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Maria Chan " + }, + { + "contributorType": "ContactPerson", + "contributorName": "Eric Schwenker " + } + ], + "titles": [ + { + "title": "A Simulated Atomic-resolution HAADF STEM Imaging Dataset Containing Unique ICSD Structure Prototypes" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The atomagined dataset is a collection of atomic-resolution images of unique ICSD structure prototypes, simulated in the high angle annular dark field (HAADF) STEM modality. 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C.", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Universite de Toulouse", + "Oak Ridge National Laboratory", + "Sandia National Laboratories" + ], + "familyName": "Kent", + "givenName": "Paul R. C." + }, + { + "creatorName": "Shulenburger, Luke", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "Argonne National Laboratory", + "Universite de Toulouse", + "Oak Ridge National Laboratory", + "Sandia National Laboratories" + ], + "familyName": "Shulenburger", + "givenName": "Luke" + } + ], + "subjects": [ + { + "subject": "QMCPack" + }, + { + "subject": "QMC" + }, + { + "subject": "Selected CI" + }, + { + "subject": "AFQMC" + }, + { + "subject": "DMC" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-09-25", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Anouar Benali", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data for \"Systematic Comparison and Cross-validation of Fixed-Node Diffusion Monte Carlo and Phaseless Auxiliary-Field Quantum Monte Carlo in Solids\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for Paper titled: \"Systematic Comparison and Cross-validation of Fixed-Node Diffusion Monte Carlo and Phaseless Auxiliary-Field Quantum Monte Carlo in Solids\" containing Input and outputfiles for AFQMC and DMC using the QMCPACK code as well as trial wavefunctions and FCIDUMP Files. Output files for the Selected CI runs are provided in the Quantum Package Format. Pyscf files for Input and output are available allowing to generate the sCI inputs. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "manna_nanoclusters_dft_fitting_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/manna_nanoclusters_dft_fitting_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/manna_nanoclusters_dft_fitting_v1.1/", + "total_size": 114829624, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manna_nanoclusters_dft_fitting_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-09-25T21:55:13.143465Z", + "source_id": "manna_nanoclusters_dft_fitting_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "manna_nanoclusters_dft_fitting" + }, + "dc": { + "identifier": { + "identifier": "10.18126/rpik-56t4", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Manna, Sukriti", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Manna", + "givenName": "Sukriti" + }, + { + "creatorName": "Sankaranarayanan, Subramanian", + "affiliations": [ + "University of Illinois at Chicago" + ], + "familyName": "Sankaranarayanan", + "givenName": "Subramanian" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-09-25", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "sukriti.manna@gmail.com", + "contributorType": "ContactPerson" + }, + { + "contributorName": "skrssank@uic.edu", + "contributorType": 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}, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-06-17T02:08:53.509594Z", + "source_id": "afqmc_gpu_kp_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "afqmc_gpu_kp" + }, + "dc": { + "identifier": { + "identifier": "10.18126/xxyt-h8gj", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Malone", + "givenName": "Fionn D.", + "creatorName": "Malone, Fionn D.", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "Laboratory for Laser Energetics University of Rochester" + ] + }, + { + "familyName": "Zhang", + "givenName": "Shuai", + "creatorName": "Zhang, Shuai", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "Laboratory for Laser Energetics University of Rochester" + ] + }, + { + "familyName": "Morales", + "givenName": "Miguel A.", + "creatorName": "Morales, Miguel A.", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "Laboratory for Laser Energetics University of Rochester" + ] + } + ], + "subjects": [ + { + "subject": "QMC" + }, + { + "subject": "QMCPACK" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-17", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "malone14@llnl.gov" + } + ], + "titles": [ + { + "title": "Accelerating Auxiliary-Field Quantum Monte Carlo Simulations of Solids with Graphical Processing Units" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Raw data for Accelerating Auxiliary-Field Quantum Monte Carlo Simulations of Solids with Graphical Processing Units" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "al_preequilibrium_paper_v1.2", + "entries": [ + { + 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"creatorName": "Kononov, Alina", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-23", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "schleife@illinois.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "kononov2@illinois.edu" + } + ], + "titles": [ + { + "title": "Pre-equilibrium stopping and charge capture in proton-irradiated aluminum sheets" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "TDDFT simulations of proton-irradiated aluminum sheets performed with Qbox/Qb@ll (https://github.com/LLNL/qball)" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "hydronet_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/hydronet_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/hydronet_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hydronet_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-10-02T01:30:17.920167Z", + "source_id": "hydronet_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "hydronet" 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"rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Mannodi-Kanakkithodi", + "givenName": "Arun", + "creatorName": "Mannodi-Kanakkithodi, Arun", + "affiliations": [ + "Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL-60439" + ] + }, + { + "familyName": "Chan", + "givenName": "Maria K.Y.", + "creatorName": "Chan, Maria K.Y.", + "affiliations": [ + "School of Materials Engineering, Purdue University, West Lafayette, IN-47907" + ] + } + ], + "subjects": [ + { + "subject": "DFT" + }, + { + "subject": "machine learning" + }, + { + "subject": "simulation" + }, + { + "subject": "semiconductors" + }, + { + "subject": "defects" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-07-14", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Arun Mannodi-Kanakkithodi (amannodi@purdue.edu)" + }, + { + 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Shell and python-based scripts are applied on the converged calculation output files to determine the complete charge-, chemical potential-, and Fermi level-dependent formation energies of ~ 2000 native point defects and extrinsic impurity atoms across a total of 34 compounds, resulting in the largest computational defect dataset to date. 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Yu, Z., Zhang, C., Voyles, P. M., He, L., Liu, X., Nygren, K. & Couet, A. Microstructure and microchemistry study of irradiation-induced precipitates in proton irradiated ZrNb alloys. Acta Mater. 178, 228\u2013240 (2019). DOI: 10.1016/j.actamat.2019.08.012. Nb precipitates are formed in a Zr matrix by proton irradiation. This data set contains Z-contrast STEM imaging and 4D STEM strain mapping of the Nb precipitates. 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This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "zhang_actamat2016_zrcual_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/zhang_actamat2016_zrcual_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhang_actamat2016_zrcual_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/zhang_actamat2016_zrcual_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-06-30T21:32:51.252730Z", + "source_id": "zhang_actamat2016_zrcual_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "zhang_actamat2016_zrcual" + }, + "dc": { + "identifier": { + "identifier": "10.18126/vefp-a28r", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Zhang", + "givenName": "Pei", + "creatorName": "Zhang, Pei", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Maldonis", + "givenName": "Jason J.", + "creatorName": "Maldonis, Jason J.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Besser", + "givenName": "M.F.", + "creatorName": "Besser, M.F.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Kramer", + "givenName": "M.J.", + "creatorName": "Kramer, M.J.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + }, + { + "subject": "fluctuation electron microscopy" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-30", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1016/j.actamat.2016.02.006", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Data for Medium-range structure and glass forming ability in Zr\u2013Cu\u2013Al bulk metallic glasses" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for Zhang, P., Maldonis, J. J., Besser, M. F., Kramer, M. J. & Voyles, P. M. \"Medium-range structure and glass forming ability in Zr\u2013Cu\u2013Al bulk metallic glasses\" Acta Mater. 109, 103\u2013114 (2016). DOI: 10.1016/j.actamat.2016.02.006. Includes variable-resolution fluctuation electron microscopy raw data and analysis and hybrid reverse Monte Carlo derived atomic structural models and related analyses." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "he_mm2015_ecm_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/he_mm2015_ecm_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/he_mm2015_ecm_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/he_mm2015_ecm_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-06-30T22:19:35.465516Z", + "source_id": "he_mm2015_ecm_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "he_mm2015_ecm" + }, + "dc": { + "identifier": { + "identifier": "10.18126/oph0-nrge", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "He", + "givenName": "Li", + "creatorName": "He, Li", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Zhang", + "givenName": "Pei", + "creatorName": "Zhang, Pei", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Besser", + "givenName": "Matthew F.", + "creatorName": "Besser, Matthew F.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Kramer", + "givenName": "Matthew Joseph", + "creatorName": "Kramer, Matthew Joseph", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison", + "Ames Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "electron correlation microscopy" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-06-30", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1017/S1431927615000641", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Data for Electron Correlation Microscopy: A New Technique for Studying Local Atom Dynamics Applied to a Supercooled Liquid" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for He, L., Zhang, P., Besser, M. F., Kramer, M. J. & Voyles, P. M. Electron Correlation Microscopy: A New Technique for Studying Local Atom Dynamics Applied to a Supercooled Liquid. Microsc. Microanal. 21, 1026\u20131033 (2015). DOI: 10.1017/S1431927615000641. Electron correlation microscopy experiments on a Pd40Ni20P20 metallic glass in the supercooled liquid state. Contains raw data and analysis." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "tsopanidis_wha_amaps_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mrr": "https://mrr.materialsdatafacility.org/data?id=5eb05be49322a400328bfff8", + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/tsopanidis_wha_amaps_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/tsopanidis_wha_amaps_v1.1/", + "total_size": 1214540557, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/tsopanidis_wha_amaps_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-05-04T16:07:51.238909Z", + "source_id": "tsopanidis_wha_amaps_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "tsopanidis_wha_amaps" + }, + "dc": { + "identifier": { + "identifier": "10.18126/aph0-olbz", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Tsopanidis, Stylianos", + "affiliations": [ + "University Carlos III of Madrid" + ], + "familyName": "Tsopanidis", + "givenName": "Stylianos" + }, + { + "creatorName": "Osovski, Shmuel", + "affiliations": [ + "Technion - Israel Institute of Technology" + ], + "familyName": "Osovski", + "givenName": "Shmuel" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "microscopy" + }, + { + "subject": "microstructures" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-05-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "tsopanidisstelios@gmail.com", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "WHA SEM Fracture Images Dataset and Activation Maps" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The WHA Dataset is a composed by SEM images of the fracture surface of WHA samples with different tungsten composition. This dataset is used to evaluate the efficiency of two Unsupervised Machine Learning algorithms that aim to cluster and classify the fracture images according to the tungsten composition. Additionally, the Activation Maps of the last convolution layer of the neural network (VGG16) that is used in an intermediate stage of the algorithms are computed, according to their weights in the next stages of the clustering algorithm." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "zhang_tensorsvd_4dstem_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/zhang_tensorsvd_4dstem_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhang_tensorsvd_4dstem_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/zhang_tensorsvd_4dstem_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-07-02T17:19:26.047803Z", + "source_id": "zhang_tensorsvd_4dstem_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "zhang_tensorsvd_4dstem" + }, + "dc": { + "identifier": { + "identifier": "10.18126/vh9q-i1l6", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Zhang", + "givenName": "Chenyu", + "creatorName": "Zhang, Chenyu", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Han", + "givenName": "Rungang", + "creatorName": "Han, Rungang", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Zhang", + "givenName": "Anru", + "creatorName": "Zhang, Anru", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "semiconductors" + }, + { + "subject": "oxides" + }, + { + "subject": "microscopy" + }, + { + "subject": "4D STEM" + }, + { + "subject": "denoising" + }, + { + "subject": "component analysis" + }, + { + "subject": "tensor SVD" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-07-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles" + } + ], + "titles": [ + { + "title": "Data for \"Denoising Atomic Resolution 4D Scanning Transmission Electron Microscopy Data with Tensor Singular Value Decomposition\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for \"Denoising Atomic Resolution 4D Scanning Transmission Electron Microscopy Data with Tensor Singular Value Decomposition\" by\nChenyu Zhang, Rungang Han, Anru R. Zhang, Paul. M. Voyles. Consists of experimental and simulated 4D STEM data both as-created and after denoising with tensor singular value decomposition. Simulated data sets are from SrTiO3 [100] and an edge dislocation in Si [110]. Experimental data sets are from SrTiO3 [100] and an epitaxial LiZnSb thin film on GaSb substrate. MATLAB code for tensor SVD denoising is also included.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ophus_mbed_01_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ophus_mbed_01_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ophus_mbed_01_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/ophus_mbed_01_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-12-03T17:05:48.657563Z", + "source_id": "ophus_mbed_01_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ophus_mbed_01" + }, + "dc": { + "identifier": { + "identifier": "10.18126/7eav-q9vl", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Hong", + "givenName": "Xuhao", + "creatorName": "Hong, Xuhao", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Zeltmann", + "givenName": "Steven E", + "creatorName": "Zeltmann, Steven E", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Savitzky", + "givenName": "Benjamin H", + "creatorName": "Savitzky, Benjamin H", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Dacosta", + "givenName": "Luis Rangel", + "creatorName": "Dacosta, Luis Rangel", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Muller", + "givenName": "Alexander", + "creatorName": "Muller, Alexander", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Bustillo", + "givenName": "Karen C", + "creatorName": "Bustillo, Karen C", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Minor", + "givenName": "Andrew M", + "creatorName": "Minor, Andrew M", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + }, + { + "familyName": "Ophus", + "givenName": "Colin", + "creatorName": "Ophus, Colin", + "affiliations": [ + "Lawrence Berkeley National Laboratory", + "University of California Berkeley" + ] + } + ], + "subjects": [ + { + "subject": "Scanning Transmission Electron Microscopy" + }, + { + "subject": "Experiment" + }, + { + "subject": "Multibeam Electron Diffraction" + }, + { + "subject": "4D-STEM" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-12-03", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "clophus@lbl.gov" + } + ], + "titles": [ + { + "title": "Multibeam Electron Diffraction - experimental datasets" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Experimental datasets for multibeam electron diffraction. Includes diffraction patterns from various 4D-STEM scans of Au nanoparticles, recorded using a multibeam aperture containing 7 outer beams spaced around a ring with radius 60 mrads, around a central beam." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "nahyunjo_prb2020_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/nahyunjo_prb2020_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/nahyunjo_prb2020_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nahyunjo_prb2020_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + 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"Slager", + "givenName": "Robert-Jan" + }, + { + "creatorName": "Yan, Jiaqiang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Yan", + "givenName": "Jiaqiang" + }, + { + "creatorName": "Wu, Yun", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Wu", + "givenName": "Yun" + }, + { + "creatorName": "Lee, Kyungchan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Lee", + "givenName": "Kyungchan" + }, + { + "creatorName": "Schrunk, Benjamin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Schrunk", + "givenName": "Benjamin" + }, + { + "creatorName": "Vishwanath, Ashvin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Vishwanath", + "givenName": "Ashvin" + }, + { + "creatorName": "Kaminski, Adam", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Oak Ridge National Laboratory" + ], + "familyName": "Kaminski", + "givenName": "Adam" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "simulation" + }, + { + "subject": "Topological matter" + }, + { + "subject": "Axion insulator" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-07-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Na Hyun Jo (njo@iastate.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Adam Kaminski (kaminski@ameslab.gov)", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Intrinsic axion insulating behavior in antiferromagnetic MnBi6Te10" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data set for publication \"Intrinsic axion insulating behavior in antiferromagnetic MnBi6Te10\" in Phys Rev B 2020" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "muley_mgfilmstability_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/muley_mgfilmstability_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/muley_mgfilmstability_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/muley_mgfilmstability_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-07-03T19:42:10.298643Z", + "source_id": "muley_mgfilmstability_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "muley_mgfilmstability" + }, + "dc": { + "identifier": { + "identifier": "10.18126/xa3c-69j6", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Muley", + "givenName": "Sachin V.", + "creatorName": "Muley, Sachin V.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Cao", + "givenName": "Chengrong", + "creatorName": "Cao, Chengrong", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Chatterjee", + "givenName": "Debaditya", + "creatorName": "Chatterjee, Debaditya", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Francis", + "givenName": "Carter", + "creatorName": "Francis, Carter", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Lu", + "givenName": "Felix P.", + "creatorName": "Lu, Felix P.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Ediger", + "givenName": "M. D.", + "creatorName": "Ediger, M. D.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Perpezko", + "givenName": "John H.", + "creatorName": "Perpezko, John H.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + }, + { + "subject": "thin films" + }, + { + "subject": "stability" + }, + { + "subject": "medium-range order" + }, + { + "subject": "fluctuation electron microscopy" + }, + { + "subject": "electron nanodiffraction" + }, + { + "subject": "nanoindentation" + }, + { + "subject": "calorimetry" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-07-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul Voyles" + } + ], + "titles": [ + { + "title": "Data for Varying Kinetic Stability, Icosahedral Ordering, and Mechanical Properties of a Model Zr-Cu-Al Metallic Glass by Sputtering" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for \"Varying Kinetic Stability, Icosahedral Ordering, and Mechanical Properties of a Model Zr-Cu-Al Metallic Glass by Sputtering\" by\nSachin V. Muley, Chengrong Cao, Debaditya Chatterjee, Carter Francis, Felix P. Lu, M. D. Ediger, John H. Perepezko, and Paul M. Voyles. This paper shows that the kinetic and thermodynamic stability of Zr65Cu27.5Al7.5 metallic glass thin films may be manipulated by changing the film deposition rate during sputtering. Changing stability increases the ordering of the glass structure, as shown by fluctuation electron microscopy and angular correlations in nanodiffraction, and increases the film's elastic modulus and hardness. The data include x-ray diffraction, calorimetry, nanoindentation, SEM, AFM, and electron nanodiffraction on the films and on a bulk ribbon glass with the same composition quenched from the melt.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "fsp_silica_bayesopt_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/fsp_silica_bayesopt_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/fsp_silica_bayesopt_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/fsp_silica_bayesopt_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-07-27T13:32:01.678880Z", + "source_id": "fsp_silica_bayesopt_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "fsp_silica_bayesopt" + }, + "dc": { + "identifier": { + "identifier": "10.18126/w809-nuxk", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Paulson", + "givenName": "Noah", + "creatorName": "Paulson, Noah", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "familyName": "Libera", + "givenName": "Joe", + "creatorName": "Libera, Joe", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "familyName": "Stan", + "givenName": "Marius", + "creatorName": "Stan, Marius", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "flame spray pyrolysis" + }, + { + "subject": "silica" + }, + { + "subject": "SiO2" + }, + { + "subject": "nanomaterials" + }, + { + "subject": "particle size distribution" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-07-27", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "npaulson@anl.gov" + }, + { + "contributorType": "ContactPerson", + "contributorName": "jlibera@anl.gov" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1016/j.matdes.2020.108972", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Flame spray pyrolysis for silica nanomaterial synthesis" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset describes 17 experimental runs for the production of silica nanomaterials via flame spray pyrolysis (FSP) from tetraethyl orthosilicate (TEOS, pure, Merck KGaA, Germany) in reagent alcohol (C2H5OH + CH3OH 94.0-96.0%, C3H8O 4.0-6.0%, VWR Chemicals BDH, USA). The dataset principally contains the various gas and liquid flowrates and the scanning mobility particle sizer outputs including particle counts for different diameter bins. Finally, the dataset contains the calculated statistics of the measured particle size distribution for each experimental run. See the following article for further details: https://doi.org/10.1016/j.matdes.2020.108972." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "singh_rolling_friction_prl_2020_v1.4", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/singh_rolling_friction_prl_2020_v1.4/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/singh_rolling_friction_prl_2020_v1.4/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/singh_rolling_friction_prl_2020_v1.4", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-06-29T20:02:02.948799Z", + "source_id": "singh_rolling_friction_prl_2020_v1.4", + "scroll_id": 0, + "version": 1, + "source_name": "singh_rolling_friction_prl_2020" + }, + "dc": { + "identifier": { + "identifier": "10.18126/oqop-2zxk", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Singh", + "givenName": "Abhinendra", + "creatorName": "Abhinendra Singh", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Ness", + "givenName": "Christopher", + "creatorName": "Christopher Ness", + "affiliations": [ + "University of Edinburgh" + ] + }, + { + "familyName": "Seto", + "givenName": "Ryohei", + "creatorName": "Ryohei Seto", + "affiliations": [ + "Wenzhou Institute" + ] + }, + { + "familyName": "de Pablo", + "givenName": "Juan J.", + "creatorName": "Juan J. de Pablo", + "affiliations": [ + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Jaeger", + "givenName": "Heinrich M.", + "creatorName": "Heinrich M. 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excitations in SixHy molecular systems using quantum Monte Carlo" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nDataset for \"Binding and excitations in SixHy molecular systems using quantum Monte Carlo\".\n\nData is categorized by each molecule." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "chaitanya_borophene_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/chaitanya_borophene_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/chaitanya_borophene_v1.1/", + "total_size": 7003, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/chaitanya_borophene_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-01-22T19:37:44.528308Z", + "source_id": "chaitanya_borophene_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "chaitanya_borophene" + }, + "dc": { + "identifier": { + "identifier": "10.18126/oizk-5a9l", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Li, Qiucheng", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Li", + "givenName": "Qiucheng" + }, + { + "creatorName": "Kolluru, Venkata Surya Chaitanya", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Kolluru", + "givenName": "Venkata Surya Chaitanya" + }, + { + "creatorName": "Rahn, Matthew S.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Rahn", + "givenName": "Matthew S." + }, + { + "creatorName": "Schwenker, Eric", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Schwenker", + "givenName": "Eric" + }, + { + "creatorName": "Li, Shaowei", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Li", + "givenName": "Shaowei" + }, + { + "creatorName": "Hennig, Richard G.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Hennig", + "givenName": "Richard G." + }, + { + "creatorName": "Darancet, Pierre", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Darancet", + "givenName": "Pierre" + }, + { + "creatorName": "Chan, Maria K. Y.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Chan", + "givenName": "Maria K. Y." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory", + "University of Florida", + "Quantum Theory Project", + "Northwestern-Argonne Institute of Science and Engineering" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-01-22", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Venkata Surya Chaitanya Kolluru", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Maria K. Y. Chan", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Synthesis of borophane polymorphs via hydrogenation of borophene" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "### Description\n\nThe dataset consists of the structure files in POSCAR format of the bare v1/6 -30 borophene and the three rectangular lattice candidate structures of the H adsorption on borophene - Bridge-1H, Top-1H and 2H.\n
\n\nFurther, the vasprun.xml files from the VASP calculations of the different characterization methods which include -\n\n* Bader charge analysis\n* Projected density of states\n* Partial charge densities near Fermi level for STM simulation\n* Vibrational mode calculations\n* Wrok function calculations with dipole corrections\n\nof the three candidate structures. The vasprun.xml files contains all the relevant input settings used to perform these calculations. By using the same settings along with the provided structure files and pseudopotentials, one can reproduce the reported data. The results from these calculations are compared with experimental observations to determine the atomisitc structure of the observed rectangular borophane lattice.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "structurefactorsilicajade_v2.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/structurefactorsilicajade_v2.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/structurefactorsilicajade_v2.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/structurefactorsilicajade_v2.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-05-12T19:37:39.364275Z", + "source_id": "structurefactorsilicajade_v2.1", + "scroll_id": 0, + "version": 2, + "source_name": "structurefactorsilicajade" + }, + "dc": { + "identifier": { + "identifier": "10.18126/mnbf-k5kn", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Shi, Ying", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ], + "familyName": "Shi", + "givenName": "Ying" + }, + { + "creatorName": "Neuefeind, J\u00f6rg", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ], + "familyName": "Neuefeind", + "givenName": "J\u00f6rg" + }, + { + "creatorName": "Bauchy, Mathieu", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ], + "familyName": "Bauchy", + "givenName": "Mathieu" + }, + { + "creatorName": "Zhou, Qi", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ], + "familyName": "Zhou", + "givenName": "Qi" + }, + { + "creatorName": "Deng, Binghui", + "affiliations": [ + "Corning Incorporated", + "Oak Ridge National Laboratory", + "University of California Los Angeles" + ], + "familyName": "Deng", + "givenName": "Binghui" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "oxides" + }, + { + "subject": "silicate" + }, + { + "subject": "glass" + }, + { + "subject": "neutron diffraction" + }, + { + "subject": "structure factor" + }, + { + "subject": "silica" + }, + { + "subject": "calcium aluminosilicate" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-05-15", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "bauchy@ucla.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "shiy3@corning.com", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1103/PhysRevB.101.134106", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1016/j.jnoncrysol.2019.03.037", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Neutron Structure Factor of Glassy Silica and Jade Glass" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Room temperature reduced neutron structure factor of glassy silica and Jade Glass measured by Time-of-flight (TOF) neutron scattering. The measurements were performed on the Nanoscale-Ordered Materials Diffractometer (NOMAD) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bcdb_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bcdb_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bcdb_v1.3/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/bcdb_v1.3", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-07-20T03:53:55.339818Z", + "source_id": "bcdb_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "bcdb" + }, + "dc": { + "identifier": { + "identifier": "10.18126/w26l-fjts", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Rebello", + "givenName": "Nathan J.", + "creatorName": "Rebello, Nathan J.", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "familyName": "", + "givenName": "CRIPT", + "creatorName": "CRIPT", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "morphologies" + }, + { + "subject": "machine learning" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-07-20", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "bcdb@mit.edu" + } + ], + "titles": [ + { + "title": "BCDB: The Block Copolymer Phase Behavior Database" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The Block Copolymer Database (BCDB) is a database platform that allows a user to search, submit, visualize, and download experimental phase measurements and their associated characterization information for di- and multi-block copolymers. This database template can accommodate any number of blocks and at the time of publication contains over 5,300 block copolymer melt phase measurements mined from literature and manually curated. The chemical structure of the polymer is encoded in BigSMILES, an extension of the Simplified Molecular-Input Line-Entry System (SMILES) into the macromolecular domain, and the user can search repeat units and functional groups using the search syntax SMARTS (SMILES Arbitrary Target Specification). The user can also query characterization and phase information using the Structured Query Language (SQL). This platform is an important step in making polymer data more accessible to the broader community, facilitates benchmarking and visualization of new data against existing samples, enables to user to search and download custom sets of block copolymer data from the literature, and will drive the development of data-driven models and machine learning applications." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "euin2as2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/euin2as2_v1.1/", + "total_size": 462739, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/euin2as2_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/euin2as2_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-12-16T21:21:56.339287Z", + "source_id": "euin2as2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "euin2as2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/u3j8-aplr", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Riberolles", + "givenName": "S.X.M.", + "creatorName": "Riberolles, S.X.M.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Trevisan", + "givenName": "T. V.", + "creatorName": "Trevisan, T. V.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Kuthanazhi", + "givenName": "B.", + "creatorName": "Kuthanazhi, B.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Heitmann", + "givenName": "T. W.", + "creatorName": "Heitmann, T. W.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Ye", + "givenName": "F.", + "creatorName": "Ye, F.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Johnston", + "givenName": "D. C.", + "creatorName": "Johnston, D. C.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Bud\u2019ko", + "givenName": "S. L.", + "creatorName": "Bud\u2019ko, S. L.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Ryan", + "givenName": "D. H.", + "creatorName": "Ryan, D. H.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Canfield", + "givenName": "P. C.", + "creatorName": "Canfield, P. C.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Kreyssig", + "givenName": "A.", + "creatorName": "Kreyssig, A.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Vishwanath", + "givenName": "A.", + "creatorName": "Vishwanath, A.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "McQueeney", + "givenName": "R. J.", + "creatorName": "McQueeney, R. J.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Wang", + "givenName": "L. L.", + "creatorName": "Wang, L. L.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Orth", + "givenName": "P. P.", + "creatorName": "Orth, P. P.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + }, + { + "familyName": "Ueland", + "givenName": "B. G.", + "creatorName": "Ueland, B. G.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "University of Missouri Research Reactor", + "Oak Ridge National Laboratory", + "McGill University", + "Harvard Unniversity" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "DFT" + }, + { + "subject": "Topological materials" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-12-16", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "simonr@iastate.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "bgueland@ameslab.gov" + }, + { + "contributorType": "ContactPerson", + "contributorName": "heinonen@anl.gov" + } + ], + "titles": [ + { + "title": "Data set for \"Magnetic crystalline-symmetry-protected axion electrodynamics and field-tunable unpinned Dirac cones in EuIn2As2\", published in Nature Communications" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "EuIn2As2 single crystal neutron scattering data collected on the TRIAX instrument at the MURR facility (MO) and on the CORRELI instrument at the Oak Ridge SNS facility (TN), presented in \"Magnetic crystalline-symmetry-protected axion electrodynamics and field-tunable unpinned Dirac cones in EuIn2As2\" published in Nature Comm." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "huang_correlation_symmetry_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/huang_correlation_symmetry_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/huang_correlation_symmetry_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/huang_correlation_symmetry_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-06-01T22:05:36.960930Z", + "source_id": "huang_correlation_symmetry_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "huang_correlation_symmetry" + }, + "dc": { + "identifier": { + "identifier": "10.18126/y6l9-ts2n", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Huang", + "givenName": "Shuoyuan", + "creatorName": "Huang, Shuoyuan", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Francis", + "givenName": "Carter", + "creatorName": "Francis, Carter", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Ketkaew", + "givenName": "Jittisa", + "creatorName": "Ketkaew, Jittisa", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Schroers", + "givenName": "Jan", + "creatorName": "Schroers, Jan", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison", + "Yale University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + }, + { + "subject": "nanodiffraction" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-06-01", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Paul M. Voyles" + } + ], + "titles": [ + { + "title": "Data for Correlation Symmetry Analysis of Electron Nanodiffraction from Amorphous Materials" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data for \"Correlation Symmetry Analysis of Electron Nanodiffraction from Amorphous Materials\" by Shuoyuan Huang, Carter Francis, Jittisa Ketkaew, Jan Schroers, Paul M. Voyles. \n\nData consist of experimental electron nanodiffraction patterns from metallic glasses. Experimental data were collected on Pd40Ni10Cu27P20 metallic glass nanowires with a 2 nm diameter, 200 kV electron probe beam on a Titan STEM using a Gatan US1000 CCD camera. The nanowires were 41 \u00b1 3 nm thick. Jupyter notebooks demonstrate analysis of this data for its rotation symmetries using the angular power spectrum and the correlation symmetry coefficients. \n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "silicajade_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/silicajade_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/silicajade_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/silicajade_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-05-12T16:29:59.113871Z", + "source_id": "silicajade_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "silicajade" + }, + "dc": { + "identifier": { + "identifier": "10.18126/rowi-jn5s", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Zhou", + "givenName": "Qi", + "creatorName": "Zhou, Qi", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "familyName": "Shi", + "givenName": "Ying", + "creatorName": "Shi, Ying", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "familyName": "Deng", + "givenName": "Binghui", + "creatorName": "Deng, Binghui", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "familyName": "Neuefeind", + "givenName": "J\u00f6rg", + "creatorName": "Neuefeind, J\u00f6rg", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + }, + { + "familyName": "Bauchy", + "givenName": "Mathieu", + "creatorName": "Bauchy, Mathieu", + "affiliations": [ + "University of California Los Angeles", + "Corning Incorporated", + "Oak Ridge National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "glass" + }, + { + "subject": "silica" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "atomic structure" + }, + { + "subject": "calcium aluminosilicate" + }, + { + "subject": "ring size distribution" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-05-12", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "bauchy@ucla.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "shiy3@corning.com" + } + ], + "titles": [ + { + "title": "Atomic Structures of Glassy Silica and Jade Glass" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset contains the atomic configurations of glassy silica (SiO2) and Jade Glass generated by molecular dynamics (MD) and force-enhanced atomic refinement (FEAR). The configurations have the LAMMPS Data format. Computed neutron structure factors and ring size distribution (using the Guttman definition) are also provided as an Excel file." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "elder_solidification_al126wtcu_c_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/elder_solidification_al126wtcu_c_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/elder_solidification_al126wtcu_c_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/elder_solidification_al126wtcu_c_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-04-27T16:56:26.017020Z", + "source_id": "elder_solidification_al126wtcu_c_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "elder_solidification_al126wtcu_c" + }, + "dc": { + "identifier": { + "identifier": "10.18126/yf3k-kcr0", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Elder, Kate L. 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M.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Stan", + "givenName": "Tiberiu", + "creatorName": "Stan, Tiberiu", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Sun", + "givenName": "Yue", + "creatorName": "Sun, Yue", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Xiao", + "givenName": "Xianghui", + "creatorName": "Xiao, Xianghui", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Voorhees", + "givenName": "Peter W.", + "creatorName": "Voorhees, Peter W.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "microstructures" + }, + { + "subject": "tomography" + }, + { + "subject": "dendrite" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-04-23", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "kateelder2022@u.northwestern.edu " + }, + { + "contributorType": "ContactPerson", + "contributorName": "p-voorhees@northwestern.edu " + } + ], + "titles": [ + { + "title": "Solidification of an Al-12.6wt%Cu alloy cooled from 626.2 C to 624.5 C" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Reconstruction of X-ray projections collected as an Al-12.6wt%Cu alloy was cooled from 626.2C to 624.5C at a cooling rate of 0.5 C/min. The experiment shows dendritic growth as the sample solidifies. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "annaberdiyev_quantum_monte_benchmarks_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/annaberdiyev_quantum_monte_benchmarks_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/annaberdiyev_quantum_monte_benchmarks_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/annaberdiyev_quantum_monte_benchmarks_v1.2/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-11-21T23:56:16.454726Z", + "source_id": "annaberdiyev_quantum_monte_benchmarks_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "annaberdiyev_quantum_monte_benchmarks" + }, + "dc": { + "identifier": { + "identifier": "10.18126/c509-4if0", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Annaberdiyev, Abdulgani", + "affiliations": [ + "North Carolina State" + ], + "familyName": "Annaberdiyev", + "givenName": "Abdulgani" + }, + { + "creatorName": "Wang, Guangming", + "affiliations": [ + "North Carolina State" + ], + "familyName": "Wang", + "givenName": "Guangming" + }, + { + "creatorName": "Melton, Cody A.", + "affiliations": [ + "North Carolina State" + ], + "familyName": "Melton", + "givenName": "Cody A." + }, + { + "creatorName": "Bennett, M. Chandler", + "affiliations": [ + "North Carolina State" + ], + "familyName": "Bennett", + "givenName": "M. Chandler" + }, + { + "creatorName": "Mitas, Lubos", + "affiliations": [ + "North Carolina State" + ], + "familyName": "Mitas", + "givenName": "Lubos" + } + ], + "subjects": [ + { + "subject": "QMC" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-11-22", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Abdulgani Annaberdiyev", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Cohesion and excitations in diamond Si by quantum Monte Carlo methods: benchmarks and systematic biases" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset for \"Quantum Monte Carlo of cohesion and excitations in diamond Si: benchmarks\". \nInputs and outputs are organized according to the various codes." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "schultz_gb_model_full_fit_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/schultz_gb_model_full_fit_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/schultz_gb_model_full_fit_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/schultz_gb_model_full_fit_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-02-02T21:37:10.657687Z", + "source_id": "schultz_gb_model_full_fit_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "schultz_gb_model_full_fit" + }, + "dc": { + "identifier": { + "identifier": "10.18126/sbcm-in13", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Afflerbach", + "givenName": "Benjamin", + "creatorName": "Afflerbach, Benjamin", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Francis", + "givenName": "Carter", + "creatorName": "Francis, Carter", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Szlufarska", + "givenName": "Izabela", + "creatorName": "Szlufarska, Izabela", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Morgan", + "givenName": "Dane", + "creatorName": "Morgan, Dane", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Voyles", + "givenName": "Paul M.", + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Schultz", + "givenName": "Lane E.", + "creatorName": "Schultz, Lane E.", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "Metallic glasses" + }, + { + "subject": "machine learning" + }, + { + "subject": "Characteristic temperatures" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-02-02", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "lsschultz@wisc.edu" + } + ], + "titles": [ + { + "title": "Characteristic Temperature Model for Metallic Glass Critical Casting Diameter" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The best GB model from the paper \"Experimental Exploration of Characteristic Temperature Contributions to Metallic Glass Forming Ability.\"" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "park_sensitivity_characterization_source_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/park_sensitivity_characterization_source_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/park_sensitivity_characterization_source_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/park_sensitivity_characterization_source_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-02-26T21:59:21.698406Z", + "source_id": "park_sensitivity_characterization_source_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "park_sensitivity_characterization_source" + }, + "dc": { + "identifier": { + "identifier": "10.18126/u4zn-6xzt", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Park, Jun-Sang", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Park", + "givenName": "Jun-Sang" + }, + { + "creatorName": "Sharma, Hemant", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Sharma", + "givenName": "Hemant" + }, + { + "creatorName": "Kenesei, Peter", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Kenesei", + "givenName": "Peter" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-02-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Jun-Sang Park", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Hemant Sharma", + "contributorType": "ContactPerson" + }, + { + "contributorName": " Peter Kenesei", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Sensitivity Characterization of the far-field high-energy diffraction microscopy instrument at the Advanced Photon Source" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "FF-HEDM data with systematic sample motions to characterize the angular and translational sensitivity of the APS HEDM instrument at the 1-ID beamline." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "shenyolosi_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/shenyolosi_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/shenyolosi_v1.3/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/shenyolosi_v1.3", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-11-11T00:10:57.551245Z", + "source_id": "shenyolosi_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "shenyolosi" + }, + "dc": { + "identifier": { + "identifier": "10.18126/n9dj-5mk0", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Morgan", + "givenName": "Dane", + "creatorName": "Morgan, Dane", + "affiliations": [ + "University of Wisconsin-Madison" + ] + }, + { + "familyName": "Shen", + "givenName": "Mingren", + "creatorName": "Shen, Mingren", + "affiliations": [ + "University of Wisconsin-Madison" + ] + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "microscopy" + }, + { + "subject": "defects" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-11-11", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "mshen32@wisc.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "ddmorgan@wisc.edu" + } + ], + "titles": [ + { + "title": "A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations " + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Codes and Data for A Deep Learning Based Automatic Defect Analysis Framework for In-situ TEM Ion Irradiations \n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "hanus_thermoreflectance_imaging_coatings_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/hanus_thermoreflectance_imaging_coatings_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hanus_thermoreflectance_imaging_coatings_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/hanus_thermoreflectance_imaging_coatings_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-23T22:26:17.300968Z", + "source_id": "hanus_thermoreflectance_imaging_coatings_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "hanus_thermoreflectance_imaging_coatings" + }, + "dc": { + "identifier": { + "identifier": "10.18126/emun-4lr3", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Hanus", + "givenName": "Riley", + "creatorName": "Hanus, Riley", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Rangnekar", + "givenName": "Sonal V.", + "creatorName": "Rangnekar, Sonal V.", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Mollah", + "givenName": "Shahab", + "creatorName": "Mollah, Shahab", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Hussain", + "givenName": "Kamal", + "creatorName": "Hussain, Kamal", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Hines", + "givenName": "Nicholas", + "creatorName": "Hines, Nicholas", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Heller", + "givenName": "Eric", + "creatorName": "Heller, Eric", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Khan", + "givenName": "Asif", + "creatorName": "Khan, Asif", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Graham", + "givenName": "Samuel", + "creatorName": "Graham, Samuel", + "affiliations": [ + "Northwestern University", + "Georgia Institute of Technology", + "University of South Carolina", + "Air Force Research Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2D materials" + }, + { + "subject": "AlGaN" + }, + { + "subject": "GaN" + }, + { + "subject": "power electronics" + }, + { + "subject": "radio frequency (RF) electronics" + }, + { + "subject": "thermal imaging" + }, + { + "subject": "thermoreflectance" + }, + { + "subject": "ultra wide band gap" + }, + { + "subject": "wide band gap" + }, + { + "subject": "semiconductors" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-23", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Lidia Kuo " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acsami.1c11528", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Thermoreflectance Imaging of (Ultra)wide Band-Gap Devices with MoS2 Enhancement Coatings" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Measuring the maximum operating temperature within the channel of ultrawide band-gap transistors is critically important since the temperature dependence of the device reliability sets operational limits such as maximum operational power. Thermoreflectance imaging (TTI) is an optimal choice to measure the junction temperature due to its submicrometer spatial resolution and submicrosecond temporal resolution. Since TTI is an imaging technique, data acquisition is orders of magnitude faster than point measurement techniques such as Raman thermometry. Unfortunately, commercially available LED light sources used in thermoreflectance systems are limited to energies less than \u223c3.9 eV, which is below the band gap of many ultrawide band-gap semiconductors (>4.0 eV). Therefore, the semiconductors are transparent to the probing light sources, prohibiting the application of TTI. To address this thermal imaging challenge, we utilize an MoS2 coating as a thermoreflectance enhancement coating that allows for the measurement of the surface temperature of (ultra)wide band-gap materials. The coating consists of a network of MoS2 nanoflakes with the c axis aligned normal to the surface and is easily removable via sonication. The method is validated using electrical and thermal characterization of GaN and AlGaN devices. We demonstrate that this coating does not measurably influence the electrical performance or the measured operating temperature. A maximum temperature rise of 49 K at 0.59 W was measured within the channel of the AlGaN device, which is over double the maximum temperature rise obtained by measuring the thermoreflectance of the gate metal. The importance of accurately measuring the peak operational temperature is discussed in the context of accelerated stress testing." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "garrison_2d_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/garrison_2d_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/garrison_2d_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/garrison_2d_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-23T22:54:57.837071Z", + "source_id": "garrison_2d_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "garrison_2d" + }, + "dc": { + "identifier": { + "identifier": "10.18126/2rpo-t1rn", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Garrison", + "givenName": "Michael D", + "creatorName": "Garrison, Michael D", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Wallace", + "givenName": "Shay Goff", + "creatorName": "Wallace, Shay Goff", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Baldwin", + "givenName": "Lawrence C.", + "creatorName": "Baldwin, Lawrence C.", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Guo", + "givenName": "Zixuan", + "creatorName": "Guo, Zixuan", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Kuo", + "givenName": "Lidia", + "creatorName": "Kuo, Lidia", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Estevez", + "givenName": "Joseph E.", + "creatorName": "Estevez, Joseph E.", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Briseno", + "givenName": "Alejandro L.", + "creatorName": "Briseno, Alejandro L.", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + }, + { + "familyName": "Baca", + "givenName": "Alfred J.", + "creatorName": "Baca, Alfred J.", + "affiliations": [ + "Northwestern University", + "US Navy Research Office" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2D materials" + }, + { + "subject": "crystals" + }, + { + "subject": "charge transfer" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-23", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": " Lidia Kuo " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acs.chemmater.1c03100", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Accelerated Decomposition Kinetics of Ammonium Perchlorate via Conformal Graphene Coating" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Ammonium perchlorate (AP) is an oxidizer material that is widely employed in applications ranging from rocketry to airbags. Previous research has suggested that efficient electron transfer plays a critical role in determining the kinetics of catalyzed AP decomposition reactions. Consequently, intimate contact between AP crystals and electron acceptors has the potential to accelerate decomposition kinetics, which motivates the development of conformal coatings with suitably tailored electronic structures. Here, we demonstrate a scalable method for conformally coating AP crystals with two atomically well-defined 2D materials with orthogonal electronic properties\u2500namely, pristine graphene, which is a zero-band gap semiconductor that has been shown to be an effective electron acceptor in diverse heterojunctions and hexagonal boron nitride (hBN), which is a wide-band gap electrical insulator. Consistent with an electron transfer mechanism, graphene-coated AP undergoes accelerated decomposition kinetics compared to uncoated (neat) or hBN-coated AP. Through extensive structural characterization including electron microscopy and X-ray diffraction, the effects of AP crystal size and crystallinity are examined. In addition, the accelerated decomposition kinetics of graphene-coated AP are quantified through thermogravimetric analysis, gas chromatography mass spectrometry, and kinetic modeling. Overall, this work establishes pristine graphene as an effective coating for promoting accelerated decomposition of AP, which enhances its utility in various applications." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "yang_evidence_coupled_nbge2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/yang_evidence_coupled_nbge2_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/yang_evidence_coupled_nbge2_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/yang_evidence_coupled_nbge2_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-08-02T20:44:10.850958Z", + "source_id": "yang_evidence_coupled_nbge2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "yang_evidence_coupled_nbge2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/uftm-ny12", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Yang", + "givenName": "Hung-Yu", + "creatorName": "Yang, Hung-Yu", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Yao", + "givenName": "Xiaohan", + "creatorName": "Yao, Xiaohan", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Plisson", + "givenName": "Vincent", + "creatorName": "Plisson, Vincent", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Tafti", + "givenName": "Fazel", + "creatorName": "Tafti, Fazel", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Balicas", + "givenName": "Luis", + "creatorName": "Balicas, Luis", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Burch", + "givenName": "Kenneth", + "creatorName": "Burch, Kenneth", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Mozaffari", + "givenName": "Shirin", + "creatorName": "Mozaffari, Shirin", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Choi", + "givenName": "Eun Sang", + "creatorName": "Choi, Eun Sang", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + }, + { + "familyName": "Chan", + "givenName": "Julia", + "creatorName": "Chan, Julia", + "affiliations": [ + "Boston College", + "National high magnetic field", + "Florida State University", + "University of Texas Dallas" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-08-02", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": " fazel.tafti@bc.edu" + } + ], + "titles": [ + { + "title": "Evidence of a coupled electron-phonon liquid in NbGe2" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Comprehensive characterization of the intermetallic compounds NbGe2 to provide evidence of electron-phonon liquid. 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Jeffrey", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "familyName": "Sofer", + "givenName": "Zden\u011bk", + "creatorName": "Sofer, Zden\u011bk", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "Sungkyunkwan University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "semiconductors" + }, + { + "subject": "photodiodes" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Lidia Kuo " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1007/s12274-020-3059-3", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Intrinsic carrier multiplication in layered Bi2O2Se avalanche photodiodes with gain bandwidth product exceeding 1 GHz" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": " Emerging layered semiconductors present multiple advantages for optoelectronic technologies including high carrier mobilities, strong light-matter interactions, and tunable optical absorption and emission. Here, metal-semiconductor-metal avalanche photodiodes (APDs) are fabricated from Bi2O2Se crystals, which consist of electrostatically bound [Bi2O2]2+ and [Se]2\u2212 layers. The resulting APDs possess an intrinsic carrier multiplication factor up to 400 at 7 K with a responsivity gain exceeding 3,000 A/W and bandwidth of ~ 400 kHz at a visible wavelength of 515.6 nm, ultimately resulting in a gain bandwidth product exceeding 1 GHz. Due to exceptionally low dark currents, Bi2O2Se APDs also yield high detectivities up to 4.6 \u00d7 1014 Jones. A systematic analysis of the photocurrent temperature and bias dependence reveals that the carrier multiplication process in Bi2O2Se APDs is consistent with a reverse biased Schottky diode model with a barrier height of ~ 44 meV, in contrast to the charge trapping extrinsic gain mechanism that dominates most layered semiconductor phototransistors. In this manner, layered Bi2O2Se APDs provide a unique platform that can be exploited in a diverse range of high-performance photodetector applications." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bennett_high_accuracy_carlo_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/bennett_high_accuracy_carlo_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bennett_high_accuracy_carlo_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/bennett_high_accuracy_carlo_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-12-08T23:15:11.440255Z", + "source_id": "bennett_high_accuracy_carlo_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "bennett_high_accuracy_carlo" + }, + "dc": { + "identifier": { + "identifier": "10.18126/j7cm-wh84", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Bennett, M. Chandler", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Bennett", + "givenName": "M. Chandler" + }, + { + "creatorName": "Reboredo, Fernando A.", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Reboredo", + "givenName": "Fernando A." + }, + { + "creatorName": "Mitas, Lubos", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Mitas", + "givenName": "Lubos" + }, + { + "creatorName": "Krogel, Jaron T.", + "affiliations": [ + "North Carolina State University" + ], + "familyName": "Krogel", + "givenName": "Jaron T." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "Monte Carlo" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-12-08", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Chandler Bennett ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "High Accuracy Transition Metal Effective Cores for Many-body Diffusion Monte Carlo" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Practical applications of the real-space diffusion Monte Carlo (DMC) method require the removal of core electrons, where currently localization approximations of semi-local potentials are generally used in the projector. Accurate calculations of complex solids and large molecules demand minimizing the impact of approximated atomic cores. Prior works have shown the errors from such approximations can be sizable in both finite and periodic systems. In this work, we show that a class of differential pseudopotentials, known as pseudo-Hamiltonians, can be constructed for the $3d$ transition metal atoms, entirely removing the need for any localization scheme in the DMC projector. As a proof of principle, we demonstrate the approach for the case of Co. In order to minimize errors in the pseudo-Hamiltonian at the many-body level, we generalize the recently proposed correlation-consistent pseudopotential generation scheme to successively close semi-local representations of the differential potentials. Our generation scheme successfully produces potentials tailored specifically for real space projector quantum Monte Carlo methods with low error at the many-body level, i.e., with many-body scattering properties very close to relativistic all-electron results. In particular, we show that the agreement with respect to atomic and molecular quantities reach chemical accuracy in many cases -- on par with the most accurate semi-local pseudopotentials available. Further, our pseudo-Hamiltonian generation scheme utilizes standard quantum chemistry codes designed only to work with semi-local pseudopotentials, enabling straightforward generation of pseudo-Hamiltonians for additional elements in future works.The entire set of input and output files used in this project are provided here." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "phthalocyanine_mos2_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/phthalocyanine_mos2_v1.1/", + "total_size": 37227015, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/phthalocyanine_mos2_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/phthalocyanine_mos2_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-11-05T01:09:58.175851Z", + "source_id": "phthalocyanine_mos2_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "phthalocyanine_mos2" + }, + "dc": { + "identifier": { + "identifier": "10.18126/xkru-gan1", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Zhou", + "givenName": "Qunfei", + "creatorName": "Zhou, Qunfei", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Liu", + "givenName": "Zhen-Fei", + "creatorName": "Liu, Zhen-Fei", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Marks", + "givenName": "Tobin", + "creatorName": "Marks, Tobin", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Darancet", + "givenName": "Pierre", + "creatorName": "Darancet, Pierre", + "affiliations": [ + "Northwestern University", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "molecular structures" + }, + { + "subject": "DFT" + }, + { + "subject": "Electronic Structure" + }, + { + "subject": " mixed-dimensional heterojunctions" + }, + { + "subject": "transition-metal phthalocyanine" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-11-05", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Qunfei Zhou" + }, + { + "contributorType": "ContactPerson", + "contributorName": "Pierre Darancet" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acs.jpca.0c10766", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acsnano.8b09166", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "\tarXiv:2107.08516", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Electronic Structure of Transition-Metal Phthalocyanine (MPc) Molecules and Mixed-Dimensional MPc-MoS2 Heterojunctions" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Density functional calculations with different functionals, B3LYP, HSE, PBE, range-separated hybrid functionals for transition-metal phthalocyanine (MPc) molecules and mixed-dimensional MPc-MoS2 heterojunctions. Calculations of dielectric screening effects of the 2D MoS2 and other substrates on the energy levels of the organic molecules. Calculations and figure plotting in Python scripts are included in jupyter notebooks." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "nature_ndbi_cats_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/nature_ndbi_cats_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nature_ndbi_cats_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/nature_ndbi_cats_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-12-13T15:35:20.099036Z", + "source_id": "nature_ndbi_cats_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "nature_ndbi_cats" + }, + "dc": { + "identifier": { + "identifier": "10.18126/p9b8-e7ms", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Schrunk", + "givenName": "Benjamin", + "creatorName": "Schrunk, Benjamin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Kushnirenko", + "givenName": "Yevhen", + "creatorName": "Kushnirenko, Yevhen", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Kuthanazhi", + "givenName": "Brinda", + "creatorName": "Kuthanazhi, Brinda", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Ahn", + "givenName": "Junyeong", + "creatorName": "Ahn, Junyeong", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Wang", + "givenName": "Lin-Lin", + "creatorName": "Wang, Lin-Lin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "O\u2019Leary", + "givenName": "Evan", + "creatorName": "O\u2019Leary, Evan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Lee", + "givenName": "Kyungchan", + "creatorName": "Lee, Kyungchan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Eaton", + "givenName": "Andrew", + "creatorName": "Eaton, Andrew", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Fedorov", + "givenName": "Alexander", + "creatorName": "Fedorov, Alexander", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Lou", + "givenName": "Rui", + "creatorName": "Lou, Rui", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Voroshnin", + "givenName": "Vladimir", + "creatorName": "Voroshnin, Vladimir", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Clark", + "givenName": "Oliver J.", + "creatorName": "Clark, Oliver J.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "\u0301anchez-Barriga", + "givenName": "J. S", + "creatorName": "\u0301anchez-Barriga, J. S", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Bud\u2019ko", + "givenName": "Sergey L.", + "creatorName": "Bud\u2019ko, Sergey L.", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + "University of Cambridge" + ] + }, + { + "familyName": "Slager", + "givenName": "Robert-Jan", + "creatorName": "Slager, Robert-Jan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Harvard University", + "Leibniz Institute for Solid State and Materials Research", + "Helmholtz-Zentrum Berlin fur Materialien und Energie", + "Lanzhou University", + 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for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Han", + "givenName": "Shen", + "creatorName": "Han, Shen", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Fu", + "givenName": "Chenguang", + "creatorName": "Fu, Chenguang", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Zhu", + "givenName": "Tiejun", + "creatorName": "Zhu, Tiejun", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Shawon", + "givenName": "A K M Ashiquzzaman", + "creatorName": "Shawon, A K M Ashiquzzaman", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, 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"Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Snyder", + "givenName": "G. 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"Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Saglik", + "givenName": "Kivanc", + "creatorName": "Saglik, Kivanc", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Aydemir", + "givenName": "Umut", + "creatorName": "Aydemir, Umut", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Cardoso-Gil", + "givenName": "Ra\u00fal", + "creatorName": "Cardoso-Gil, Ra\u00fal", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Svanidze", + "givenName": "E", + "creatorName": "Svanidze, E", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Funahashi", + "givenName": "Ryoji", + "creatorName": "Funahashi, Ryoji", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Powell", + "givenName": "Anthony V", + "creatorName": "Powell, Anthony V", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Mukherjee", + "givenName": "Shriparna", + "creatorName": "Mukherjee, Shriparna", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Tippireddy", + "givenName": "Sahil", + "creatorName": "Tippireddy, Sahil", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Vaqueiro", + "givenName": "Paz", + "creatorName": "Vaqueiro, Paz", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Gascoin", + "givenName": "Franck", + "creatorName": "Gascoin, Franck", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Kyratsi", + "givenName": "Theodora", + "creatorName": "Kyratsi, Theodora", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Sauerschnig", + "givenName": "Philipp", + "creatorName": "Sauerschnig, Philipp", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + }, + { + "familyName": "Mori", + "givenName": "Takao", + "creatorName": "Mori, Takao", + "affiliations": [ + "University of Manchester", + "Iskenderun Technical University", + "Jawaharlal Nehru Centre for Advanced Scientific Research", + "Shanghai Institute of Ceramics", + "Center for High Pressure Science and Technology Advanced Research", + "Chinese Academy of Sciences", + "Zhejiang University", + "Michigan State University", + "Northwestern University", + "National Institute of Advanced Industrial Science and Technology", + "Ko\u00e7 University", + "Max-Planck-Institut", + " National Institute of Advanced Industrial Science and Technology", + "University of Reading", + "Laboratoire CRISMAT UMR 6508 CNRS ENSICAEN", + "University of Cyprus", + "National Institute for Materials Science", + "University of Tsukuba" + ] + } + ], + "subjects": [ + { + "subject": "semiconductors" + }, + { + "subject": "energy materials" + }, + { + "subject": "oxides" + }, + { + "subject": "Thermoelectrics" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-04-01", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Robert Freer" + }, + { + "contributorType": "ContactPerson", + "contributorName": " Jeffery Snyder" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1088/2515-7655/ac49dc", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Key properties of inorganic thermoelectric materials tables" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This Dataset presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The 12 families of materials included in these tables are primarily selected on the basis of well established, internationally-recognised performance and promise for current and future applications: Tellurides, Skutterudites, Half Heuslers, Zintls, Mg-Sb Antimonides, Clathrates, FeGa3\u2013type materials, Actinides and Lanthanides, Oxides, Sulfides, Selenides, Silicides, Borides and Carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "sangwan_visualizing_thermally_semiconductors_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/sangwan_visualizing_thermally_semiconductors_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/sangwan_visualizing_thermally_semiconductors_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sangwan_visualizing_thermally_semiconductors_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-25T22:51:53.513004Z", + "source_id": "sangwan_visualizing_thermally_semiconductors_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "sangwan_visualizing_thermally_semiconductors" + }, + "dc": { + "identifier": { + "identifier": "10.18126/v4tm-nw3i", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Rangnekar, Sonal V.", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Rangnekar", + "givenName": "Sonal V." + }, + { + "creatorName": "Kang, Joohoon", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Kang", + "givenName": "Joohoon" + }, + { + "creatorName": "Shen, Jianan", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Shen", + "givenName": "Jianan" + }, + { + "creatorName": "Lee, Hong\u2010Sub", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Lee", + "givenName": "Hong\u2010Sub" + }, + { + "creatorName": "Lam, David", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Lam", + "givenName": "David" + }, + { + "creatorName": "Shen, Junhua", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Shen", + "givenName": "Junhua" + }, + { + "creatorName": "Liu, Xiaolong", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Liu", + "givenName": "Xiaolong" + }, + { + "creatorName": "Moraes, Ana C. M.", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Moraes", + "givenName": "Ana C. M." + }, + { + "creatorName": "Kuo, Lidia", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Kuo", + "givenName": "Lidia" + }, + { + "creatorName": "Gu, Jie", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Gu", + "givenName": "Jie" + }, + { + "creatorName": "Wang, Haihua", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Wang", + "givenName": "Haihua" + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + " Sungkyunkwan University", + "Kangwon National University", + "Shaanxi University of Science and Technology" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2D materials" + }, + { + "subject": "memristors" + }, + { + "subject": "solution processing" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Lidia Kuo ", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1002/adfm.202107385", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Visualizing Thermally Activated Memristive Switching in Percolating Networks of Solution\u2010Processed 2D Semiconductors" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Memristive systems present a low-power alternative to silicon-based elec-tronics for neuromorphic and in-memory computation. 2D materials have been increasingly explored for memristive applications due to their novel biomimetic functions, ultrathin geometry for ultimate scaling limits, and potential for fabricating large-area, flexible, and printed neuromorphic devices. While the switching mechanism in memristors based on single 2D nanosheets is similar to conventional oxide memristors, the switching mechanism in nanosheet composite films is complicated by the interplay of multiple physical processes and the inaccessibility of the active area in a two-terminal vertical geometry. Here, the authors report thermally activated mem-ristors fabricated from percolating networks of diverse solution-processed 2D semiconductors including MoS2, ReS2, WS2, and InSe. The mechanisms underlying threshold switching and negative differential resistance are eluci-dated by designing large-area lateral memristors that allow the direct observa-tion of filament and dendrite formation using in situ spatially resolved optical, chemical, and thermal analyses. The high switching ratios (up to 103) that are achieved at low fields (\u22484 kV cm\u22121) are explained by thermally assisted electrical discharge that preferentially occurs at the sharp edges of 2D nanosheets. Overall, this work establishes percolating networks of solution-processed 2D semiconductors as a platform for neuromorphic architectures." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "moore_ultrasensitive_molecular_materials_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/moore_ultrasensitive_molecular_materials_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/moore_ultrasensitive_molecular_materials_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/moore_ultrasensitive_molecular_materials_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-25T22:41:18.638663Z", + "source_id": "moore_ultrasensitive_molecular_materials_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "moore_ultrasensitive_molecular_materials" + }, + "dc": { + "identifier": { + "identifier": "10.18126/fipx-ox7u", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Moore", + "givenName": "David C.", + "creatorName": "Moore, David C.", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Jawaid", + "givenName": "Ali", + "creatorName": "Jawaid, Ali", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Busch", + "givenName": "Robert", + "creatorName": "Busch, Robert", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Brothers", + "givenName": "Michael", + "creatorName": "Brothers, Michael", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Miesle", + "givenName": "Paige", + "creatorName": "Miesle, Paige", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Miesle", + "givenName": "Adam", + "creatorName": "Miesle, Adam", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Rao", + "givenName": "Rahul", + "creatorName": "Rao, Rahul", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Lee", + "givenName": "Jonghoon", + "creatorName": "Lee, Jonghoon", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Beagle", + "givenName": "Lucas K.", + "creatorName": "Beagle, Lucas K.", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Motala", + "givenName": "Michael", + "creatorName": "Motala, Michael", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Wallace", + "givenName": "Shay Goff", + "creatorName": "Wallace, Shay Goff", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Downing", + "givenName": "Julia R.", + "creatorName": "Downing, Julia R.", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Roy", + "givenName": "Ajit", + "creatorName": "Roy, Ajit", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Muratore", + "givenName": "Christopher", + "creatorName": "Muratore, Christopher", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Vaia", + "givenName": "Richard", + "creatorName": "Vaia, Richard", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Kim", + "givenName": "Steve", + "creatorName": "Kim, Steve", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + }, + { + "familyName": "Glavin", + "givenName": "Nicholas R.", + "creatorName": "Glavin, Nicholas R.", + "affiliations": [ + "Northwestern University", + "Air Force Research Laboratory" + ] + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Lidia Kuo " + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1002/adfm.202106830", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Ultrasensitive Molecular Sensors Based on Real\u2010Time Impedance Spectroscopy in Solution\u2010Processed 2D Materials" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Chemical sensors based on solution-processed 2D nanomaterials represent an extremely attractive approach toward scalable and low-cost devices. Through the implementation of real-time impedance spectroscopy and development of a three-element circuit model, redox exfoliated MoS2 nanoflakes demonstrate an ultrasensitive empirical detection limit of NO2 gas at 1 ppb, with an extrapolated ultimate detection limit approaching 63 ppt. This sensor construct reveals a more than three orders of magnitude improvement from conventional direct current sensing approaches as the traditionally dominant interflake interactions are bypassed in favor of selectively extracting intraflake doping effects. This same approach allows for an all solution-processed, flexible 2D sensor to be fabricated on a polyimide substrate using a combination of graphene contacts and drop-casted MoS2 nanoflakes, exhibiting similar sensitivity limits. Finally, a thermal annealing strategy is used to explore the tunability of the nanoflake interactions and subsequent circuit model fit, with a demonstrated sensitivity improvement of 2\u00d7 with thermal annealing at 200 \u00b0C." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "foam_db_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/foam_db_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/foam_db_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/foam_db_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-06-04T05:26:26.663877Z", + "source_id": "foam_db_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "foam_db" + }, + "dc": { + "identifier": { + "identifier": "10.18126/6h74-leb4", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Landauer, Alexander K", + "affiliations": [ + "National Institute of Standards and Technology" + ], + "familyName": "Landauer", + "givenName": "Alexander K" + }, + { + "creatorName": "Kafka, Orion L", + "affiliations": [ + "National Institute of Standards and Technology" + ], + "familyName": "Kafka", + "givenName": "Orion L" + }, + { + "creatorName": "Moser, Newell H", + "affiliations": [ + "National Institute of Standards and Technology" + ], + "familyName": "Moser", + "givenName": "Newell H" + }, + { + "creatorName": "Forster, Aaron M", + "affiliations": [ + "National Institute of Standards and Technology" + ], + "familyName": "Forster", + "givenName": "Aaron M" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "microscopy" + }, + { + "subject": "microstructures" + }, + { + "subject": "foam" + }, + { + "subject": "mechanical properties" + }, + { + "subject": "vinyl nitrile" + }, + { + "subject": "impact mitigation" + }, + { + "subject": " elastomer" + }, + { + "subject": " computer tomography" + }, + { + "subject": " digital image correlation" + }, + { + "subject": "impact protection" + }, + { + "subject": " impact mitigating materials" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-06-04", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": " Aaron Forster ", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.18434/mds2-2606", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "A Materials Dataset for Elastomeric Foam Impact Mitigating Materials" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The database includes data for structure-properties relationships and mechanical modeling of elastic impact protection foams from a variety of imaging (micro-computed tomography, digital image correlation) and force-sensing instruments (dynamic mechanical analysis, universal test system) under a wide range of experimental conditions and modes. The data repository includes directories for: dynamic mechanical analysis raw data, results, and analysis tools; intermediate rate (servo-hydraulic UTS based) raw data including 2D digital image correlation (DIC) images, results, and analysis tools; quasi-static rate (electro-mechanical UTS based) raw data including 2D digital image correlation (DIC), results, and analysis tools; and, micro-computed tomography data including raw volume images, filtered images, binarized images, other results, and analysis tools. For more information see the readme and data documentation in each respective directory. A complete manuscript describing data collection, analysis, and database documentation is to be published." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "doan_datasets_accelerating_representations_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/doan_datasets_accelerating_representations_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/doan_datasets_accelerating_representations_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/doan_datasets_accelerating_representations_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-03-29T21:32:03.215104Z", + "source_id": "doan_datasets_accelerating_representations_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "doan_datasets_accelerating_representations" + }, + "dc": { + "identifier": { + "identifier": "10.18126/82c7-6cn8", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Doan, Hieu A.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Doan", + "givenName": "Hieu A." + }, + { + "creatorName": "Li, Chenyang", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Li", + "givenName": "Chenyang" + }, + { + "creatorName": "Ward, Logan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Ward", + "givenName": "Logan" + }, + { + "creatorName": "Zhou, Mingxia", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Zhou", + "givenName": "Mingxia" + }, + { + "creatorName": "Curtiss, Larry A.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Curtiss", + "givenName": "Larry A." + }, + { + "creatorName": "Assary, Rajeev S.", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago" + ], + "familyName": "Assary", + "givenName": "Rajeev S." + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "simulation" + }, + { + "subject": "DFT" + }, + { + "subject": "catalyst" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-03-29", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Hieu A. Doan ", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Datasets for Accelerating Catalysts Screening via Machine-learned Local Coordination Graph Representations" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "A priori catalyst designs from reliable first principles simulations and emerging artificial intelligence tools are desired to accelerate materials development. In the context of upgrading biomass materials via deoxygenation reaction to value-added chemicals, molybdenum carbides (Mo2C) have been considered among the best and economically viable catalysts. One of the bottlenecks related to longer term stability of Mo2C catalysts is the susceptibility to surface oxidation, which requires the use of excess hydrogen for active site regeneration. By using dopants to tune the oxygen affinity of Mo2C surfaces, it is possible to design new doped Mo2C catalysts with desired reactivity and stability. Here, we first employed Density Functional Theory (DFT) to perform high-throughput calculations of oxygen binding energies (BEO) on various pristine and doped Mo2C surfaces. We evaluated a total of 20,177 oxygen adsorption structures consisting of 7 low Miller-index surfaces, 23 d-block elements as single-atom dopants, all possible surface terminations, dopant locations, and adsorption sites. We make the outputs including 1865 pristine and 18312 doped relaxed geometries from VASP simulations available here." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-24T12:23:45.678463Z", + "source_id": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "gabriel_aluminum_bayesian_weighted_dft_md_experiments_cph" + }, + "dc": { + "identifier": { + "identifier": "10.18126/to2p-lmot", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Gabriel, Joshua J.", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Gabriel", + "givenName": "Joshua J." + }, + { + "creatorName": "Paulson, Noah H.", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Paulson", + "givenName": "Noah H." + }, + { + "creatorName": "Duong, Thien C.", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Duong", + "givenName": "Thien C." + }, + { + "creatorName": "Becker, Chandler A.", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Becker", + "givenName": "Chandler A." + }, + { + "creatorName": "Tavazza, Francesca", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Tavazza", + "givenName": "Francesca" + }, + { + "creatorName": "Kattner, Ursula R.", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Kattner", + "givenName": "Ursula R." + }, + { + "creatorName": "Stan, Marius", + "affiliations": [ + "Argonne National Laboratory", + "National Institute of Standards and Technology" + ], + "familyName": "Stan", + "givenName": "Marius" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "DFT" + }, + { + "subject": "MD" + }, + { + "subject": "Uncertainty Quantification" + }, + { + "subject": "Bayesian automated weighting" + }, + { + "subject": "heat capacity" + }, + { + "subject": "enthalpy" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-02-24", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "jgabriel@anl.gov", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1016/j.mtla.2021.101216", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Datasets with uncertainty quantification from DFT, MD, and experiments and their rescaled uncertainties after Bayesian automated weighting" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Excel spreadsheet contains sub-sheets of \n1. the raw DFT, MD, and Experimental datasets named according to the convention adopted in https://doi.org/10.1016/j.mtla.2021.101216\n2. the mean rescaled uncertainty of each heat capacity dataset of the solid and liquid phase of aluminum (Cp_Solid and Cp_Liquid) \n3. the mean rescaled uncertainty of each heat capacity dataset of the solid and liquid phase of aluminum (H_Solid and H_Liquid)\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "3d_cnn_dendrites_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/3d_cnn_dendrites_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/3d_cnn_dendrites_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/3d_cnn_dendrites_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-02-18T04:36:49.616796Z", + "source_id": "3d_cnn_dendrites_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "3d_cnn_dendrites" + }, + "dc": { + "identifier": { + "identifier": "10.18126/cxf3-hrum", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "James", + "givenName": "Jim", + "creatorName": "James, Jim", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "familyName": "Pruyne", + "givenName": "Nathan", + "creatorName": "Pruyne, Nathan", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "familyName": "Stan", + "givenName": "Tiberiu", + "creatorName": "Stan, Tiberiu", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "familyName": "Schwarting", + "givenName": "Marcus", + "creatorName": "Schwarting, Marcus", + "affiliations": [ + "Argonne National Laboratory", + "Northwestern University", + "University of Chicago", + "Korea Advanced Institute of Science and Technology" + ] + }, + { + "familyName": "Yeom", + "givenName": "Jiwon", + "creatorName": "Yeom, Jiwon", + "affiliations": [ + "Argonne National 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Folders beginning with \"c-\" contain image volumes, while other folders contain manually segmented images used for training 3D convolutional neural networks. These networks are used to segment dendrites across all published image volumes." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "cdznx_db_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/cdznx_db_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/cdznx_db_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/cdznx_db_v1.2/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-03-06T17:10:49.817847Z", + "source_id": "cdznx_db_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "cdznx_db" + }, + "dc": { + "identifier": { + "identifier": "10.18126/ak3i-s746", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Mannodi-Kanakkithodi, Arun", + "affiliations": [ + "Purdue University" + ], + "familyName": "Mannodi-Kanakkithodi", + "givenName": "Arun" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "semiconductors" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-03-06", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "Arun Mannodi-Kanakkithodi", + "contributorType": "ContactPerson" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://iopscience.iop.org/article/10.1088/1361-651X/ac59d8", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "DFT Dataset of Ternary and Quaternary II\u2013VI Zincblende Semiconductor Alloys" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset contains high-throughput density functional theory (DFT) computations on ternary and quaternary alloys of II-VI zincblende semiconductors with Cd or Zn at the cation site and S, Se or Te at the anion site. Computed properties include formation and mixing energies, electronic band gaps, and optical absorption spectra, from different levels of theory (GGA-PBE, HSE06, with and without spin-orbit coupling)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "marras_physical_property_micelles_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/marras_physical_property_micelles_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/marras_physical_property_micelles_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/marras_physical_property_micelles_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-04-01T17:03:48.968297Z", + "source_id": "marras_physical_property_micelles_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "marras_physical_property_micelles" + }, + "dc": { + "identifier": { + "identifier": "10.18126/gtyw-0yag", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Marras", + "givenName": "Alexander E.", + "creatorName": "Marras, Alexander E.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Campagna", + "givenName": "Trinity R.", + "creatorName": "Campagna, Trinity R.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Vieregg", + "givenName": "Jeffrey R.", + "creatorName": "Vieregg, Jeffrey R.", + "affiliations": [ + "University of Chicago" + ] + }, + { + "familyName": "Tirrell", + "givenName": "Matthew V.", + "creatorName": "Tirrell, Matthew V.", + "affiliations": [ + "University of Chicago" + ] + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "polyelectrolytes" + }, + { + "subject": "micelles" + }, + { + "subject": "SAXS" + }, + { + "subject": "experiment" + }, + { + "subject": "CHiMaD" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-04-01", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "marras@uchicago.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "mtirrell@uchicago.edu" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acs.macromol.1c00743", + "relatedIdentifierType": "DOI" + } + ], + "titles": [ + { + "title": "Physical Property Scaling Relationships for Polyelectrolyte Complex Micelles" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": 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"familyName": "Luetkens", + "givenName": "Hubertus", + "creatorName": "Luetkens, Hubertus", + "affiliations": [ + "Boston College" + ] + }, + { + "familyName": "Fabbris", + "givenName": "Gilberto", + "creatorName": "Fabbris, Gilberto", + "affiliations": [ + "Boston College" + ] + }, + { + "familyName": "Graf", + "givenName": "Michael J.", + "creatorName": "Graf, Michael J.", + "affiliations": [ + "Boston College" + ] + }, + { + "familyName": "Haskel", + "givenName": "Daniel", + "creatorName": "Haskel, Daniel", + "affiliations": [ + "Boston College" + ] + }, + { + "familyName": "Ran", + "givenName": "Ying", + "creatorName": "Ran, Ying", + "affiliations": [ + "Boston College" + ] + }, + { + "familyName": "Tafti", + "givenName": "Fazel", + "creatorName": "Tafti, Fazel", + "affiliations": [ + "Boston College" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "topological" + }, + { + "subject": "metals and alloys" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-26", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Fazel Tafti " + } + ], + "titles": [ + { + "title": "First Demonstration of Tuning Between the Kitaev and Ising Limits in a Honeycomb Lattice" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Recent observations of novel spin-orbit coupled states have generated tremendous interest in $4d/5d$ transition metal systems.\n\nA prime example is the $J_{\\text{eff}}=\\frac{1}{2}$ state in iridate materials and \\RCL\\ that drives Kitaev interactions. Here, by tuning the competition between spin-orbit interaction ($\\lambda_{\\text{SOC}}$) and trigonal crystal field splitting ($\\Delta_\\text{T}$), we restructure the spin-orbital wave functions into a novel $\\mu=\\frac{1}{2}$ state that drives Ising interactions. This is done via a topochemical reaction that converts \\LRO\\ to \\ALRO, leading to an enhanced trigonal distortion and a diminished spin-orbit coupling in the latter compound. Using perturbation theory, we present an explicit expression for the new $\\mu=\\frac{1}{2}$ state in the limit $\\Delta_\\text{T}\\gg \\lambda_{\\text{SOC}}$ realized in \\ALRO, different from the conventional $J_\\text{eff}=\\frac{1}{2}$ state in the limit $\\lambda_{\\text{SOC}}\\gg \\Delta_\\text{T}$ realized in \\LRO. The change of ground state is followed by a dramatic change of magnetism from a 6~K spin-glass in \\LRO\\ to a 94~K antiferromagnet in \\ALRO. These results open a pathway for tuning materials between the two limits and creating a rich magnetic phase diagram. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "dumi_dmc_hgraphene_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/dumi_dmc_hgraphene_v1.3" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/dumi_dmc_hgraphene_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/dumi_dmc_hgraphene_v1.3/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-01-20T23:07:28.031464Z", + "source_id": "dumi_dmc_hgraphene_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "dumi_dmc_hgraphene" + }, + "dc": { + "identifier": { + "identifier": "10.18126/s1wc-tyac", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Dumi, Amanda", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Dumi", + "givenName": "Amanda" + }, + { + "creatorName": "Upadhyay, Shiv", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Upadhyay", + "givenName": "Shiv" + }, + { + "creatorName": "Bernasconi, Leonardo", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Bernasconi", + "givenName": "Leonardo" + }, + { + "creatorName": "Shin, Hyeondeok", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Shin", + "givenName": "Heondeok" + }, + { + "creatorName": "Benali, Anouar", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Benali", + "givenName": "Anouar" + }, + { + "creatorName": "Jordan, Kenneth D.", + "affiliations": [ + "University of Pittsburgh", + "Argonne National Laboratory" + ], + "familyName": "Jordan", + "givenName": "Kenneth D." + } + ], + "subjects": [ + { + "subject": "DMC" + }, + { + "subject": "QMC" + }, + { + "subject": "QMCPack" + }, + { + "subject": "PWSCF" + }, + { + "subject": "Nexus" + }, + { + "subject": "graphene" + }, + { + "subject": "diffusion Monte Carlo" + }, + { + "subject": "variational Monte Carlo" + }, + { + "subject": "VMC" + }, + { + "subject": "quantum Monte Carlo" + }, + { + "subject": "CPSFM" + }, + { + "subject": "simulation" + } + ], + "publicationYear": 2022, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2022-01-21", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "aed63@pitt.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "shu8@pitt.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "benali@anl.gov", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Dataset for \"The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The files and data used for \"The binding of atomic hydrogen on graphene from density functional theory and diffusion Monte Carlo calculations\".\n\nA.B. and H.S were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials.\nAn award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program.\nThe DMC and PW-DFT calculations used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE-AC02-06CH11357. The DFT calculation using Gaussian orbitals were carried out on computing resources in the University of Pittsburgh's Center for Research Computing. K.D.J. acknowledges NSF (CBET-2028826) for partial support of this work. S.U. was supported in part by the Pittsburgh Quantum Institute (PQI) Graduate Quantum Leader Award. 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A more detailed description can be found in the \u201cdataset overview\u201d section below\n * Chemical Inventory.csv - the hand curated file of all chemicals used in the construction of the perovskite dataset. This file includes identifiers, chemical properties, and other information.\n * ExcessMolarVolumeData.xlsx - record of experimental data, computations, and final dataset used in the generation of the excess molar volume plots.\n * MLModelMetrics.xlsx - all of the ML metrics organized in one place (excludes reactant set specific breakdown, see ML_Logs.zip for those files).\n * OrganoammoniumDensityDataset.xlsx - complete set of the data used to generate the density values. Example calculations included.\n * model_matchup_main.py - python pipeline used to generate all of the ML runs associated with the article. More detailed instructions on the operation of this code is included in the \u201cML Code\u201d Section below. This file is also hosted on\n * GIT: https://github.com/ipendlet/MLScripts/blob/master/temp_densityconc/model_matchup_main_20191231.py \n * SolutionVolumeDataset - complete set of 219 solutions in the perovskite dataset. Tabs include the automatically generated reagent information from ESCALATE, hand curated reagent information from early runs, and the generation of the dataset used in the creation of Figures\n * error_auditing.zip - code and historical datasets used for reporting the dataset auditing.\n * \u201cAllCode.zip\u201d which contains:\n * model_matchup_main_20191231.py - python pipeline used to generate all of the ML runs associated with the article. More detailed instructions on the operation of this code is included in the \u201cML Code\u201d Section below. This file is also hosted on\n * GIT: https://github.com/ipendlet/MLScripts/blob/master/temp_densityconc/0045.perovskitedata.csv \n * VmE_CurveFitandPlot.py - python code for generating the third order polynomial fit to the VmE vs mole fraction of FAH included in the main text. Requires the \u2018MolFractionResults.csv\u2019 to function (also included).\n * Calculation_Vm_Ve_CURVEFITTING.nb - mathematica code for generating the third order polynomial fit to the VmE vs mole fraction of FAH included in the main text. \n * Covariance_Analysis.py - python code for ingesting and plotting the covariance of features and volumes in the perovskite dataset. Includes renaming dictionaries used for the publication.\n * FeatureComparison_Plotting.py - python code for reading in and plotting features for the \u2018GBT\u2019 and \u2018OHGBT\u2019 folders in this directory. The code parses the contents of these folders and generates feature comparison metrics used for Figure 9 and the associated Figure S8. Some assembly required.\n * Requirements.txt - all of the packages used in the generation of this paper\n * 0045.perovskitedata.csv - the main dataset described throughout the article. This file is required to run some of the code and is therefore kept near the code. \n * \u201cML_Logs.zip\u201d which contains:\n * A folder describing every model generated for this article. In each folder there are a number of files:\n * Features_named_important.csv and features_value_importance.csv - these files are linked together and describe the weighted feature contributions from features (only present for GBT models)\n * AnalysisLog.txt - Log file of the run including all options, data curation and model training summaries \n * LeaveOneOut_Summary.csv - Results of the leave-one-reactant set-out studies on the model (if performed)\n * LOOModelInfo.txt - Hyperparameter information for each model in the study (associated with the given dataset, sometimes includes duplicate runs).\n * STTSModelInfo.txt - Hyperparameter information for each model in the study (associated with the given dataset, sometimes includes duplicate runs).\n * StandardTestTrain_Summary.csv - Results of the 6 fold cross validation ML performance (for the hold out case)\n * LeaveOneOut_FullDataset_ByAmine.csv - Results of the leave-one-reactant set-out studies performed on the full dataset (all experiments) specified by reactant set (delineated by the amine)\n * LeaveOneOut_StratifiedData_ByAmine.csv - Results of the leave-one-reactant set-out studies performed on a random stratified sample (96 random experiments) specified by reactant set (delineated by the amine)\n * model_matchup_main_*.py - code used to generate all of the runs contained in a particular folder. The code is exactly what was used at run time to generate a given dataset (requires 0045.perovskitedata.csv file to run)." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "swatek_prb_rapids_2020_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/swatek_prb_rapids_2020_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/swatek_prb_rapids_2020_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/swatek_prb_rapids_2020_v1.1/" + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2020-03-06T19:10:47.176848Z", + "source_id": "swatek_prb_rapids_2020_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "swatek_prb_rapids_2020" + }, + "dc": { + "identifier": { + "identifier": "10.18126/x3ye-f0tl", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "creatorName": "Swatek, Przemyslaw", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Swatek", + "givenName": "Przemyslaw" + }, + { + "creatorName": "Wu, Yun", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Wu", + "givenName": "Yun" + }, + { + "creatorName": "Wang, Lin-Lin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Wang", + "givenName": "Lin-Lin" + }, + { + "creatorName": "Lee, Kyungchan", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Lee", + "givenName": "Kyungchan" + }, + { + "creatorName": "Schrunk, Benjamin", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Schrunk", + "givenName": "Benjamin" + }, + { + "creatorName": "Yan, Jiaqiang", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Yan", + "givenName": "Jiaqiang" + }, + { + "creatorName": "Kaminski, Adam", + "affiliations": [ + "Ames Laboratory", + "Iowa State University", + "Oak Ridge National Laboratory", + "University of Tennessee" + ], + "familyName": "Kaminski", + "givenName": "Adam" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "DFT" + }, + { + "subject": "ARPES" + }, + { + "subject": "topological insulator" + } + ], + "publicationYear": 2020, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2020-03-06", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorName": "kaminski@ameslab.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "pswatek@ameslab.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "heinonen@anl.gov", + "contributorType": "ContactPerson" + } + ], + "titles": [ + { + "title": "Data set for \"Gapless Dirac surface states in the antiferromagnetic topological insulator MnBi2Te4\" published in the Phsycial Review B(R) 2020" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Data set for \"Gapless Dirac surface states in the antiferromagnetic topological insulator MnBi2Te4\" by P. 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Chandler", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ], + "familyName": "Bennett", + "givenName": "M. Chandler" + }, + { + "creatorName": "Wang, Guangming", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ], + "familyName": "Wang", + "givenName": "Guangming" + }, + { + "creatorName": "Mitas, Lubos", + "affiliations": [ + "North Carolina State University", + "Oak Ridge National Laboratory" + ], + "familyName": "Mitas", + "givenName": "Lubos" + } + ], + "subjects": [ + { + "subject": "ccECP" + }, + { + "subject": "ECP" + }, + { + "subject": "pseudopotential" + }, + { + "subject": "correlation energy" + }, + { + "subject": "atomic energy" + }, + { + "subject": "FN-DMC" + }, + { + "subject": "kinetic energy" + }, + { + "subject": "CPSFM" + } + ], + "publicationYear": 2019, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2019-07-30", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for \"Accurate atomic total energies for correlation consistent effective core potentials\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nDataset for \"Accurate atomic total energies for correlation consistent effective core potentials\"\n\nIncluded are input and output files for various codes. For each code, outputs are organized per atom.\n\nFunding: U.S. Department of Energy\n\nContact: Abdulgani Annaberdiyev " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "sdsa_sem_images_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/sdsa_sem_images_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sdsa_sem_images_v1.2/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/sdsa_sem_images_v1.2", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-10-12T16:58:28.681199Z", + "source_id": "sdsa_sem_images_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "sdsa_sem_images" + }, + "dc": { + "identifier": { + "identifier": "10.18126/09ow-8g8k", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Doerk", + "givenName": "Gregory S.", + "creatorName": "Doerk, Gregory S.", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Stein", + "givenName": "Aaron", + "creatorName": "Stein, Aaron", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "familyName": "Yager", + "givenName": "Kevin G.", + "creatorName": "Yager, Kevin G.", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "polymer" + }, + { + "subject": "experiment" + }, + { + "subject": "microscopy" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2022, + "dates": [ + { + "date": "2022-10-12", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "SEM images of sDSA morphologies" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Gregory S. Doerk" + }, + { + "contributorType": "ContactPerson", + "contributorName": "Kevin G. yager" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Scanning electron microscope (SEM) images collected on a combinatorial samplel used in an autonomous SAXS mapping experiment. The sample is a selective directed self-assembly (sDSA) combinatorial sample. Sample was a grid of lithographically-defined chemical patterns (varying pitch and dose) with a blend of block copolymer materials cast on top." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "manna_sio2_polymorphs_functional_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/manna_sio2_polymorphs_functional_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/manna_sio2_polymorphs_functional_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/manna_sio2_polymorphs_functional_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2022-10-27T20:26:21.405338Z", + "source_id": "manna_sio2_polymorphs_functional_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "manna_sio2_polymorphs_functional" + }, + "dc": { + "identifier": { + "identifier": "10.18126/8wuy-z519", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Manna", + "givenName": "Sukriti", + "creatorName": "Manna, Sukriti", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "familyName": "Sankaranarayanan", + "givenName": "Subramanian KRS", + "creatorName": "Sankaranarayanan, Subramanian KRS", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "familyName": "Darancet", + "givenName": "Pierre", + "creatorName": "Darancet, Pierre", + "affiliations": [ + "Argonne National Laboratory", + "University of Illinois at Chicago" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "ceramics" + }, + { + "subject": "semiconductors" + }, + { + "subject": "oxides" + }, + { + "subject": "DFT" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2022, + "dates": [ + { + "date": "2022-10-27", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for SiO2 polymorphs using metaGGA functional" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Sukriti Manna" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "VASP run directory for SiO2 polymorphs" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kuo_mos2_mse_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/kuo_mos2_mse_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kuo_mos2_mse_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kuo_mos2_mse_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-25T21:48:44.452514Z", + "source_id": "kuo_mos2_mse_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kuo_mos2_mse" + }, + "dc": { + "identifier": { + "identifier": "10.18126/d5dp-eglo", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Kuo, Lidia", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Kuo", + "givenName": "Lidia" + }, + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Rangnekar, Sonal V.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Rangnekar", + "givenName": "Sonal V." + }, + { + "creatorName": "Chu, Ting\u2010Ching", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Chu", + "givenName": "Ting\u2010Ching" + }, + { + "creatorName": "Lam, David", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Lam", + "givenName": "David" + }, + { + "creatorName": "Zhu, Zhehao", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Zhu", + "givenName": "Zhehao" + }, + { + "creatorName": "Richter, Lee J.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Richter", + "givenName": "Lee J." + }, + { + "creatorName": "Li, Ruipeng", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Li", + "givenName": "Ruipeng" + }, + { + "creatorName": "Szyd\u0142owska, Beata M.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Szyd\u0142owska", + "givenName": "Beata M." + }, + { + "creatorName": "Downing, Julia R.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Downing", + "givenName": "Julia R." + }, + { + "creatorName": "Luijten, Benjamin J.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Luijten", + "givenName": "Benjamin J." + }, + { + "creatorName": "Lauhon, Lincoln J.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Lauhon", + "givenName": "Lincoln J." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "Brookhaven National Laboratory", + "National Institute of Standards and Technology " + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "semiconductors" + }, + { + "subject": "2D materials" + }, + { + "subject": "additive manufacturing" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "All\u2010Printed Ultrahigh\u2010Responsivity MoS2 Nanosheet Photodetectors Enabled by Megasonic Exfoliation" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1002/adma.202203772", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Printed 2D materials, derived from solution-processed inks, offer scalable and cost-effective routes to mechanically flexible optoelectronics. With micrometer-scale control and broad processing latitude, aerosol-jet printing (AJP) is of particular interest for all-printed circuits and systems. Here, AJP is utilized to achieve ultrahigh-responsivity photodetectors consisting of well-aligned, percolating networks of semiconducting MoS2 nanosheets and graphene electrodes on flexible polyimide substrates. Ultrathin (\u22481.2 nm thick) and high-aspect-ratio (\u22481 \u03bcm lateral size) MoS2 nanosheets are obtained by electrochemical intercalation followed by megasonic atomization during AJP, which not only aerosolizes the inks but also further exfoliates the nanosheets. The incorporation of the high-boiling-point solvent terpineol into the MoS2ink is critical for achieving a highly aligned and flat thin-film morphology following AJP as confirmed by grazing-incidence wide-angle X-ray scattering and atomic force microscopy. Following AJP, curing is achieved with photonic annealing, which yields quasi-ohmic contacts and photoactive channels with responsivities exceeding 103 A W\u22121 that outperform previously reported all-printed visible-light photodetectors by over three orders of magnitude. Megasonic exfoliation coupled with properly designed AJP ink formula-tions enables the superlative optoelectronic properties of ultrathin MoS2nanosheets to be preserved and exploited for the scalable additive manufac-turing of mechanically flexible optoelectronics" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "gr_covid_biosensors_2022_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/gr_covid_biosensors_2022_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/gr_covid_biosensors_2022_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/gr_covid_biosensors_2022_v1.2/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-29T19:45:48.704630Z", + "source_id": "gr_covid_biosensors_2022_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "gr_covid_biosensors_2022" + }, + "dc": { + "identifier": { + "identifier": "10.18126/d1vz-v939", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Pola, C\u00edcero C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Pola", + "givenName": "C\u00edcero C" + }, + { + "creatorName": "Rangnekar, Sonal V", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Rangnekar", + "givenName": "Sonal V" + }, + { + "creatorName": "Sheets, Robert", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Sheets", + "givenName": "Robert" + }, + { + "creatorName": "Szyd\u0142owska, Beata M", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Szyd\u0142owska", + "givenName": "Beata M" + }, + { + "creatorName": "Downing, Julia R", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Downing", + "givenName": "Julia R" + }, + { + "creatorName": "Parate, Kshama W", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Parate", + "givenName": "Kshama W" + }, + { + "creatorName": "Wallace, Shay G", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Wallace", + "givenName": "Shay G" + }, + { + "creatorName": "Tsai, Daphne", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Tsai", + "givenName": "Daphne" + }, + { + "creatorName": "Hersam, Mark C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Hersam", + "givenName": "Mark C" + }, + { + "creatorName": "Gomes, Carmen L", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Gomes", + "givenName": "Carmen L" + }, + { + "creatorName": "Claussen, Jonathan C", + "affiliations": [ + "Northwestern University", + "Iowa State University" + ], + "familyName": "Claussen", + "givenName": "Jonathan C" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2d materials" + }, + { + "subject": "sensors" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-29", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Aerosol-jet-printed graphene electrochemical immunosensors for rapid and label-free detection of SARS-CoV-2 in saliva" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1088/2053-1583/ac7339", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Rapid, inexpensive, and easy-to-use coronavirus disease 2019 (COVID-19) home tests are key tools in addition to vaccines in the world wide fight to eliminate national and local shutdowns. However, currently available tests for SARS-CoV-2, the virus that causes COVID-19, are too expensive,\npainful, and irritating, or not sufficiently sensitive for routine, accurate home testing. Herein, we employ custom-formulated graphene inks and aerosol jet printing to create a rapid electrochemical immunosensor for direct detection of SARS-CoV-2 spike receptor-binding domain (RBD) in saliva samples acquired noninvasively. This sensor demonstrated limits of detection that are considerably lower than most commercial SARS-CoV-2 antigen tests (22.91 \u00b1 4.72 pg ml\u22121 for spike RBD and 110.38 \u00b1 9.00 pg ml\u22121 for spike S1) as well as fast response time (\u223c30 min), which was facilitated by the functionalization of printed graphene electrodes in a single-step with SARS-CoV-2 polyclonal antibody through the carbodiimide reaction without the need for nanoparticle functionalization or secondary antibody or metallic nanoparticle labels. This immunosensor presents a wide linear sensing range from 1 to 1000 ng ml\u22121 and does not react with other coexisting influenza viruses such as H1N1 hemagglutinin. By combining high-yield graphene ink synthesis, automated printing, high antigen selectivity, and rapid testing capability, this work offers a promising alternative to current SARS-CoV-2 antigen tests." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "wallace_biosensor_2022_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/wallace_biosensor_2022_v1.3" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/wallace_biosensor_2022_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/wallace_biosensor_2022_v1.3/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-30T21:49:36.199770Z", + "source_id": "wallace_biosensor_2022_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "wallace_biosensor_2022" + }, + "dc": { + "identifier": { + "identifier": "10.18126/7s4s-6p2s", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Wallace, Shay Goff", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Wallace", + "givenName": "Shay Goff" + }, + { + "creatorName": "Brothers, Michael C", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Brothers", + "givenName": "Michael C" + }, + { + "creatorName": "Brooks, Zachary E", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Brooks", + "givenName": "Zachary E" + }, + { + "creatorName": "Rangnekar, Sonal V", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Rangnekar", + "givenName": "Sonal V" + }, + { + "creatorName": "Lam, David", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Lam", + "givenName": "David" + }, + { + "creatorName": "Lawrence, Michael J St", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Lawrence", + "givenName": "Michael J St" + }, + { + "creatorName": "Rojas, William A Gaviria", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Rojas", + "givenName": "William A Gaviria" + }, + { + "creatorName": "Putz, Karl W", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Putz", + "givenName": "Karl W" + }, + { + "creatorName": "Kim, Steve S", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Kim", + "givenName": "Steve S" + }, + { + "creatorName": "Hersam, Mark C", + "affiliations": [ + "Northwestern University", + "air force research laboratory" + ], + "familyName": "Hersam", + "givenName": "Mark C" + } + ], + "subjects": [ + { + "subject": "experiment" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-04", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Fully printed and flexible multi-material electrochemical aptasensor platform enabled by selective graphene biofunctionalization" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1088/2631-8695/ac5e27", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The demand for flexible biochemical sensors has increased with advances in computational functionality and wireless communication. Advances in materials science and biochemistry have enabled the development and fabrication of biosensors for selective detection of biological analytes leveraging ink-printed technologies, including in flexible form-factors. However, despite these advances, minimal effort has been devoted to translating the multi-material, three-electrode electrochemical cell, which is widely regarded as the standard for laboratory-scale studies, into a flexible form-factor for use in immunosensors, especially in a manner that is compatible with rapid and scalable additive manufacturing. Here, we report a fully printed and flexible electrochemical non-enzymatic immunosensor platform that integrates four chemically compatible inks and a non-covalent, two-step biofunctionalization scheme. The robustness of the platform is demonstrated using a model aptasensor that enables lysozyme detection using both electrochemical impedance spectroscopy and square wave voltammetry. The flexible, fully ink-printed aptasensor shows competitive performance to commercially available rod/disc electrodes in a bath cell. Overall, this work establishes a methodology for high-throughput fabrication of robust, flexible, multi-material, three-electrode immunosensors that can be generalized to a range of biosensor applications." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "hyun_nanoletters2022_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/hyun_nanoletters2022_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/hyun_nanoletters2022_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/hyun_nanoletters2022_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-29T03:36:29.959095Z", + "source_id": "hyun_nanoletters2022_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "hyun_nanoletters2022" + }, + "dc": { + "identifier": { + "identifier": "10.18126/32mg-akx2", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Hyun", + "givenName": "Woo Jin", + "creatorName": "Hyun, Woo Jin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Thomas", + "givenName": "Cory M.", + "creatorName": "Thomas, Cory M.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Chaney", + "givenName": "Lindsay E.", + "creatorName": "Chaney, Lindsay E.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Mazarin de Moraes", + "givenName": "Ana Carolina", + "creatorName": "Mazarin de Moraes, Ana Carolina", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "batteries" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-29", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Screen-Printable Hexagonal Boron Nitride Ionogel Electrolytes for Mechanically Deformable Solid-State Lithium-Ion Batteries" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acs.nanolett.2c01364", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Ionogel electrolytes present several benefits for solid-state lithium-ion batteries including nonflammability, favorable electrochemical properties, and high thermal stability. However, limited processing methods are currently available for ionogel electrolytes, restricting their practical applications. Here, we present a screen-printable ionogel electrolyte formulation based on hexagonal boron nitride (hBN) nanoplatelets. To achieve screen-printable rheological properties, hBN nanoplatelets are mixed with an imidazolium ionic liquid in ethyl lactate. Following screen printing, the resulting spatially uniform and mechanically flexible hBN ionogel electrolytes achieve high room-temperature ionic conductivities >1 mS cm\u20131 and stiff mechanical moduli >1 MPa. These hBN ionogel electrolytes enable the fabrication of fully screen-printed lithium-ion batteries with high cycling stability, rate performance, and mechanical resilience against flexion and external forces, thus providing a robust energy storage solution that is compatible with scalable additive manufacturing." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "thomas_bladecoat_hbn_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/thomas_bladecoat_hbn_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/thomas_bladecoat_hbn_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/thomas_bladecoat_hbn_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-29T17:30:32.502464Z", + "source_id": "thomas_bladecoat_hbn_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "thomas_bladecoat_hbn" + }, + "dc": { + "identifier": { + "identifier": "10.18126/6fqv-23mo", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Thomas", + "givenName": "Cory M.", + "creatorName": "Thomas, Cory M.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hyun", + "givenName": "Woo Jin", + "creatorName": "Hyun, Woo Jin", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Huang", + "givenName": "Hsien Cheng", + "creatorName": "Huang, Hsien Cheng", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Zeng", + "givenName": "Davy", + "creatorName": "Zeng, Davy", + "affiliations": [ + "Northwestern University" + ] + }, + { + "familyName": "Hersam", + "givenName": "Mark C.", + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "batteries" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-29", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Blade-Coatable Hexagonal Boron Nitride Ionogel Electrolytes for Scalable Production of Lithium Metal Batteries" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acsenergylett.2c00535", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Solid-state electrolytes have attracted significant attention for rechargeable lithium-ion batteries due to their potential to enable higher energy density technologies and improve cell safety by removing volatile liquid electrolytes. However, existing solid-state electrolyte materials lack sufficient electrochemical performance or require expensive and time-consuming processing methods that have prevented their wide-scale adoption. Here, a blade-coatable hexagonal boron nitride ionogel electrolyte is introduced that exhibits high room temperature ionic conductivity (>1 mS cm\u20131), is stable against lithium metal anodes, and can be applied over a wide area in a thin (<40 \u03bcm) and crack-free film. Furthermore, this blade-coatable slurry has a tunable viscosity to enable its use in existing battery manufacturing infrastructure. The resulting blade-coated hBN ionogel electrolyte is employed in a lithium metal battery with a LiFePO4 cathode, exhibiting superlative rate capability at room temperature with a 78% capacity retention after 500 cycles at a rate of 1C." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "atomvison_atomistic_stm_images_2d_materials_unique_chemical_compositions_structure_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "projects": { + "foundry": { + "splits": [ + { + "path": "stmimages.h5", + "label": "train", + "type": "train" + } + ], + "keys": [ + { + "description": "simulated STM images of various 2D materials using the Tersoff-Hamann approach", + "type": "input", + "key": [ + "images" + ] + }, + { + "description": "bias of STM, negative or positive", + "type": "input", + "key": [ + "bias_voltage" 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+ "date": "2023-04-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "AtomVison: Atomistic STM images of 2D materials with unique chemical compositions and structure" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "axelrod_photodock_docking_drugs_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/axelrod_photodock_docking_drugs_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/axelrod_photodock_docking_drugs_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/axelrod_photodock_docking_drugs_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2023-01-10T20:36:56.673546Z", + "source_id": "axelrod_photodock_docking_drugs_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "axelrod_photodock_docking_drugs" + }, + "dc": { + "identifier": { + "identifier": "10.18126/41tz-igf4", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Axelrod", + "givenName": "Simon", + "creatorName": "Axelrod, Simon", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "familyName": "Shakhnovich", + "givenName": "Eugene", + "creatorName": "Shakhnovich, Eugene", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "familyName": "Gomez-Bombarelli", + "givenName": "Rafael", + "creatorName": "Gomez-Bombarelli, Rafael", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-01-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "PhotoDock: Docking scores and poses of photoactive drugs" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Simon Axelrod (simonaxelrod83@gmail.com)" + }, + { + "contributorType": "ContactPerson", + "contributorName": "Eugene Shakhnovich (shakhnovich@chemistry.harvard.edu) " + }, + { + "contributorType": "ContactPerson", + "contributorName": "Rafael Gomez-Bombarelli (rafagb@mit.edu)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Here you can find the docking data from our paper, \"Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling\" (https://arxiv.org/abs/2302.11490). 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+ "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Mukhopadhyay", + "givenName": "Swarnav" + }, + { + "creatorName": "Liu, Rui", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Liu", + "givenName": "Rui" + }, + { + "creatorName": "Liu, Cheng", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Liu", + "givenName": "Cheng" + }, + { + "creatorName": "Evans, Paul G.", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Evans", + "givenName": "Paul G." + }, + { + "creatorName": "Pasayat, Shubhra S.", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Pasayat", + "givenName": "Shubhra S." + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-18", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "MOCVD of InGaN on ScAlMgO4 on Al2O3 Substrates with Improved Surface Morphology and Crystallinity" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": " https://doi.org/10.3390/cryst13030446", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": " https://doi.org/10.3390/cryst13030446", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Pasayat, Shubhra (shubhra@ece.wisc.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "All the AFM, XRD and PL raw data files related to the published work" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "rucl3_lpe_2022_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/rucl3_lpe_2022_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/rucl3_lpe_2022_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rucl3_lpe_2022_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-25T20:12:25.065039Z", + "source_id": "rucl3_lpe_2022_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "rucl3_lpe_2022" + }, + "dc": { + "identifier": { + "identifier": "10.18126/9z5q-zwzu", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Lam, David", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Lam", + "givenName": "David" + }, + { + "creatorName": "Lebedev, Dmitry", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Lebedev", + "givenName": "Dmitry" + }, + { + "creatorName": "Kuo, Lidia", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Kuo", + "givenName": "Lidia" + }, + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Szyd\u0142owska, Beata M.", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Szyd\u0142owska", + "givenName": "Beata M." + }, + { + "creatorName": "Ferraresi, Filippo", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Ferraresi", + "givenName": "Filippo" + }, + { + "creatorName": "S\u00f6ll, Aljoscha", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "S\u00f6ll", + "givenName": "Aljoscha" + }, + { + "creatorName": "Sofer, Zden\u011bk", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Sofer", + "givenName": "Zden\u011bk" + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University", + "University of Chemistry and Technology Prague" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "2D materials" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Liquid-Phase Exfoliation of Magnetically and Optoelectronically Active Ruthenium Trichloride Nanosheets" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acsnano.2c04888", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "\"Kuo, Lidia\" (lidiakuo2023@u.northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\u03b1-RuCl3 is a layered transition metal halide that possesses a range of exotic magnetic, optical, and electronic properties including fractional excitations indicative of a proximate Kitaev quantum spin liquid (QSL). While previous reports have explored these properties on idealized single crystals or mechanically exfoliated samples, the scalable production of \u03b1-RuCl3 nanosheets has not yet been demonstrated. Here, we perform liquid-phase exfoliation (LPE) of \u03b1-RuCl3 through an electrochemically assisted approach, which yields ultrathin, electron-doped \u03b1-RuCl3 nanosheets that are then assembled into electrically conductive large-area thin films. The crystalline integrity of the \u03b1-RuCl3 nanosheets following LPE is confirmed through a wide range of structural and chemical analyses. Moreover, the physical properties of the LPE \u03b1-RuCl3 nanosheets are investigated through electrical, optical, and magnetic characterization methods, which reveal a structural phase transition at 230 K that is consistent with the onset of Kitaev paramagnetism in addition to an antiferromagnetic transition at 2.6 K. Intercalated ions from the electrochemical LPE protocol favorably alter the optical response of the \u03b1-RuCl3 nanosheets, enabling large-area Mott insulator photodetectors that operate at telecommunications-relevant infrared wavelengths near 1.55 \u03bcm. These photodetectors show a linear photocurrent response as a function of incident power, which suggests negligible trap-mediated recombination or photothermal effects, ultimately resulting in a photoresponsivity of \u22482 mA/W." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "multiresource_ai_v2.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/multiresource_ai_v2.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/multiresource_ai_v2.1/", + "total_size": 36192949, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/multiresource_ai_v2.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-15T13:57:14.838475Z", + "source_id": "multiresource_ai_v2.1", + "scroll_id": 0, + "version": 2, + "source_name": "multiresource_ai" + }, + "dc": { + "identifier": { + "identifier": "10.18126/ld7m-5bb5", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Ward, Logan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Ward", + "givenName": "Logan" + }, + { + "creatorName": "Pauloski, J. Gregory", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Pauloski", + "givenName": "J. Gregory" + }, + { + "creatorName": "Hayot-Sasson, Valerie", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Hayot-Sasson", + "givenName": "Valerie" + }, + { + "creatorName": "Chard, Ryan", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Chard", + "givenName": "Ryan" + }, + { + "creatorName": "Babuji, Yadu", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Babuji", + "givenName": "Yadu" + }, + { + "creatorName": "Sivaraman, Ganesh", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Sivaraman", + "givenName": "Ganesh" + }, + { + "creatorName": "Choudhury, Sutanay", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Choudhury", + "givenName": "Sutanay" + }, + { + "creatorName": "Chard, Kyle", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Chard", + "givenName": "Kyle" + }, + { + "creatorName": "Thakur, Rajeev", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Thakur", + "givenName": "Rajeev" + }, + { + "creatorName": "Foster, Ian", + "affiliations": [ + "Argonne National Laboratory", + "University of Chicago", + "Pacific Northwest National Laboratory" + ], + "familyName": "Foster", + "givenName": "Ian" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "simulation" + }, + { + "subject": "energy materials" + }, + { + "subject": "molecular structures" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-15", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.18126/hy6c-vyh6", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Logan Ward (lward@anl.gov)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The data and software needed leveraging multiple computing resources together to design molecules and fit interatomic potentials. We also include the data from runs of the application described in our associated paper." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "axelrod_azoflip_spinflip_derivatives_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/axelrod_azoflip_spinflip_derivatives_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/axelrod_azoflip_spinflip_derivatives_v1.3/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/axelrod_azoflip_spinflip_derivatives_v1.3", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "resource_type": "dataset", + "organizations": [ + "MDF Open" + ], + "ingest_date": "2021-10-01T03:33:09.861988Z", + "source_id": "axelrod_azoflip_spinflip_derivatives_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "axelrod_azoflip_spinflip_derivatives" + }, + "dc": { + "identifier": { + "identifier": "10.18126/unc8-336t", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "CC-BY 4.0" + } + ], + "creators": [ + { + "familyName": "Axelrod", + "givenName": "Simon", + "creatorName": "Axelrod, Simon", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "familyName": "Shakhnovich", + "givenName": "Eugene", + "creatorName": "Shakhnovich, Eugene", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + }, + { + "familyName": "Gomez-Bombarelli", + "givenName": "Rafael", + "creatorName": "Gomez-Bombarelli, Rafael", + "affiliations": [ + "Harvard University", + "Massachusetts Institute of Technology" + ] + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "simulation" + }, + { + "subject": "TDDFT" + }, + { + "subject": "DFT" + } + ], + "publicationYear": 2021, + "publisher": "Materials Data Facility", + "dates": [ + { + "date": "2021-10-01", + "dateType": "Accepted" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Simon Axelrod " + } + ], + "titles": [ + { + "title": "AzoFlip: Spin-flip TDDFT Data for Non-adiabatic Dynamics of Azobenzene Derivatives" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Here you can find information about the quantum chemistry data accompanying our papers, \"Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential\" (https://www.nature.com/articles/s41467-022-30999-w), and \"Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential\" (https://pubs.acs.org/doi/10.1021/acscentsci.2c00897). If you use this data, please cite our papers.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "zhu_transistor_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/zhu_transistor_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/zhu_transistor_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zhu_transistor_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-19T20:56:16.859508Z", + "source_id": "zhu_transistor_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "zhu_transistor" + }, + "dc": { + "identifier": { + "identifier": "10.18126/e9d0-pc68", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Zhu, Zhehao", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Zhu", + "givenName": "Zhehao" + }, + { + "creatorName": "Kim, Joon-Seok", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Kim", + "givenName": "Joon-Seok" + }, + { + "creatorName": "Moody, Michael J.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Moody", + "givenName": "Michael J." + }, + { + "creatorName": "Lauhon, Lincoln J.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Lauhon", + "givenName": "Lincoln J." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "semiconductors" + }, + { + "subject": "microscopy" + }, + { + "subject": "printed electronics" + }, + { + "subject": "thin-film transistors" + }, + { + "subject": "2D materials" + }, + { + "subject": "transition metal dichalcogenides" + }, + { + "subject": "Kelvin probe force microscopy" + }, + { + "subject": "finite element simulation" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-19", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Edge and Interface Resistances Create Distinct Trade-Offs When Optimizing the Microstructure of Printed van der Waals Thin-Film Transistors" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://pubs.acs.org/doi/10.1021/acsnano.2c09527", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Zhehao Zhu (zhehaozhu2022@u.northwestern.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Lincoln Lauhon (lauhon@northwestern.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Raw experimental data, and Python notebooks for data analysis and device simulation. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "mbsmd_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/mbsmd_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/mbsmd_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/mbsmd_v1.2/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-02-10T15:42:32.989087Z", + "source_id": "mbsmd_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "mbsmd" + }, + "dc": { + "identifier": { + "identifier": "10.18126/g26r-f6oz", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Hamilton, Brenden W.", + "affiliations": [ + "Purdue University" + ], + "familyName": "Hamilton", + "givenName": "Brenden W." + }, + { + "creatorName": "Strachan, Alejandro", + "affiliations": [ + "Purdue University" + ], + "familyName": "Strachan", + "givenName": "Alejandro" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "mechanochemistry" + }, + { + "subject": "TATB" + }, + { + "subject": "spiropyran" + }, + { + "subject": "activation energy" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-02-10", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Datasets for: \"Many-Body Mechanochemistry: Intra-molecular Strain in Condensed Matter Chemistry\"" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.26434/chemrxiv-2022-bvdmp", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Alejandro Strachan (strachan@purdue.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Dataset includes all activation barriers for mechanochemical simulations of TATB and Spiropyran using the MBsMD method. TATB reaction counts and Spiropyran free energy are also included." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kononov_identifying_native_\u03b1alumina_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kononov_identifying_native_%CE%B1alumina_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kononov_identifying_native_\u03b1alumina_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/kononov_identifying_native_\u03b1alumina_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-03-23T21:34:40.319895Z", + "source_id": "kononov_identifying_native_\u03b1alumina_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kononov_identifying_native_\u03b1alumina" + }, + "dc": { + "identifier": { + "identifier": "10.18126/bwgz-gel3", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Kononov", + "givenName": "Alina", + "creatorName": "Kononov, Alina", + "affiliations": [ + "Sandia National Laboratories" + ] + }, + { + "familyName": "Lee", + "givenName": "Cheng-Wei", + "creatorName": "Lee, Cheng-Wei", + "affiliations": [ + "Colorado School of Mines" + ] + }, + { + "familyName": "Shapera", + "givenName": "Ethan", + "creatorName": "Shapera, Ethan", + "affiliations": [ + "Graz University of Technology" + ] + }, + { + "familyName": "Schleife", + "givenName": "Andre", + "creatorName": "Schleife, Andre", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "oxides" + }, + { + "subject": "defects" + }, + { + "subject": "DFT" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-03-23", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Identifying native point defect configurations in \u03b1-alumina" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "Alina Kononov (akonono@sandia.gov)" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "DFT calculations for native point defects in aluminum oxide performed using VASP" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "stanley_machine_learning_photovoltaics_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/stanley_machine_learning_photovoltaics_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/stanley_machine_learning_photovoltaics_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/stanley_machine_learning_photovoltaics_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T21:00:09.085121Z", + "source_id": "stanley_machine_learning_photovoltaics_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "stanley_machine_learning_photovoltaics" + }, + "dc": { + "identifier": { + "identifier": "10.18126/tu66-3hpl", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Stanley, Jared C.", + "familyName": "Stanley", + "givenName": "Jared C." + }, + { + "creatorName": "Gagliardi, Alessio", + "familyName": "Gagliardi", + "givenName": "Alessio" + } + ], + "subjects": [ + { + "subject": "perovskite" + }, + { + "subject": "machine learning" + }, + { + "subject": "materials science" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for 'Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics'" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.4043330", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nDatasets used in the publication \"Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics\" [doi:10.1002/adts.201900178].\n\nAll structures were relaxed with the following parameters using Quantumwise QATK 2017:\n\n- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)\n- 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4\u00c5x5.7\u00c5x11.4\u00c5-structures (forces < 0.01eV/\u00c5)\n- 300K Fermi-Dirac-smearing\n- a 6x12x6 k-point grid (Monkhorst-Pack)\n\n\nSpecifically, the included files are:\n\ndb_2.data: the actual database used for model building (json-format)\nlead_set.data: the \"external\" test set used to test predictive power with out of sample compounds (json-format)\nload_stanley_c.py: a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as ASE-atoms\n\nThe format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):\n{\n \"\" : {\n \"trajectory\" : n/a,\n \"energy\" : total DFT energy in eV,\n \"rstruc\" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),\n \"gaps\" : { \"opt_gap\", \"ind_gap } - both direct and indirect gap,\n \"effective_mass\" : n/a,\n \"iterations\" : number of relaxation steps,\n \"calc\" : some calculation metadata,\n \"ustruc\" : unrelaxed input structure,\n \n }\n}\nMissing ids relate to structures filtered out, because the calculation didn't converge.\n\nSome code which works with a different representation of this data can be found at https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "lee_repository_machinelearning_antiferromagnet_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/lee_repository_machinelearning_antiferromagnet_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/lee_repository_machinelearning_antiferromagnet_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lee_repository_machinelearning_antiferromagnet_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T20:50:49.440512Z", + "source_id": "lee_repository_machinelearning_antiferromagnet_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "lee_repository_machinelearning_antiferromagnet" + }, + "dc": { + "identifier": { + "identifier": "10.18126/grx6-uzq5", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Lee, Jackson", + "familyName": "Lee", + "givenName": "Jackson" + }, + { + "creatorName": "Carbone, Matthew R.", + "familyName": "Carbone", + "givenName": "Matthew R." + }, + { + "creatorName": "Yin, Weiguo", + "familyName": "Yin", + "givenName": "Weiguo" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "spectral function" + }, + { + "subject": "correlated materials" + }, + { + "subject": "t-J model" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Data Repository for: Machine-learning the spectral function of a hole in a quantum antiferromagnet" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7527378", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\n The machine-learning dataset of 51^3 ~1.3 \u00d7 10^5 density of states (DOS) of a mobile hole in the t-t'-t''-J model theoretically generated by using the self-consistent Born approximation in the three-dimensional parameter space of t\u2032 \u2208 [\u22120.5, 0.5], t\u2032\u2032 \u2208 [\u22120.5, 0.5] and J \u2208 [0.2, 1.0], with each parameter sampled on a 51-point uniform grid. The dataset is randomly partitioned into an 80/10/10 training (T), validation (V), and testing T split. Note that each DOS A(\u03c9) was calculated on a 1201-point uniform grid of \u03c9 \u2208 [\u22126t, 6t], then it was resampled on a 301-point uniform grid for the forward problem and on a 354-point uniform grid for the inverse problem. The dataset used in the inverse problem is limited to the parameter space of t\u2032 \u2208 [\u22120.5, 0], t\u2032\u2032 \u2208 [0, 0.5] and J \u2208 [0.2, 1.0]. To open the enclosed .npz files, use numpy.load() in python3.\n \n This work was supported by U.S. Department of Energy (DOE) the Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-SC0012704. This project was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI). This project was supported in part by the Brookhaven National Laboratory (BNL), Condensed Matter Physics and Materials Science Division under the BNL Supplemental Undergraduate Research Program (SURP).\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "rojas_chemtastesdb_curated_tastants_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/rojas_chemtastesdb_curated_tastants_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/rojas_chemtastesdb_curated_tastants_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rojas_chemtastesdb_curated_tastants_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T21:14:39.407456Z", + "source_id": "rojas_chemtastesdb_curated_tastants_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "rojas_chemtastesdb_curated_tastants" + }, + "dc": { + "identifier": { + "identifier": "10.18126/5pq3-zbho", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Rojas, Cristian", + "familyName": "Rojas", + "givenName": "Cristian" + }, + { + "creatorName": "Ballabio, Davide", + "familyName": "Ballabio", + "givenName": "Davide" + }, + { + "creatorName": "Pacheco Sarmiento, Karen", + "familyName": "Pacheco Sarmiento", + "givenName": "Karen" + }, + { + "creatorName": "Pacheco Jaramillo, Elisa", + "familyName": "Pacheco Jaramillo", + "givenName": "Elisa" + }, + { + "creatorName": "Mendoza, Mateo", + "familyName": "Mendoza", + "givenName": "Mateo" + }, + { + "creatorName": "Garc\u00eda, Fernando", + "familyName": "Garc\u00eda", + "givenName": "Fernando" + } + ], + "subjects": [ + { + "subject": "chemistry" + }, + { + "subject": "machine learning" + }, + { + "subject": "taste" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "ChemTastesDB: A Curated Database of Molecular Tastants" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.6528835", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nChemTastesDB is a database that includes curated information of 2944 molecular tastants. ChemTastesDB constitutes a useful tool for the scientific community to expand the information of molecular tastants, which could assist in the analysis of the relationships between molecular structure and taste, as well as in silico (QSAR) studies for taste prediction by means of diverse machine learning approaches.\n\nMolecules are labelled in one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, multitaste and miscellaneous). ChemTastesDB provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. Moreover, the molecular structure in the HyperChem (.hin) format of each chemical is provided.\n\nThis is version 1.2 of the ChemTastesDB.\n\nWhat's new in version 1.2:\n\nChemical information (for instance, name, PubChem CID or CAS number) for some tastants has been included.\n\nThe database is freeware and may be used if proper reference is given to the authors. Preferably refer to the following paper:\nRojas, C., Ballabio, D., Pacheco Sarmiento, K., Pacheco Jaramillo, E., Mendoza, M., & Garc\u00eda, F. (2022). ChemTastesDB: A curated database of molecular tastants. Food Chemistry: Molecular Sciences, 4, 100090. https://doi.org/10.1016/j.fochms.2022.100090.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "zheng_iupac_dissociationconstants_v10_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/zheng_iupac_dissociationconstants_v10_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/zheng_iupac_dissociationconstants_v10_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/zheng_iupac_dissociationconstants_v10_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-04-24T20:58:49.353339Z", + "source_id": "zheng_iupac_dissociationconstants_v10_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "zheng_iupac_dissociationconstants_v10" + }, + "dc": { + "identifier": { + "identifier": "10.18126/cy58-tndq", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Zheng", + "givenName": "Jonathan", + "creatorName": "Zheng, Jonathan" + } + ], + "subjects": [ + { + "subject": "pKa" + }, + { + "subject": "machine learning" + }, + { + "subject": "dissociation" + }, + { + "subject": "chemistry" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-04-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "IUPAC Dissociation-Constants: v1.0" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.5281/zenodo.7236453", + "relatedIdentifierType": "DOI" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThis release includes the labeled data, digitization report, method keys, and partial reference keys as part of the IUPAC Digitized pKa Dataset.\n\nThis is the first release of this dataset. The detailed process of digitization and curation of this dataset can be found in the IUPAC pKa Data Digitization Report attached in this repository. Our validation process is ongoing and will continue. Please be advised that a few errors and inconsistencies may still exist.\n\nThe digitized books in this collection include:\n\nSerjeant: International Union of Pure and Applied Chemistry, E. P Serjeant and Boyd Dempsey. Ionisation Constants of Organic Acids in Aqueous Solution; Oxford/Pergamon, 1979 (Oxford IUPAC chemical data series)\nPerrin: International Union of Pure and Applied Chemistry, DD Perrin. Dissociation Constants of Organic Bases in Aqueous Solution; Butterworths, 1965\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "banszerus_particlehole_symmetry_dots_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/banszerus_particlehole_symmetry_dots_v1.3/", + "total_size": 5792337, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/banszerus_particlehole_symmetry_dots_v1.3/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/banszerus_particlehole_symmetry_dots_v1.3", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + 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"Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Contains Faraday cup electrostatic data for volcanic ash samples, for the investigation of their triboelectric charging properties. There is also density, shape, and size data for the volcanic ash samples.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "O'Hara, Tom (tom.ohara@bristol.ac.uk)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/n3dx-dh16" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "9cde500f-87e2-493b-aa9c-b31a8346b045", + "source_id": "9cde500f-87e2-493b-aa9c-b31a8346b045", + "versioned_source_id": "9cde500f-87e2-493b-aa9c-b31a8346b045-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-09-03T10:44:34.308400Z" + }, + "data": { + "endpoint_path": 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"University of Delaware" + ] + }, + { + "creatorName": "Sturchio, Neil C.", + "familyName": "Sturchio", + "givenName": "Neil C.", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + }, + { + "creatorName": "Fenter, Paul", + "familyName": "Fenter", + "givenName": "Paul", + "affiliations": [ + "Argonne National Laboratory", + "University of Delaware" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1021/acs.est.4c03567", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "fenter@anl.gov", + "contributorType": "ContactPerson" + } + ], + "identifier": { + 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+ "title": "Data ver 2 for: Learning Orderings in Crystalline Materials with Symmetry-Aware Graph Neural Networks" + } + ], + "creators": [ + { + "creatorName": "Peng, Jiayu", + "familyName": "Peng", + "givenName": "Jiayu", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "creatorName": "Damewood, James", + "familyName": "Damewood", + "givenName": "James", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "creatorName": "Karaguesian, Jessica", + "familyName": "Karaguesian", + "givenName": "Jessica", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "creatorName": "Lunger, Jaclyn R.", + "familyName": "Lunger", + "givenName": "Jaclyn R.", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + }, + { + "creatorName": "G\u00f3mez-Bombarelli, Rafael", + "familyName": "G\u00f3mez-Bombarelli", + "givenName": "Rafael", + "affiliations": [ + "Massachusetts Institute of Technology" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data for our paper \"Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks\". If you use this data, please cite our paper.", + "descriptionType": "Other" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Jiayu Peng (jypeng@mit.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ncqt-rh18" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "64d19901-f3a7-4ed6-937a-65a6108abe34", + "organization": "MDF Open", + "source_id": "64d19901-f3a7-4ed6-937a-65a6108abe34", + "versioned_source_id": "64d19901-f3a7-4ed6-937a-65a6108abe34-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-09-20T04:19:45.571253Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/64d19901-f3a7-4ed6-937a-65a6108abe34/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/64d19901-f3a7-4ed6-937a-65a6108abe34/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "e32dae46-863d-4c13-a9ef-a36eefa38a78-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Data-driven Low-rank Approximation for Electron-hole Kernel and Acceleration of Time-dependent GW Calculations" + } + ], + "creators": [ + { + "creatorName": "Hou, Bowen", + "familyName": "Hou", + "givenName": "Bowen", + "affiliations": [ + "Yale University" + ] + }, + { + "creatorName": "Wu, Jinyuan", + "familyName": "Wu", + "givenName": "Jinyuan", + "affiliations": [ + "Yale University" + ] + }, + { + "creatorName": "Lee, Victor Chang", + "familyName": "Lee", + "givenName": "Victor Chang", + "affiliations": [ + "Yale University" + ] + }, + { + "creatorName": "Guo, Jiaxuan", + "familyName": "Guo", + "givenName": "Jiaxuan", + "affiliations": [ + "Yale University" + ] + }, + { + "creatorName": "Liu, Luna Y.", + "familyName": "Liu", + "givenName": "Luna Y.", + "affiliations": [ + "Yale University" + ] + }, + { + "creatorName": "Qiu, Diana Y.", + "familyName": "Qiu", + "givenName": "Diana Y.", + "affiliations": [ + "Yale University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Many-body electron-hole interactions are essential for understanding non-linear optical processes and ultrafast spectroscopy of materials. Recent first principles approaches based on nonequilibrium Green\u2019s function formalisms, such as the time-dependent adiabatic GW (TD-aGW) approach, can predict the nonequilibrium dynamics of excited states including electron-hole interactions. However, the high dimensionality of the electron-hole kernel poses significant computational challenges for scalability. Here, we develop a data-driven low-rank approximation for the electron-hole kernel, leveraging localized excitonic effects in the Hilbert space of crystalline systems. Through singular value decomposition (SVD) analysis, we show that the subspace of non-zero singular values, containing the key information of the electron-hole kernel, retains a small size even as the k-grid grows, ensuring computational feasibility with extremely dense k-grids for converged calculations. Utilizing this low-rank property, we achieve at least 95% compression of the kernel and an orderof-magnitude speedup of TD-aGW calculations. Our method, rooted in physical interpretability, outperforms existing machine learning approaches by avoiding intensive training processes and eliminating time-accumulated errors, providing a general framework for high-throughput, nonequilibrium simulation of light-driven dynamics in materials.\n\nThis dataset includes all the raw data of MoS2 BSE kernel from BerkeleyGW, the TD-aGW generated data, and post-processed data used for plot.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "TD-aGW" + }, + { + "subject": "SVD" + }, + { + "subject": "Non-equilibrium Dynamics" + }, + { + "subject": "Electron-hole Interaction" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Bowen Hou (bowen.hou@yale.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/1f89-fc48" + } + }, + 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"creatorName": "Ringel, Benjamin M.", + "familyName": "Ringel", + "givenName": "Benjamin M.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Semeraro, Federico", + "familyName": "Semeraro", + "givenName": "Federico", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Ferguson, Joseph C.", + "familyName": "Ferguson", + "givenName": "Joseph C.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Barnard, Harold S.", + "familyName": "Barnard", + "givenName": "Harold S.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Dias, Bruno", + "familyName": "Dias", + "givenName": "Bruno", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Schlep\u00fctz, Christian M.", + "familyName": "Schlep\u00fctz", + "givenName": "Christian M.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Barnard, Edward S.", + "familyName": "Barnard", + "givenName": "Edward S.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Schickler, Sam", + "familyName": "Schickler", + "givenName": "Sam", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Levy, Kara", + "familyName": "Levy", + "givenName": "Kara", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Shacterman, Shawn", + "familyName": "Shacterman", + "givenName": "Shawn", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Benioff-White, Talia", + "familyName": "Benioff-White", + "givenName": "Talia", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Davis, Julian", + "familyName": "Davis", + "givenName": "Julian", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "MacDowell, Alastair A.", + "familyName": "MacDowell", + "givenName": "Alastair A.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Parkinson, Dilworth Y.", + "familyName": "Parkinson", + "givenName": "Dilworth Y.", + "affiliations": [ + "Department of Aerospace Engineering, Grainger College of Engineering, University of Illinois at Urbana-Champaign, 104 S Wright Street, Urbana, 61802, IL, USA", + "Analytical Mechanics Associates, NASA Ames Research Center, Moffett Field, 94035, CA, USA", + "Department of Mechanical Engineering, Stanford University, 440 Escondido Mall, Stanford, 94305, CA, USA", + "Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA", + "Swiss Light Source, Paul Scherrer Institute, Forschungsstrasse 111, 5232, Villigen, Switzerland", + "Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA" + ] + }, + { + "creatorName": "Panerai, Francesco", + "familyName": "Panerai", + "givenName": "Francesco", + "affiliations": [ + "Department of 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Each file is a Numpy array corresponding to one simulation frame. The first section each file name is the simulation number, from 0000_out to 0199_out. The second section of each file name is the tracked phase: composition, ostwald 1, ostwald 2, ostwald 3, or ostwald 4. The last digits of each file name is the simulation timestep, from 0 to 30,000,000 with an interval of 30,000 for a total of 101 timesteps included per simulation per phase. Each array contains only 0s and 1s, with 0 corresponding to out of phase and 1 corresponding to in phase. At each timestep, the sum of all four individual phases will equal the compositional phase. The simulation parameters used were randomized but checked to ensure mictrostructures generated. A dump with these parameters is available in excel format from the authors.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "diffusion" + }, + { + "subject": "microstructures" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "ahgesch@crimson.ua.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "hucz@ua.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ccqv-t437" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "34c73a10-2b22-404c-9c6f-652569720a28", + "organization": "MDF Open", + "source_id": "34c73a10-2b22-404c-9c6f-652569720a28", + "versioned_source_id": "34c73a10-2b22-404c-9c6f-652569720a28-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-05-16T14:42:37.707474Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/34c73a10-2b22-404c-9c6f-652569720a28/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/34c73a10-2b22-404c-9c6f-652569720a28/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "be9d86d2-dbc9-4b0a-813f-6bd2784aaa18-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Datasets for Stacking polymorphism of PtSe2: its implication to layer-dependent metal-insulator transitions" + } + ], + "creators": [ + { + "creatorName": "Ahn, Jeonghwan", + "familyName": "Ahn", + "givenName": "Jeonghwan", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Hong, Iuegyun", + "familyName": "Hong", + "givenName": "Iuegyun", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Lee, Gwangyoung", + "familyName": "Lee", + "givenName": "Gwangyoung", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Shin, Hyeondeok", + "familyName": "Shin", + "givenName": "Hyeondeok", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Benali, Anouar", + "familyName": "Benali", + "givenName": "Anouar", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Kwon, Yongkyung", + "familyName": "Kwon", + "givenName": "Yongkyung", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Krogel, Jaron T.", + "familyName": "Krogel", + "givenName": "Jaron T.", + "affiliations": [ + "Konkuk University", + "Oak Ridge National Laboratory", + "Argonne National Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Datasets for Datasets for Stacking polymorphism of PtSe2: its implication to layer-dependent metal-insulator transitions published on npj 2D Mater Appl 9, 34 (2025). https://doi.org/10.1038/s41699-025-00544-6 ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1038/s41699-025-00544-6", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "DFT" + }, + { + "subject": "QMCPack" + }, + { + "subject": "QMC" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Jaron T. 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Polymerization resins comprised of dicyclopentadiene (DCPD) with 5 wt% 5-ethylidene-2-norbornene (ENB) added, grubbs\u2019 second generation catalyst, and tributyl phosphite (TBP) inhibitor (8000:1:1 monomer:initiator:inhibitor ratio). Polymerization fronts were triggered using concentrated solutions of grubbs\u2019 third generation catalyst in various solvents.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "DSC" + }, + { + "subject": "properties" + }, + { + "subject": "characterization" + }, + { + "subject": "chemistry" + }, + { + "subject": "glass transition temperature" + }, + { + "subject": "FROMP" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "annacc2@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/t1j5-4111" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "organization": "MDF Open", + "source_id": "ad2a1532-e3dd-4b12-bded-8653466b3a9b", + "versioned_source_id": "ad2a1532-e3dd-4b12-bded-8653466b3a9b-1.2", + "version": "1.2", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-05-13T15:20:24.247558Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ad2a1532-e3dd-4b12-bded-8653466b3a9b/1.2/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ad2a1532-e3dd-4b12-bded-8653466b3a9b/1.2/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "442f453e-e296-455a-a200-6ca6779cefca-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Videos of Chemically Activated Frontal Polymerization of Thermosets " + } + ], + "creators": [ + { + "creatorName": "Cramblitt, Anna C.", + "familyName": "Cramblitt", + "givenName": "Anna C.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Lessard, Jacob J.", + "familyName": "Lessard", + "givenName": "Jacob J.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Video recordings of the frontal ring-opening metathesis polymerization (FROMP) of thermoset polymer specimens in 10mm glass test tubes were collected in order to measure the front speed resulting from varied front initiation methods. Maximum front temperature was also measured. Polymerization resins comprised of dicyclopentadiene (DCPD) with 5 wt% 5-ethylidene-2-norbornene (ENB) added, grubbs\u2019 second generation catalyst, and tributyl phosphite (TBP) inhibitor (8000:1:1 monomer:initiator:inhibitor ratio). Polymerization fronts were triggered using concentrated solutions of grubbs\u2019 third generation catalyst in various solvents.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "video" + }, + { + "subject": "chemistry" + }, + { + "subject": "front speed" + }, + { + "subject": "front temperature" + }, + { + "subject": "FROMP" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "annacc2@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/183e-2r15" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "442f453e-e296-455a-a200-6ca6779cefca", + "organization": "MDF Open", + "source_id": "442f453e-e296-455a-a200-6ca6779cefca", + "versioned_source_id": "442f453e-e296-455a-a200-6ca6779cefca-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-05-13T15:05:58.688071Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/442f453e-e296-455a-a200-6ca6779cefca/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/442f453e-e296-455a-a200-6ca6779cefca/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "1be66992-4956-44a6-834a-c790ca0559f9-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Datasets for \"Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo\"" + } + ], + "creators": [ + { + "creatorName": "Lopez, Annette", + "familyName": "Lopez", + "givenName": "Annette", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "creatorName": "Melton, Cody A.", + "familyName": "Melton", + "givenName": "Cody A.", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "creatorName": "Ahn, Jeonghwan", + "familyName": "Ahn", + "givenName": "Jeonghwan", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "creatorName": "Rubenstein, Brenda M.", + "familyName": "Rubenstein", + "givenName": "Brenda M.", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + }, + { + "creatorName": "Krogel, Jaron T.", + "familyName": "Krogel", + "givenName": "Jaron T.", + "affiliations": [ + "Brown University", + "Sandia National Laboratory", + "Oak Ridge National Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Datasets for \"Identifying Band Inversions in Topological Materials Using Diffusion Monte Carlo\". 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Basset (klbasse@sandia.gov)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Amelia A. Henriksen (aahenri@sandia.gov)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset constitutes the output from a novel workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni-Fe thin films, as outlined in accompanying paper \u201cA Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films.\u201d Approximately four films were created for each unique set of process conditions. These process conditions span seven different chemical bath solutions (and one repeated solution with the addition of agitation), and current densities varied between 1 and 200 mA/cm^2. The in-situ voltage time series data for these electrodepositions is included to capture process information. Six characterization modalities are represented: X-ray diffraction, X-ray fluorescence, nanoindentation, optical microscopy, laser profilometry, and tribological (friction and wear) testing.\n\nWe acknowledge the Laboratory Directed Research and Development program for providing funding for this study. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy\u2019s National Nuclear Security Administration under contract DE-NA0003525.\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "sandia_vdos_xrd_si_fe_mg_si_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/sandia_vdos_xrd_si_fe_mg_si_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/sandia_vdos_xrd_si_fe_mg_si_v1.1/", + "total_size": 0, + "endpoint_path": 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"givenName": "Andr\u00e9" + }, + { + "creatorName": "Kanai, Yosuke", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "University of North Carolina at Chapel Hill" + ], + "familyName": "Kanai", + "givenName": "Yosuke" + }, + { + "creatorName": "Correa, Alfredo A.", + "affiliations": [ + "Lawrence Livermore National Laboratory", + "University of North Carolina at Chapel Hill" + ], + "familyName": "Correa", + "givenName": "Alfredo A." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "nuclear materials" + }, + { + "subject": "DFT" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-10-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Accurate atomistic first-principles calculations of electronic stopping" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "https://doi.org/10.1103/PhysRevB.91.014306", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "lward@anl.gov", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset contains the outputs of real time time-dependent density functional theory (RT-TDDFT) calculations of a proton traveling at high velocities through face-centered cubic aluminum." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "pyebsdindexdemo_v1.4", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/pyebsdindexdemo_v1.4" + }, + "data": { + "link": 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Jupyter notebooks are included to create simulated patterns of known orientation with varying levels of noise, which then can be indexed, with the index orientation compared to the simulated orientation. Additionally, a test Ti64 dataset is included, with a Jupyter notebook, that is used to demonstrate typical usage of PyEBSDIndex. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "jang_vu_vitrimer_calculations_v1.4", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/jang_vu_vitrimer_calculations_v1.4" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/jang_vu_vitrimer_calculations_v1.4/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/jang_vu_vitrimer_calculations_v1.4/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-09-21T16:38:42.803367Z", + "source_id": "jang_vu_vitrimer_calculations_v1.4", + "scroll_id": 0, + "version": 1, + "source_name": "jang_vu_vitrimer_calculations" + }, + "dc": { + "identifier": { + "identifier": "10.18126/c9zx-r4gs", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Jang, Seongon", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Jang", + "givenName": "Seongon" + }, + { + "creatorName": "Alvarez, Erick I. Hernandez", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Alvarez", + "givenName": "Erick I. Hernandez" + }, + { + "creatorName": "Chen, Chen", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Chen", + "givenName": "Chen" + }, + { + "creatorName": "Jing, Brian B.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Jing", + "givenName": "Brian B." + }, + { + "creatorName": "Shen, Chengtian", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Shen", + "givenName": "Chengtian" + }, + { + "creatorName": "Braun, Paul V.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Braun", + "givenName": "Paul V." + }, + { + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Schleife", + "givenName": "Andr\u00e9" + }, + { + "creatorName": "Schroeder, Charles M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Schroeder", + "givenName": "Charles M." + }, + { + "creatorName": "Evans, Christopher M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Evans", + "givenName": "Christopher M." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "polymers" + }, + { + "subject": "DFT" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-09-22", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Control of lithium salt partitioning, coordination, and solvation in vitrimer electrolytes" + } + ], + "contributors": [ + { + "contributorName": "Erick I. Hernandez Alvarez (eih2@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Structures for polyethylene oxide (PEO) molecules (repeating units 2, 4, and 6) with a vinylogous urethane cross linker. Pre-relaxed and relaxed structures are provided. A Li+ ion is placed along different sites of the polymer backbone to calculate the binding energies at different oxygen sites." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "nagarajan_2023_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/nagarajan_2023_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/nagarajan_2023_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/nagarajan_2023_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-10-16T21:27:42.646406Z", + "source_id": "nagarajan_2023_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "nagarajan_2023" + }, + "dc": { + "identifier": { + "identifier": "10.18126/3a2c-lkac", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Maxwell B Nagarajan Daniel S Reynolds, Sebastien G M Uel, Jason W Bjork, Bryan A Baker, Amy K McNulty, Susan L Woulfe, Jennifer A Lewis, Alexander J Ainscough", + "affiliations": [ + "Harvard University", + "3M Company" + ], + "familyName": "Maxwell B Nagarajan Daniel S Reynolds, Sebastien G M Uel, Jason W Bjork, Bryan A Baker, Amy K McNulty, Susan L Woulfe, Jennifer A Lewis", + "givenName": "Alexander J Ainscough" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-10-16", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Biomimetic Human Skin Model Patterned with Rete Ridges" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1088/1758-5090/acfc29", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "max.nagarajan@gmail.com", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Image data from Biomimetic human skin model patterned with rete ridges by Nagarajan et al., 2023. Data is organized by figure. " + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "lyc_v1.3", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/lyc_v1.3" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/lyc_v1.3/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/lyc_v1.3/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-09-25T22:27:30.418890Z", + "source_id": "lyc_v1.3", + "scroll_id": 0, + "version": 1, + "source_name": "lyc" + }, + "dc": { + "identifier": { + "identifier": "10.18126/csff-i4um", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Ballal Ahammed, Elif Ertekin", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ], + "familyName": "Ballal Ahammed", + "givenName": "Elif Ertekin" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-09-26", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Raw data set for LYC paper" + } + ], + "contributors": [ + { + "contributorName": "\"Ahammed, Ballal\" (ahammed2@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Raw dataset for LYC paper" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "smc2023_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/smc2023_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/smc2023_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/smc2023_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-07-25T15:12:13.879352Z", + "source_id": "smc2023_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "smc2023" + }, + "dc": { + "identifier": { + "identifier": "10.18126/gmi9-62tm", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Kramer, Sharlotte L. B.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Kramer", + "givenName": "Sharlotte L. B." + }, + { + "creatorName": "Ivanoff A., Thomas", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Ivanoff A.", + "givenName": "Thomas" + }, + { + "creatorName": "Corona, Edmundo", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Corona", + "givenName": "Edmundo" + } + ], + "subjects": [ + { + "subject": "SMC" + }, + { + "subject": "SMC2023" + }, + { + "subject": "Sandia" + }, + { + "subject": "Benchmark Challenge" + }, + { + "subject": "Simulation" + }, + { + "subject": "Experiment" + }, + { + "subject": "Fasteners" + }, + { + "subject": "Al2024" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-07-25", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Sandia Mechanics Challenge 2023 Information Packet" + } + ], + "contributors": [ + { + "contributorName": "slkrame@sandia.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "tivanof@sandia.gov", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "\nThe Sandia Mechanics Challenge (SMC) provides the solid-mechanics community a forum for assessing its ability to predict mechanical behavior in structures and materials through a blind, round-robin format. Computationalists are asked to predict the behavior of an unfamiliar geometry given experimental calibration data, their predictions are compared to experimental measurements of the SMC scenario, and then the participants assess and compare their approaches, documenting their findings. The SMC broadens the scope of Sandia-hosted benchmarking problems that previously focused on ductile failure through the Sandia Fracture Challenges, enabling an enduring, community-wide self-assessment of predictive capabilities for a variety of mechanics topics. The SMC is part of the Structural Reliability Partnership, which offers other benchmarking challenges hosted by several participating institutions.\n\nThe topic of the SMC2023 is threaded fastener joints, specifically the deformation and failure of a structure with a threaded fastener joint in a drop scenario. Much of the research on predicting threaded fastener joint behavior has been on joints in vibrational environments or loss of joint preload. Less attention has been paid to the large deformation and failure of threaded fastener joints, even though analyses of such scenarios are vital to safety assessments of structures with threaded fastener joints. These analyses require modeling of several phenomena including multi-axial joint mechanics, material deformation and failure at intermediate strain rates, and contact mechanics. This SMC is calling attention to these diverse and challenging analyses, rallying the solid-mechanics community to identify and address potential shortcomings.\n\nThis information packet enables participants to perform their predictions. The SMC scenario is a drop of a metal structure with a cantilevered cylinder attached to a block by threaded fasteners. The information packet includes a description of the SMC scenario and geometry, calibration data for the metal materials and fasteners, and quantities of interest from the predictions that serve as the basis for comparison to the experimental data of the SMC scenario. Participants will be asked to perform their predictions in a limited amount of time, on the order of a few months, to mimic real-world time constraints of safety assessments. After the predictions are returned, the SMC organizers will conduct the initial comparisons of the predictions and experiments, and then all participants will work together to assess the predictive capabilities and report the results. The SMC will enable the solid-mechanics community to define the state-of-the-art in modeling the behavior and failure of structures with threaded fastener joints, highlighting where future research could improve the approaches." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "kroll_tcbpaper_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/kroll_tcbpaper_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/kroll_tcbpaper_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/kroll_tcbpaper_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-07-24T16:45:24.114244Z", + "source_id": "kroll_tcbpaper_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "kroll_tcbpaper" + }, + "dc": { + "identifier": { + "identifier": "10.18126/e8y5-ayft", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Kroll", + "givenName": "Katharina T", + "creatorName": "Kroll, Katharina T", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Mata", + "givenName": "Mariana M", + 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Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Micallef", + "givenName": "Virginie", + "creatorName": "Micallef, Virginie", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Carpy", + "givenName": "Alejandro", + "creatorName": "Carpy, Alejandro", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Hiratsuka", + "givenName": "Ken", + "creatorName": "Hiratsuka, Ken", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Morizane", + "givenName": "Ryuji", + "creatorName": "Morizane, Ryuji", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Moisan", + "givenName": "Annie", + "creatorName": "Moisan, Annie", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Gubler", + "givenName": "Marcel", + "creatorName": "Gubler, Marcel", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Walz", + "givenName": "Antje-Christine", + "creatorName": "Walz, Antje-Christine", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Marrer-Berger", + "givenName": "Estelle", + "creatorName": "Marrer-Berger, Estelle", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + }, + { + "familyName": "Lewis", + "givenName": "Jennifer A", + "creatorName": "Lewis, Jennifer A", + "affiliations": [ + "Harvard University", + "Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA", + "Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland", + "Roche Pharma Research & Early Development, Roche Innovation Center Munich, Munich, Germany", + "Complex in vitro Systems, Safety Assessment, Genentech Inc.", + "Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA", + "Harvard Stem Cell Institute, Cambridge, Massachusetts, USA" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-07-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Dataset for Kroll, Mata, Homan et al. \"Immune-infiltrated Kidney Organoid-on-Chip Model for Assessing T Cell Bispecific Antibodies\", PNAS, 2023" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "kkroll@g.harvard.edu" + }, + { + "contributorType": "ContactPerson", + "contributorName": "katharinakroll76@gmail.com" + }, + { + "contributorType": "ContactPerson", + "contributorName": "jalewis@seas.harvard.edu" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This directory contains the raw flow cytometry and imaging data as well as Matlab code for\nKroll, Mata, Homan et al 2023 \u201cImmune-infiltrated Kidney Organoid-on-Chip Model for\nAssessing T Cell Bispecific Antibodies\u201d\nPlease refer to the manuscript for a full description of methods and data processing, external\ncode/data sources, and primary references.\nThe directory is split up by figure and supplemental figure." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ht_ebsd_additive_kovar_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/ht_ebsd_additive_kovar_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ht_ebsd_additive_kovar_v1.1/", + "total_size": 7037874774, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/ht_ebsd_additive_kovar_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-07-14T17:40:06.922511Z", + "source_id": "ht_ebsd_additive_kovar_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "ht_ebsd_additive_kovar" + }, + "dc": { + "identifier": { + "identifier": "10.18126/7d9u-edev", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Fowler, J. Elliott", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Fowler", + "givenName": "J. Elliott" + }, + { + "creatorName": "Ruggles, Tim J.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Ruggles", + "givenName": "Tim J." + }, + { + "creatorName": "Cillessen, Dale E.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Cillessen", + "givenName": "Dale E." + }, + { + "creatorName": "Johnson, Kyle L.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Johnson", + "givenName": "Kyle L." + }, + { + "creatorName": "Jauregui, Luis J.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Jauregui", + "givenName": "Luis J." + }, + { + "creatorName": "Henriksen, Amelia A.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Henriksen", + "givenName": "Amelia A." + }, + { + "creatorName": "Bianco, Nathan R.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Bianco", + "givenName": "Nathan R." + }, + { + "creatorName": "Boyce, Brad L.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Boyce", + "givenName": "Brad L." + } + ], + "subjects": [ + { + "subject": "EBSD" + }, + { + "subject": "high-throughput" + }, + { + "subject": "laser powder bed fusion" + }, + { + "subject": "Kovar" + }, + { + "subject": "experiment" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-07-17", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "High Throughput EBSD Characterization of Additive Kovar" + } + ], + "contributors": [ + { + "contributorName": "J. 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Other methodological distinctions between version 1 and version 2 samples are highlighted in the methods section of our Readme.\n\nThe dataset consists of raw channel text files (ctf), usable header information and EBSD data points extracted from each ctf, and LPBF build process parameters for each additively manufactured sample. The high throughput EBSD innovations used to generate this data are thoroughly described in our corresponding paper, \"High Throughput Microstructural Characterization using Automated Electron Backscatter Diffraction.\"" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "exp_dl_electron_counting_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/exp_dl_electron_counting_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/exp_dl_electron_counting_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/exp_dl_electron_counting_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-07-28T21:57:58.173247Z", + "source_id": "exp_dl_electron_counting_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "exp_dl_electron_counting" + }, + "dc": { + "identifier": { + "identifier": "10.18126/xv6r-y3lr", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Wei, Jingrui", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Wei", + "givenName": "Jingrui" + }, + { + "creatorName": "Hagopian, Nicholas", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Hagopian", + "givenName": "Nicholas" + }, + { + "creatorName": "Voyles, Paul", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Voyles", + "givenName": "Paul" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "microscopy" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-07-31", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Experimental data for evaluation of deep learning approach for high-accuracy electron counting of MAPS-type direct electron detectors" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.18126/rgew-xbw4", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1093/micmic/ozad067.346", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Paul Voyles(paul.voyles@wisc.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Experimental data of Celeritas XS direct detector for evaluation of deep learning counting approach as well as conventional connected component labeling counting and integrating. 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"Christian H.", + "affiliations": [ + "Max-Planck-Institut f\u00fcr Eisenforschung" + ] + }, + { + "creatorName": "Ghiringhelli, Luca M.", + "familyName": "Ghiringhelli", + "givenName": "Luca M.", + "affiliations": [ + "NOMAD Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2023", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This repository contains HAADF images and FFT-HAADF descriptors, 80/20 splits for train and validation. See more details about the work at https://doi.org/10.48550/arXiv.2303.12702", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "foundry" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/424k-7s66" + } + }, + "mdf": { + "organization": "Foundry", + "source_name": "automatic_identification_crystal_tructures", + "source_id": "automatic_identification_crystal_tructures", + "versioned_source_id": "automatic_identification_crystal_tructures-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-06T16:40:12.454946Z" + }, + "projects": { + "foundry": { + "short_name": "automatic_identification_crystal_tructures", + "data_type": "hdf5", + "task_type": [ + "supervised" + ], + "domain": [ + "materials science" + ], + "n_items": 31470, + "splits": [ + { + "label": "train", + "path": "train.h5", + "type": "train" + }, + { + "label": "validation", + "path": "val.h5", + "type": "test" + } + ], + "keys": [ + { + "key": [ + "haadf" + ], + "type": "input", + "description": "input, simulated HAADF images of difference crystal lattice" + }, + { + "key": [ + "fft_haadf" + ], + "type": "input", + "description": "fast fourier transform power spectrum of the HAADF images with hann windowing and thresholding" + }, + { + "key": [ + "label_onehot" + ], + "type": "target", + "description": "output, one hot label for class of crystal structure" + }, + { + "key": [ + "label_encoding" + ], + "type": "input", + "description": "tabular information describing the classes of crystal structure" + } + ] + } + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//foundry/automatic_identification_crystal_tructures/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/foundry/automatic_identification_crystal_tructures/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "287cae7a-8304-4e0e-a13d-cf4effbdfc36-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "In-Situ MCT of Loaded Parachute Textiles: MIL-C-7020 T.III and MIL-C-44378 T.II" + } + ], + "creators": [ + { + "creatorName": "Phillippe, Cutler", + "familyName": "Phillippe", + "givenName": "Cutler", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Villafa\u00f1e, Laura", + "familyName": "Villafa\u00f1e", + "givenName": "Laura", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Panerai, Francesco", + "familyName": "Panerai", + "givenName": "Francesco", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "In situ X-ray micro-tomography imaging of textiles under loaded conditions. Results are presented for two textiles commonly used in parachute systems, MIL-C-7020 Type III and MIL-C-44378 Type II. The materials are subjected to incremental tension using a custom apparatus that loads the fabric radially in-plane via a plunger, and the material microstructure is imaged sequentially at steady load conditions. Micro-tomography images are processed using learning-aided segmentation. \n\nIncluded are:\n\nThe raw x-ray reconstructions (tif stacks) with x-ray meta-data.\n\nSegmented warp and weft tows (both in tif stack form).\n\nU-NET 3D models trained for segmentation with validation loss meta-data.\n\nLabelled warp and weft tows (both in tif stack form).\n\nTensile tester plunger deflection measurements and associated radiographs for deflection measurement.\n\nRaw and processed load cell data with estimated radial stress boundary condition at lip of plunger.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.2514/6.2024-1003", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "machine learning" + }, + { + "subject": "microstructures" + }, + { + "subject": "microscopy" + }, + { + "subject": "polymers" + } + ], + "contributors": [ + { + "contributorName": "\"Cutler Phillippe\" (cutlerp2@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "\"Francesco Panerai\" (fpanerai@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "\"Laura Villafa\u00f1e\" (lvillafa@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/82fx-q319" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "versioned_source_id": "287cae7a-8304-4e0e-a13d-cf4effbdfc36-1.0", + "source_name": "287cae7a-8304-4e0e-a13d-cf4effbdfc36", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-15T01:59:57.122214Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/287cae7a-8304-4e0e-a13d-cf4effbdfc36/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/287cae7a-8304-4e0e-a13d-cf4effbdfc36/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bff0b7c9-7eda-4008-97ea-8603c1883230-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Effects of Dynamic Disulfide Bonds on Mechanical Behavior in Glassy Epoxy Thermosets" + } + ], + "creators": [ + { + "creatorName": "Lewis, Broderick", + "familyName": "Lewis", + "givenName": "Broderick", + "affiliations": [ + "Northwestern University" + ] + }, + { + "creatorName": "Dennis, Joseph M.", + "familyName": "Dennis", + "givenName": "Joseph M.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "creatorName": "Shull, Kenneth R.", + "familyName": "Shull", + "givenName": "Kenneth R.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cEffects of Dynamic Disulfide Bonds on Mechanical Behavior in Glassy Epoxy Thermosets.\u201d B. Lewis, J.M. Dennis & K.R. Shull, ACS Appl. Polym. Mater. 2023, 2583\u20132595 (2023) (http://dx.doi.org/10.1021/acsapm.2c02194).\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Lewis et al. - 2023 - Effects of Dynamic Disulfide Bonds on Mechanical B.pdf: the journal-formatted pdf.\n3. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n4. data: directory of raw data files called by the python scripts.\n5. figures: the figures generated by the makeplots.py in the python directory. These figures will all be regenerated when makeplots.py is run. ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1021/acsapm.2c02194", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "fracture toughness" + } + ], + "contributors": [ + { + "contributorName": "k-shull@northwestern.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/wjxd-j196" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "versioned_source_id": "bff0b7c9-7eda-4008-97ea-8603c1883230-1.0", + "source_name": "bff0b7c9-7eda-4008-97ea-8603c1883230", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-16T19:31:16.102110Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bff0b7c9-7eda-4008-97ea-8603c1883230/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bff0b7c9-7eda-4008-97ea-8603c1883230/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Investigations of the high-frequency dynamic properties of polymeric systems with quartz crystal resonators" + } + ], + "creators": [ + { + "creatorName": "Shull, Kenneth R.", + "familyName": "Shull", + "givenName": "Kenneth R.", + "affiliations": [ + "Northwestern University" + ] + }, + { + "creatorName": "Taghon, Meredith", + "familyName": "Taghon", + "givenName": "Meredith", + "affiliations": [ + "Northwestern University" + ] + }, + { + "creatorName": "Wang, Qifeng", + "familyName": "Wang", + "givenName": "Qifeng", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cInvestigations of the high-frequency dynamic properties of polymeric systems with quartz crystal resonators.\u201d K.R. Shull, M. Taghon & Q. Wang, Biointerphases 15, 021012 (2020) (http://dx.doi.org/10.1116/1.5142762)\n.\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Shull et al. - 2020 - Investigations of the high-frequency dynamic prope.pdf: the journal-formatted pdf.\n3. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n4. data: directory of raw data files called by the python scripts.\n5. figures: the figures generated by the makeplots.py in the python directory. These figures will all be regenerated when makeplots.py is run. ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1116/1.5142762", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "QCM" + }, + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "rheology" + } + ], + "contributors": [ + { + "contributorName": "k-shull@northwestern.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/7kfk-jr95" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "versioned_source_id": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4-1.0", + "source_name": "d58bad80-138c-4fb4-afb5-6786dc0bc7d4", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-16T03:46:58.398478Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/d58bad80-138c-4fb4-afb5-6786dc0bc7d4/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/d58bad80-138c-4fb4-afb5-6786dc0bc7d4/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "58d58130-8356-4290-9baf-5b614bc3d712-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Processing Polyelectrolyte Complexes with Deep Eutectic Solvents" + } + ], + "creators": [ + { + "creatorName": "Chen, YuLing", + "familyName": "Chen", + "givenName": "YuLing", + "affiliations": [ + "Northwestern University" + ] + }, + { + "creatorName": "Shull, Kenneth R.", + "familyName": "Shull", + "givenName": "Kenneth R.", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \n\n\u201cProcessing Polyelectrolyte Complexes with Deep Eutectic Solvents.\u201d Y. Chen & K.R. Shull, ACS Macro Lett. 10, 1243\u20131247 (2021) (http://dx.doi.org/10.1021/acsmacrolett.1c00494).\n\nThe dataset includes the following files and directories:\n\n1. readme.md: Markdown file of this description.\n2. Chen and Shull - 2021 - Processing Polyelectrolyte Complexes with Deep Eut.pdf: the journal-formatted pdf.\n3. SI.pdf: published supporting information.\n4. python: directory of python scripts used to generate the data-containing figures in the main publication and the SI.\n5. data: directory of raw data files called by the python scripts.\n\n", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1021/acsmacrolett.1c00494", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "polyelectrolytes" + }, + { + "subject": "ionic liquids" + } + ], + "contributors": [ + { + "contributorName": "k-shull@northwestern.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/kqj3-jg60" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "58d58130-8356-4290-9baf-5b614bc3d712", + "versioned_source_id": "58d58130-8356-4290-9baf-5b614bc3d712-1.0", + "source_name": "58d58130-8356-4290-9baf-5b614bc3d712", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-15T16:11:24.037039Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/58d58130-8356-4290-9baf-5b614bc3d712/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/58d58130-8356-4290-9baf-5b614bc3d712/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "790c76bc-c489-4d86-b861-0afd1bea21bb-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Using X-ray computed microtomography to determine subsample-specific cosmogenic noble gas production rates of E (enstatite) chondrites" + } + ], + "creators": [ + { + "creatorName": "Mijjum, Moshammat", + "familyName": "Mijjum", + "givenName": "Moshammat", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "creatorName": "Andrews, Benjamin J.", + "familyName": "Andrews", + "givenName": "Benjamin J.", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "creatorName": "McCoy, Timothy J.", + "familyName": "McCoy", + "givenName": "Timothy J.", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "creatorName": "Corrigan, Catherine M.", + "familyName": "Corrigan", + "givenName": "Catherine M.", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "creatorName": "Caffee, Marc W.", + "familyName": "Caffee", + "givenName": "Marc W.", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + }, + { + "creatorName": "Tremblay, Marissa M.", + "familyName": "Tremblay", + "givenName": "Marissa M.", + "affiliations": [ + "Purdue University", + "Smithsonian National Museum of Natural History" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This repository contains X-ray computed microtomography .DCM files of the following meteorites: CEC 024, CEC 025, CEC 028, NWA 974, Indarch (USNM 449), and Ufana (USNM 6006). The images can be compiled to generate 3D renderings of subsamples. Segmented data can be located in the Dragonfly ORS files. ", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "microtomography" + }, + { + "subject": "meteorites" + }, + { + "subject": "machine learning" + } + ], + "contributors": [ + { + "contributorName": "Moshammat Mijjum (mmijjum@purdue.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/b873-4886" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "versioned_source_id": "790c76bc-c489-4d86-b861-0afd1bea21bb-1.0", + "source_name": "790c76bc-c489-4d86-b861-0afd1bea21bb", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-02-22T16:44:51.677848Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/790c76bc-c489-4d86-b861-0afd1bea21bb/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/790c76bc-c489-4d86-b861-0afd1bea21bb/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "02b4ceb5-9263-40ae-acd5-136210301819-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Systematic Improvement of DMC Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gaps " + } + ], + "creators": [ + { + "creatorName": "Shin, Hyeondeok", + "familyName": "Shin", + "givenName": "Hyeondeok", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "creatorName": "Gasperich, Kevin", + "familyName": "Gasperich", + "givenName": "Kevin", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "creatorName": "Rojas, Tomas", + "familyName": "Rojas", + "givenName": "Tomas", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "creatorName": "Ngo, Anh T.", + "familyName": "Ngo", + "givenName": "Anh T.", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "creatorName": "Krogel, Jaron T.", + "familyName": "Krogel", + "givenName": "Jaron T.", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + }, + { + "creatorName": "Benali, Anouar", + "familyName": "Benali", + "givenName": "Anouar", + "affiliations": [ + "Argonne National Laboratory", + "Oak Ridge National Laboratory", + "University of Illinois at Chicago" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data Description for \"Systematic Improvement of DMC Calculations in Transition Metal Oxides: sCI-Driven Wavefunction Optimization for Reliable Band Gaps\"\nRefer to README File in top directory to navigate the data", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "energy materials" + }, + { + "subject": "oxides" + }, + { + "subject": "QMCPack" + }, + { + "subject": "QMC" + } + ], + "contributors": [ + { + "contributorName": "Anouar Benali", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/z20s-pg52" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "02b4ceb5-9263-40ae-acd5-136210301819", + "versioned_source_id": "02b4ceb5-9263-40ae-acd5-136210301819-1.0", + "source_name": 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"affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Sharma, Hemant", + "familyName": "Sharma", + "givenName": "Hemant", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Kenesei, Peter", + "familyName": "Kenesei", + "givenName": "Peter", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Niezgoda, Stephen R.", + "familyName": "Niezgoda", + "givenName": "Stephen R.", + "affiliations": [ + "Air Force Research Laboratory", + " The Ohio State University", + "University of Dayton", + "BlueHalo LLC", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Mills, 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This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional (3D) reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed ``low solvus, high refractory'' (LSHR) nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available on the Materials Data Facility (MDF) for further investigation.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "high energy diffraction microscopy" + }, + { + "subject": "hedm" + }, + { + "subject": "ebsd" + }, + { + "subject": "electron backscatter diffraction" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "parkjs@anl.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "hsharma@anl.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "gregory.sparks.2.ctr@us.af.mil", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/4y0p-v604" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + 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+ ] + }, + { + "creatorName": "Sornberger, Parker", + "familyName": "Sornberger", + "givenName": "Parker", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "creatorName": "Odom, Susan A.", + "familyName": "Odom", + "givenName": "Susan A.", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + }, + { + "creatorName": "Risko, Chad", + "familyName": "Risko", + "givenName": "Chad", + "affiliations": [ + "Center for Applied Energy Research", + "University of Kentucky" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains cleaned data for 35,729 molecules marked as `public` in the D3TaLES database at \nthe time of this dataset's creation. The calculations data were generated through a high-throughput molecular computational workflow \nusing density functional theory (DFT) carried out at the (IP-tuned) LC-\u03c9HPBE/Def2SVP level of \ntheory via the Gaussian16 (rev A.03) software suite. More details can be found in the associated\npublication: [https://doi.org/10.1039/D3DD00081H](https://doi.org/10.1039/D3DD00081H). \nThis dataset excludes molecules with outlier properties. An outlier \nis considered a property value greater than 3 standard deviations from the mean. \n\n\nThe dataset contains over 90 possible properties for each molecule. All energy properties are given \nin eV. Properties that begin with `solv_` were calculated in implicit solvent conditions simulating \nacetonitrile (dielectric constant 35.688). More information can be found in the README.md file, and \ncomplete documentation for molecular and species properties can \nbe found in the D3TaLES documentation at [https://d3tales.as.uky.edu/docs](https://d3tales.as.uky.edu/docs).", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1039/D3DD00081H", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "energy materials" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Chad Risko (chad.risko@uky.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/v5sj-6q93" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "organization": "MDF Open", + "source_id": "9bb8800f-9cda-4957-ad12-60ad2a381177", + "versioned_source_id": "9bb8800f-9cda-4957-ad12-60ad2a381177-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-20T18:27:54.020216Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/9bb8800f-9cda-4957-ad12-60ad2a381177/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/9bb8800f-9cda-4957-ad12-60ad2a381177/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "7f70db70-4550-4286-9744-71566906d8b6-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Differential Scanning Calorimetry to Determine Reactivity of Stable Viscosity-modified Thermoset Resins in Frontal Polymerization" + } + ], + "creators": [ + { + "creatorName": "Krishnan, Pranav", + "familyName": "Krishnan", + "givenName": "Pranav", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "creatorName": "Lessard, Jacob", + "familyName": "Lessard", + "givenName": "Jacob", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "creatorName": "Suslick, Benjamin", + "familyName": "Suslick", + "givenName": "Benjamin", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "creatorName": "Cramblitt, Anna", + "familyName": "Cramblitt", + "givenName": "Anna", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "creatorName": "Balta, Javier", + "familyName": "Balta", + "givenName": "Javier", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Utah" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Differential Scanning Calorimetry (DSC) experiments were conducted on thermoset resins (95 wt% dicyclopentadiene (DCPD), 5 wt% 5-ethylidene-2-norbornene (ENB)) containing varying concentrations of catalyst and inhibitor. The samples were tested at a fixed heating rate in order to compare the enthalpy of reaction (\u2206Hr) released during the ring-opening metathesis (ROMP) reaction. This dataset is intended to be used as a reference for the range of \u2206Hr values typically achieved using the 95:5 wt% DCPD:ENB monomer resin. ", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "DSC" + }, + { + "subject": "chemistry" + }, + { + "subject": "reactivity" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Krishnan, Pranav (pranav12@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Lessard, Jacob (jacob.Lessard@utah.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Suslick, Benjamin (suslick@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Cramblitt, Anna (annacc2@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Balta, Javier (jbalta@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Arretche, Ignacio (ia6@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/4c5v-p868" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "7f70db70-4550-4286-9744-71566906d8b6", + "organization": "MDF Open", + "source_id": "7f70db70-4550-4286-9744-71566906d8b6", + "versioned_source_id": "7f70db70-4550-4286-9744-71566906d8b6-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-01-07T17:31:24.764609Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/7f70db70-4550-4286-9744-71566906d8b6/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/7f70db70-4550-4286-9744-71566906d8b6/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ebcfac93-17d0-418f-b3ce-fd4d8a851674-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Pressure-Driven Solid\u2013Solid Phase Transformations of Isotropic Particles Across Diverse Crystal Structure Types" + } + ], + "creators": [ + { + "creatorName": "Du, Hongjin", + "familyName": "Du", + "givenName": "Hongjin", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Pan, Hillary", + "familyName": "Pan", + "givenName": "Hillary", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Dshemuchadse, Julia", + "familyName": "Dshemuchadse", + "givenName": "Julia", + "affiliations": [ + "Cornell University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by H. Du, H. Pan, and J. Dshemuchadse,\u201cPressure-Driven Solid\u2013Solid Phase Transformations of Isotropic Particles Across Diverse Crystal Structure Types\u201d, in publication (2025). In this work, we investigated the influence of pressure on the behavior of 16 crystal structure types that have been shown to self-assemble in molecular dynamics simulations using isotropic, pairwise interaction potentials. We studied these diverse structures using a range of computational models as a function of pressure, characterized the high-pressure phases, identified four previously unknown crystal structure types, and categorized the observed phase transformations. This dataset includes the representative simulation trajectories (in .gsd file format) mentioned in the main text and the Supplemental Material. A README.txt file is included to assist with parsing the data. We hope that this dataset will be useful for future research on pressure-induced phase transformations in both experimental and simulation studies.", + "descriptionType": "Other" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Hongjin Du (hd329@cornell.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ka47-wp46" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "source_id": "ebcfac93-17d0-418f-b3ce-fd4d8a851674", + "versioned_source_id": "ebcfac93-17d0-418f-b3ce-fd4d8a851674-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-01-16T17:05:38.659630Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ebcfac93-17d0-418f-b3ce-fd4d8a851674/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ebcfac93-17d0-418f-b3ce-fd4d8a851674/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "81d55710-5bec-4e71-91b0-6f269e8da85a-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Data set for \"\u201cAssessment of diagenesis in archeological human second metacarpal bones using the intensity of the small angle x- ray scattering D-period peak\"" + } + ], + "creators": [ + { + "creatorName": "Gardner, L.L.", + "familyName": "Gardner", + "givenName": "L.L.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Abramovich, W.", + "familyName": "Abramovich", + "givenName": "W.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Romanov, M.", + "familyName": "Romanov", + "givenName": "M.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Flohr, S.", + "familyName": "Flohr", + "givenName": "S.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Kierdorf, U.", + "familyName": "Kierdorf", + "givenName": "U.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Kierdorf, H.", + "familyName": "Kierdorf", + "givenName": "H.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Almer, J.D.", + "familyName": "Almer", + "givenName": "J.D.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Jacobsen, S.D.", + "familyName": "Jacobsen", + "givenName": "S.D.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Gonzalez Aviles, G.", + "familyName": "Gonzalez Aviles", + "givenName": "G.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Park, J.-S.", + "familyName": "Park", + "givenName": "J.-S.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Stock, S.R.", + "familyName": "Stock", + "givenName": "S.R.", + "affiliations": [ + "Northwestern University", + "DePaul University", + " University of Hildesheim", + "Argonne National Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This data set contains raw wide angle and small angle x-ray scattering patterns from archaeological human metacarpal bones. These scattering patterns were analyzed using GSAS-2 focusing on the D-periods to assess the diagenesis in these bones. ", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "biomaterials" + }, + { + "subject": "microstructures" + }, + { + "subject": "x-ray diffraction" + }, + { + "subject": "wide angle x-ray scattering" + }, + { + "subject": "WAXS" + }, + { + "subject": "small angle x-ray scattering" + }, + { + "subject": "SAXS" + }, + { + "subject": "diagenesis" + }, + { + "subject": "bone" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Stuart Stock (stuart.r.stock@gmail.com)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/bhj3-3c20" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "source_id": "81d55710-5bec-4e71-91b0-6f269e8da85a", + "versioned_source_id": "81d55710-5bec-4e71-91b0-6f269e8da85a-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-21T14:15:31.270585Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/81d55710-5bec-4e71-91b0-6f269e8da85a/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/81d55710-5bec-4e71-91b0-6f269e8da85a/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "5eba44ad-00f7-474c-8d72-7134cfd4f091-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Dataset of Simulated Shock Deformed Microstructures and X-ray Diffraction Patterns for Single Crystal and Nanocrystalline Cu for use with Machine-Learning" + } + ], + "creators": [ + { + "creatorName": "Vizoso, Daniel", + "familyName": "Vizoso", + "givenName": "Daniel", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "creatorName": "Tsurkan, Phillip", + "familyName": "Tsurkan", + "givenName": "Phillip", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "creatorName": "Ma, Ke", + "familyName": "Ma", + "givenName": "Ke", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "creatorName": "Dongare, Avinash M.", + "familyName": "Dongare", + "givenName": "Avinash M.", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + }, + { + "creatorName": "Dingreville, R\u00e9mi", + "familyName": "Dingreville", + "givenName": "R\u00e9mi", + "affiliations": [ + "Sandia National Laboratory", + "University of Connecticut" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Molecular dynamics (LAMMPS) dump files, simulated X-ray diffraction (XRD) profiles, and microstructural descriptor text files. Systems used are single crystal Cu shocked along the [100], [110], [111], and [112] directions, as well as a shocked nanocrystalline Cu system with random grain orientations. Scripts and an organized Python dataset are provided for generating data and running machine-learning models. \n", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "metals" + }, + { + "subject": "defects" + }, + { + "subject": "microstructures" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "diffraction" + }, + { + "subject": "shock" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "R\u00e9mi Dingreville (rdingre@sandia.gov)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Avinash M. Dongare (dongare@uconn.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/jezj-6852" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "source_id": "5eba44ad-00f7-474c-8d72-7134cfd4f091", + "versioned_source_id": "5eba44ad-00f7-474c-8d72-7134cfd4f091-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-18T22:25:25.590549Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/5eba44ad-00f7-474c-8d72-7134cfd4f091/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/5eba44ad-00f7-474c-8d72-7134cfd4f091/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "77552592-f0b9-4df2-bcd5-8f470026c26e-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Neural Networks for Prediction of Electronic Excitation Dynamics" + } + ], + "creators": [ + { + "creatorName": "Shapera, Ethan P.", + "familyName": "Shapera", + "givenName": "Ethan P.", + "affiliations": [ + "University at Buffalo" + ] + }, + { + "creatorName": "Lee, Cheng-Wei", + "familyName": "Lee", + "givenName": "Cheng-Wei", + "affiliations": [ + "Colorado School of Mines" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Dataset for \"Neural Networks for Prediction of Electronic Excitation Dynamics\". Contains rt-TDDFT data and descriptor files.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": " \t https://doi.org/10.48550/arXiv.2409.14042", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "molecular structures" + }, + { + "subject": "high-throughput" + }, + { + "subject": "DFT" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Ethan P Shapera (epshaper@buffalo.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/y1j1-g019" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "source_id": "77552592-f0b9-4df2-bcd5-8f470026c26e", + "versioned_source_id": 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It includes inputs and outputs for Quantum Espresso, RMG, and QMCPACK codes. Nexus workflow scripts are also included wherever possible.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "DFT" + }, + { + "subject": "QMC" + }, + { + "subject": "metals and alloys" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Abdulgani Annaberdiyev", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/k8k2-ad62" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "source_id": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c", + "versioned_source_id": "c7cd2c77-5607-4681-9955-f0a7f9f4d78c-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-01-06T22:45:53.112981Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c7cd2c77-5607-4681-9955-f0a7f9f4d78c/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/c7cd2c77-5607-4681-9955-f0a7f9f4d78c/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "1d473c7a-1028-4868-bfdd-ca20afa16d81-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "A Coarse-Grained Simulation Toolkit for Metal\u2013Organic Framework Synthesis" + } + ], + "creators": [ + { + "creatorName": "Scott, Reum N.", + "familyName": "Scott", + "givenName": "Reum N.", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Milner, Phillp J.", + "familyName": "Milner", + "givenName": "Phillp J.", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Dshemuchadse, Julia", + "familyName": "Dshemuchadse", + "givenName": "Julia", + "affiliations": [ + "Cornell University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by Reum N. Scott, Phillip J. Milner, and Julia Dshemuchadse, \"A Coarse-Grained Simulation Toolkit for Metal\u2013Organic Framework Synthesis\", in publication (2025). In this study, the self-assembly of different MOF structures is simulated from the MOFs' components (metal nodes and organic linkers), which were constructed in a coarse-grained model from isotropic beads, reflecting the symmetries of the molecular components. In addition to simulating the spontaneous growth of 34 different MOF nets (with linear, trigonal planar, tetragonal planar, as well as tetrahedral linkers), the effects of isoreticular relationships (linker length), competing interactions in layered MOFs, and the assembly of nets with additional degrees of freedom are demonstrated. This dataset includes representative simulation trajectories (in .gsd file format) documenting the growth of each of the MOF nets. A README.txt file is included to assist with parsing the data.\n\nThis material is based upon work supported by the National Science Foundation MPS-Ascend Postdoctoral Research Fellowship under Grant No. DMR-2139237. This work was supported by the donors of ACS Petroleum Research Fund under Grant 66310-DNI10. P.J.M. acknowledges support from the National Science Foundation (CBET-2047627).", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1039/d5cp02121a", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "Molecular Dynamics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Scott, Reum (rns76@cornell.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/75tn-px24" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "source_id": "1d473c7a-1028-4868-bfdd-ca20afa16d81", + "versioned_source_id": "1d473c7a-1028-4868-bfdd-ca20afa16d81-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-07-09T17:51:25.065040Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/1d473c7a-1028-4868-bfdd-ca20afa16d81/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/1d473c7a-1028-4868-bfdd-ca20afa16d81/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "3c249c48-eaa0-488b-866b-57ca9a53f39a-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Kinetic pathways of solid\u2013solid phase transitions dictated by short-range interactions" + } + ], + "creators": [ + { + "creatorName": "Pan, Hillary", + "familyName": "Pan", + "givenName": "Hillary", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Dshemuchadse, Julia", + "familyName": "Dshemuchadse", + "givenName": "Julia", + "affiliations": [ + "Cornell University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by H. Pan and J. Dshemuchadse, ``Kinetic pathways of solid--solid phase transitions dictated by short-range interactions'' Proc. Natl. Acad. Sci. USA (2025). In this study, we determine three distinct transformation pathways from body-centered cubic (bcc) to face-centered cubic (fcc) crystal structures upon cooling in minimal, one-component systems governed by isotropic pair potentials. These include: (1) a two-step bcc-to-bct-to-fcc transition, (2) a direct bcc-to-fcc transition, and (3) a microstructure-dependent transition with three main pathways. We show that in each case, particle-level rearrangements follow distinct kinetic pathways, leading to different microstructures of the resulting fcc phase. We also characterize the reverse transitions upon heating and report a previously unobserved fcc-to-bcc transformation pathway. \n\nThis dataset includes all seven high-resolution solid--solid phase transition simulations referenced in the manuscript, provided as GSD files:\n\n- bcc-to-bct: LJGP_2well_bcc_bct.gsd\n- bct-to-fcc: LJGP_2well_bct_fcc.gsd\n- direct bcc-to-fcc: OPP_bcc_fcc.gsd\n- three microstructure-dependent transformations: LJGP_1well_mix_fcc.gsd, LJGP_1well_gb_fcc.gsd, LJGP_1well_bcc_hcp.gsd\n- reverse fcc-to-bcc: LJGP_2well_fcc_bcc.gsd\n\nWe hope that this dataset will be useful for theorists, computationalists, and experimentalists interested in the mechanisms of solid--solid phase transitions. 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Within quantum mechanical first principles approaches, Kohn-Sham density functional theory (DFT) is the preeminent tool for understanding electronic structure, and the high-dimensional wavefunctions calculated in this approach serve as the building block for downstream calculations of correlated many-body excitations and related physical observables. Here, we use variational autoencoders (VAE) for the unsupervised learning of high-dimensional DFT wavefunctions and show that these wavefunctions lie in a low-dimensional manifold within the latent space. Our model autonomously determines the optimal representation of the electronic structure, avoiding limitations due to manual feature engineering and selection in prior work. To demonstrate the utility of the latent space representation of the DFT wavefunction, we use it for the supervised training of neural networks (NN) for downstream prediction of the quasiparticle bandstructures within the GW formalism, which includes many-electron correlations beyond DFT. The GW prediction achieves a low error of 0.11 eV for a combined test set of metals and semiconductors drawn from the Computational 2D Materials Database (C2DB), suggesting that latent space representation captures key physical information from the original data. Finally, we explore the interpretability of the VAE representation and show that the successful representation learning and downstream prediction by our model is derived from the smoothness of the VAE latent space, which also enables the generation of wavefunctions on arbitrary points in latent space. Our work provides a novel and general machine-learning framework for investigating electronic structure and many-body physics.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "DFT" + }, + { + "subject": "machine learning" + }, + { + "subject": "VAE" + }, + { + "subject": "Many-body Interaction" + }, + { + "subject": "GW" + }, + { + "subject": "Unsupervised Learning" + }, + { + "subject": "Representation Learning" + }, + { + "subject": "Simulation" + }, + { + "subject": "Condensed Matter Physics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Bowen Hou\" (bowen.hou@yale.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/rpa1-yp91" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "organization": "MDF Open", + "source_id": "b483f85e-9af3-4cd8-a83b-ea178c925312", + "versioned_source_id": "b483f85e-9af3-4cd8-a83b-ea178c925312-1.3", + "version": "1.3", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-07-11T17:49:09.427501Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b483f85e-9af3-4cd8-a83b-ea178c925312/1.3/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/b483f85e-9af3-4cd8-a83b-ea178c925312/1.3/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "6eb6f3b6-8e8f-4454-b998-591da2419ba8-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Quartz Crystal Microbalance with Dissipation Monitoring for Studying Soft Matter at Interfaces" + } + ], + "creators": [ + { + "creatorName": "Johannsmann, Diethelm", + "familyName": "Johannsmann", + "givenName": "Diethelm", + "affiliations": [ + "Institute of Physical Chemistry, Clausthal University of Technology, Clausthal-Zellerfeld, Germany", + "Department of Bioengineering, University of Washington, Seattle, Washington, USA", + "Advanced Wave Sensors S.L., Valencia, Spain" + ] + }, + { + "creatorName": "Reviakine, Ilya", + "familyName": "Reviakine", + "givenName": "Ilya", + "affiliations": [ + "Institute of Physical Chemistry, Clausthal University of Technology, Clausthal-Zellerfeld, Germany", + "Department of Bioengineering, University of Washington, Seattle, Washington, USA", + "Advanced Wave Sensors S.L., Valencia, Spain" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "The two files contain raw QCM-D data for adsorption of streptavidin on biotinylated SAM on gold and adsorption of coffee cream on bare gold surface, plots, input for PyQTM, and the output of the fitting to the viscoelastic model performed in PyQTM. These files correspond to Figure 3a,b (streptavidin) and Figure 3b,c (coffee cream), as well as Supplementary Table 1 and Supplementary Table 2 in Johannsmann and Reviakine (2024) Quartz Crystal Microbalance with Dissipation Monitoring for Studying Soft Matter at Interfaces, Nature Methods Protocols Primers. ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1038/s43586-024-00340-4", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "QCM" + }, + { + "subject": "QCM-D" + }, + { + "subject": "Streptavidin" + }, + { + "subject": "Coffee Cream" + }, + { + "subject": "rheology" + }, + { + "subject": "Viscoelasticity" + }, + { + "subject": "Viscoelastic model" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "johannsmann@pc.tu-clausthal.de", + "contributorType": "ContactPerson" + }, + { + "contributorName": "reviakin@uw.edu ", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/vxx9-qw76" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "6eb6f3b6-8e8f-4454-b998-591da2419ba8", + "organization": "MDF Open", + "source_id": "6eb6f3b6-8e8f-4454-b998-591da2419ba8", + "versioned_source_id": "6eb6f3b6-8e8f-4454-b998-591da2419ba8-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-07-31T11:24:42.618370Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6eb6f3b6-8e8f-4454-b998-591da2419ba8/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/6eb6f3b6-8e8f-4454-b998-591da2419ba8/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ca66ffd0-5315-41ba-bfb0-67e1b311481d-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Analyzed Experimental Data and Molecular Dynamics Data for \"Composition-Dependent Dynamics in a Multicomponent Supercooled Liquid Measured with Nanometer Spatial Resolution\"" + } + ], + "creators": [ + { + "creatorName": "Huang, Shuoyuan", + "familyName": "Huang", + "givenName": "Shuoyuan", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Annamareddy, Ajay", + "familyName": "Annamareddy", + "givenName": "Ajay", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Francis, Carter", + "familyName": "Francis", + "givenName": "Carter", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Schultz, Lane E.", + "familyName": "Schultz", + "givenName": "Lane E.", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Ketkaew, Jittisa", + "familyName": "Ketkaew", + "givenName": "Jittisa", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Ediger, Mark", + "familyName": "Ediger", + "givenName": "Mark", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Yu, Lian", + "familyName": "Yu", + "givenName": "Lian", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Schroers, Jan", + "familyName": "Schroers", + "givenName": "Jan", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Morgan, Dane", + "familyName": "Morgan", + "givenName": "Dane", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + }, + { + "creatorName": "Voyles, Paul M.", + "familyName": "Voyles", + "givenName": "Paul M.", + "affiliations": [ + "University of Wisconsin\u2013Madison", + "Yale University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Analyzed experimental data and molecular dynamics data for \"Composition-Dependent Dynamics in a Multicomponent Supercooled Liquid Measured with Nanometer Spatial Resolution\". Raw experimental data is in a separate archive, DOI: (TBA). Archive includes electron correlation microscopy and symmetry coefficient analysis of experimental 5D STEM data on Pt57.5Cu14.7Ni5.3Pt22.5 and Pd43Cu27Ni10Pt20 supercooled liquids and molecular dynamics trajectories computed with a machine-learning potential for Pt57.5Cu14.7Ni5.3Pt22.5 and associated analyses.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "electron correlation microscopy" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Paul M. Voyles (paul.voyles@wisc.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Dane Morgan (ddmorgan@wisc.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ddp4-nv34" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "ca66ffd0-5315-41ba-bfb0-67e1b311481d", + "source_id": "ca66ffd0-5315-41ba-bfb0-67e1b311481d", + "versioned_source_id": "ca66ffd0-5315-41ba-bfb0-67e1b311481d-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-07-25T19:54:05.282649Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ca66ffd0-5315-41ba-bfb0-67e1b311481d/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ca66ffd0-5315-41ba-bfb0-67e1b311481d/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "484c94e2-c05a-4f13-a7a5-ca8e875b01b5-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Candidate perovskite compositions from \"Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility\" by Jacobs et al" + } + ], + "creators": [ + { + "creatorName": "Engler, Will", + "familyName": "Engler", + "givenName": "Will", + "affiliations": [ + "University of Chicago" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "A list of ~19 million perovskite composition strings, delimited by newlines. These are the perovskites screened in \"Machine Learning Materials Properties with Accurate Predictions, Uncertainty Estimates, Domain Guidance, and Persistent Online Accessibility\" by Jacobs et al. ([link](https://doi.org/10.48550/arXiv.2406.15650))\n\nDerived from [files shared on FigShare](https://figshare.com/articles/dataset/Machine_Learning_Materials_Properties_with_Accurate_Predictions_Uncertainty_Estimates_Domain_Guidance_and_Persistent_Online_Accessibility/26077015). This text file is essentially just the first column of all the subfiles in MATERIALS_SCREENING_NEW.zip. This file is helpful for reproducing the screening from scratch.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.48550/arXiv.2406.15650", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "willengler@uchicago.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/zbrg-k749" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "484c94e2-c05a-4f13-a7a5-ca8e875b01b5", + "source_id": "484c94e2-c05a-4f13-a7a5-ca8e875b01b5", + "versioned_source_id": "484c94e2-c05a-4f13-a7a5-ca8e875b01b5-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-08-12T21:22:57.434871Z" + }, + "data": { + "endpoint_path": 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"Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Pungsrisai, Tipsiri", + "familyName": "Pungsrisai", + "givenName": "Tipsiri", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Wahab, Oluwasegun J.", + "familyName": "Wahab", + "givenName": "Oluwasegun J.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Alden, Sasha E.", + "familyName": "Alden", + "givenName": "Sasha E.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Zhong, Yaxu", + "familyName": "Zhong", + "givenName": "Yaxu", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Choi, Myung-Hoon", + "familyName": "Choi", + "givenName": "Myung-Hoon", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Verma, Ekta", + "familyName": "Verma", + "givenName": "Ekta", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Bentley, Anne K.", + "familyName": "Bentley", + "givenName": "Anne K.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Bailey, Kathleen O.", + "familyName": "Bailey", + "givenName": "Kathleen O.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Skrabalak, Sara E.", + "familyName": "Skrabalak", + "givenName": "Sara E.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Ye, Xingchen", + "familyName": "Ye", + "givenName": "Xingchen", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Willets, Katherine A.", + "familyName": "Willets", + "givenName": "Katherine A.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + }, + { + "creatorName": "Baker, Lane A.", + "familyName": "Baker", + "givenName": "Lane A.", + "affiliations": [ + "Texas A&M University", + "Temple University", + "Indiana University Bloomington", + "Lewis and Clark College" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + 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The first dataset (t0) was collected after the microstructure was sintered (1425\u00b0C, 1hr, oxygen) and pre-annealed (1400\u00b0C, 10 hr, forming gas). The second dataset (t1) was collected after an additional annealing step at 1400\u00b0C for 70 hr under forming gas. The data is provided in a .dream3D file, which includes post-processing information completed in Dream3d and grain information (orientation, centroid, grain size) in voxel arrays. See Dream3D for more information about the file format and storage. For more details about the data cleanup and material processing, please see the papers listed below.\n\nV. Muralikrishnan, H. Liu, B. Conry, C.J. Marvel, M.P. Harmer, G.S. Rohrer, M.R. Tonks, R.M. Suter, C.E. Krill III, A.R. Krause, Observations of unexpected grain boundary migration in SrTiO3. Scripta Materialia. 2023; 222:115022. 10.1016/j.scriptamat.2022.115055.\nV. Muralikrishnan, Z. Xu, G.S. Rohrer, A.R. Krause, The effect of local geometry and relative energy on grain boundary area changes during grain growth in SrTiO3. Journal of the American Ceramic Society. 2025; 108[4]:e20319.\n", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1016/j.scriptamat.2022.115055", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "10.1111/jace.20319", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "ceramics" + }, + { + "subject": "oxides" + }, + { + "subject": "3D microstructure evolution" + }, + { + "subject": "high energy x-ray diffraction microscopy" + }, + { + "subject": "experiment" + }, + { + "subject": "microstructures" + }, + { + "subject": "strontium titanate" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Amanda Krause (amandakr@andrew.cmu.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/d8mx-0391" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "12b54dfc-0584-4665-9a56-595ec6dcac85", + "source_id": "12b54dfc-0584-4665-9a56-595ec6dcac85", + "versioned_source_id": "12b54dfc-0584-4665-9a56-595ec6dcac85-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-07-21T12:41:18.008014Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/12b54dfc-0584-4665-9a56-595ec6dcac85/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/12b54dfc-0584-4665-9a56-595ec6dcac85/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "74d6b3d9-c33c-47c4-9c9a-c14ca773d3c8-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials" + } + ], + "creators": [ + { + "creatorName": "Maxson, Tristan", + "familyName": "Maxson", + "givenName": "Tristan", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "creatorName": "Soyemi, Ademola", + "familyName": "Soyemi", + "givenName": "Ademola", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "creatorName": "Zhang, Xinglong", + "familyName": "Zhang", + "givenName": "Xinglong", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "creatorName": "Chen, Benjamin Wei Jie", + "familyName": "Chen", + "givenName": "Benjamin Wei Jie", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + }, + { + "creatorName": "Szilvasi, Tibor", + "familyName": "Szilvasi", + "givenName": "Tibor", + "affiliations": [ + "University of Alabama", + "Institute of High Performance Computing (IHPC)" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Here we provide the following zipped to facilitate easy downloading.\n\n- `Analysis.zip`: Additional analysis scripts and minor datasets used to validate observable properties. ~11 MB\n- `Speed.zip`: The LAMMPS input for performing our speed tests for each MLIP. ~55 KB\n\nThe datasets are provided unzipped as the file size is quite large and only particular datasets may be of interest.\n- `Datasets/`: The database of datasets used in the benchmark with 3 splits. ~3.8 GB\n\nPlease note that the Zr-O dataset is derived from the referenced work by Waters et. al with permission.\n- Waters, M. J.; Rondinelli, J. M. Benchmarking Structural Evolution Methods for Training of Machine Learned Interatomic Potentials. J. Phys.: Condens. Matter 2022, 34 (38), 385901", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.5281.zenodo.14584499", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "10.1021/acs.jcim.5c01262", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "10.1088/1361-648X/ac7f73", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "oxides" + }, + { + "subject": "simulation" + }, + { + "subject": "DFT" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Maxson, Tristan (tgmaxson@gmail.com)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Szilv\u00e1si, Tibor (tszilvasi@ua.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { 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Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "creatorName": "Park, Inkyu", + "familyName": "Park", + "givenName": "Inkyu", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + 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"Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + }, + { + "creatorName": "Amjadi, Morteza", + "familyName": "Amjadi", + "givenName": "Morteza", + "affiliations": [ + "Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea", + "School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom", + "James Watt School of Engineering, University of Glasgow, G12 8QQ, United Kingdom", + "Institute of Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, EH9 3FF, United Kingdom" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + 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The layout and file names of the included files are based on an accompanying publication covering the topic of \"Wearable, Near Temperature Insensitive Laser-Induced Graphene Nanocomposite Strain Sensors\".\n\nSimon Wanstall and Tom Jacquin contributed equally to this work.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "laser-induced graphene" + }, + { + "subject": "nanocomposites" + }, + { + "subject": "soft wearable sensors" + }, + { + "subject": "temperature insensitivity" + }, + { + "subject": "soft robotics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Amjadi, Morteza\" (Morteza.Amjadi@glasgow.ac.uk)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/e1f9-bn46" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "organization": "MDF Open", + "source_id": "7e599fc3-f015-4d16-9082-101b2d3b54c6", + "versioned_source_id": "7e599fc3-f015-4d16-9082-101b2d3b54c6-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-07-14T14:10:09.707689Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/7e599fc3-f015-4d16-9082-101b2d3b54c6/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/7e599fc3-f015-4d16-9082-101b2d3b54c6/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "e52f77de-6756-4ca9-8fdb-f4791b395c1f-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/e52f77de-6756-4ca9-8fdb-f4791b395c1f/1.0/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/e52f77de-6756-4ca9-8fdb-f4791b395c1f/1.0/" + }, + "mdf": { + "organization": "MDF Open", + "resource_type": "dataset", + "domains": [ + "materials" + ], + "ingest_date": "2024-05-02T02:19:16.267445Z", + "acl": [ + "public" + ], + "source_id": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "version": "1.0", + "source_name": "e52f77de-6756-4ca9-8fdb-f4791b395c1f", + "versioned_source_id": "e52f77de-6756-4ca9-8fdb-f4791b395c1f-1.0" + }, + "dc": { + "identifier": { + "identifier": "10.18126/wy01-4e11", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "creators": [ + { + "creatorName": "Martirossyan, Maya M.", + "affiliations": [ + "Cornell University" + ], + "familyName": "Martirossyan", + "givenName": "Maya M." + }, + { + "creatorName": "Spellings, Matthew", + "affiliations": [ + "Cornell University" + ], + "familyName": "Spellings", + "givenName": "Matthew" + }, + { + "creatorName": "Pan, Hillary", + "affiliations": [ + "Cornell University" + ], + "familyName": "Pan", + "givenName": "Hillary" + }, + { + "creatorName": "Dshemuchadse, Julia", + "affiliations": [ + "Cornell University" + ], + "familyName": "Dshemuchadse", + "givenName": "Julia" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "titles": [ + { + "title": "Local structural features elucidate crystallization of complex structures" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": " https://doi.org/10.48550/arXiv.2401.13765", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "jd732@cornell.edu", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "This dataset accompanies the \u201cLocal structural features elucidate crystallization of complex structures\u201d https://doi.org/10.1021/acsnano.4c01290 (Martirossyan, et al. 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A README.txt file is included for parsing the data." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Spin-space averaged Fe\u2013C diffusion calculations from density functional theory" + } + ], + "creators": [ + { + "creatorName": "Wirth, Luke J.", + "familyName": "Wirth", + "givenName": "Luke J.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Trinkle, Dallas R.", + "familyName": "Trinkle", + "givenName": "Dallas R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data set for spin-space averaged Fe\u2013C diffusion calculations with VASP. 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Activation energies from these inform diffusion calculations of carbon in bcc Fe at finite temperatures and under field.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "DFT" + }, + { + "subject": "diffusion" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Dallas Trinkle (dtrinkle@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/fttq-w045" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "source_id": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4", + "versioned_source_id": "37a80cc7-5b0d-42ee-9d79-c4c6dac507a4-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-07-16T14:57:00.160394Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/37a80cc7-5b0d-42ee-9d79-c4c6dac507a4/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/37a80cc7-5b0d-42ee-9d79-c4c6dac507a4/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "6114d6c9-6c76-497f-bf4b-f1033c4b1679-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Atom Probe Tomography dataset" + } + ], + "creators": [ + { + "creatorName": "Ulfig, Robert", + "familyName": "Ulfig", + "givenName": "Robert", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "creatorName": "Reinhard, David", + "familyName": "Reinhard", + "givenName": "David", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "creatorName": "Jacobs, Ryan", + "familyName": "Jacobs", + "givenName": "Ryan", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + }, + { + "creatorName": "Voyles, Paul", + "familyName": "Voyles", + "givenName": "Paul", + "affiliations": [ + "CAMECA Instruments, Inc.", + "University of Wisconsin\u2013Madison" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Collection of Atom Probe Tomography (APT) dataset files for various materials. 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Pan and J. Dshemuchadse, \u201cTargeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions\u201d ACS Nano 17(8), 7157\u20137169 (2023). In this work, we designed a new isotropic pair potential, in which all energy wells and maxima can be tuned independently. We use our interaction potential design to systematically explore self-assembled crystal structure configurations, specifically targeting low-coordinated structures. We report the computational self-assembly of 20 new crystal structure types, 14 of which are low-coordinated, and we investigate the relationship between features of the interaction potential and the resulting structures. This dataset includes over 2000 final simulation frames of the resulting crystal droplets (.gsd) and the corresponding interaction potential and simulation parameters (.json) used in this study. A README.txt file is included for parsing the data. We hope that this dataset will be useful for a variety of future work, both fundamental in nature as well as data-centric applications related to self-assembly, interaction potentials, and complex crystal structures. 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Scott", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Julia Dshemuchadse", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Phillip J. Milner", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "These files contain the data supporting all of the results in Reum N. Scott, et. al, Metal\u2013organic frameworks (MOFs) are crystalline materials that self-assemble from inorganic nodes and organic linkers, and isoreticular chemistry allows for modular and synthetic reagents of various sizes. In this study, a MOF's components\u2014metal nodes and organic linkers\u2014are constructed in a coarse-grained model from isotropic beads, retaining the basic symmetries of the molecular components. Lennard-Jones and Weeks\u2013Chandler\u2013Andersen pair potentials are used to model attractive and repulsive particle interactions, respectively. We analyze the crystallinity of the self-assembled products and explore the role of modulators\u2014molecules that compete with the organic linkers in binding to the metal nodes, and which we construct analogously\u2014during the self-assembly process of defect-engineered MOFs. Coarse-grained simulation allows for the uncoupling of experimentally interdependent variables to broadly map and determine essential MOF self-assembly conditions, among which are properties of the modulator: binding strength, size (steric hindrance), and concentration. Of these, the simulated modulator's binding strength has the most pronounced effect on the resulting MOF's crystal size." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "schleife2018_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/schleife2018_v1.1/", + "total_size": 585846333, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/schleife2018_v1.1/" + }, + "services": { + "mdf_publish": "https://petreldata.net/mdf/detail/schleife2018_v1.1", + "mdf_search": "This dataset was ingested to MDF Search." + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-10-24T16:38:18.431004Z", + "source_id": "schleife2018_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "schleife2018" + }, + "dc": { + "identifier": { + "identifier": "10.18126/2kdh-3lwi", + "identifierType": "DOI" + }, + "creators": [ + { + "familyName": "Schleife", + "givenName": "Andr\u00e9", + "creatorName": "Schleife, Andr\u00e9", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Ward", + "givenName": "Logan", + "creatorName": "Ward, Logan", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Blaiszik", + "givenName": "Ben", + "creatorName": "Blaiszik, Ben", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + }, + { + "familyName": "Foster", + "givenName": "Ian", + "creatorName": "Foster, Ian", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "University of Chicago", + "Argonne National Laboratory" + ] + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "nuclear materials" + }, + { + "subject": "time dependent density functional theory" + }, + { + "subject": "aluminum" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-10-24", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Time-Dependent Density Functional Theory Calculations of Proton Radiation in Aluminum" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.18126/UY0K-BWKQ", + "relatedIdentifierType": "DOI" + }, + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1103/PhysRevB.91.014306", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorType": "ContactPerson", + "contributorName": "lwrad@anl.gov" + }, + { + "contributorType": "ContactPerson", + "contributorName": "schleife@illinois.edu" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Input file and outputs of Real Time Time-Dependent Density Functional Data (RT-TDDFT) computations of proton radiation in Aluminum. This dataset contains four additional trajectories in Aluminum using the same settings as in [Schleife et al. 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initial state, t10 - after the 10th annealing treatment\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "5decm_1_v1.4", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/5decm_1_v1.4" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/5decm_1_v1.4/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/5decm_1_v1.4/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2023-11-17T02:40:03.425766Z", + "source_id": "5decm_1_v1.4", + "scroll_id": 0, + "version": 1, + "source_name": "5decm_1" + }, + "dc": { + "identifier": { + "identifier": "10.18126/y2vi-ntsk", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Huang, Shuoyuan", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Huang", + "givenName": "Shuoyuan" + }, + { + "creatorName": "Voyles, Paul M.", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Voyles", + "givenName": "Paul M." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "microscopy" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2023, + "dates": [ + { + "date": "2023-11-17", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Data for Momentum Transfer Resolved Electron Correlation Microscopy" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1016/j.ultramic.2023.113886", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "paul.voyles@wisc.edu", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "5D electron correlation microscopy data from a Pt57.5Cu14.7Ni5.2P22.5 metallic glass. 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G.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Kotula", + "givenName": "Paul G." + }, + { + "creatorName": "Hinojos, Alejandro E.", + "affiliations": [ + "Sandia National Laboratory" + ], + "familyName": "Hinojos", + "givenName": "Alejandro E." + } + ], + "subjects": [ + { + "subject": "thin films" + }, + { + "subject": "molybednum" + }, + { + "subject": "magnetron sputter deposition" + }, + { + "subject": "multimodal data" + }, + { + "subject": "experiment" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2022, + "dates": [ + { + "date": "2022-12-21", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Sputter-deposited Mo thin films: multimodal characterization of structure, surface morphology, density, residual stress, electrical resistivity, and mechanical response" + } + ], + "contributors": [ + { + "contributorName": "aahenri@sandia.gov", + "contributorType": "ContactPerson" + }, + { + "contributorName": "dpadams@sandia.gov", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Multimodal datasets of materials are rich sources of information which can be leveraged for expedited discovery of process-structure-property relationships and for designing materials with specific structures and/or properties. Here, we provide a multimodal dataset of magnetron sputter-deposited molybdenum (Mo) thin films, which are used in a variety of industries including high temperature applications, photovoltaics, and microelectronics. A process space consisting of 27 unique combinations of sputter power and Argon (Ar) deposition pressure was explored. We include an extensive set of eight experimental characterization modalities for each unique power/pressure combination: X-ray diffraction, scanning electron microscopy, residual stress, resistivity, atomic force microscopy, rutherford backscattering, nanoindentation, and transmission electron microscopy with automated crystal orientation mapping data. We also include simulated experiments over an expanded set of powers, pressures and working distances to capture process data in addition to this experimental structure and property data. We acknowledge the Laboratory Directed Research and Development program for providing funding for this study. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the view of the U.S. DOE or the United States Government." + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "82393b42-7252-4d33-ad97-dfda59f22e33-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Electron Microscopy and White Light Interferometry Fractography Images of Ti64 Compact Tension Specimens" + } + ], + "creators": [ + { + "creatorName": "Jones, Katelyn", + "familyName": "Jones", + "givenName": "Katelyn", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Shade, Paul A.", + "familyName": "Shade", + "givenName": "Paul A.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "John, Reji", + "familyName": "John", + "givenName": "Reji", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Musinski, William D.", + "familyName": "Musinski", + "givenName": "William D.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Pilchak, Adam L.", + "familyName": "Pilchak", + "givenName": "Adam L.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Chapman, Michael G.", + "familyName": "Chapman", + "givenName": "Michael G.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Holm, Elizabeth A.", + "familyName": "Holm", + "givenName": "Elizabeth A.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + }, + { + "creatorName": "Rollett, Anthony D.", + "familyName": "Rollett", + "givenName": "Anthony D.", + "affiliations": [ + "Carnegie Mellon University", + "Air Force Research Lab", + "University of Wisconsin\u2013Milwaukee", + "University of Michigan Ann Arbor" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "The study of fatigue fracture surfaces presents a perfect opportunity for machine learning (ML) and computer vision (CV) techniques. This work compiles a multimodal set of images taken on the fractured surfaces of fatigue crack growth experiments on Ti-6Al-4V compact tension specimens. Secondary electron (SE), back scattered electron (BSE), and scanning white-light interferometry (SWLI) images were taken on the samples. A set of 102 SE images was taken from near the crack initiation site up to 14mm from the initiation site in regular 1mm increments. The other set of SE images, in addition to the BSE and SWLI, were from large area scans that were stitched together but can be cropped for future ML usage. Labeled data sets such as this can improve the development of ML tools for fractography studies.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1016/j.ijfatigue.2023.107915", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "machine learning" + }, + { + "subject": "ti64" + }, + { + "subject": "fatigue" + }, + { + "subject": "microscopy" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "SEM" + }, + { + "subject": "BSE" + }, + { + "subject": "Scanning white light interferometry" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "ktj@andrew.cmu.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "paul.shade.1@us.af.mil", + "contributorType": "ContactPerson" + }, + { + "contributorName": "reji.john@us.af.mil", + "contributorType": "ContactPerson" + }, + { + "contributorName": "michael.chapman.22.ctr@us.af.mil", + "contributorType": "ContactPerson" + }, + { + "contributorName": "adam.pilchak@prattwhitney.com", + "contributorType": "ContactPerson" + }, + { + "contributorName": "eaholm@umich.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "musinski@uwm.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "rollett@andrew.cmu.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/qpb3-7246" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "82393b42-7252-4d33-ad97-dfda59f22e33", + "source_id": "82393b42-7252-4d33-ad97-dfda59f22e33", + "versioned_source_id": "82393b42-7252-4d33-ad97-dfda59f22e33-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-03T05:10:10.531823Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/82393b42-7252-4d33-ad97-dfda59f22e33/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/82393b42-7252-4d33-ad97-dfda59f22e33/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "3034ae5d-f0a8-439d-908b-e1d41d275279-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Dataset for Thermogravimetric analysis of commercial tungsten molecular precursors for vapor phase materials growth processes" + } + ], + "creators": [ + { + "creatorName": "Currie, Taylor M.", + "familyName": "Currie", + "givenName": "Taylor M.", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "McNealy-James, Terrick", + "familyName": "McNealy-James", + "givenName": "Terrick", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "Berriel, S. Novia", + "familyName": "Berriel", + "givenName": "S. Novia", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "Preradovic, Konstantin", + "familyName": "Preradovic", + "givenName": "Konstantin", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "Sattelberger, Alfred P.", + "familyName": "Sattelberger", + "givenName": "Alfred P.", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "Banerjee, Parag", + "familyName": "Banerjee", + "givenName": "Parag", + "affiliations": [ + "University of Central Florida" + ] + }, + { + "creatorName": "Jurca, Titel", + "familyName": "Jurca", + "givenName": "Titel", + "affiliations": [ + "University of Central Florida" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset is associated with the following publication: T. M. Currie, T. McNealy-James, S. N. Berriel, K. Preradovic, A. P. Sattelberger, P. Banerjee, and T. Jurca, RSC Advances, 2024.\n\nThis dataset includes relevant TGA data, Temperature (\u00b0C) vs. Mass (%), for all eight precursors discussed in our publication. Please see our publication and associated supplementary information for more details. \n\nIf using this dataset, please cite both this page and our publication.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "thermogravimetric analysis" + }, + { + "subject": "precursor chemistry" + }, + { + "subject": "atomic layer deposition" + }, + { + "subject": "chemical vapor deposition" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Titel Jurca (titel.jurca@ucf.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ga5y-dh42" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "organization": "MDF Open", + "source_id": "3034ae5d-f0a8-439d-908b-e1d41d275279", + "versioned_source_id": "3034ae5d-f0a8-439d-908b-e1d41d275279-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-11-25T22:23:01.887936Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/3034ae5d-f0a8-439d-908b-e1d41d275279/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/3034ae5d-f0a8-439d-908b-e1d41d275279/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ace76b96-9175-4e1a-810f-ef0a54fc2c45-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Asymmetric porous catalyst structures for low temperature photocatalytic dry reforming of methane" + } + ], + "creators": [ + { + "creatorName": "Moore, William", + "familyName": "Moore", + "givenName": "William", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Shoji, Shusaku", + "familyName": "Shoji", + "givenName": "Shusaku", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Tsaur, Lieihn", + "familyName": "Tsaur", + "givenName": "Lieihn", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Yu, Fei", + "familyName": "Yu", + "givenName": "Fei", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Thedford, R. Paxton", + "familyName": "Thedford", + "givenName": "R. Paxton", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Tait, William R", + "familyName": "Tait", + "givenName": "William R", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Raisi, M. Sadegh", + "familyName": "Raisi", + "givenName": "M. Sadegh", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Saha, Aniruddha", + "familyName": "Saha", + "givenName": "Aniruddha", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Hur, Kayhun", + "familyName": "Hur", + "givenName": "Kayhun", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Reese, Austin", + "familyName": "Reese", + "givenName": "Austin", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Kozbek, Ali Y.", + "familyName": "Kozbek", + "givenName": "Ali Y.", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Hesse, Sarah", + "familyName": "Hesse", + "givenName": "Sarah", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Gruner, Sol M.", + "familyName": "Gruner", + "givenName": "Sol M.", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Yeghiazarian, Lilit", + "familyName": "Yeghiazarian", + "givenName": "Lilit", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Sobhani, Sadaf", + "familyName": "Sobhani", + "givenName": "Sadaf", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Suntivich, Jin", + "familyName": "Suntivich", + "givenName": "Jin", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + }, + { + "creatorName": "Wiesner, Ulrich B.", + "familyName": "Wiesner", + "givenName": "Ulrich B.", + "affiliations": [ + "Cornell University", + "National Insitute for Materials Science", + "University of Cincinnati", + "Korea Institute of Science and Technology" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data associated with the article titled \"Asymmetric porous catalyst structures for low temperature photocatalytic dry reforming of methane\"", + "descriptionType": "Other" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Ulrich Wiesner (ubw1@cornell.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "William Moore (wlm68@cornell.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/xr9e-js38" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "source_id": "ace76b96-9175-4e1a-810f-ef0a54fc2c45", + "versioned_source_id": "ace76b96-9175-4e1a-810f-ef0a54fc2c45-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-18T18:08:51.505959Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ace76b96-9175-4e1a-810f-ef0a54fc2c45/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ace76b96-9175-4e1a-810f-ef0a54fc2c45/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "55490765-6506-4532-9e80-4f8c32195466-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/55490765-6506-4532-9e80-4f8c32195466/1.0/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/55490765-6506-4532-9e80-4f8c32195466/1.0/" + }, + "mdf": { + "organization": "MDF Open", + "resource_type": "dataset", + "domains": [ + "materials" + ], + "ingest_date": "2024-11-19T18:19:58.885764Z", + "acl": [ + "public" + ], + "source_id": "55490765-6506-4532-9e80-4f8c32195466", + "version": "1.0", + "source_name": "55490765-6506-4532-9e80-4f8c32195466", + "versioned_source_id": "55490765-6506-4532-9e80-4f8c32195466-1.0" + }, + "dc": { + "identifier": { + "identifier": "10.18126/93xq-zt25", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "creators": [ + { + "creatorName": "Islam, Showmic", + "affiliations": [ + "University of Nebraska\u2013Lincoln" + ], + "familyName": "Islam", + "givenName": "Showmic" + }, + { + "creatorName": "Turner, Joseph A.", + "affiliations": [ + "University of Nebraska-Lincoln" + ], + "familyName": "Turner", + "givenName": "Joseph A." + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "microstructures" + }, + { + "subject": "metals and alloys" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "titles": [ + { + "title": "Dataset for Quantifying the Effect of Ultrasonic Scattering from a Distribution of Grain Sizes" + } + ], + "contributors": [ + { + "contributorName": "sislam2@unl.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "jaturner@unl.edu", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The dataset consists of synthetic polycrystals created using DREAM.3D. All synthetics have the same mean grain size. There are sets of thirty synthetics and each set has a different grain-size distribution width. Both cropped and uncropped synthetics are included as described in the article \"Dataset for Quantifying the Effect of Ultrasonic Scattering from a Distribution of Grain Sizes.\"" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "camp_2023-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Dataset of NMC battery Tests from CAMP, 2023 Release" + } + ], + "creators": [ + { + "creatorName": "Ward, Logan", + "familyName": "Ward", + "givenName": "Logan", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Kubal, Joseph", + "familyName": "Kubal", + "givenName": "Joseph", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Babinec, Susan J.", + "familyName": "Babinec", + "givenName": "Susan J.", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Lu, Wenquan", + "familyName": "Lu", + "givenName": "Wenquan", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Dunlop, Allison", + "familyName": "Dunlop", + "givenName": "Allison", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Trask, Steve", + "familyName": "Trask", + "givenName": "Steve", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Polzin, Bryant", + "familyName": "Polzin", + "givenName": "Bryant", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Jansen, Andrew", + "familyName": "Jansen", + "givenName": "Andrew", + "affiliations": [ + "Argonne National Laboratory" + ] + }, + { + "creatorName": "Paulson, Noah H.", + "familyName": "Paulson", + "givenName": "Noah H.", + "affiliations": [ + "Argonne National Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Collection of cycle life tests of Li-ion batteries from the Cell Analysis, Modeling, and Prototyping (CAMP) Facility at Argonne National Laboratory. The dataset contains all 300 cells used by Paulson et al. to study the effect of feature engineering on machine learning models to predict the life of batteries, which were selected because they have a graphite anode, used low charging rates, and were tested for at least 100 cycles. We also include 302 cells that failed to meet acceptence criteria for that study. Each cell is stored in the HDF5 format of Argonne's Battery Data Toolkit v0.4.0, which includes battery metadata, the raw signal from the testing equipment, and per-cycle summaries of battery performance.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1016/j.jpowsour.2022.231127", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/s90e-dq90" + } + }, + "mdf": { + "source_name": "camp_2023", + "organization": "MDF Open", + "source_id": "camp_2023", + "versioned_source_id": "camp_2023-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-11-19T19:08:20.970886Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/camp_2023/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/camp_2023/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "237d3189-ae38-496a-88c9-760c5fa25419-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Improvement of Cellular Pattern Organization and Clarity Through Centrifugal Force " + } + ], + "creators": [ + { + "creatorName": "Mehanna, Lauren E.", + "familyName": "Mehanna", + "givenName": "Lauren E.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Boyd, James D.", + "familyName": "Boyd", + "givenName": "James D.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Remus-Williams, Shelley", + "familyName": "Remus-Williams", + "givenName": "Shelley", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Racca, Nicole M.", + "familyName": "Racca", + "givenName": "Nicole M.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Spraggins P., Dawson", + "familyName": "Spraggins P.", + "givenName": "Dawson", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Grady, Martha E.", + "familyName": "Grady", + "givenName": "Martha E.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + }, + { + "creatorName": "Berron, Brad J.", + "familyName": "Berron", + "givenName": "Brad J.", + "affiliations": [ + "University of Kentucky", + "University of Michigan" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "subjects": [ + { + "subject": "cell patterning" + }, + { + "subject": "adhesion" + }, + { + "subject": "centrifugation" + }, + { + "subject": "shear flow" + }, + { + "subject": "streptavidin" + }, + { + "subject": "cell density" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Lauren E. Mehanna (lauren.mehanna@uky.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/g7qt-bp50" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "237d3189-ae38-496a-88c9-760c5fa25419", + "source_id": "237d3189-ae38-496a-88c9-760c5fa25419", + "versioned_source_id": "237d3189-ae38-496a-88c9-760c5fa25419-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-12-05T19:05:45.795399Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/237d3189-ae38-496a-88c9-760c5fa25419/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/237d3189-ae38-496a-88c9-760c5fa25419/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "e6d1f230-74af-4f53-9f5b-836aa5efe72e-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "A Programmable Nanoparticle Conversion Pathway to Monodisperse Polyelemental High Entropy Alloy, Intermetallic, and Multiphase Nanoparticles" + } + ], + "creators": [ + { + "creatorName": "Kar, Nabojit", + "familyName": "Kar", + "givenName": "Nabojit", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "creatorName": "Leonardi, Alberto", + "familyName": "Leonardi", + "givenName": "Alberto", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "creatorName": "McCoy, Maximilian", + "familyName": "McCoy", + "givenName": "Maximilian", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "creatorName": "Selvaraj, Rukshanthan", + "familyName": "Selvaraj", + "givenName": "Rukshanthan", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + }, + { + "creatorName": "Skrabalak, Sara E.", + "familyName": "Skrabalak", + "givenName": "Sara E.", + "affiliations": [ + "Department of Chemistry, Indiana University, Bloomington, IN 47405, USA", + "Physical Sciences, Diamond Light Source (United Kingdom), Diamond House \u2013 Harwell Science & Innovation Campus, Didcot, Oxfordshire OX11 0DE, UK", + "Earth and Atmospheric Sciences, Indiana University, 1001 East 10th street, Bloomington, Indiana 47405-1405, USA" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data Collection associated with the above titled paper, published in Angewandte Chemie. Abstract: Polyelemental nanoparticles (PE NPs), those consisting of four or more elements, exhibit unique properties from synergistic compositional effects. Examples include high entropy alloys, high entropy intermetallics, and multiphase types, including Janus and core-shell architectures. Although colloidal syntheses offer excellent structural control for mono- and bi-elemental compositions, achieving the same control for PE NPs remains challenging. Here, this challenge is addressed\nwith a NP conversion strategy wherein different types of PE NPs \u2013 including high entropy alloy, high entropy intermetallic, and multiphase Janus nanoparticles \u2013 are achieved through thermal transformation of readily synthesized colloidal coreshell NPs. Through systematic variations in stoichiometry and metal identity to the core-shell precursor NPs, along with atomistic simulations that probe phase stabilities, we deduce that the final mixing states of the various NPs are governed by the balance between the enthalpy and entropy of mixing. Moreover, our annealing method allows us to trap NPs at intermediate states of mixing, creating distinct surface ensembles that were evaluated as catalysts for the hydrogen evolution reaction. This study is the first, to our knowledge, to report colloidally derived precursor NPs enabling the synthesis of all types of PE NPs in a single process. This NP conversion strategy offers a general route to diverse PE NPs.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1002/anie.202505523", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "doi.org/10.1002/anie.202505523", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "inorganic Nanoparticles" + }, + { + "subject": "high entropy alloy" + }, + { + "subject": "Polyelemental nanoparticle" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "nabojitkar@gmail.com", + "contributorType": "ContactPerson" + }, + { + "contributorName": "sskrabal@iu.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/0a1r-zt14" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "source_id": "e6d1f230-74af-4f53-9f5b-836aa5efe72e", + "versioned_source_id": "e6d1f230-74af-4f53-9f5b-836aa5efe72e-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-04-26T02:16:13.283743Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/e6d1f230-74af-4f53-9f5b-836aa5efe72e/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/e6d1f230-74af-4f53-9f5b-836aa5efe72e/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Terahertz spectroscopic characterization of Ge3Sb2Te6 compounds for active applications" + } + ], + "creators": [ + { + "creatorName": "Kumar, Krishna", + "familyName": "Kumar", + "givenName": "Krishna", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "creatorName": "Zabyako, Miroslavna Kovylina", + "familyName": "Zabyako", + "givenName": "Miroslavna Kovylina", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "creatorName": "Alvarez-Sanchis, Jose Antonio", + "familyName": "Alvarez-Sanchis", + "givenName": "Jose Antonio", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "creatorName": "De Zarate Diaz, David Ortiz", + "familyName": "De Zarate Diaz", + "givenName": "David Ortiz", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "creatorName": "Vidal, Borja", + "familyName": "Vidal", + "givenName": "Borja", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + }, + { + "creatorName": "Garcia-Meca, Carlos", + "familyName": "Garcia-Meca", + "givenName": "Carlos", + "affiliations": [ + "Universitat Politecnica de Valenica" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "The uploaded data comprises three primary folders titled: THz-TDS, Raman data, and XRD data\n\nTHz-TDS\nThis folder has four subfolders, each contains terahertz time-domain transmission data. In every data file, the first column represents the time delay, and the second column corresponds to the transmitted electric field (e-field). \nThe measurements are named in the format: \"LnpmXYi_11h_1\", where:\n n = 1 for GST225, 3 for GST326;\nm =1 for as-grown, 2 for sample annealed at 155 C, 3 at 205 C, 4 at 255 C, 5 at 305 C, 6 at 355 C, and 7 at 405 C;\nX = Z indicates sapphire substrate and without X is silicon substrate;\nY = M for thin-film+substrate, S for bare substrate only, R for reference;\ni = 1, 2 or 3 indicates measurement spots 1, 2 and 3 respectively. \nIn cases where the file name is prefixed by 'replica', for example replicaLnpmXYi_11h_1, it refers to newly fabricated samples measured under the same conditions. \n\n\nRaman data\nRaman measurements are named in the format: \"Ln_i_laser 785nm_F25_r50_250_ob10_g600_t2s_ac20_spotj\", where:\nn = 2 for GST225, 3 for GST326;\ni = 1 to 7, representing the as-grown state and annealed samples at 155 C, 205 C, 255 C, 305 C, 355 C and 405 C, respectively;\nj = 1 to 3, indicating the three different measurement spots per sample.\nEach Raman dataset contains two columns: the first column is the Raman shift, and second column is the corresponding intensity. \n\nXRD data\nXRD files are named in the format: \"Ln.i_exported\", where:\nn = 2 for GST225, 3 for GST326;\ni = 1 to 7, corresponding to the as-grown sample and annealed samples at 155 C, 205 C, 255 C, 305 C, 355 C and 405 C, respectively.\nEach XRD file contains two columns: the first column is the diffraction angle (2 theta), and the second column is the intensity. ", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "spectroscopy" + }, + { + "subject": "experiment" + } + ], + "rightsList": [ + { + "rights": "MIT License" + } + ], + "contributors": [ + { + "contributorName": "Krishna Kumar (krishnacelos.isp@gmail.com)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/1v02-w971" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "source_id": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4", + "versioned_source_id": "c4cf2068-fc7a-469e-af39-0aa60bf20cf4-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-04-16T17:00:39.946154Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c4cf2068-fc7a-469e-af39-0aa60bf20cf4/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/c4cf2068-fc7a-469e-af39-0aa60bf20cf4/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "0989ee07-e015-494d-8805-8e2703e76763-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "LabDCT data for investigation of 3D strain heterogeneity and fracture studied by X-ray tomography and crystal plasticity in an aluminium alloy" + } + ], + "creators": [ + { + "creatorName": "Gille, Maryse", + "familyName": "Gille", + "givenName": "Maryse", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "creatorName": "Proudhon, Henry", + "familyName": "Proudhon", + "givenName": "Henry", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "creatorName": "Oddershede, Jette", + "familyName": "Oddershede", + "givenName": "Jette", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "creatorName": "Quey, Romain", + "familyName": "Quey", + "givenName": "Romain", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + }, + { + "creatorName": "Morgeneyer, Thilo F.", + "familyName": "Morgeneyer", + "givenName": "Thilo F.", + "affiliations": [ + "Mines Paris", + "Xnovo Technology ApS", + "Mines Saint-Etienne" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Sample and materials\n* Material: 6016 T4 aluminum alloy\n* Average grain size: 40 \u00b5m\n* Shape: Wide dogbone to fulfil plane strain conditions during tensile deformation\n\n\nExperiment and characterisation\n* LabDCT grain map of interior volume of undeformed sample\n* X-ray imaging equipment: Zeiss Xradia 520 Versa X-ray microscope with LabDCT Pro module\n* Absorption contrast scan: 60kV-4W, 4X detector, 1601 projections, 4.45 \u00b5m voxel size\n* Diffraction contrast scan: 110kV-9W, flat panel detector, helical phyllotaxis HART scanning scheme, 1800 projections\n\n\nData\n* Map dimensions: \n - x = 0.44 mm (sample thickness)\n - y = 0.24 mm (direction of near-zero strain in the plane strain condition, ~6 grain diameters width)\n - z = 1.00 mm (tensile direction)\n* Voxel size: 4 \u00b5m\n* Number of grains: 1666, all of which are at least 27 voxels, and no voids\n* Context: This is a cropped volume at the center of the sample, not the full volume analysed in the associated publication \n* Format: .h5", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1016/j.ijplas.2024.104146", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "LabDCT" + }, + { + "subject": "plane strain deformation" + }, + { + "subject": "CP-FE simulations" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "microstructures" + }, + { + "subject": "X-ray imaging" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": " \"Oddershede, Jette\" (joddershede@xnovotech.com)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "\"Proudhon, Henry\" (henry.proudhon@minesparis.psl.eu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/yc1m-nk41" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "0989ee07-e015-494d-8805-8e2703e76763", + "source_id": "0989ee07-e015-494d-8805-8e2703e76763", + "versioned_source_id": "0989ee07-e015-494d-8805-8e2703e76763-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-03-26T08:46:40.678208Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/0989ee07-e015-494d-8805-8e2703e76763/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/0989ee07-e015-494d-8805-8e2703e76763/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "8d0831be-baf6-4829-8187-e2e45192df1e-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Molecular dynamics trajectories of ZnCl2 aqueous solutions from 1 m to 30 m " + } + ], + "creators": [ + { + "creatorName": "Cao, Chuntian", + "familyName": "Cao", + "givenName": "Chuntian", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "Zhang, Chunyi", + "familyName": "Zhang", + "givenName": "Chunyi", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "Wu, Xifan", + "familyName": "Wu", + "givenName": "Xifan", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "Carbone, Matthew R.", + "familyName": "Carbone", + "givenName": "Matthew R.", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "van Dam, Hubertus", + "familyName": "van Dam", + "givenName": "Hubertus", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "Yoo, Shinjae", + "familyName": "Yoo", + "givenName": "Shinjae", + "affiliations": [ + "Brookhaven National Laboratory" + ] + }, + { + "creatorName": "Lu, Deyu", + "familyName": "Lu", + "givenName": "Deyu", + "affiliations": [ + "Brookhaven National Laboratory" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains molecular dynamics (MD) trajectories of aqueous zinc chloride (ZnCl\u2082) solutions at concentrations ranging from 1 mol/kg to 30 mol/kg, with 30 mol/kg representing the \"water-in-salt\" regime. Simulations were performed using deep neural network potential trained with DFT data. Details about the neural network potential can be found in the publication: PRX Energy 4, 023004 (10.1103/PRXEnergy.4.023004). \n\nThe simulation used a 0.5 fs timestep. For each concentration, the dataset includes three 100-ns trajectories for a smaller system (about 1600 atoms) and one 20-ns trajectory for a larger system (about 15,000 atoms). These trajectories can be used to study ion solvation, structural organization, and dynamical behavior as a function of concentration.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1103/PRXEnergy.4.023004", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "https://doi.org/10.24435/materialscloud:xb-4f", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "machine learning" + }, + { + "subject": "energy materials" + }, + { + "subject": "diffusion" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Chuntian Cao\" (ccao@bnl.gov)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/8h2q-g409" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "8d0831be-baf6-4829-8187-e2e45192df1e", + "source_id": "8d0831be-baf6-4829-8187-e2e45192df1e", + "versioned_source_id": "8d0831be-baf6-4829-8187-e2e45192df1e-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-05-05T19:14:19.780879Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8d0831be-baf6-4829-8187-e2e45192df1e/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/8d0831be-baf6-4829-8187-e2e45192df1e/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "915db26c-222b-4f06-9352-643a2f36415d-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Glassy Dynamics of Epoxy-Amine Thermosets Containing Dynamic, Aromatic Disulfides" + } + ], + "creators": [ + { + "creatorName": "Broderick Lewis Cheol Park, and Kenneth R. Shull, Joseph M. Dennis", + "familyName": "Broderick Lewis Cheol Park, and Kenneth R. Shull", + "givenName": "Joseph M. Dennis", + "affiliations": [ + "Northwestern University", + "Army Research Laboratory", + "NASA Langley Research Center" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \u201cGlassy Dynamics of Epoxy-Amine Thermosets Containing Dynamic, Aromatic Disulfides.\u201d B. Lewis, J.M. Dennis, C. Park & K.R. Shull, Macromolecules 57, 7112\u20137122 (2024) (http://dx.doi.org/10.1021/acs.macromol.4c01012). The dataset includes the following files and directories:\n- readme.md: Markdown file of this description. \n- Lewis et al. - 2024 - Glassy Dynamics of Epoxy-Amine Thermosets Containi.pdf: the journal-formatted pdf. \n- ma4c01012_si_001.pdf: Published supporting information. \n- python: python scripts used to generate the figures in the paper and in the supporting information.\n- data: raw data accessed by the python scripts.\n- figures: figures generated by the python scripts. ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1021/acs.macromol.4c01012", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "glass transition" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Kenneth R. 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Hall and Kenneth R. Shull, Broderick Lewis", + "familyName": "Margaret A. Hall and Kenneth R. Shull", + "givenName": "Broderick Lewis", + "affiliations": [ + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains the raw data and python scripts used to generate the figures in the main body and supporting information of the following paper: \u201cThermomechanical Characterization of High Tg Disulfide-Containing Thermoplastic Polyimides.\u201d M.A. Hall, B. Lewis & K.R. Shull, Macromolecules 58, 2065\u20132072 (2025) (http://dx.doi.org/10.1021/acs.macromol.4c02693)\n- The dataset includes the following files and directories:\n- readme.md: Markdown file of this description. \n- Hall et al. - 2025 - Thermomechanical Characterization of High Tg Disulfide-Containing Thermoplastic Polyimide. pdf. \n- supporting_info.pdf: Published supporting information. \n- python directory: python scripts for generating Figures 2,4 and 5 in the main body of the paper, and Figures 12-15 in the supporting information.\n- DMA directory: raw dynamic mechanical data accessed by the python scripts\n- QCM directory: raw quartz crystal microblance data accessed by the python scripts.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "QCM" + }, + { + "subject": "Mechanics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Kenneth R. 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Files are in GSD (https://gsd.readthedocs.io) format, and are able to be rendered by OVITO (https://www.ovito.org/).", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.48550/arXiv.2412.04588", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": " Erin G. Teich (et106@wellesley.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/91ax-1e09" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "source_id": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e", + "versioned_source_id": "c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-03-31T03:39:19.643863Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/c3b78c81-f4e4-4bc2-b1cb-d15ecadbdc9e/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "0f2c24a3-71fa-49cc-8339-df4f9d443f95-1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Fe-Cu-Ni liquid ternary calculations with EAM" + } + ], + "creators": [ + { + "creatorName": "Trinkle, Dallas R.", + "familyName": "Trinkle", + "givenName": "Dallas R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "# Data set for Fe-Cu-Ni liquid ternary calculations with LAMMPS\n\nOutput from a series of runs using the `FeCuNi.eam.alloy` EAM potential with LAMMPS to compute Gibbs free energies. The runs are from 1200K to 3000K in 300K increments, over a variety of concentrations. Chemical potential differences are extracted and analyzed to produce a ternary phase diagram. Nonequilibrium Hamiltonian integration is used to compute absolute Gibbs free energies for the pure liquids.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "EAM" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Dallas R. Trinkle\" (dtrinkle@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/p0gx-tt30" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "organization": "MDF Open", + "source_id": "0f2c24a3-71fa-49cc-8339-df4f9d443f95", + "versioned_source_id": "0f2c24a3-71fa-49cc-8339-df4f9d443f95-1.2", + "version": "1.2", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-03-21T19:19:14.334322Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/0f2c24a3-71fa-49cc-8339-df4f9d443f95/1.2/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/0f2c24a3-71fa-49cc-8339-df4f9d443f95/1.2/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "8550cbeb-e710-439f-96c3-2aaa5ded6a30-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Block copolymer self-assembly directed mesoporous gyroidal strontium titanate with room-temperature ferromagnetism" + } + ], + "creators": [ + { + "creatorName": "Moore, William", + "familyName": "Moore", + "givenName": "William", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Yang, Ningning", + "familyName": "Yang", + "givenName": "Ningning", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Nason, Abigail K.", + "familyName": "Nason", + "givenName": "Abigail K.", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Freychet, Guillaume", + "familyName": "Freychet", + "givenName": "Guillaume", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Beaucage, Peter A.", + "familyName": "Beaucage", + "givenName": "Peter A.", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Thom-Levy, Julia", + "familyName": "Thom-Levy", + "givenName": "Julia", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Gruner, Sol M.", + "familyName": "Gruner", + "givenName": "Sol M.", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + }, + { + "creatorName": "Wiesner, Ulrich", + "familyName": "Wiesner", + "givenName": "Ulrich", + "affiliations": [ + "Cornell University", + "Universite Grenoble Alpes", + "Kavli Institute at Cornell for Nanoscale Science" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data supporting Block copolymer self-assembly directed mesoporous gyroidal strontium titanate with room-temperature ferromagnetism", + "descriptionType": "Other" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"William Moore\" (wlm68@cornell.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Ulrich Wiesner (ubw1@cornell.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/hpbm-f471" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "source_id": "8550cbeb-e710-439f-96c3-2aaa5ded6a30", + "versioned_source_id": "8550cbeb-e710-439f-96c3-2aaa5ded6a30-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-04-02T18:23:30.570894Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8550cbeb-e710-439f-96c3-2aaa5ded6a30/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/8550cbeb-e710-439f-96c3-2aaa5ded6a30/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "jolley_correlative_v1.2", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/jolley_correlative_v1.2" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/jolley_correlative_v1.2/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/jolley_correlative_v1.2/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2024-01-26T15:03:14.322348Z", + "source_id": "jolley_correlative_v1.2", + "scroll_id": 0, + "version": 1, + "source_name": "jolley_correlative" + }, + "dc": { + "identifier": { + "identifier": "10.18126/t27t-9iao", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Jolley, Bryce R.", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ], + "familyName": "Jolley", + "givenName": "Bryce R." + }, + { + "creatorName": "Uchic, Michael D.", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ], + "familyName": "Uchic", + "givenName": "Michael D." + }, + { + "creatorName": "Sparkman, Daniel", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ], + "familyName": "Sparkman", + "givenName": "Daniel" + }, + { + "creatorName": "Chapman, Michael", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ], + "familyName": "Chapman", + "givenName": "Michael" + }, + { + "creatorName": "Schwalbach, Edwin", + "affiliations": [ + "UES Inc.", + "Air Force Research Laboratory" + ], + "familyName": "Schwalbach", + "givenName": "Edwin" + } + ], + "subjects": [ + { + "subject": "Serial Sectioning Data" + }, + { + "subject": "Additive Manufacturing" + }, + { + "subject": "Computed Tomography Data" + }, + { + "subject": " Data Registration" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2024, + "dates": [ + { + "date": "2024-01-26", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Correlative X-ray Computed Tomography and Optical Microscopy Serial Sectioning Data of Additive Manufactured Ti-6Al-4V using External Fiducial Markers" + } + ], + "contributors": [ + { + "contributorName": "Bryce R. Jolley", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Daniel Sparkman", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The x-ray computed tomography (XCT) data from five different scans are available for a volume of interest (VOI). Mechanical polish serial sectioning optical microscopy data (SS) for that same VOI is also provided. Each computed tomography system collected data with different processing parameters and acquired different voxel sizes for the sample. The reconstruction from each XCT scan is presented as a stack of TIFs registered with every other data set. These registered TIF stacks only represent the VOI that will correlate with the provided SS data. \nThe projections for each individual scan are provided in the format they were exported as. Scans A, B, and C contain projection images in 32-bit float format and are provided with corresponding reference and dark current images. Scan C projections are rotated by 90 degrees. Scan D consists of transmission radiographs scaled by 100 in 16-bit integer format with no reference image or dark current image. Scan E consists of transmission radiographs in 16-bit integer format scaled by 65535. Scans A and B include a \u2018_shifts.txt\u2019 text file containing the x and y shift of the detector for each projection. Scan A also consisted of an image taken at four different detector positions for each projection angle, and the detector position is included for each of these. The projections are over views from 0 to 360 degrees in a nominally circular trajectory. The corresponding view angle for each projection is provided in the \u2018_angles.txt\u2019 file for each scan. If reconstruction algorithms are to be used on these projections, registration of a generated reconstruction to the SS data will need to be accomplished independently. A portion of the projections (1812) for data set E was lost due to a data transfer error. The remaining 1329 projections are provided. \nThe SS data is presented as a stack of TIFs for the VOI that correlates with the multiple XCT scans. The in-plane resolution of the SS data is 2.8 \u03bcm. The average slice thickness for this stack of TIFs is 2.01 \u03bcm. The z-height map data is provided as a text file for the actual thicknesses. Accordingly, to effectively transfer the stack of SS images into a 3D data volume, the slices of the stack should be spaced vertically with the average of 2.01 \u03bcm between images or according to the actual thicknesses as given from the z-height map. \n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "4dclustering_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/4dclustering_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/4dclustering_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/4dclustering_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2024-01-22T01:14:33.920211Z", + "source_id": "4dclustering_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "4dclustering" + }, + "dc": { + "identifier": { + "identifier": "10.18126/z6m9-o3hv", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Carter Francis, Paul M. Voyles", + "affiliations": [ + "University of Wisconsin\u2013Madison" + ], + "familyName": "Carter Francis", + "givenName": "Paul M. Voyles" + } + ], + "subjects": [ + { + "subject": "microscopy" + }, + { + "subject": "metallic glasses" + }, + { + "subject": "4DSTEM" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2024, + "dates": [ + { + "date": "2024-01-22", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Clustering Diffraction Vectors from Overlapping Structures in 4D STEM Data Sets" + } + ], + "contributors": [ + { + "contributorName": "Paul Voyles (paul.voyles@wisc.edu)", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "The following contains two folders containing the raw and processed data for the Paper ``Clustering Diffraction Vectors from Overlapping Structures in 4-D STEM Data Sets`` There are 4 datasets included.\nThe first three datasets are from a PdCuSi thin film sputtered metallic glasses:\n1. 4nmSample2pt5mradCompressed.zspy\n2. 9nmSample2pt5mradCompressed.zspy\n3. 13nmSample2pt5mradCompressed.zspy\nThe last dataset is an Al nano crystal standard sample.\nAll of the datasets are saved in the ``.zspy`` file format (defined in the ``rosettasciio`` package) using the ``Zarr.ZipStore`` formalism.\nIf you want to load the data first install hyperspy\n```\n$pip install hyperspy\n# or\n$conda install hyperspy\n```\nThen you can load the data using:\n```python\nimport hyperspy.api as hs\nimport zarr\nstore = zarr.ZipStore(\"file_you_want_to_read.zspy\")\ns = hs.load(store, lazy=True) # lazy so you don't load the entire data into memory" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "sotck_nov17_mummy_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/sotck_nov17_mummy_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/sotck_nov17_mummy_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/sotck_nov17_mummy_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2024-01-31T03:21:43.899548Z", + "source_id": "sotck_nov17_mummy_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "sotck_nov17_mummy" + }, + "dc": { + "identifier": { + "identifier": "10.18126/o00b-7reh", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Stock, S. R.", + "affiliations": [ + "Metropolitan State Univ. of Denver" + ], + "familyName": "Stock", + "givenName": "S. R." + }, + { + "creatorName": "Stock, M. K.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Stock", + "givenName": "M. K." + }, + { + "creatorName": "Almer, J. D.", + "affiliations": [ + "Argonne National Laboratory" + ], + "familyName": "Almer", + "givenName": "J. D." + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": " diffraction" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2024, + "dates": [ + { + "date": "2024-01-31", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Combined computed tomography and position-resolved X-ray diffraction of an intact Roman-era Egyptian portrait mummy" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "http://dx.doi.org/10.1098/rsif.2020.0686", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "almer@anl.gov", + "contributorType": "ContactPerson" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Diffraction data and associated metadata for \nStock SR, Stock MK, Almer JD. 2020 Combined computed tomography and position-resolved X-ray diffraction of an intact Roman-era Egyptian portrait mummy. J. R. Soc. Interface 17: 20200686. http://dx.doi.org/10.1098/rsif.2020.0686" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "rangnekar_electroluminescence_megasonically_films_v1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "services": { + "mdf_search": "This dataset was ingested to MDF Search.", + "mdf_publish": "https://petreldata.net/mdf/detail/rangnekar_electroluminescence_megasonically_films_v1.1" + }, + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/rangnekar_electroluminescence_megasonically_films_v1.1/", + "total_size": 0, + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/rangnekar_electroluminescence_megasonically_films_v1.1/" + }, + "mdf": { + "organizations": [ + "MDF Open" + ], + "resource_type": "dataset", + "ingest_date": "2024-01-22T14:11:28.058528Z", + "source_id": "rangnekar_electroluminescence_megasonically_films_v1.1", + "scroll_id": 0, + "version": 1, + "source_name": "rangnekar_electroluminescence_megasonically_films" + }, + "dc": { + "identifier": { + "identifier": "10.18126/pviz-km4p", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Rangnekar, Sonal V.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Rangnekar", + "givenName": "Sonal V." + }, + { + "creatorName": "Sangwan, Vinod K.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Sangwan", + "givenName": "Vinod K." + }, + { + "creatorName": "Jin, Mengru", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Jin", + "givenName": "Mengru" + }, + { + "creatorName": "Khalaj, Maryam", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Khalaj", + "givenName": "Maryam" + }, + { + "creatorName": "Szyd\u0142owska, Beata M.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Szyd\u0142owska", + "givenName": "Beata M." + }, + { + "creatorName": "Dasgupta, Anushka", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Dasgupta", + "givenName": "Anushka" + }, + { + "creatorName": "Kuo, Lidia", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Kuo", + "givenName": "Lidia" + }, + { + "creatorName": "Kurtz, Heather E.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Kurtz", + "givenName": "Heather E." + }, + { + "creatorName": "Marks, Tobin J.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Marks", + "givenName": "Tobin J." + }, + { + "creatorName": "Hersam, Mark C.", + "affiliations": [ + "Northwestern University" + ], + "familyName": "Hersam", + "givenName": "Mark C." + } + ], + "publisher": "Materials Data Facility", + "publicationYear": 2024, + "dates": [ + { + "date": "2024-01-22", + "dateType": "Accepted" + } + ], + "titles": [ + { + "title": "Electroluminescence from Megasonically Solution-Processed MoS2 Nanosheet Films" + } + ], + "relatedIdentifiers": [ + { + "relationType": "IsPartOf", + "relatedIdentifier": "10.1021/acsnano.3c06034", + "relatedIdentifierType": "DOI" + } + ], + "contributors": [ + { + "contributorName": "Sonal V. 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A description of this metadata format is provided in the notebook `code/data_format.ipynb`, and a Python schema is provided in `code/metadata.py`.\n\n# Simulated data\n\nIn addition to raw datasets + metadata, simulated datasets contain additional information. This information is summarized in the `code/data_format.ipynb` notebook. The supercell used for simulation is provided in the XCrysDen/XSF file format as well as the Prismatic-flavor XYZ file format.\n\n# License information & acknowledements\n\nThe MATLAB code provided is a modified fork of Yi Jiang's `fold_slice`, which is based on the PtychoShelves package developed at the Paul Scherrer Institut. In addition to the authors at PSI, the code contains work from the following authors:\n\n - Zhen Chen\n - Yi Jiang\n - Colin Gilgenbach\n - Michael Xu\n - Xi Chen\n\nThe code can be found on GitHub at https://github.com/LeBeauGroup/fold_slice, commit 7c9816f0584ff142c90aa836ed744a299397dad6.\n\nThe code is licensed under the same license as the original PtychoShelves code, which is a academic non-commercial license from the Paul Scherr Institut. The complete license agreement can be found in the `LICENSE.md` file in `code/fold_slice`. All reproductions of this code must be distributed under this same license.\n\nThe remainder of the code and data in this repository is licensed under the [Creative Commons Attribution Sharealike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) (CC BY-SA 4.0) license. This license allows for copying, reproduction, and transformation, but requires modified versions to be released under the same license with proper attribution.", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1093/mam/ozae055", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "semiconductors" + }, + { + "subject": "microscopy" + }, + { + "subject": "ptychography" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Colin Gilgenbach\" (hexane@mit.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "\"James M. LeBeau\" (lebeau@mit.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/b2sj-nt57" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "source_id": "6d571f53-a034-47e5-b3e3-d01adf437ae8", + "versioned_source_id": "6d571f53-a034-47e5-b3e3-d01adf437ae8-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-06-07T06:26:51.155530Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6d571f53-a034-47e5-b3e3-d01adf437ae8/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/6d571f53-a034-47e5-b3e3-d01adf437ae8/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "4f8e7a42-09a2-45aa-af07-0994d14d2a54-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Adhesion and Motility Quantification to Assess Cancer Cell Aggression" + } + ], + "creators": [ + { + "creatorName": "Mehanna, Lauren E.", + "familyName": "Mehanna", + "givenName": "Lauren E.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "creatorName": "Boyd, James D.", + "familyName": "Boyd", + "givenName": "James D.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "creatorName": "Walker, Chloe G.", + "familyName": "Walker", + "givenName": "Chloe G.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "creatorName": "Osborne, Adrianna R.", + "familyName": "Osborne", + "givenName": "Adrianna R.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "creatorName": "Grady, Martha E.", + "familyName": "Grady", + "givenName": "Martha E.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + }, + { + "creatorName": "Berron, Brad J.", + "familyName": "Berron", + "givenName": "Brad J.", + "affiliations": [ + "University of Kentucky", + "Clemson University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "During epithelial-to-mesenchymal transition (EMT), cancer cells lose their cell-cell adhesion junctions as they become more metastatic, altering cell motility and focal adhesion disassembly associated with increased detachment from the primary tumor and a migratory response into nearby tissue and vasculature. Current in vitro strategies characterizing a cell\u2019s metastatic potential heavily favor quantifying the presence of cell adhesion biomarkers through biochemical analysis; however, mechanical cues such as adhesion and motility directly relate to cancer metastasis and can be quantified without needing to first identify a cell specific biomarker for a particular type of cancer. This paper presents a comprehensive comparison of two functional metrics of cancer aggression, wound closure migration velocity and cell detachment from a culture surface, for three pairs of epithelial cancer cell lines (breast, endometrium, tongue tissue origins). On average, cell lines with low metastatic potential (MCF-7, Ishikawa, and Cal-27) were more aggressive through wound closure migration compared to loss of cell adhesion. On the other hand, cell lines with high metastatic potential (MDA-MB-231, KLE, and SCC-25) were on average more aggressive through loss of cell adhesion compared to wound closure migration. This trend was true independent of the tissue type where the cells originated, indicating that there is a relationship between metastatic potential and the predominate type of cancer aggression. Our work presents one of the first combined studies observing functional cell migration and adhesion across cancer cell lines from selected tissue origins, without needing to identify tissue-specific biomarkers to achieve success. Using functional metrics to assess cancer cell aggression provides a powerful clinical tool for future label-free bioseparation platforms.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "adhesion" + }, + { + "subject": "shear flow" + }, + { + "subject": "cancer metastasis" + }, + { + "subject": "metastatic potential" + }, + { + "subject": "wound closure" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Lauren E. Mehanna (lauren.mehanna@uky.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/2zmk-n291" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "source_id": "4f8e7a42-09a2-45aa-af07-0994d14d2a54", + "versioned_source_id": "4f8e7a42-09a2-45aa-af07-0994d14d2a54-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-05-28T21:05:56.674298Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/4f8e7a42-09a2-45aa-af07-0994d14d2a54/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/4f8e7a42-09a2-45aa-af07-0994d14d2a54/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e-1.1", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Insights into the Nucleation and Growth of BiOCl Nanoparticles by In Situ X- ray Pair Distribution Function Analysis and In Situ Liquid Cell TEM" + } + ], + "creators": [ + { + "creatorName": "Gordon, Matthew N.", + "familyName": "Gordon", + "givenName": "Matthew N.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Junkers, Laura S.", + "familyName": "Junkers", + "givenName": "Laura S.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Googasian, Jack S.", + "familyName": "Googasian", + "givenName": "Jack S.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Mathiesen, Jette K.", + "familyName": "Mathiesen", + "givenName": "Jette K.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Zhan, Xun", + "familyName": "Zhan", + "givenName": "Xun", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Morgan, David Gene", + "familyName": "Morgan", + "givenName": "David Gene", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Jensen, Kirsten M. \u00d8.", + "familyName": "Jensen", + "givenName": "Kirsten M. \u00d8.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + }, + { + "creatorName": "Skrabalak, Sara E.", + "familyName": "Skrabalak", + "givenName": "Sara E.", + "affiliations": [ + "Indiana University Bloomington", + "University of Copenhagen" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Data Collection associated with the above titled paper, pulished in Nanoscale.\nAbstract: The synthesis of bismuth oxyhalides as defined nanostructures is hindered by their fast nucleation and growth in aqueous solutions. Using our recently developed single-source precursor, the formation of bismuth oxychloride in such solutions can be slowed significantly. As reported herein, this advance enables BiOCl formation to be investigated by in situ X-ray total scattering and in situ liquid cell transmission electron microscopy. In situ pair distribution function analysis of X-ray total scattering data reveals the local order of atomic structures throughout the synthesis, while in situ liquid cell transmission electron microscopy allows for tracking the growth of individual nanoparticles. Through this work, the precursor complex is shown to give rise to BiOCl upon heating in solution without the observation of structurally distinct intermediates. The emerging nanoparticles have a widened interlayer spacing, which moderately decreases as the particles grow. Mechanistic insights into the formation of bismuth oxyhalide nanoparticles, including the absence of distinct intermediates within the available time resolution, will help facilitate future design of controlled BiOX nanostructures. ", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "semiconductors" + }, + { + "subject": "microscopy" + }, + { + "subject": "oxides" + }, + { + "subject": "morphologies" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Sara E. Skrabalak (sskrabal@indiana.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/vzbt-a750" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e", + "organization": "MDF Open", + "source_id": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e", + "versioned_source_id": "393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-06-06T08:35:07.095739Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/393bd8bf-c7bc-400e-8a51-6f01f7a9bb0e/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "f6031a4c-1492-4865-b956-b3fb6fe8ad28-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Ion-Exchange Effects in One-Dimensional Lepidocrocite TiO2: A Cryogenic Scanning Transmission Electron Microscopy and Density Functional Theory Study" + } + ], + "creators": [ + { + "creatorName": "Lagunas, Francisco", + "familyName": "Lagunas", + "givenName": "Francisco", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Bugallo, David", + "familyName": "Bugallo", + "givenName": "David", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Karimi, Fatemeh", + "familyName": "Karimi", + "givenName": "Fatemeh", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Yang, Yingjie", + "familyName": "Yang", + "givenName": "Yingjie", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Badr, Hussein", + "familyName": "Badr", + "givenName": "Hussein", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Cope, Jacob", + "familyName": "Cope", + "givenName": "Jacob", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Ferral, Emilio", + "familyName": "Ferral", + "givenName": "Emilio", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Barsoum, Michel", + "familyName": "Barsoum", + "givenName": "Michel", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Hu, Yong-Jie", + "familyName": "Hu", + "givenName": "Yong-Jie", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + }, + { + "creatorName": "Klie, Robert", + "familyName": "Klie", + "givenName": "Robert", + "affiliations": [ + "University of Illinois at Chicago", + "Drexel University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "STEM data ", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.1021/acs.chemmater.3c02773", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "flagun2@uic.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "rfklie@uic.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/2ad3-8930" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "f6031a4c-1492-4865-b956-b3fb6fe8ad28", + "source_id": "f6031a4c-1492-4865-b956-b3fb6fe8ad28", + "versioned_source_id": "f6031a4c-1492-4865-b956-b3fb6fe8ad28-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2024-06-28T21:59:46.347339Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/f6031a4c-1492-4865-b956-b3fb6fe8ad28/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/f6031a4c-1492-4865-b956-b3fb6fe8ad28/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "827b15e0-4bd2-4914-804b-2cdbc713c437-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Combinatorial CuNiAl As-Sputtered and Annealed XRD Data" + } + ], + "creators": [ + { + "creatorName": "Alwen, Adie", + "familyName": "Alwen", + "givenName": "Adie", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + }, + { + "creatorName": "Ziegner, Mirko", + "familyName": "Ziegner", + "givenName": "Mirko", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + }, + { + "creatorName": "Peter, Nicolas", + "familyName": "Peter", + "givenName": "Nicolas", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + }, + { + "creatorName": "Schwaiger, Ruth", + "familyName": "Schwaiger", + "givenName": "Ruth", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + }, + { + "creatorName": "Hodge, Andrea", + "familyName": "Hodge", + "givenName": "Andrea", + "affiliations": [ + "University of South Carolina", + "Forschungszentrum Julich Institute for Energy and Climate Research Structure and Function of Materials (IEK-2)" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2024", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "XRD dataset for 169 unique CuNiAl compositions analyzed as-sputtered and annealed. The CuNiAl alloys were annealed at 400 C for 3 hours in a vacuum furnace. 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The structures are relaxed in density functional theory (DFT) using lattice Green function-based flexible boundary conditions (LGF FBC). The energetics of the dislocations are calculated using the energy density method (EDM) integrated over Bader volumes and charge-neutral volumes using the weight method ([doi:10.1063/1.3553716](https://doi.org/10.1063/1.3553716)). For comparison, we include the computation of the same dislocations using significantly larger cells with the Mendelev EAM potential and Zhang GAP potential for Fe, which had been earlier shown to perform well at predicting the core structures.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "defects" + }, + { + "subject": "DFT" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Dallas R. 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"University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "creatorName": "Calder, Stuart A.", + "familyName": "Calder", + "givenName": "Stuart A.", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "creatorName": "Aczel, Adam A.", + "familyName": "Aczel", + "givenName": "Adam A.", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "creatorName": "Fabbris, Gilberto", + "familyName": "Fabbris", + "givenName": "Gilberto", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of 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University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "creatorName": "Graf, Michael J.", + "familyName": "Graf", + "givenName": "Michael J.", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + }, + { + "creatorName": "Tafti, Fazel", + "familyName": "Tafti", + "givenName": "Fazel", + "affiliations": [ + "Boston College", + "Argonne National Laboratory", + "University of Illinois at Chicago", + "Brown University", + "Oak Ridge National Laboratory", + "University of Ausburg", + "Paul Scherrer Institute" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], 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Data analysis was performed using in-house MATLAB code as described in the paper and available upon request. The set includes all data used to generate the figures in the paper.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "experiment" + }, + { + "subject": "superparamagnetism" + }, + { + "subject": "magnetoelasticity" + }, + { + "subject": "nanoparticles" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "epesposito@uchicago.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/b5s9-6r94" + } + }, + "mdf": { + "acl": [ + "public" + ], + "source_name": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "organization": "MDF Open", + "source_id": "8ac08d59-a322-4629-a40b-867c55cbd4a1", + "versioned_source_id": "8ac08d59-a322-4629-a40b-867c55cbd4a1-1.1", + "version": "1.1", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-03-09T22:56:09.655117Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8ac08d59-a322-4629-a40b-867c55cbd4a1/1.1/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/8ac08d59-a322-4629-a40b-867c55cbd4a1/1.1/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "8ac08d59-a322-4629-a40b-867c55cbd4a1-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Dataset for Actuating Superparamagnetic Nanoparticle Monolayers" + } + ], + "creators": [ + { + "creatorName": "Esposito, Edward P.", + "familyName": "Esposito", + "givenName": "Edward P.", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "creatorName": "Rios, Hector Manuel Lopez", + "familyName": "Rios", + "givenName": "Hector Manuel Lopez", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "creatorName": "de la Cruz, Monica Olvera", + "familyName": "de la Cruz", + "givenName": "Monica Olvera", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + }, + { + "creatorName": "Jaeger, Heinrich M.", + "familyName": "Jaeger", + "givenName": "Heinrich M.", + "affiliations": [ + "University of Chicago", + "Northwestern University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "The dataset includes experimental and simulated data collected for the paper \"Actuating Superparamagnetic Nanoparticle Monolayers\". 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Using nanoindentation data from the experiments described in \"A Workflow for Accelerating Multimodal Data Collection for Electrodeposited Films,\" this dataset includes 9600 expert-labelled indents: 64 indents per sample over 150 samples. These 150 samples consitute NiFe thin films electrodeposited under a variety of current densities and solutions--creating rich coverage for a variety of anomaly types. The dataset includes all load-displacement curve data, machine-derived summary data for each indent (including computed hardness and reduced modulus), expert annotations, initial anomaly labels, and a second set of anomaly labels for higher fidelity ground truth. The software used to facilitate the consistency checks/reverification process for the second label set is openly available at https://github.com/sandialabs/nanoindentation-consistency-checker.\n\n*Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy\u2019s National Nuclear Security Administration under contract DE-NA0003525.*", + "descriptionType": "Other" + } + ], + "relatedIdentifiers": [ + { + "relatedIdentifier": "https://doi.org/10.1007/s40192-023-00315-5", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + }, + { + "relatedIdentifier": "https://doi.org/10.18126/v84f-we14", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } + ], + "subjects": [ + { + "subject": "nanoindentation" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "anomaly detection" + }, + { + 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Details of the experiment and how the data was generated can be found in our in Advanced Intelligent Systems (https://doi.org/10.1002/aisy.202400293). This dataset contains Herschel-Bulkley rheological data on 15 materials used in Direct-Ink-Write 3D printing. Each material was printed in a test print pattern which was photographed, and has its own table of print data and links to the segmented pattern. The code used to process the data is also included. \n\nDISTRIBUTION STATEMENT A: Approved for public release: distribution unlimited. 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Dataset contents include SBA-15 Characterization through TEM, BET, and TGA; Polymer characterization with HT-SEC; SANS Profiles of SBA-15, Contrast match determination vs temperature, and SANS profiles of dPE in SBA-15.", + "descriptionType": "Other" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": " bdv5051@psu.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/06wx-2589" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "source_id": "b2237392-8c20-40de-9ead-f91c9ee1840e", + "versioned_source_id": "b2237392-8c20-40de-9ead-f91c9ee1840e-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-09-02T15:55:57.011434Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/b2237392-8c20-40de-9ead-f91c9ee1840e/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/b2237392-8c20-40de-9ead-f91c9ee1840e/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/6fad1e75-e902-49c0-88ca-4e72fbb6f7ab/1.0/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/6fad1e75-e902-49c0-88ca-4e72fbb6f7ab/1.0/" + }, + "mdf": { + "organization": "MDF Open", + "resource_type": "dataset", + "domains": [ + "materials" + ], + "ingest_date": "2025-09-08T19:45:06.551064Z", + "acl": [ + "public" + ], + "source_id": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab", + "version": "1.0", + "source_name": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab", + "versioned_source_id": "6fad1e75-e902-49c0-88ca-4e72fbb6f7ab-1.0" + }, + "dc": { + "identifier": { + "identifier": "10.18126/wms3-v894", + "identifierType": "DOI" + }, + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "creators": [ + { + "creatorName": "Kittithat Krongchon Tawfiqur Rakib, Daniel Palmer, Elif Ertekin, and Harley T. 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We hope that this dataset will be useful for future work studying the assembly of colloidal crystals and polyhedral particles.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "colloids" + }, + { + "subject": "Monte Carlo" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Julia Dshemuchadse (jd732@cornell.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/tykw-5q20" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d", + "source_id": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d", + "versioned_source_id": "ebe16bf6-4c29-4f80-bbc8-0404c8fb015d-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-11-03T21:13:38.018561Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ebe16bf6-4c29-4f80-bbc8-0404c8fb015d/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ebe16bf6-4c29-4f80-bbc8-0404c8fb015d/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "ca0ec216-0ee8-4942-b303-fb648740d229-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Data for \"Softening of shear-thickening in suspensions by the addition of large particles\"" + } + ], + "creators": [ + { + "creatorName": "Pleosse, Alice", + "familyName": "Pleosse", + "givenName": "Alice", + "affiliations": [ + "University of Chicago" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": " alice.pelosse@espci.psl.eu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/0kyc-6g69" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "ca0ec216-0ee8-4942-b303-fb648740d229", + "source_id": "ca0ec216-0ee8-4942-b303-fb648740d229", + "versioned_source_id": "ca0ec216-0ee8-4942-b303-fb648740d229-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-11-13T22:12:47.085215Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/ca0ec216-0ee8-4942-b303-fb648740d229/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/ca0ec216-0ee8-4942-b303-fb648740d229/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "f3b5bdb0-ac14-462e-b287-6958d053af10-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Dataset for \"Softening of shear-thickening in suspensions by the addition of large particles\" A. 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Crack propagation data associated with the paper \" Stress-aided thermal activation of crack propagation in multidentate hydrogen bonding adhesives\" currently under review. Briefly, cured DGEBA-Tris polymers adhered to mica are peeled away by the sudden movement of a glass spacer, and the crack between the materials propagates, slowing down until it reaches equillibrium. By knowing the length of the crack and material properties one can calculate the strain energy release rate *G* at all times through the equation *G = (9 \u00b5 d^3 h^2)/(24 l^4)*, where *\u00b5* is the shear modulus of the material (25 GPa), *h* is the height of the spacer used to initate the crack, *d* is the thickness of the mica sheet, and *l* is the crack length. By analyzing the movement of the crack *G* can be correlated with the instantaneous crack velocity. Data was collected using a home-built microscope to record the position of the crack over time. Also included are the presented control curves for DGEBA and mica controls.

\n\n

Each CSV file is named with the temperature at which the measurement was performed and the identifier. It contains a header listing the height of the spacer used and the thickness of the mica sheet for each sample. The first two columns are the time and time stamp of the associated image. Then, the crack length is presented for each image, calculated from the known position of the spacer. The next four columns contain intermediate steps to obtain this crack length: first, the \"Mean Crack Edge\" records the position of the vertical mean of the crack in the image, calculated from the left edge of the frame. Then the \"Crack edge mean\" reports the error assocated with this mean crack edge, which largely comes from slight tilts to the crack front. The column \"Motor Position\" contains the position of the motor that moves the camera, allowing for absolute positioning beyond the frame edge. Larger values inidcate further to the right in the frame. Finally, the \"Absolute crack position\" column contains the sum of the crack edge and the motor moment positioning. The 8th column reports the calculated crack velocity, found as the derivative of crack position vs time after smoothing using a non-uniform savgol filter. The 9th column reports an alternative method of obtaining crack velocity through the differentiation of an interpolated function, which was not used in analysis. Finally, we have the calculated strain energy release rate and the associated error, which mostly comes from errors associated with determining the mica thickness.

", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "fuel cells" + }, + { + "subject": "glasses" + }, + { + "subject": "electrochemical testing" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "high-entropy alloys" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "microscopy" + }, + { + "subject": "simulation" + }, + { + "subject": "carbides" + }, + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "fuel cells" + }, + { + "subject": "glasses" + }, + { + "subject": "electrochemical testing" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "high-entropy alloys" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "microscopy" + }, + { + "subject": "simulation" + }, + { + "subject": "carbides" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/faxs-ga32" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "36a0cbae-899b-4adb-84d9-28d72f786659", + "versioned_source_id": "36a0cbae-899b-4adb-84d9-28d72f786659-1.0", + "source_name": "36a0cbae-899b-4adb-84d9-28d72f786659", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-01-27T17:08:24.250043Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/36a0cbae-899b-4adb-84d9-28d72f786659/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/36a0cbae-899b-4adb-84d9-28d72f786659/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Peeling adhesion of DGEBA-Tris polymer to mica" + } + ], + "creators": [ + { + "creatorName": "Zach Lamberty", + "givenName": "Zach", + "familyName": "Lamberty", + "affiliations": [ + "University of California", + "Berkeley" + ] + }, + { + "creatorName": "Ngon Tran", + "givenName": "Ngon", + "familyName": "Tran", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + }, + { + "creatorName": "Daniel Knorr Jr.", + "givenName": "Daniel", + "familyName": "Knorr Jr.", + "affiliations": [ + "U.S. DEVCOM Army Research Laboratory" + ] + }, + { + "creatorName": "Joelle Frechette", + "givenName": "Joelle", + "familyName": "Frechette", + "affiliations": [ + "University of California", + "Berkeley" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "Crack propagation data associated with the paper \" Stress-aided thermal activation of crack propagation in multidentate hydrogen bonding adhesives\" currently under review. Briefly, cured DGEBA-Tris polymers adhered to mica are peeled away by the sudden movement of a glass spacer, and the crack between the materials propagates, slowing down until it reaches equillibrium. By knowing the length of the crack and material properties one can calculate the strain energy release rate *G* at all times through the equation *G = (9 \u00b5 d^3 h^2)/(24 l^4)*, where *\u00b5* is the shear modulus of the material (25 GPa), *h* is the height of the spacer used to initate the crack, *d* is the thickness of the mica sheet, and *l* is the crack length. By analyzing the movement of the crack *G* can be correlated with the instantaneous crack velocity. Data was collected using a home-built microscope to record the position of the crack over time. Also included are the presented control curves for DGEBA and mica controls.\n\nEach CSV file is named with the temperature at which the measurement was performed and the identifier. It contains a header listing the height of the spacer used and the thickness of the mica sheet for each sample. The first two columns are the time and time stamp of the associated image. Then, the crack length is presented for each image, calculated from the known position of the spacer. The next four columns contain intermediate steps to obtain this crack length: first, the \"Mean Crack Edge\" records the position of the vertical mean of the crack in the image, calculated from the left edge of the frame. Then the \"Crack edge mean\" reports the error assocated with this mean crack edge, which largely comes from slight tilts to the crack front. The column \"Motor Position\" contains the position of the motor that moves the camera, allowing for absolute positioning beyond the frame edge. Larger values inidcate further to the right in the frame. Finally, the \"Absolute crack position\" column contains the sum of the crack edge and the motor moment positioning. The 8th column reports the calculated crack velocity, found as the derivative of crack position vs time after smoothing using a non-uniform savgol filter. The 9th column reports an alternative method of obtaining crack velocity through the differentiation of an interpolated function, which was not used in analysis. Finally, we have the calculated strain energy release rate and the associated error, which mostly comes from errors associated with determining the mica thickness.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "fuel cells" + }, + { + "subject": "glasses" + }, + { + "subject": "electrochemical testing" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "high-entropy alloys" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "microscopy" + }, + { + "subject": "simulation" + }, + { + "subject": "carbides" + }, + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "fuel cells" + }, + { + "subject": "glasses" + }, + { + "subject": "electrochemical testing" + }, + { + "subject": "electron microscopy" + }, + { + "subject": "high-entropy alloys" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "microscopy" + }, + { + "subject": "simulation" + }, + { + "subject": "carbides" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/gm2r-kt69" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "versioned_source_id": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8-1.0", + "source_name": "bc47f085-8265-4e7d-9bfc-fa981bd98cd8", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-01-31T19:30:11.060765Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bc47f085-8265-4e7d-9bfc-fa981bd98cd8/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bc47f085-8265-4e7d-9bfc-fa981bd98cd8/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "c54c9fa0-c601-4258-9314-3b531af104e2-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Real-Time Nanoscale Visualization of Cholesterol Monohydrate Nucleation and Growth" + } + ], + "creators": [ + { + "creatorName": "Lioudmila V. Sorokina", + "givenName": "Lioudmila", + "familyName": "V. Sorokina", + "affiliations": [] + }, + { + "creatorName": "Azadeh Amiri", + "givenName": "Azadeh", + "familyName": "Amiri", + "affiliations": [] + }, + { + "creatorName": "Pavel L. Rehak", + "givenName": "Pavel", + "familyName": "L. Rehak", + "affiliations": [] + }, + { + "creatorName": " Alireza Ghorbani", + "givenName": "", + "familyName": "Alireza Ghorbani", + "affiliations": [] + }, + { + "creatorName": "Abhijit H. Phakatkar", + "givenName": "Abhijit", + "familyName": "H. Phakatkar", + "affiliations": [] + }, + { + "creatorName": "Petr Kr\u00e1l", + "givenName": "Petr", + "familyName": "Kr\u00e1l", + "affiliations": [] + }, + { + "creatorName": "Tolou Shokuhfar", + "givenName": "Tolou", + "familyName": "Shokuhfar", + "affiliations": [] + }, + { + "creatorName": " Reza Shahbazian \u2013 Yassar", + "givenName": "", + "familyName": "Reza Shahbazian \u2013 Yassar", + "affiliations": [] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by Lioudmila V. Sorokina, Azadeh Amiri, Pavel L. Rehak, Alireza Ghorbani, Abhijit H. Phakatkar, Petr Kr\u00e1l, Tolou Shokuhfar, and Reza Shahbazian \u2013 Yassar, entitled \u201cReal-Time Nanoscale Visualization of Cholesterol Monohydrate Nucleation and Growth\u201d and published in Journal of Crystal Growth (2025).\n\nThis study utilized in-situ graphene liquid cell transmission electron microscopy to capture real-time nanoscale events of cholesterol monohydrate (ChM) nucleation and growth. The results revealed ChM triclinic forms through a combination of non-classical and classical modes, specifically, a modified Stranski \u2013 Krastanov mechanism. ChM triclinic nucleates from an amorphous precursor, which grows on triclinic surfaces as an epilayer. These epilayers coalesce into 2D layers formed along a preferred lattice plane, enabling 3D growth. Molecular dynamics simulations revealed that the amorphous to crystalline transition occurs via the self-assembly of small clusters, interconnected by filaments, which regrow into bilayers with exposed polar groups. These superstructures adsorb on the surfaces of crystalline cholesterol, form islands, which spread and form nuclei of a new bilayer. This study underscores the significance of homoepitaxy in ChM growth and may provide additional insights into biologically relevant processes, such as ChM nucleation on lipid droplets. Overall, this study lays the foundation for investigating the mechanisms of ChM growth from solution in real time and on the nanoscale.\n\nA README.txt file is included to assist with analyzing the data. This material is based upon work partially supported by National Science Foundation DMR-1710049 and DMR-2212123. \n\n", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "TEM" + }, + { + "subject": "liquid cell TEM" + }, + { + "subject": "graphene liquid cell" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "cholesterol" + }, + { + "subject": "nucleation" + }, + { + "subject": "stranski-krastanov" + }, + { + "subject": "nanoscale" + }, + { + "subject": "crystallization" + }, + { + "subject": "cholesterol monohydrate" + }, + { + "subject": "triclinic" + }, + { + "subject": "TEM" + }, + { + "subject": "liquid cell TEM" + }, + { + "subject": "graphene liquid cell" + }, + { + "subject": "molecular dynamics" + }, + { + "subject": "cholesterol" + }, + { + "subject": "nucleation" + }, + { + "subject": "stranski-krastanov" + }, + { + "subject": "nanoscale" + }, + { + "subject": "crystallization" + }, + { + "subject": "cholesterol monohydrate" + }, + { + "subject": "triclinic" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/2hem-2r63" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "c54c9fa0-c601-4258-9314-3b531af104e2", + "versioned_source_id": "c54c9fa0-c601-4258-9314-3b531af104e2-1.0", + "source_name": "c54c9fa0-c601-4258-9314-3b531af104e2", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-01-06T01:51:44.136819Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/c54c9fa0-c601-4258-9314-3b531af104e2/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/c54c9fa0-c601-4258-9314-3b531af104e2/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "8d34c45e-0b99-4f48-9d04-2890576e09c6-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Nickel as a Modifier of Calcium Oxalate: An In-Situ Liquid Cell TEM Investigation of Nucleation and Growth" + } + ], + "creators": [ + { + "creatorName": " Lioudmila V. Sorokina", + "givenName": "", + "familyName": "Lioudmila V. Sorokina", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "creatorName": "Abhijit H. Phakatkar", + "givenName": "Abhijit", + "familyName": "H. Phakatkar", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "creatorName": "Pavel Rehak", + "givenName": "Pavel", + "familyName": "Rehak", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "creatorName": "Petr Kr\u00e1l", + "givenName": "Petr", + "familyName": "Kr\u00e1l", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "creatorName": "Tolou Shokuhfar", + "givenName": "Tolou", + "familyName": "Shokuhfar", + "affiliations": [ + "University of Illinois Chicago" + ] + }, + { + "creatorName": " Reza Shahbazian \u2013 Yassar", + "givenName": "", + "familyName": "Reza Shahbazian \u2013 Yassar", + "affiliations": [ + "University of Illinois Chicago" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by Lioudmila V. Sorokina, Abhijit H. Phakatkar, Pavel Rehak, Petr Kr\u00e1l, Tolou Shokuhfar, and Reza Shahbazian \u2013 Yassar, entitled \u201cNickel as a Modifier of Calcium Oxalate: An In-Situ Liquid Cell TEM Investigation of Nucleation and Growth\u201d and published in Nanoscale (2024). \n\nIn this work, we leveraged in-situ graphene liquid cell transmission electron microscopy (GLC TEM) to observe real-time, nanoscale transformations of calcium oxalate crystals in the presence of nickel ions (Ni2+). The results showed that Ni2+ form Ni-water complexes, acting as a shape-directing species, generating a unique morphology and altering growth kinetics. Transient adsorption of Ni-water complexes resulted in a metastable phase formation of calcium oxalate trihydrate. Atomistic molecular dynamics simulations confirmed that Ni2+ acts as a weak inhibitor which slows down the CaOx crystallization, elucidating that Ni2+ impacts small-sized CaOx clusters by bringing more water into the clusters. This work highlighted the intricacies behind the effect of Ni2+ on CaOx biomineralization that were made possible to discern using in-situ GLC TEM.\n\nA README.txt file is included to assist with analyzing the data. This material is based upon work partially supported by a grant from the National Science Foundation (NSF-DMR Award No. 1710049). Additional support from NSF-DMR Award No. 1809439 for the microscopy efforts is acknowledged.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "TEM" + }, + { + "subject": "in situ TEM" + }, + { + "subject": "liquid cell" + }, + { + "subject": "graphene liquid cell" + }, + { + "subject": "EDS" + }, + { + "subject": "HAADF" + }, + { + "subject": "calcium oxalate" + }, + { + "subject": "biomineralization" + }, + { + "subject": "nickel" + }, + { + "subject": "TEM" + }, + { + "subject": "in situ TEM" + }, + { + "subject": "liquid cell" + }, + { + "subject": "graphene liquid cell" + }, + { + "subject": "EDS" + }, + { + "subject": "HAADF" + }, + { + "subject": "calcium oxalate" + }, + { + "subject": "biomineralization" + }, + { + "subject": "nickel" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/fy22-ry45" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "versioned_source_id": "8d34c45e-0b99-4f48-9d04-2890576e09c6-1.0", + "source_name": "8d34c45e-0b99-4f48-9d04-2890576e09c6", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-31T23:04:17.279446Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/8d34c45e-0b99-4f48-9d04-2890576e09c6/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/8d34c45e-0b99-4f48-9d04-2890576e09c6/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "79bf6888-a761-4366-987d-05cb836c2239-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Videos and front speeds of frontal ring-opening metathesis polymerization (FROMP) of DCPD/ENB with norbornene-functionalized PDMS comonomers" + } + ], + "creators": [ + { + "creatorName": "Clarke, Brandon R.", + "familyName": "Clarke", + "givenName": "Brandon R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "creatorName": "Kim, Yun Seong", + "familyName": "Kim", + "givenName": "Yun Seong", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "creatorName": "Galewsky, Benjamin", + "familyName": "Galewsky", + "givenName": "Benjamin", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "creatorName": "Lewis, Jennifer A.", + "familyName": "Lewis", + "givenName": "Jennifer A.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + }, + { + "creatorName": "Tawfick, Sameh H.", + "familyName": "Tawfick", + "givenName": "Sameh H.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign", + "Harvard University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset contains videos and front speed measurements for 16 frontal ring-opening metastasis polymerization experiments of dicyclopentadiene (DCPD)/5-ethylidene-2-norbornene (ENB) resins and norbornene-functionalized polydimethylsiloxane (nor-PDMS) comonomers. Each run was carried out in a 10 mm diameter glass test tube and recorded to quantify front propagation behavior. Reported front speeds were extracted by video tracking and reported maximum front temperatures were measured with a thermocouple.\n", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "experiment" + }, + { + "subject": "polymers" + }, + { + "subject": "frontal ring-opening metastasis polymerization" + }, + { + "subject": "DCPD" + }, + { + "subject": "PDMS" + }, + { + "subject": "ENB" + }, + { + "subject": "chemistry" + }, + { + "subject": "front speeds" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "\"Clarke, Brandon R. (bclarke@seas.harvard.edu)\"", + "contributorType": "ContactPerson" + }, + { + "contributorName": " \"Arretche, Ignacio (ia6@illinois.edu\"", + "contributorType": "ContactPerson" + }, + { + "contributorName": " \"Lewis, Jennifer A. (jalewis@seas.harvard.edu)\"", + "contributorType": "ContactPerson" + }, + { + "contributorName": " \"Tawfick, Sameh H. (tawfick@illinois.edu)\"", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/46bb-sz19" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "79bf6888-a761-4366-987d-05cb836c2239", + "source_id": "79bf6888-a761-4366-987d-05cb836c2239", + "versioned_source_id": "79bf6888-a761-4366-987d-05cb836c2239-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-09T23:14:39.730126Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/79bf6888-a761-4366-987d-05cb836c2239/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/79bf6888-a761-4366-987d-05cb836c2239/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "a48853a2-2ba0-433b-8eda-6c020240fd84-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Understanding the Structural Origin of Chirality in Magic-Size Semiconductor Nanoclusters through Self-Assembly Simulations" + } + ], + "creators": [ + { + "creatorName": "Hongjin Du", + "givenName": "Hongjin", + "familyName": "Du", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Ellery J. Hendrix", + "givenName": "Ellery", + "familyName": "J. Hendrix", + "affiliations": [ + "University of Michigan" + ] + }, + { + "creatorName": "Richard D. Robinson", + "givenName": "Richard", + "familyName": "D. Robinson", + "affiliations": [ + "Cornell University" + ] + }, + { + "creatorName": "Julia Dshemuchadse", + "givenName": "Julia", + "familyName": "Dshemuchadse", + "affiliations": [ + "Cornell University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset accompanies the manuscript by H. Du, E. J. Hendrix, R. D. Robinson, and J. Dshemuchadse, \"Understanding the Structural Origin of Chirality in Magic-Size Semiconductor Nanoclusters through Self-Assembly Simulations'', Nano Letters, in publication (2025). In this work, we investigate the formation of magic-size clusters (MSCs) in zincblende-forming semiconductor systems using molecular dynamics simulations with a minimal, chemically agnostic pair potential. We simulate the self-assembly of binary clusters across a range of sizes and stoichiometries, reproducing key structural features observed in the experimentally synthesized MSCs. In particular, we examine the emergence of chirality in distorted icosahedral motifs. This dataset includes the final frames of simulation trajectories for systems with total particle numbers ranging from *N* = 4 to 110, provided in .xyz format. A README.txt file is included to assist with parsing the data. This material is based upon work supported by the National Science Foundation under Grant No. CHE-2003586.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "magic-size clusters" + }, + { + "subject": "chirality" + }, + { + "subject": "molecular dynamics simulations" + }, + { + "subject": "self-assembly" + }, + { + "subject": "magic-size clusters" + }, + { + "subject": "chirality" + }, + { + "subject": "molecular dynamics simulations" + }, + { + "subject": "self-assembly" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/wcfw-q906" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "versioned_source_id": "a48853a2-2ba0-433b-8eda-6c020240fd84-1.0", + "source_name": "a48853a2-2ba0-433b-8eda-6c020240fd84", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-11T16:42:15.791582Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a48853a2-2ba0-433b-8eda-6c020240fd84/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/a48853a2-2ba0-433b-8eda-6c020240fd84/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M204/Tributyl Phosphite Resins" + } + ], + "creators": [ + { + "creatorName": "Cope, Reid", + "familyName": "Cope", + "givenName": "Reid", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Curran, Ethan M.", + "familyName": "Curran", + "givenName": "Ethan M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Cramblitt, Anna C.", + "familyName": "Cramblitt", + "givenName": "Anna C.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Galewsky, Ben", + "familyName": "Galewsky", + "givenName": "Ben", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Sottos, Nancy R.", + "familyName": "Sottos", + "givenName": "Nancy R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Moore, Jeffrey S.", + "familyName": "Moore", + "givenName": "Jeffrey S.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Tawfick, Sameh", + "familyName": "Tawfick", + "givenName": "Sameh", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst 2nd Generation (M204), and Tributyl Phosphite in varying ratios.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "FROMP" + }, + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "kinetics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "annacc2@illinois.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "n-sottos@illinois.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "jsmoore@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/tg8t-mq17" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "source_id": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2", + "versioned_source_id": "a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-02-02T23:13:37.160497Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/a7e6c5f0-dbb7-4dd9-b090-2a3507b45ef2/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "70d169fd-7f83-43e7-b584-e13444d7fc9c-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M207/Tributyl Phosphite Resins" + } + ], + "creators": [ + { + "creatorName": "Curran, Ethan M.", + "familyName": "Curran", + "givenName": "Ethan M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Cramblitt, Anna C.", + "familyName": "Cramblitt", + "givenName": "Anna C.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Galewsky, Ben", + "familyName": "Galewsky", + "givenName": "Ben", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Sottos, Nancy R.", + "familyName": "Sottos", + "givenName": "Nancy R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst M207, and Tributyl Phosphite in varying ratios.", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "FROMP" + }, + { + "subject": "kinetics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "Cramblitt Anna C (annacc2@illinois.edu)", + "contributorType": "ContactPerson" + }, + { + "contributorName": "Sottos Nancy R (n-sottos@illinois.edu)", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/28dg-wn78" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "source_id": "70d169fd-7f83-43e7-b584-e13444d7fc9c", + "versioned_source_id": "70d169fd-7f83-43e7-b584-e13444d7fc9c-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-05T19:16:50.579936Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/70d169fd-7f83-43e7-b584-e13444d7fc9c/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/70d169fd-7f83-43e7-b584-e13444d7fc9c/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "91e964ab-89ea-4995-9717-64d34053162f-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Front Speed Measurements of the Frontal Polymerization of Cyclooctadiene/Grubbs Catalyst M202/Tributyl Phosphite Resins" + } + ], + "creators": [ + { + "creatorName": "Curran, Ethan M.", + "familyName": "Curran", + "givenName": "Ethan M.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Cramblitt, Anna C.", + "familyName": "Cramblitt", + "givenName": "Anna C.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Arretche, Ignacio", + "familyName": "Arretche", + "givenName": "Ignacio", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Galewsky, Ben", + "familyName": "Galewsky", + "givenName": "Ben", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + }, + { + "creatorName": "Sottos, Nancy R.", + "familyName": "Sottos", + "givenName": "Nancy R.", + "affiliations": [ + "University of Illinois at Urbana\u2013Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": "Dataset" + }, + "descriptions": [ + { + "description": "Included in this data set are videos and front speed measurements for cyclooctadiene (COD) polymers synthesized using frontal ring opening metathesis polymerization (FROMP). The resins are composed of Cyclooctadiene, Grubbs Catalyst M202, and Tributyl Phosphite in varying ratios.\n\n", + "descriptionType": "Other" + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "FROMP" + }, + { + "subject": "kinetics" + } + ], + "rightsList": [ + { + "rights": "Creative Commons Attribution 4.0" + } + ], + "contributors": [ + { + "contributorName": "annacc2@illinois.edu", + "contributorType": "ContactPerson" + }, + { + "contributorName": "n-sottos@illinois.edu", + "contributorType": "ContactPerson" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/x0xf-9q27" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_name": "91e964ab-89ea-4995-9717-64d34053162f", + "source_id": "91e964ab-89ea-4995-9717-64d34053162f", + "versioned_source_id": "91e964ab-89ea-4995-9717-64d34053162f-1.0", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-12T20:29:57.794376Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/91e964ab-89ea-4995-9717-64d34053162f/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/91e964ab-89ea-4995-9717-64d34053162f/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "524a881b-f627-4c21-91cc-790399cef860-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Accurate atomic correlation and total energies for correlation consistent effective core potentials II: Rb-Xe elements" + } + ], + "creators": [ + { + "creatorName": "Aqsa Shaikh", + "givenName": "Aqsa", + "familyName": "Shaikh", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "creatorName": "Omar Madany", + "givenName": "Omar", + "familyName": "Madany", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "creatorName": "Benjamin Kincaid", + "givenName": "Benjamin", + "familyName": "Kincaid", + "affiliations": [ + "North Carolina State University" + ] + }, + { + "creatorName": "Lubos Mitas", + "givenName": "Lubos", + "familyName": "Mitas", + "affiliations": [ + "North Carolina State University" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2025", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "A complete dataset of inputs and outputs for paper \"Accurate atomic correlation and total energies for correlation consistent effective core potentials II: Rb\u2212Xe elements\" by Aqsa Shaikh et al.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "QMC" + }, + { + "subject": "DMC" + }, + { + "subject": "accurate energies" + }, + { + "subject": "correlation" + }, + { + "subject": "quantum chemistry" + }, + { + "subject": "simulation" + }, + { + "subject": "QMC" + }, + { + "subject": "DMC" + }, + { + "subject": "accurate energies" + }, + { + "subject": "correlation" + }, + { + "subject": "quantum chemistry" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/gc97-qa02" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "524a881b-f627-4c21-91cc-790399cef860", + "versioned_source_id": "524a881b-f627-4c21-91cc-790399cef860-1.0", + "source_name": "524a881b-f627-4c21-91cc-790399cef860", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2025-12-18T22:55:36.588844Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/524a881b-f627-4c21-91cc-790399cef860/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/524a881b-f627-4c21-91cc-790399cef860/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "data": { + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e/1.0/", + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e/1.0/" + }, + "mdf": { + "organization": "MDF Open", + "resource_type": "dataset", + "domains": [ + "materials" + ], + "ingest_date": "2026-01-08T16:41:22.907238Z", + "acl": [ + "public" + ], + "source_id": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e", + "version": "1.0", + "versioned_source_id": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e-1.0", + "source_name": "1a1b2005-e0dc-4f68-bbcd-4f684c0e3e2e" + }, + "dc": { + "identifier": { + "identifier": "10.18126/t0ca-5645", + "identifierType": "DOI" + }, + "creators": [ + { + "creatorName": "Irene Calvo Almazan", + "affiliations": [ + "University of Zaragoza", + "Spain" + ], + "givenName": "Irene", + "familyName": "Calvo Almazan" + }, + { + "creatorName": "Anusheela Das", + "affiliations": [ + "Argonne National Laboratory" + ], + "givenName": "Anusheela", + "familyName": "Das" + }, + { + "creatorName": "Ana Suzana", + "affiliations": [ + "Argonne National Laboratory" + ], + "givenName": "Ana", + "familyName": "Suzana" + }, + { + "creatorName": "Fernando Bartolome", + "affiliations": [ + "University of Zaragoza", + "Spain" + ], + "givenName": "Fernando", + "familyName": "Bartolome" + }, + { + "creatorName": "Paul Fenter", + "affiliations": [ + "Argonne National Laboratory" + ], + "givenName": "Paul", + "familyName": "Fenter" + } + ], + "subjects": [ + { + "subject": "simulation" + }, + { + "subject": "microscopy" + }, + { + "subject": "theory" + }, + { + "subject": "Coherent X-ray Reflectivity" + }, + { + "subject": "crystal structure" + }, + { + "subject": "X-ray diffraction" + }, + { + "subject": "DFT" + }, + { + "subject": "semiconductors" + }, + { + "subject": "experiment" + }, + { + "subject": "fuel cells" + }, + { + "subject": "simulation" + }, + { + "subject": "microscopy" + }, + { + "subject": "theory" + }, + { + "subject": "Coherent X-ray Reflectivity" + }, + { + "subject": "crystal structure" + }, + { + "subject": "X-ray diffraction" + }, + { + "subject": "DFT" + }, + { + "subject": "semiconductors" + }, + { + "subject": "experiment" + }, + { + "subject": "fuel cells" + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "titles": [ + { + "title": "Original Data from \"Imaging Surface Topography with Coherent X-ray Reflectivity: Theory, Kinematics and Simulations\"" + } + ], + "descriptions": [ + { + "descriptionType": "Other", + "description": "Archived Data are from a publication Physical Review B. See manuscript at [https://doi.org/10.1103/q75z-gk3l](https://doi.org/10.1103/q75z-gk3l)\n\nThe results are simulations of coherent X-ray reflectivity for a one-dimensional surface (along x) with a assumed topography, h(x), where x is in units of Angstroms and h(x), where h(x) clat is the surface height in angstroms (we use a layer spacing of clat = 3 Angstroms).\n\nSimulations assume a beam energy of 10 keV with a Gaussian beam shape having a width of 0.5 micrometers. Simulated scattering intensities are calculated for vertical momentum transfer centered at Q0 = 1.99 Ang^-1 = 0.95 QB, where QB = 2 * pi/clat, is the momentum transfer of the Bragg peak. The complex valued structure factor is calculated as a function of vertical and lateral momentum transfer (Qx, Qz), over a range of momentum transfer with full width of deltaQ = 0.01 QB in both the vertical and lateral directions. The effective density, rho_eff(x,z), is then obtained by inverse fourier transform.\n\nDescription of Archived data: Topography: this file contains the surface topography that is used in all calculations (shown in Figure 1, 5av, 5bv, 6ai, 6bi). This is in the form: x, h(x)\n\nFigure 2a: This includes documents the complex valued intrinsic effective density for an ideally terminated lattice calculated for each layer ,n, as a function of height, z. The figure shows the results for n = 1 to 20, over the range -200 Angstrom < z < 200 Angstrom. This archived data file includes the calculation for n = 1 to 500, and for 4500 Angstrom < z < 4500 Angstrom. This was calculated for the attenuation parameter beta = 0.99. Data is in the form: n, z, abs(rho_eff(n,z)), angle(rho_eff(n,z))\n\nFigure 2bc: This is effectve interfacial density as a function of height, which corresponds to the sum of intrinsic effective densities for all layers. Data is in the form: z, abs(rho_eff(z)), angle(rho_eff(z)).\n\nFigure 4abc_i: This documents the effective densities determined from the complex structure factors, a) using the orthogonal ROI, b) the experimental effective densites obtained from data that simulates the effect of the tilt of the Ewald sphere, and c) the density after unskewing experimental densities. Data are in the form: x, z, abs(rho_eff_orth(x,z)), abs(rho_eff_EXP(x,z)), abs(rho_eff_EXP_unskew(x,z)).\n\nFigure 4abii: This documents the structure factor magnitudes that contain a) the orthogonal ROI, b) the experimentally observed intensities that included the tilting the Ewald sphere. Data are in the form: qx, qz, I_tot(qx,qz), I_tot_exp(qx,qz). Here, qz is the reduced momentum transfer, qz = Qz - QB, and QB is the momentum transfer of the Bragg peak.\n\nFigure 4c_ii: This documents the structure factor magnitudes that contain c) the experimentally observed intensities recovered from the unskewed densities. Data are in the form: qx, qz, I_tot_exp_unskew(qx,qz). Here, qz is the reduced momentum transfer, qz = Qz - QB, and QB is the momentum transfer of the Bragg peak.\n\nFigure 5a_i_ii: This documents the unskewed effective densities determined from the complex structure factors, a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.95 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew(x,z)), abs(rho_eff_exp_unskew(x,z)).\n\nFigure 5b_i_ii: This documents the unskewed effective densities determined from the complex structure factors, a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.55 QB and deltaQ = 0.01 QB. Data are in the form: x,z,abs(rho_eff_exp_unskew(x,z)),abs(rho_eff_exp_unskew(x,z)).\n\nFigure 6a_ii_iii: This documents the unskewed effective densities determined from the complex structure factors after Fourier interpolation along the surface normal direction, including a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.95 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew_interp(x,z)), abs(rho_eff_exp_unskew_interp(x,z)).\n\nFigure 6b_ii_iii: This documents the unskewed effective densities determined from the complex structure factors after Fourier interpolation along the surface normal direction, including a) the magnitude, and ii) phase of the effective density calculated at Q0 = 0.55 QB and deltaQ = 0.01 QB. Data are in the form: x, z, abs(rho_eff_exp_unskew_interp(x,z)), abs(rho_eff_exp_unskew_interp(x,z)).\n\n" + } + ], + "resourceType": { + "resourceTypeGeneral": "Dataset" + } + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "bfecb497-b118-4332-ac7c-99de2430bcdd-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Adhesion Tape Loop Data" + } + ], + "creators": [ + { + "creatorName": "andrew b. croll", + "givenName": "andrew", + "familyName": "b. croll", + "affiliations": [ + "North Dakota State University" + ] + }, + { + "creatorName": "Harmeet Singh", + "givenName": "Harmeet", + "familyName": "Singh", + "affiliations": [ + "Indian Institutes of Technology (IITs) Gandhinagar" + ] + }, + { + "creatorName": "Krishnan Suryanarayanan", + "givenName": "Krishnan", + "familyName": "Suryanarayanan", + "affiliations": [ + "Indian Institutes of Technology (IITs) Gandhinagar" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "Data used to compute state diagram for the Adhesive Tape Loop. Loops are rectangular prisms made of various modulus elastomer which are bent into a loop shape and adhered to itself. Loops may be stable, may open a small amount then reach equilibrium or may open completely. This spreadsheet contains all the necessary information from measurements and the steps in calculation to recreate the state diagram in our paper.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "simulation" + }, + { + "subject": "phase field" + }, + { + "subject": "surface properties" + }, + { + "subject": "polymers" + }, + { + "subject": "adhesion" + }, + { + "subject": "simulation" + }, + { + "subject": "phase field" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/7hxx-t317" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "versioned_source_id": "bfecb497-b118-4332-ac7c-99de2430bcdd-1.0", + "source_name": "bfecb497-b118-4332-ac7c-99de2430bcdd", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-02-05T21:54:29.703601Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/bfecb497-b118-4332-ac7c-99de2430bcdd/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/bfecb497-b118-4332-ac7c-99de2430bcdd/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "058f62f4-9b99-4410-9211-7ab781326ab3-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Linear viscoelastic measurements of commerical therapy putties for design and intuition" + } + ], + "creators": [ + { + "creatorName": "Maxwell C. Marsh", + "givenName": "Maxwell", + "familyName": "C. Marsh", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "creatorName": "Rebecca E. Corman", + "givenName": "Rebecca", + "familyName": "E. Corman", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "creatorName": "Nabil Ramlawi", + "givenName": "Nabil", + "familyName": "Ramlawi", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "creatorName": "Samya Sen", + "givenName": "Samya", + "familyName": "Sen", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + }, + { + "creatorName": "Randy H. Ewoldt", + "givenName": "Randy", + "familyName": "H. Ewoldt", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset complements the publication Mapping Linear Viscoelasticity for Design and Tactile Intuition (Corman & Ewoldt, 2019). It contains rigorous shear rheometry measurements for six commercial silicone putties, including creep recovery, SAOS, LAOS, and stress relaxation.\n\nAdditional, unpublished data was generated as part of a graduate rheology course taught by Randy Ewoldt at University of Illinois Urbana-Champaign. Together, these datasets provide a comprehensive example of linear viscoelasticity of a commercial material.\n\nThe collection is intended both as a pedagogical resource for developing physical intuition in linear viscoelasticity and as a reference dataset for data interpretation, model fitting, and comparative rheological analysis.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "viscoelasticity" + }, + { + "subject": "polymers" + }, + { + "subject": "experiment" + }, + { + "subject": "viscoelasticity" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/ff0v-7304" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "058f62f4-9b99-4410-9211-7ab781326ab3", + "versioned_source_id": "058f62f4-9b99-4410-9211-7ab781326ab3-1.0", + "source_name": "058f62f4-9b99-4410-9211-7ab781326ab3", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-02-06T23:44:08.381794Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/058f62f4-9b99-4410-9211-7ab781326ab3/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/058f62f4-9b99-4410-9211-7ab781326ab3/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "Data for Thermodynamics and Kinetics of Li at the Ag-Li Battery Interface" + } + ], + "creators": [ + { + "creatorName": "Grace Lu", + "givenName": "Grace", + "familyName": "Lu", + "affiliations": [ + "University of California", + "Irvine" + ] + }, + { + "creatorName": "Dallas R. Trinkle", + "givenName": "Dallas", + "familyName": "R. Trinkle", + "affiliations": [ + "University of Illinois Urbana-Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset complements the publication Thermodynamics and Kinetics of Li at the Ag-Li Battery Interface. In this project, we calculate thermodynamic properties for 6 different interfaces: KS, NW, Bain, Pitsch, FCC (111), and FCC (100). For all six interfaces, this dataset contains the per atom energies and vacancy formation energies for every atom calculated using the MACE-MP-0 pretrained potential. Also included are the MACE-MP-0 surface energies of each Ag and Li slab and MACE-MP-0 interfacial energies. For the two FCC surfaces, this dataset also contains DFT calculations for the surface and interfacial energies. Additionally, for the FCC surfaces, we include calculated migration barriers for vacancies across the Ag-Li interface using MACE-MP-0.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "defects" + }, + { + "subject": "machine learning potential" + }, + { + "subject": "DFT" + }, + { + "subject": "simulation" + }, + { + "subject": "diffusion" + }, + { + "subject": "lithium-silver" + }, + { + "subject": "vacancy" + }, + { + "subject": "defects" + }, + { + "subject": "machine learning potential" + }, + { + "subject": "DFT" + }, + { + "subject": "simulation" + }, + { + "subject": "diffusion" + }, + { + "subject": "lithium-silver" + }, + { + "subject": "vacancy" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/gfpd-mm33" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "versioned_source_id": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc-1.0", + "source_name": "a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-02-11T02:17:59.726353Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/a1b0e8c0-0399-4d29-8c6c-cebff07dd2fc/1.0/" + } + } + } + ] + }, + { + "@datatype": "GMetaResult", + "@version": "2017-09-01", + "subject": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd-1.0", + "entries": [ + { + "entry_id": null, + "matched_principal_sets": [], + "content": { + "dc": { + "titles": [ + { + "title": "A Multi-Alloys and Multi-Modal Dataset for Spatial Intelligence Learning of Microstructure\u2013Plasticity\u2013Mechanical Property Relationships in Metals" + } + ], + "creators": [ + { + "creatorName": "Mathieu Calvat", + "givenName": "Mathieu", + "familyName": "Calvat", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "creatorName": "Gregory Sparks", + "givenName": "Gregory", + "familyName": "Sparks", + "affiliations": [ + "US Air Force Research Laboratory" + ] + }, + { + "creatorName": "Dhruv Anjaria", + "givenName": "Dhruv", + "familyName": "Anjaria", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "creatorName": "Chris Bean", + "givenName": "Chris", + "familyName": "Bean", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + }, + { + "creatorName": "Haoren Wang", + "givenName": "Haoren", + "familyName": "Wang", + "affiliations": [ + "University of California San Diego" + ] + }, + { + "creatorName": "Maik Rajkowski", + "givenName": "Maik", + "familyName": "Rajkowski", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "creatorName": "Aditya Srinivasan Tirunilai", + "givenName": "Aditya", + "familyName": "Srinivasan Tirunilai", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "creatorName": "Guillaume Laplanche", + "givenName": "Guillaume", + "familyName": "Laplanche", + "affiliations": [ + "Ruhr-Universit\u00e4t Bochum" + ] + }, + { + "creatorName": "St\u00e9phane Forsik", + "givenName": "St\u00e9phane", + "familyName": "Forsik", + "affiliations": [ + "Carpenter Technology Corporation" + ] + }, + { + "creatorName": "Kenneth Vecchio", + "givenName": "Kenneth", + "familyName": "Vecchio", + "affiliations": [ + "University of California San Diego" + ] + }, + { + "creatorName": "J.C. Stinville", + "givenName": "J.C.", + "familyName": "Stinville", + "affiliations": [ + "University of Illinois at Urbana-Champaign" + ] + } + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": { + "resourceTypeGeneral": "Dataset" + }, + "descriptions": [ + { + "description": "This dataset provides a large-scale, multi-alloy, multimodal collection of spatially resolved microstructural states, local plastic deformation fields, and corresponding macroscopic mechanical properties for metallic materials. It is designed to enable material spatial intelligence, that is, data-driven learning of relationships between heterogeneous microstructure, plastic deformation resolved down to the nanometer scale over large fields of view, and bulk mechanical response.\nThe dataset combines high-resolution microstructure characterization, such as electron backscatter diffraction (EBSD) measurements, with full-field plasticity measurements obtained using high-resolution digital image correlation. These measurements are acquired over large fields of view and multiple deformation states and are systematically paired with macroscopic mechanical properties such as yield strength, hardening behavior, tensile strength, ductility, fatigue strength, and creep strength.\nMultiple alloys and processing conditions are included, capturing a wide range of chemistry, structure, and microstructural heterogeneity, deformation modes, and property envelopes. The dataset spans alloys with face-centered cubic, body-centered cubic, and hexagonal close-packed crystal structures, and includes both wrought and additively manufactured processing routes. Mechanical properties and deformation fields are captured at both room temperature and elevated temperature.\nBy explicitly linking local microstructural features, localized plastic deformation, and macroscopic mechanical behavior within a unified spatial framework, this dataset provides a foundational resource for developing, benchmarking, and validating machine-learning and physics-informed models aimed at microstructure-aware property prediction, mechanism discovery, and data-driven alloy design.", + "descriptionType": "Abstract" + } + ], + "subjects": [ + { + "subject": "metals" + }, + { + "subject": "mechanical properties" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "experiment" + }, + { + "subject": "defects" + }, + { + "subject": "microscopy" + }, + { + "subject": "spectroscopy" + }, + { + "subject": "Multi-Alloys" + }, + { + "subject": "Multi-Modal" + }, + { + "subject": "Spatial Intelligence" + }, + { + "subject": "Microstructure" + }, + { + "subject": "EBSD" + }, + { + "subject": "High-resolution digital image correlation" + }, + { + "subject": "Plasticity localization" + }, + { + "subject": "Fatigue" + }, + { + "subject": "Creep" + }, + { + "subject": "Invar" + }, + { + "subject": "room temperature" + }, + { + "subject": "microstructure" + }, + { + "subject": "polymers" + }, + { + "subject": "benchmark" + }, + { + "subject": "crystal structure" + }, + { + "subject": "software" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "continuum" + }, + { + "subject": "metals" + }, + { + "subject": "mechanical properties" + }, + { + "subject": "metals and alloys" + }, + { + "subject": "experiment" + }, + { + "subject": "defects" + }, + { + "subject": "microscopy" + }, + { + "subject": "spectroscopy" + }, + { + "subject": "Multi-Alloys" + }, + { + "subject": "Multi-Modal" + }, + { + "subject": "Spatial Intelligence" + }, + { + "subject": "Microstructure" + }, + { + "subject": "EBSD" + }, + { + "subject": "High-resolution digital image correlation" + }, + { + "subject": "Plasticity localization" + }, + { + "subject": "Fatigue" + }, + { + "subject": "Creep" + }, + { + "subject": "Invar" + }, + { + "subject": "room temperature" + }, + { + "subject": "microstructure" + }, + { + "subject": "polymers" + }, + { + "subject": "benchmark" + }, + { + "subject": "crystal structure" + }, + { + "subject": "software" + }, + { + "subject": "containerization" + }, + { + "subject": "images" + }, + { + "subject": "continuum" + } + ], + "identifier": { + "identifierType": "DOI", + "identifier": "10.18126/xhm6-sx12" + } + }, + "mdf": { + "acl": [ + "public" + ], + "organization": "MDF Open", + "source_id": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "versioned_source_id": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd-1.0", + "source_name": "f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd", + "version": "1.0", + "domains": [ + "materials" + ], + "resource_type": "dataset", + "ingest_date": "2026-02-16T20:17:49.163069Z" + }, + "data": { + "endpoint_path": "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec//mdf_open/f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd/1.0/", + "link": "https://app.globus.org/file-manager?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec&origin_path=/mdf_open/f1a1d102-16b7-4f53-b9e6-5e1ad57adbcd/1.0/" + } + } + } + ] + } + ] +} \ No newline at end of file diff --git a/examples/WALKTHROUGH.md b/examples/WALKTHROUGH.md new file mode 100644 index 0000000..d2cb3b6 --- /dev/null +++ b/examples/WALKTHROUGH.md @@ -0,0 +1,380 @@ +# MDF Agent Walkthrough + +Two ways to publish datasets to the Materials Data Facility: **human-driven CLI** or **AI-assisted zero-touch publishing**. + +--- + +## Example 1: Human Workflow (CLI) + +You're a materials scientist with DFT calculation results. Here's how to publish them. + +### Step 1: Initialize your dataset + +```bash +cd examples/dft-alloys + +mdf init . \ + --title "High-Throughput DFT Study of Binary Intermetallic Alloys" \ + --author "Jane Doe" \ + --author "John Smith" \ + --author "Alice Chen" \ + --description "Formation energies and electronic properties for 15 Al-X intermetallics" +``` + +**Output:** +``` +Initialized MDF repository at . + Title: High-Throughput DFT Study of Binary Intermetallic Alloys + Authors: Jane Doe, John Smith, Alice Chen +``` + +### Step 2: Stage your data files + +```bash +mdf add calculations.csv parameters.json --discover +``` + +**Output:** +``` +Staged: + + calculations.csv + + parameters.json +``` + +The `--discover` flag automatically extracts metadata: +- CSV: 6 columns, 15 rows (composition, formation_energy, volume, etc.) +- JSON: VASP 6.3.2, PBE functional, PAW pseudopotentials + +### Step 3: Commit your changes + +```bash +mdf commit -m "Initial dataset with 15 Al-X intermetallic calculations" +``` + +**Output:** +``` +Committed: Initial dataset with 15 Al-X intermetallic calculations + 2 files recorded +``` + +### Step 4: Check repository status + +```bash +mdf status +``` + +**Output:** +``` +No files staged + +Commits (1): +┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ # ┃ Message ┃ Files ┃ Time ┃ +┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ 1 │ Initial dataset with 15 Al-X intermetallic calc… │ 2 │ 2026-01-31T06:15:30 │ +└────┴───────────────────────────────────────────────────┴───────┴─────────────────────┘ +``` + +### Step 5: Add data sources to manifest + +Edit `mdf.yaml` to specify where data lives: + +```yaml +title: High-Throughput DFT Study of Binary Intermetallic Alloys +authors: + - Jane Doe + - John Smith + - Alice Chen +description: Formation energies and electronic properties for 15 Al-X intermetallics + +data_sources: + - "./calculations.csv" + - "./parameters.json" + +tags: + - DFT + - intermetallics + - VASP + - formation-energy + +acl: + - public +``` + +### Step 6: Validate before publishing + +```bash +mdf validate +``` + +**Output:** +``` +Validation passed +``` + +### Step 7: Preview the submission + +```bash +mdf publish --dry-run +``` + +**Output:** (syntax-highlighted JSON) +```json +{ + "dc": { + "titles": [{"title": "High-Throughput DFT Study of Binary Intermetallic Alloys"}], + "creators": [ + {"creatorName": "Doe, Jane", "familyName": "Doe", "givenName": "Jane"}, + {"creatorName": "Smith, John", "familyName": "Smith", "givenName": "John"}, + {"creatorName": "Chen, Alice", "familyName": "Chen", "givenName": "Alice"} + ], + "publisher": "Materials Data Facility", + "publicationYear": "2026", + "resourceType": {"resourceTypeGeneral": "Dataset", "resourceType": "Dataset"}, + "descriptions": [{"description": "Formation energies...", "descriptionType": "Other"}] + }, + "data_sources": ["./calculations.csv", "./parameters.json"], + "test": false, + "update": false +} +``` + +### Step 8: Publish to MDF Connect + +```bash +# Test submission (sandbox) +mdf publish --submit --test + +# Production submission +mdf publish --submit +``` + +**Output:** +``` +Published successfully! + Source ID: mdf-12345678 +``` + +--- + +## Example 2: AI Agent Workflow (Claude Code) + +--- + +## Example 3: Streaming (Local MVP) + +This example uses the local v2 backend to simulate streaming data from instruments. + +### Step 1: Start the local backend + +```bash +cd cs/aws/v2 +./local_start.sh +``` + +Optional: start the flow simulator to auto-advance statuses: + +```bash +START_FLOW_SIM=true ./local_start.sh +``` + +### Step 2: Create a stream + +```bash +mdf stream create --title "Argonne XRD+TGA Stream" --lab-id anl-xrd-tga --api-url http://127.0.0.1:8080 +``` + +### Step 3: Append files + +```bash +mdf stream append --stream-id --file-count 2 --total-bytes 24576 --api-url http://127.0.0.1:8080 +``` + +### Step 4: Snapshot the stream into a dataset + +```bash +mdf stream snapshot --stream-id --api-url http://127.0.0.1:8080 +``` + +### Step 5: Check status and close + +```bash +mdf stream status --stream-id --api-url http://127.0.0.1:8080 +mdf stream close --stream-id --api-url http://127.0.0.1:8080 +``` + +For a narrated demo, run: + +```bash +./cs/aws/v2/local_lab_stream_demo.sh +``` + +The demo simulates an Argonne lab run (XRD + TGA), shows stream creation, +file appends, status updates, and a final snapshot into a dataset record. + +You're using Claude Code and want to publish XRD data with zero manual work. + +### The Conversation + +**You:** +> I have XRD patterns in `examples/xrd-patterns/`. Can you publish this to MDF? + +**Claude:** +> I'll analyze your data and prepare it for MDF. Let me scan the folder first. + +```python +# Claude uses the MDF Agent skill handlers +from mdf_agent.skill.handlers import scan_folder, create_manifest, validate_and_preview + +# Step 1: Scan and understand the data +metadata = scan_folder("examples/xrd-patterns") +# Returns: table schemas for CSVs, instrument config from YAML +``` + +> I found 2 XRD pattern files and instrument configuration. Here's what I detected: +> +> **Files:** +> - `sample_001.csv` - 15 data points (two_theta, intensity, d_spacing) +> - `sample_002.csv` - 15 data points (two_theta, intensity, d_spacing) +> - `instrument.yaml` - Rigaku SmartLab, Cu K-alpha, scan 20-90° +> +> **Samples:** +> - Fe2O3 (ball milled) +> - Fe3O4 (hydrothermal synthesis) +> +> What title and authors should I use? + +**You:** +> Title: "XRD Patterns of Iron Oxide Nanoparticles" +> Authors: Me (Ben Johnson, MIT) and my advisor (Sarah Williams, MIT) + +**Claude:** +```python +# Step 2: Create the manifest +manifest = create_manifest( + path="examples/xrd-patterns", + title="XRD Patterns of Iron Oxide Nanoparticles", + authors=["Ben Johnson", "Sarah Williams"], + description="X-ray diffraction patterns for Fe2O3 and Fe3O4 nanoparticles synthesized via ball milling and hydrothermal methods" +) +``` + +> I've created the manifest. Let me add the data sources and validate. + +```python +# Step 3: Validate and preview +result = validate_and_preview("examples/xrd-patterns") +``` + +> **Validation passed!** Here's what will be submitted: +> +> ```json +> { +> "dc": { +> "titles": [{"title": "XRD Patterns of Iron Oxide Nanoparticles"}], +> "creators": [ +> {"creatorName": "Johnson, Ben", ...}, +> {"creatorName": "Williams, Sarah", ...} +> ], +> ... +> }, +> "data_sources": ["./sample_001.csv", "./sample_002.csv"], +> "test": false +> } +> ``` +> +> Ready to publish? I can submit to the test environment first. + +**You:** +> Yes, submit to test first. + +**Claude:** +```python +# Step 4: Publish +from mdf_agent.skill.handlers import publish + +result = publish( + path="examples/xrd-patterns", + test=True, + submit=True +) +``` + +> **Published successfully!** +> +> - Source ID: `mdf-xrd-iron-oxide-2026` +> - Status: Processing in test environment +> - Check status: `mdf status --source-id mdf-xrd-iron-oxide-2026` +> +> Once you verify it looks correct, say "publish to production" and I'll submit the final version. + +--- + +## What Just Happened? + +| Step | Human (CLI) | AI Agent | +|------|-------------|----------| +| Understand data | You read files manually | `scan_folder()` auto-extracts | +| Create manifest | `mdf init` with flags | `create_manifest()` from conversation | +| Add metadata | Edit YAML by hand | Claude fills from context | +| Validate | `mdf validate` | `validate_and_preview()` | +| Publish | `mdf publish --submit` | `publish(submit=True)` | + +**The AI workflow reduces a 10-step process to a conversation.** + +--- + +## Try It Yourself + +```bash +# Clone this repo +git clone https://github.com/materials-data-facility/mdf_client +cd mdf_client + +# Install +pip install -e . + +# Human workflow +cd examples/dft-alloys +mdf init . --title "My Dataset" --author "Your Name" +mdf add *.csv *.json +mdf commit -m "Initial commit" +mdf validate +mdf publish --dry-run + +# AI workflow +# Open Claude Code in examples/xrd-patterns and say: +# "Publish this data to MDF" +``` + +--- + +## Sample Data Included + +### `examples/dft-alloys/` +- `calculations.csv` - Formation energies for 15 Al-X intermetallics +- `parameters.json` - VASP calculation settings +- `README.txt` - Paper abstract with authors and DOI + +### `examples/xrd-patterns/` +- `sample_001.csv` - XRD pattern for Fe2O3 +- `sample_002.csv` - XRD pattern for Fe3O4 +- `instrument.yaml` - Rigaku SmartLab configuration + +--- + +## Key Commands + +| Command | Description | +|---------|-------------| +| `mdf init` | Create new dataset repository | +| `mdf add` | Stage files (with `--discover` for auto-metadata) | +| `mdf commit` | Record staged files | +| `mdf status` | Show repository state | +| `mdf validate` | Check manifest before publishing | +| `mdf publish` | Submit to MDF Connect (`--dry-run` for preview) | +| `mdf clone` | Derive from existing dataset | + +--- + +*MDF Agent: From data folder to published dataset in minutes, not hours.* diff --git a/examples/check_search_index.py b/examples/check_search_index.py new file mode 100644 index 0000000..234919b --- /dev/null +++ b/examples/check_search_index.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python3 +"""Check whether a submission made it into search. + +Uses the mdf_agent BackendClient (handles auth automatically). + +Usage: + python examples/check_search_index.py + python examples/check_search_index.py --query "search-e2e" + python examples/check_search_index.py --source-id mdf-ee1868394a384d048a5ded436fc306a9 +""" + +import argparse +import json +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) + +from mdf_agent.core.backend_client import BackendClient + +STAGING_URL = "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging" + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--query", default=None, help="Search query") + parser.add_argument("--source-id", default=None, help="Look up specific source_id") + parser.add_argument("--limit", type=int, default=10) + args = parser.parse_args() + + if not args.query and not args.source_id: + parser.print_help() + sys.exit(1) + + client = BackendClient.authenticated(base_url=STAGING_URL, service_instance="prod") + + if args.source_id: + print(f"Status for {args.source_id}:") + status = client.status(args.source_id) + print(json.dumps(status, indent=2, default=str)) + + if args.query: + print(f"\nSearch: {args.query!r}") + results = client.search(args.query, limit=args.limit) + print(json.dumps(results, indent=2, default=str)) + + +if __name__ == "__main__": + main() diff --git a/examples/demo_full_lifecycle.py b/examples/demo_full_lifecycle.py new file mode 100644 index 0000000..126a8f2 --- /dev/null +++ b/examples/demo_full_lifecycle.py @@ -0,0 +1,508 @@ +#!/usr/bin/env python3 +"""MDF Connect v2 -- Full Lifecycle Demo + +Starts a local v2 backend, then walks through: + + 1. Repository workflow: init -> add -> commit -> validate -> publish + 2. Streaming workflow: create -> upload -> preview -> snapshot + 3. Curation workflow: pending -> approve (DOI minting) + 4. Discovery: search -> dataset card -> citation + +Run: + cd /path/to/mdf_client + python examples/demo_full_lifecycle.py +""" + +from __future__ import annotations + +import base64 +import json +import os +import shutil +import signal +import subprocess +import sys +import tempfile +import time +from pathlib import Path + +# --------------------------------------------------------------------------- +# rich imports +# --------------------------------------------------------------------------- +from rich.console import Console +from rich.panel import Panel +from rich.syntax import Syntax +from rich.table import Table +from rich import box + +console = Console(width=100) + +# --------------------------------------------------------------------------- +# Paths +# --------------------------------------------------------------------------- +REPO_ROOT = Path(__file__).resolve().parents[1] +BACKEND_DIR = REPO_ROOT / "cs" / "aws" +EXAMPLE_ALLOYS = REPO_ROOT / "examples" / "dft-alloys" +EXAMPLE_XRD = REPO_ROOT / "examples" / "xrd-patterns" + +API_URL = "http://127.0.0.1:8080" +DEV_USER = "demo-researcher" + +# --------------------------------------------------------------------------- +# Utilities +# --------------------------------------------------------------------------- + +def section(title: str) -> None: + console.print() + console.rule(f"[bold cyan]{title}[/bold cyan]", style="cyan") + console.print() + + +def step(label: str) -> None: + console.print(f" [bold white]> {label}[/bold white]") + + +def ok(msg: str = "done") -> None: + console.print(f" [green]{msg}[/green]") + + +def show_json(data: dict, title: str = "") -> None: + text = json.dumps(data, indent=2, default=str) + syntax = Syntax(text, "json", theme="monokai", line_numbers=False) + if title: + console.print(Panel(syntax, title=f"[bold]{title}[/bold]", border_style="blue", padding=(0, 1))) + else: + console.print(syntax) + + +def fail(msg: str) -> None: + console.print(f" [bold red]FAILED: {msg}[/bold red]") + sys.exit(1) + + +def check(result: dict, label: str) -> dict: + if not result.get("success"): + fail(f"{label}: {result.get('error', result)}") + ok(label) + return result + + +# --------------------------------------------------------------------------- +# Backend lifecycle +# --------------------------------------------------------------------------- + +def start_backend() -> subprocess.Popen: + """Start the v2 backend as a subprocess.""" + env = { + **os.environ, + "STORE_BACKEND": "sqlite", + "SQLITE_PATH": "/tmp/mdf_demo.db", + "SQLITE_STREAMS_PATH": "/tmp/mdf_demo_streams.db", + "STORAGE_BACKEND": "local", + "FILE_STORE_PATH": "/tmp/mdf_demo_files", + "AUTH_MODE": "dev", + "ALLOW_ALL_CURATORS": "true", + "USE_MOCK_DATACITE": "true", + "ASYNC_DISPATCH_MODE": "inline", + "LOG_LEVEL": "WARNING", + "PYTHONPATH": str(BACKEND_DIR), + } + + # Clean up prior demo state + for p in ["/tmp/mdf_demo.db", "/tmp/mdf_demo_streams.db"]: + if os.path.exists(p): + os.unlink(p) + if os.path.exists("/tmp/mdf_demo_files"): + shutil.rmtree("/tmp/mdf_demo_files") + + proc = subprocess.Popen( + [sys.executable, "-m", "uvicorn", "v2.app:app", + "--host", "127.0.0.1", "--port", "8080", "--log-level", "warning"], + cwd=str(BACKEND_DIR), + env=env, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + return proc + + +def wait_for_backend(timeout: float = 10.0) -> None: + """Poll /health until the backend is ready.""" + import httpx + + deadline = time.monotonic() + timeout + while time.monotonic() < deadline: + try: + r = httpx.get(f"{API_URL}/health", timeout=1.0) + if r.status_code == 200: + return + except Exception: + pass + time.sleep(0.3) + fail("Backend did not start within timeout") + + +# --------------------------------------------------------------------------- +# BackendClient helper (thin wrapper so we don't need mdf_agent installed) +# --------------------------------------------------------------------------- + +def client(): + """Return an authenticated BackendClient for the local demo.""" + sys.path.insert(0, str(REPO_ROOT / "src")) + from mdf_agent.core.backend_client import BackendClient + return BackendClient(base_url=API_URL, user_id=DEV_USER) + + +# --------------------------------------------------------------------------- +# Demo scenario +# --------------------------------------------------------------------------- + +def demo_repository_workflow(c) -> str: + """Part 1: Git-style repository workflow.""" + section("Part 1: Repository Workflow") + console.print( + " The [cyan]mdf[/cyan] CLI gives researchers a git-style workflow.\n" + " [dim]init -> add -> commit -> validate -> publish[/dim]\n" + ) + + work_dir = Path(tempfile.mkdtemp(prefix="mdf_demo_repo_")) + + # Copy example data + step("Creating dataset directory with DFT alloy calculations") + shutil.copy(EXAMPLE_ALLOYS / "calculations.csv", work_dir / "calculations.csv") + shutil.copy(EXAMPLE_ALLOYS / "parameters.json", work_dir / "parameters.json") + ok(f"{work_dir}") + + # Init + step("mdf init --title '...' --author '...'") + from mdf_agent.core.agent import MDFAgent + agent = MDFAgent.init( + path=str(work_dir), + title="High-Throughput DFT Study of Binary Intermetallic Alloys", + authors=["Jane Doe", "John Smith", "Alice Chen"], + description="Formation energies and electronic properties for 15 Al-X intermetallics computed with VASP PBE.", + ) + ok("Repository initialized") + + # Add files and register data sources + step("mdf add calculations.csv parameters.json") + staged = agent.add("calculations.csv", "parameters.json") + agent.add_data_source("./calculations.csv") + agent.add_data_source("./parameters.json") + agent.save_manifest() + ok(f"Staged {len(staged)} files") + + # Commit + step("mdf commit -m 'Initial DFT calculation results'") + agent.commit("Initial DFT calculation results") + ok("Committed") + + # Validate + step("mdf validate") + validation = agent.validate() + errors = validation.get("errors", []) + if errors: + fail(f"Validation errors: {errors}") + ok(f"Validation passed ({len(validation.get('warnings', []))} warnings)") + + # Build & preview payload + step("mdf publish (dry run)") + payload = agent.build_submission(test=True) + show_json(payload, "Submission Payload (dry run)") + + # Publish to local backend + step("mdf publish --submit --service local --dev-user demo-researcher") + result = c.submit(payload) + result = check(result, "Published") + source_id = result["source_id"] + console.print(f" [dim]source_id:[/dim] [cyan]{source_id}[/cyan] version: {result.get('version', '1.0')}") + + shutil.rmtree(work_dir, ignore_errors=True) + return source_id + + +def demo_streaming_workflow(c) -> str: + """Part 2: Streaming data workflow.""" + section("Part 2: Streaming Workflow") + console.print( + " Streams let instruments push data in real-time.\n" + " [dim]create -> upload -> preview -> snapshot[/dim]\n" + ) + + # Create stream + step("mdf stream create --title 'XRD Beamline 12-ID'") + result = c.stream_create( + title="XRD Beamline 12-ID Live Acquisition", + lab_id="APS-12-ID", + organization="Argonne National Laboratory", + ) + result = check(result, "Stream created") + stream_id = result["stream_id"] + console.print(f" [dim]stream_id:[/dim] [cyan]{stream_id}[/cyan]") + + # Upload XRD data files + step("mdf stream upload sample_001.csv sample_002.csv") + for fname in ["sample_001.csv", "sample_002.csv"]: + content = (EXAMPLE_XRD / fname).read_bytes() + up = c.stream_upload(stream_id, fname, content) + if not up.get("success"): + fail(f"Upload {fname}: {up.get('error')}") + ok("2 files uploaded") + + # Also upload the instrument config + step("mdf stream upload instrument.yaml") + content = (EXAMPLE_XRD / "instrument.yaml").read_bytes() + up = c.stream_upload(stream_id, "instrument.yaml", content) + check(up, "instrument.yaml uploaded") + + # List files + step("mdf stream files --stream-id ...") + files_result = c.stream_list_files(stream_id) + if files_result.get("success"): + files = files_result.get("files", []) + table = Table(title="Stream Files", box=box.ROUNDED, border_style="blue") + table.add_column("Filename", style="cyan") + table.add_column("Size", justify="right") + table.add_column("Type", style="dim") + for f in files: + size_kb = f.get("size_bytes", 0) / 1024 + table.add_row( + f.get("filename", ""), + f"{size_kb:.1f} KB", + f.get("content_type", ""), + ) + console.print(table) + + # Preview + step("GET /stream/{id}/preview") + preview = c.stream_preview(stream_id) + if preview.get("success") and preview.get("previews"): + first = preview["previews"][0] + pdata = first.get("preview", {}) + if pdata.get("columns") and pdata.get("sample_rows"): + table = Table( + title=f"Preview: {first.get('filename', '?')}", + box=box.ROUNDED, + border_style="green", + ) + for col in pdata["columns"]: + table.add_column(col) + for row in pdata["sample_rows"][:5]: + table.add_row(*[str(row.get(c, "")) for c in pdata["columns"]]) + console.print(table) + ok("Live preview rendered") + + # Stream status + step("mdf stream status --stream-id ...") + ss = c.stream_status(stream_id) + if ss.get("success"): + st = ss["stream"] + console.print( + f" [dim]status:[/dim] {st.get('status')} " + f"[dim]files:[/dim] {st.get('file_count')} " + f"[dim]bytes:[/dim] {st.get('total_bytes')}" + ) + + # Snapshot -> submission + step("mdf stream snapshot --title 'XRD Session Feb 2026'") + snap = c.stream_snapshot( + stream_id, + title="XRD Session Feb 2026 - Perovskite Thin Films", + ) + snap = check(snap, "Snapshot created") + snap_source_id = snap["source_id"] + console.print(f" [dim]source_id:[/dim] [cyan]{snap_source_id}[/cyan] version: {snap.get('version', '1.0')}") + + return snap_source_id + + +def demo_curation_workflow(c, source_id: str) -> None: + """Part 3: Curation workflow -- submit -> review -> approve -> DOI.""" + section("Part 3: Curation & DOI Minting") + console.print( + " Curators review submitted datasets before publication.\n" + " [dim]pending_curation -> approve -> DOI minted[/dim]\n" + ) + + # Move to pending_curation + step("Curator sets status to pending_curation") + result = c.update_status(source_id, "1.0", "pending_curation") + check(result, "Status updated") + + # Show pending queue + step("GET /curation/pending") + import httpx + resp = httpx.get( + f"{API_URL}/curation/pending", + headers={"X-User-Id": DEV_USER}, + timeout=10.0, + ) + pending = resp.json() + if pending.get("success"): + subs = pending.get("submissions", []) + console.print(f" [dim]{pending.get('pending_count', len(subs))} dataset(s) awaiting review[/dim]") + for s in subs: + console.print(f" - [cyan]{s.get('source_id')}[/cyan] v{s.get('version')} \"{s.get('title', '?')}\"") + + # Approve with DOI + step(f"POST /curation/{source_id}/approve (mint DOI)") + resp = httpx.post( + f"{API_URL}/curation/{source_id}/approve", + headers={"X-User-Id": DEV_USER}, + json={"notes": "Excellent dataset, well-documented.", "mint_doi": True}, + timeout=10.0, + ) + approve = resp.json() + if approve.get("success") or approve.get("status") == "published": + ok(f"Approved! Status: {approve.get('status', 'published')}") + doi_info = approve.get("doi") or approve.get("doi_job", {}) + if isinstance(doi_info, dict) and doi_info.get("doi"): + console.print(f" [dim]DOI:[/dim] [bold green]{doi_info['doi']}[/bold green]") + elif isinstance(doi_info, dict) and doi_info.get("success"): + console.print(f" [dim]DOI:[/dim] [bold green]{doi_info.get('doi', 'mock-doi')}[/bold green]") + else: + console.print(f" [yellow]Approve response:[/yellow] {json.dumps(approve, indent=2)}") + + +def demo_discovery(c, source_ids: list[str]) -> None: + """Part 4: Search, dataset cards, and citations.""" + section("Part 4: Discovery & Citation") + console.print( + " Published datasets are searchable and citable.\n" + " [dim]search -> card -> citation[/dim]\n" + ) + + # Search + step("mdf search 'alloy'") + result = c.search("alloy", search_type="datasets", limit=5) + if result.get("results"): + table = Table(title="Search Results", box=box.ROUNDED, border_style="magenta") + table.add_column("#", style="dim", width=3) + table.add_column("Title") + table.add_column("Source ID", style="cyan") + table.add_column("Status", style="dim") + for i, item in enumerate(result["results"], 1): + table.add_row( + str(i), + (item.get("title") or "Untitled")[:50], + item.get("source_id", ""), + item.get("status", ""), + ) + console.print(table) + else: + console.print(" [dim]No search results (expected for fresh db)[/dim]") + + # Dataset card + for sid in source_ids: + step(f"mdf backend card {sid}") + card_result = c.get_card(sid) + if card_result.get("success"): + card = card_result["card"] + lines = [] + lines.append(f"[bold]{card.get('title', 'Untitled')}[/bold]") + lines.append(f"[dim]{card.get('description', '')}[/dim]") + lines.append("") + if card.get("authors"): + lines.append(f"[dim]Authors:[/dim] {', '.join(card['authors'])}") + if card.get("organization"): + lines.append(f"[dim]Org:[/dim] {card['organization']}") + if card.get("doi"): + lines.append(f"[dim]DOI:[/dim] [green]https://doi.org/{card['doi']}[/green]") + lines.append(f"[dim]Status:[/dim] {card.get('status', '?')}") + stats = card.get("stats", {}) + if stats: + lines.append(f"[dim]Sources:[/dim] {stats.get('data_sources_count', '?')} | Types: {', '.join(stats.get('file_types', []))}") + profile = card.get("profile_summary", {}) + if profile: + lines.append(f"[dim]Files:[/dim] {profile.get('total_files', '?')} ({profile.get('total_bytes', 0)} bytes)") + if profile.get("formats"): + lines.append(f"[dim]Formats:[/dim] {profile['formats']}") + + console.print(Panel( + "\n".join(lines), + title=f"[cyan]{sid}[/cyan] v{card.get('version', '1.0')}", + border_style="blue", + padding=(1, 2), + )) + break # Just show the first one in detail + + # Citation + step(f"mdf backend cite {source_ids[0]} --format bibtex") + cite = c.get_citation(source_ids[0], format="all") + if cite.get("success"): + for fmt in ["apa", "bibtex"]: + text = cite.get(fmt, "") + if text: + console.print(Panel(text, title=fmt.upper(), border_style="green", padding=(0, 1))) + else: + console.print(f" [dim]Citation not available: {cite.get('error', '?')}[/dim]") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main() -> None: + console.print() + console.print(Panel.fit( + "[bold white]MDF Connect v2 -- Full Lifecycle Demo[/bold white]\n\n" + "[dim]This demo starts a local v2 backend and walks through the\n" + "complete dataset lifecycle: create, upload, publish, curate,\n" + "search, and cite -- all running on your machine.[/dim]", + border_style="bright_blue", + padding=(1, 3), + )) + + # ----------------------------------------------------------------------- + section("Starting Local Backend") + step("Launching v2 FastAPI server on :8080") + + proc = start_backend() + try: + wait_for_backend() + ok("Backend healthy") + + # Create the client + c = client() + + # Run each demo section + source_id_repo = demo_repository_workflow(c) + source_id_stream = demo_streaming_workflow(c) + demo_curation_workflow(c, source_id_repo) + demo_discovery(c, [source_id_repo, source_id_stream]) + + c.close() + + # --------------------------------------------------------------- + section("Summary") + console.print( + " [bold green]All steps completed successfully.[/bold green]\n" + ) + + table = Table(box=box.SIMPLE, padding=(0, 2)) + table.add_column("Workflow", style="bold") + table.add_column("Source ID", style="cyan") + table.add_column("Status") + table.add_row("Repository publish", source_id_repo, "[green]published (DOI minted)[/green]") + table.add_row("Stream snapshot", source_id_stream, "[blue]submitted[/blue]") + console.print(table) + + console.print() + console.print(" [dim]Backend ran at[/dim] [cyan]http://127.0.0.1:8080[/cyan]") + console.print(" [dim]SQLite DB:[/dim] /tmp/mdf_demo.db") + console.print(" [dim]File store:[/dim] /tmp/mdf_demo_files/") + console.print() + + finally: + step("Shutting down backend") + proc.send_signal(signal.SIGTERM) + try: + proc.wait(timeout=5) + except subprocess.TimeoutExpired: + proc.kill() + ok("Backend stopped") + console.print() + + +if __name__ == "__main__": + main() diff --git a/examples/demo_staging_globus.py b/examples/demo_staging_globus.py new file mode 100644 index 0000000..9fddf5b --- /dev/null +++ b/examples/demo_staging_globus.py @@ -0,0 +1,373 @@ +#!/usr/bin/env python3 +"""MDF Connect v2 -- Staging + Globus Auth Demo + +Tests the deployed staging backend with real Globus authentication. +Walks through: + + 1. Health check against the staging API + 2. Stream workflow: create -> upload files -> list -> verify on Globus + 3. Repository workflow: init -> add -> commit -> publish (dry-run + submit) + 4. Discovery: search -> dataset card -> citation + +Prerequisites: + 1. Deploy the staging stack: + cd cs/aws && sam build && ./deploy.sh staging + 2. Authenticate with Globus: + mdf login --service prod + 3. Run this script with the staging API URL: + python examples/demo_staging_globus.py https://.execute-api.us-east-1.amazonaws.com/staging + +The script creates a stream and uploads test CSV data to the Globus HTTPS +endpoint at data.materialsdatafacility.org under /mdf/staging/. +""" + +from __future__ import annotations + +import json +import os +import shutil +import sys +import tempfile +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "src")) + +from mdf_agent.core.backend_client import BackendClient + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def heading(title: str) -> None: + print(f"\n{'=' * 60}") + print(f" {title}") + print(f"{'=' * 60}\n") + + +def step(label: str) -> None: + print(f" > {label}") + + +def ok(msg: str = "done") -> None: + print(f" OK: {msg}") + + +def fail(msg: str) -> None: + print(f" FAILED: {msg}") + sys.exit(1) + + +def show(data: dict, indent: int = 4) -> None: + prefix = " " * indent + for line in json.dumps(data, indent=2, default=str).splitlines(): + print(f"{prefix}{line}") + + +def check(result: dict, label: str) -> dict: + if not result.get("success"): + fail(f"{label}: {result.get('error', result)}") + ok(label) + return result + + +# --------------------------------------------------------------------------- +# Test CSV data (small but real enough to exercise the pipeline) +# --------------------------------------------------------------------------- + +SAMPLE_CSV = """\ +material,formation_energy_eV,bandgap_eV,lattice_a,lattice_c,space_group +Al3Ti, -0.42, 0.00, 3.854, 8.584, I4/mmm +AlNi, -0.67, 0.00, 2.881, 2.881, Pm-3m +Al3Ni, -0.41, 0.00, 6.611, 7.366, Pnma +AlFe, -0.30, 0.00, 2.909, 2.909, Pm-3m +Al2Cu, -0.18, 0.00, 6.063, 4.872, I4/mcm +""" + +PARAMS_JSON = json.dumps({ + "method": "DFT-PBE", + "code": "VASP 6.4", + "encut_eV": 520, + "kpoints": "8x8x8 Monkhorst-Pack", + "smearing": "Methfessel-Paxton, 0.2 eV", +}, indent=2) + + +# --------------------------------------------------------------------------- +# Demo functions +# --------------------------------------------------------------------------- + +def demo_health(client: BackendClient, api_url: str) -> None: + heading("Health Check") + step(f"GET {api_url}/health") + import httpx + resp = httpx.get(f"{api_url}/health", timeout=15.0) + if resp.status_code == 200: + ok(f"status={resp.status_code} body={resp.text.strip()}") + else: + fail(f"status={resp.status_code} body={resp.text[:200]}") + + +def demo_stream(client: BackendClient) -> str: + heading("Stream Workflow (data -> Globus endpoint)") + + # Create stream + step("Creating stream: 'Staging Test - Alloy DFT Data'") + result = client.stream_create( + title="Staging Test - Alloy DFT Data", + organization="MDF Staging Test", + ) + result = check(result, "Stream created") + stream_id = result["stream_id"] + print(f" stream_id: {stream_id}") + + # Upload CSV + step("Uploading calculations.csv (intermetallic formation energies)") + up = client.stream_upload(stream_id, "calculations.csv", SAMPLE_CSV.encode()) + check(up, "calculations.csv uploaded") + if up.get("file"): + print(f" stored at: {up['file'].get('path', '?')}") + print(f" backend: {up['file'].get('storage_backend', '?')}") + + # Upload params + step("Uploading parameters.json (DFT settings)") + up = client.stream_upload(stream_id, "parameters.json", PARAMS_JSON.encode()) + check(up, "parameters.json uploaded") + + # List files + step("Listing stream files") + files_result = client.stream_list_files(stream_id) + if files_result.get("success"): + files = files_result.get("files", []) + print(f" {len(files)} file(s):") + for f in files: + size = f.get("size_bytes", 0) + print(f" - {f.get('filename'):30s} {size:>6d} bytes ({f.get('storage_backend', '?')})") + if f.get("download_url"): + print(f" url: {f['download_url'][:80]}...") + else: + print(f" (list_files returned: {files_result})") + + # Stream status + step("Checking stream status") + ss = client.stream_status(stream_id) + if ss.get("success"): + st = ss["stream"] + print(f" status: {st.get('status')}") + print(f" file_count: {st.get('file_count')}") + print(f" total_bytes: {st.get('total_bytes')}") + + # Snapshot -> creates a submission from the stream + step("Creating snapshot (stream -> dataset submission)") + snap = client.stream_snapshot(stream_id, title="Alloy DFT Snapshot - Staging Test") + snap = check(snap, "Snapshot created") + snap_source_id = snap["source_id"] + print(f" source_id: {snap_source_id}") + print(f" version: {snap.get('version', '1.0')}") + + return snap_source_id + + +def demo_publish(client: BackendClient) -> str: + heading("Repository Workflow (init -> add -> commit -> publish)") + + work_dir = Path(tempfile.mkdtemp(prefix="mdf_staging_")) + + try: + # Write test data + (work_dir / "calculations.csv").write_text(SAMPLE_CSV) + (work_dir / "parameters.json").write_text(PARAMS_JSON) + + from mdf_agent.core.agent import MDFAgent + + # Init + step(f"mdf init (in {work_dir.name})") + agent = MDFAgent.init( + path=str(work_dir), + title="Staging Test - Binary Intermetallic Alloys", + authors=["Demo User"], + description="Formation energies for Al-X intermetallics (staging test).", + ) + ok("Repository initialized") + + # Add + commit + step("mdf add calculations.csv parameters.json") + staged = agent.add("calculations.csv", "parameters.json") + ok(f"Staged {len(staged)} files") + + step("mdf commit -m 'Initial data'") + agent.commit("Initial data") + ok("Committed") + + # Validate + step("mdf validate") + val = agent.validate() + errors = val.get("errors", []) + warnings = val.get("warnings", []) + if errors: + fail(f"Validation errors: {errors}") + ok(f"Passed ({len(warnings)} warning(s))") + + # Dry run + step("mdf publish (dry run)") + payload = agent.build_submission(test=True) + ds = payload.get("data_sources", []) + print(f" title: {payload.get('title')}") + print(f" authors: {payload.get('authors')}") + print(f" data_sources: {len(ds)} file(s)") + for src in ds: + print(f" - {src}") + ok("Dry run payload built (data_sources auto-populated from commits)") + + # Submit to staging backend + step("Submitting to staging backend") + result = client.submit(payload) + result = check(result, "Published") + source_id = result["source_id"] + print(f" source_id: {source_id}") + print(f" version: {result.get('version', '1.0')}") + return source_id + + finally: + shutil.rmtree(work_dir, ignore_errors=True) + + +def demo_discovery(client: BackendClient, source_ids: list[str]) -> None: + heading("Discovery (search -> card -> citation)") + + step("Searching for 'staging test'") + result = client.search("staging test", limit=5) + hits = result.get("results", []) + print(f" {len(hits)} result(s)") + for h in hits: + print(f" - [{h.get('source_id')}] {h.get('title', '?')[:60]}") + + for sid in source_ids: + step(f"Dataset card: {sid}") + card_result = client.get_card(sid) + if card_result.get("success"): + card = card_result["card"] + print(f" title: {card.get('title')}") + print(f" status: {card.get('status')}") + print(f" authors: {card.get('authors')}") + if card.get("doi"): + print(f" doi: {card['doi']}") + else: + print(f" (card not available: {card_result.get('error', '?')})") + + step(f"Citation: {sid}") + cite = client.get_citation(sid, format="all") + if cite.get("success"): + if cite.get("bibtex"): + print(f" BibTeX:\n {cite['bibtex'][:120]}...") + if cite.get("apa"): + print(f" APA: {cite['apa'][:120]}...") + else: + print(f" (citation not available: {cite.get('error', '?')})") + break # Just show first + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main() -> None: + if len(sys.argv) < 2: + print("Usage: python examples/demo_staging_globus.py ") + print() + print("Example:") + print(" python examples/demo_staging_globus.py https://abc123.execute-api.us-east-1.amazonaws.com/staging") + print() + print("Get the URL after deploying:") + print(" aws cloudformation describe-stacks \\") + print(" --stack-name mdf-connect-v2-staging \\") + print(" --query 'Stacks[0].Outputs[?OutputKey==`ApiUrl`].OutputValue' \\") + print(" --output text") + sys.exit(1) + + api_url = sys.argv[1].rstrip("/") + + print() + print("MDF Connect v2 -- Staging + Globus Auth Demo") + print(f"API: {api_url}") + print() + + # Create authenticated client (requests MDF Connect + data scopes; + # will open browser for login if needed) + step("Authenticating with Globus (MDF Connect + data.materialsdatafacility.org scopes)") + client = BackendClient.authenticated( + base_url=api_url, + service_instance="prod", # staging uses prod Globus auth + ) + ok(f"Client ready -> {api_url}") + + # Debug: show token state and validate directly against Globus + step("Token diagnostics") + import httpx as _httpx + print(f" Bearer token: {client._token[:20]}...{client._token[-10:]}" if client._token else " Bearer token: NONE") + print(f" Data token: {client._globus_data_token[:20]}...{client._globus_data_token[-10:]}" if client._globus_data_token else " Data token: NONE") + + step("Validating Bearer token directly against Globus Auth") + _r = _httpx.get( + "https://auth.globus.org/v2/oauth2/userinfo", + headers={"Authorization": f"Bearer {client._token}"}, + timeout=10.0, + ) + print(f" Globus userinfo (Bearer): {_r.status_code}") + if _r.status_code == 200: + _ui = _r.json() + print(f" sub={_ui.get('sub')} email={_ui.get('email')}") + else: + print(f" Response: {_r.text[:300]}") + + step("Validating Data token directly against Globus Auth") + _r2 = _httpx.get( + "https://auth.globus.org/v2/oauth2/userinfo", + headers={"Authorization": f"Bearer {client._globus_data_token}"}, + timeout=10.0, + ) + print(f" Globus userinfo (Data): {_r2.status_code}") + + step("Testing auth against staging API") + _r3 = _httpx.get( + f"{api_url}/submissions", + headers={"Authorization": f"Bearer {client._token}"}, + timeout=15.0, + ) + print(f" GET /submissions -> {_r3.status_code}") + if _r3.status_code != 200: + print(f" Response: {_r3.text[:300]}") + + try: + # 1. Health check + demo_health(client, api_url) + + # 2. Stream workflow (uploads to Globus endpoint) + stream_source_id = demo_stream(client) + + # 3. Repository publish workflow + publish_source_id = demo_publish(client) + + # 4. Discovery + demo_discovery(client, [publish_source_id, stream_source_id]) + + # Summary + heading("Summary") + print(" All steps completed successfully.\n") + print(f" Stream snapshot: {stream_source_id}") + print(f" Repo submission: {publish_source_id}") + print(f" API: {api_url}") + print(f" Globus storage: https://data.materialsdatafacility.org/tmp/staging/") + print() + print(" Files were uploaded to the Globus HTTPS endpoint.") + print(" View them at: https://app.globus.org/file-manager") + print(" Collection: MDF (82f1b5c6-6e9b-11e5-ba47-22000b92c6ec)") + print(" Path: /tmp/staging/") + print() + + finally: + client.close() + + +if __name__ == "__main__": + main() diff --git a/examples/dft-alloys/.mdf/state.json b/examples/dft-alloys/.mdf/state.json new file mode 100644 index 0000000..6a61c42 --- /dev/null +++ b/examples/dft-alloys/.mdf/state.json @@ -0,0 +1,14 @@ +{ + "version": "1", + "root": "/Users/ben/Desktop/git/mdf_client/examples/dft-alloys", + "staged_files": [], + "commits": [ + { + "message": "Initial data", + "timestamp": "2026-01-31T06:10:20.895512Z", + "staged_files": [ + "calculations.csv" + ] + } + ] +} \ No newline at end of file diff --git a/examples/dft-alloys/README.txt b/examples/dft-alloys/README.txt new file mode 100644 index 0000000..8e3c647 --- /dev/null +++ b/examples/dft-alloys/README.txt @@ -0,0 +1,31 @@ +High-Throughput DFT Study of Binary Intermetallic Alloys +========================================================= + +Authors: Jane Doe (1), John Smith (1,2), Alice Chen (3) + +Affiliations: + 1. Department of Materials Science, MIT + 2. Argonne National Laboratory + 3. Stanford University + +DOI: 10.1234/example-dataset-2026 + +Abstract +-------- +We present a comprehensive density functional theory (DFT) study of +formation energies, magnetic properties, and electronic structures for +15 binary intermetallic alloys in the Al-X system (X = Fe, Ni, Co, Ti, Cr). + +All calculations were performed using VASP 6.3.2 with the PBE functional +and PAW pseudopotentials. Structures were fully relaxed until forces +converged below 0.01 eV/A. + +Key findings: +- Ti3Al exhibits the lowest formation energy (0.089 eV/atom) +- Fe-Al alloys show significant magnetic moments (up to 2.34 muB) +- Bandgaps range from metallic (0.0 eV) to 0.45 eV for FeAl3 + +This dataset supports reproducibility and enables machine learning +studies of structure-property relationships in intermetallic compounds. + +License: CC-BY-4.0 diff --git a/examples/dft-alloys/calculations.csv b/examples/dft-alloys/calculations.csv new file mode 100644 index 0000000..307721b --- /dev/null +++ b/examples/dft-alloys/calculations.csv @@ -0,0 +1,16 @@ +composition,formation_energy_eV_atom,volume_A3,magnetic_moment_muB,bandgap_eV,space_group +Fe3Al,0.123,45.67,2.34,0.0,Fm-3m +FeAl,0.234,34.56,0.12,0.0,Pm-3m +FeAl3,0.345,56.78,0.05,0.45,I4/mmm +Fe2Al5,0.456,78.90,0.08,0.32,Cmcm +FeAl2,0.287,42.15,0.03,0.28,P1 +Ni3Al,0.198,43.21,0.45,0.0,Pm-3m +NiAl,0.312,27.89,0.02,0.0,Pm-3m +NiAl3,0.423,65.43,0.01,0.18,Pnma +Co3Al,0.156,41.23,1.67,0.0,Pm-3m +CoAl,0.267,28.45,0.34,0.0,Pm-3m +Ti3Al,0.089,67.89,0.0,0.0,P63/mmc +TiAl,0.145,32.56,0.0,0.0,P4/mmm +TiAl3,0.234,54.32,0.0,0.12,I4/mmm +Cr3Al,0.178,38.90,0.89,0.0,Pm-3m +CrAl,0.289,29.67,0.23,0.0,Pm-3m diff --git a/examples/dft-alloys/mdf.yaml b/examples/dft-alloys/mdf.yaml new file mode 100644 index 0000000..eace94c --- /dev/null +++ b/examples/dft-alloys/mdf.yaml @@ -0,0 +1,21 @@ +title: High-Throughput DFT Study of Binary Intermetallic Alloys +authors: +- Jane Doe +- John Smith +- Alice Chen +description: Formation energies and electronic properties for 15 Al-X intermetallics +data_sources: +- ./calculations.csv +- ./parameters.json +auto_metadata: + mdf: + table_schema: + file: calculations.csv + columns: + - name: composition + - name: formation_energy_eV_atom + - name: volume_A3 + - name: magnetic_moment_muB + - name: bandgap_eV + - name: space_group + row_count: 15 diff --git a/examples/dft-alloys/parameters.json b/examples/dft-alloys/parameters.json new file mode 100644 index 0000000..8882803 --- /dev/null +++ b/examples/dft-alloys/parameters.json @@ -0,0 +1,27 @@ +{ + "calculation_method": { + "software": "VASP", + "version": "6.3.2", + "functional": "PBE", + "pseudopotentials": "PAW_PBE", + "encut_eV": 520, + "kpoints_density": 8000, + "smearing": { + "method": "Methfessel-Paxton", + "sigma_eV": 0.2 + } + }, + "convergence_criteria": { + "energy_eV": 1e-6, + "force_eV_A": 0.01 + }, + "magnetic_settings": { + "spin_polarized": true, + "initial_magmom": "default" + }, + "relaxation": { + "ions": true, + "cell_shape": true, + "cell_volume": true + } +} diff --git a/examples/test_datacite_mint.py b/examples/test_datacite_mint.py new file mode 100644 index 0000000..076400a --- /dev/null +++ b/examples/test_datacite_mint.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Standalone DataCite test DOI minting script. + +Tests the DataCiteClient directly against api.test.datacite.org. +No server, no Lambda, no DynamoDB -- just the DataCite REST API. + +Usage: + python examples/test_datacite_mint.py +""" + +import sys +import os +import time + +# Add cs/aws to path so we can import v2.datacite +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "cs", "aws")) + +from v2.datacite import DataCiteClient + + +def main(): + # Test credentials for api.test.datacite.org + client = DataCiteClient( + username="Globus.TEST", + password="NTroFAzElE", + api_url="https://api.test.datacite.org", + prefix="10.23677", + test_mode=True, + ) + + timestamp = int(time.time()) + source_id = f"mdf-test-{timestamp}" + + print(f"{'=' * 60}") + print(f" DataCite Test DOI Minting") + print(f" Source ID: {source_id}") + print(f" API: https://api.test.datacite.org") + print(f"{'=' * 60}") + + # --- Step 1: Mint a DOI (publish=True → findable) --- + print("\n[1] Minting DOI (publish=True)...") + metadata = { + "title": f"MDF Test Dataset {timestamp}", + "authors": [ + {"given_name": "Test", "family_name": "User", "affiliation": "MDF"}, + {"name": "Materials Data Facility"}, + ], + "description": "Automated test of DataCite DOI minting from MDF v2 pipeline.", + "keywords": ["test", "mdf", "materials-data"], + "license": "CC-BY-4.0", + "publication_year": 2026, + } + + result = client.mint_doi(source_id=source_id, metadata=metadata, publish=True) + print(f" Success: {result.get('success')}") + print(f" DOI: {result.get('doi')}") + print(f" URL: {result.get('url')}") + print(f" State: {result.get('state')}") + + if not result.get("success"): + print(f" Error: {result.get('error')}") + print("\nAborting -- mint failed.") + client.close() + sys.exit(1) + + doi = result["doi"] + + # --- Step 2: Verify via GET --- + print("\n[2] Verifying DOI via GET...") + fetched = client.get_doi(doi) + if fetched and fetched.get("data"): + attrs = fetched["data"]["attributes"] + print(f" DOI: {fetched['data']['id']}") + print(f" State: {attrs.get('state')}") + print(f" Title: {attrs.get('titles', [{}])[0].get('title', 'N/A')}") + else: + print(" Warning: could not fetch DOI (may take a moment to propagate)") + + # --- Step 3: Update the DOI (new title) --- + print("\n[3] Updating DOI with new title...") + metadata["title"] = f"MDF Test Dataset {timestamp} (Updated)" + update_result = client.mint_doi(source_id=source_id, metadata=metadata, publish=True) + print(f" Success: {update_result.get('success')}") + print(f" Updated: {update_result.get('updated', False)}") + print(f" State: {update_result.get('state')}") + + # --- Step 4: Test draft mode --- + print("\n[4] Testing draft mode (publish=False)...") + draft_source_id = f"mdf-test-draft-{timestamp}" + draft_result = client.mint_doi( + source_id=draft_source_id, + metadata={ + "title": f"MDF Draft Test {timestamp}", + "authors": [{"name": "Test User"}], + }, + publish=False, + ) + print(f" Success: {draft_result.get('success')}") + print(f" DOI: {draft_result.get('doi')}") + print(f" State: {draft_result.get('state')}") + + # --- Step 5: Publish the draft --- + if draft_result.get("success"): + print("\n[5] Publishing the draft DOI...") + publish_result = client.mint_doi( + source_id=draft_source_id, + metadata={ + "title": f"MDF Draft Test {timestamp} (Now Published)", + "authors": [{"name": "Test User"}], + }, + publish=True, + ) + print(f" Success: {publish_result.get('success')}") + print(f" State: {publish_result.get('state')}") + + # --- Summary --- + print(f"\n{'=' * 60}") + print(" Summary") + print(f"{'=' * 60}") + print(f" Findable DOI: {doi}") + print(f" Draft DOI: {draft_result.get('doi', 'N/A')}") + print(f" Verify at: https://commons.datacite.org/doi.org/{doi}") + print(f"{'=' * 60}") + + client.close() + print("\nDone.") + + +if __name__ == "__main__": + main() diff --git a/examples/test_domains_external_e2e.py b/examples/test_domains_external_e2e.py new file mode 100644 index 0000000..d0a0ae4 --- /dev/null +++ b/examples/test_domains_external_e2e.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 +"""E2E test of domains and external import fields on staging. + +Tests: + [1] Submit dataset with domains field → verify it appears in status + [2] Submit externally-imported dataset → verify external_doi, external_url, + external_source appear in status + [3] Submit dataset with both domains and external import → verify both + +Usage: + python examples/test_domains_external_e2e.py +""" + +import sys +from pathlib import Path + +# Add src to path +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) + +from mdf_agent.core.backend_client import BackendClient + +STAGING_URL = "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging" + + +def heading(title): + print(f"\n{'=' * 60}") + print(f" {title}") + print(f"{'=' * 60}\n") + + +def step(num, title): + print(f"[{num}] {title}") + + +def main(): + heading("MDF v2 Staging E2E — Domains & External Import") + + print("Authenticating with Globus...") + client = BackendClient.authenticated(base_url=STAGING_URL, service_instance="prod") + print(" Authenticated.\n") + + # ── Step 1: Submit with domains ───────────────────────────────────── + step(1, "Submit dataset with domains=['materials', 'chemistry']") + + result1 = client.submit({ + "title": "Domains E2E Test Dataset", + "authors": [ + {"name": "MDF Test Suite", "given_name": "MDF", "family_name": "Test Suite"}, + ], + "description": "E2E test: domains field.", + "keywords": ["test", "e2e", "domains"], + "data_sources": ["https://data.materialsdatafacility.org/test/e2e/domains.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "domains": ["materials", "chemistry"], + }) + if not result1.get("success"): + print(f" FAIL: {result1}") + sys.exit(1) + + sid1 = result1["source_id"] + print(f" Source ID: {sid1}") + print(f" Version: {result1['version']}") + + status1 = client.status(sid1) + sub1 = status1.get("submission", {}) + mdata1 = sub1.get("dataset_mdata", {}) + print(f" Status: {sub1.get('status')}") + print(f" Domains: {mdata1.get('domains')}") + print() + + # ── Step 2: Submit with external import fields ────────────────────── + step(2, "Submit externally-imported dataset") + + result2 = client.submit({ + "title": "External Import E2E Test Dataset", + "authors": [ + {"name": "MDF Test Suite", "given_name": "MDF", "family_name": "Test Suite"}, + ], + "description": "E2E test: external import from Zenodo.", + "keywords": ["test", "e2e", "external-import"], + "data_sources": ["https://data.materialsdatafacility.org/test/e2e/external.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "external_doi": "10.5281/zenodo.1234567", + "external_url": "https://zenodo.org/record/1234567", + "external_source": "Zenodo", + }) + if not result2.get("success"): + print(f" FAIL: {result2}") + sys.exit(1) + + sid2 = result2["source_id"] + print(f" Source ID: {sid2}") + print(f" Version: {result2['version']}") + + status2 = client.status(sid2) + sub2 = status2.get("submission", {}) + mdata2 = sub2.get("dataset_mdata", {}) + print(f" Status: {sub2.get('status')}") + print(f" external_doi: {mdata2.get('external_doi')}") + print(f" external_url: {mdata2.get('external_url')}") + print(f" external_source: {mdata2.get('external_source')}") + print() + + # ── Step 3: Submit with both domains and external import ──────────── + step(3, "Submit dataset with both domains and external import") + + result3 = client.submit({ + "title": "Combined Domains + External Import E2E Test", + "authors": [ + {"name": "MDF Test Suite", "given_name": "MDF", "family_name": "Test Suite"}, + ], + "description": "E2E test: both domains and external import fields.", + "keywords": ["test", "e2e", "combined"], + "data_sources": ["https://data.materialsdatafacility.org/test/e2e/combined.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "domains": ["biology"], + "external_doi": "10.5061/dryad.abc123", + "external_url": "https://datadryad.org/stash/dataset/abc123", + "external_source": "Dryad", + }) + if not result3.get("success"): + print(f" FAIL: {result3}") + sys.exit(1) + + sid3 = result3["source_id"] + print(f" Source ID: {sid3}") + print(f" Version: {result3['version']}") + + status3 = client.status(sid3) + sub3 = status3.get("submission", {}) + mdata3 = sub3.get("dataset_mdata", {}) + print(f" Status: {sub3.get('status')}") + print(f" Domains: {mdata3.get('domains')}") + print(f" external_doi: {mdata3.get('external_doi')}") + print(f" external_url: {mdata3.get('external_url')}") + print(f" external_source: {mdata3.get('external_source')}") + print() + + # ── Summary ───────────────────────────────────────────────────────── + heading("Assertions") + + checks = [ + ("Test 1: domains round-trip", + mdata1.get("domains") == ["materials", "chemistry"]), + ("Test 2: external_doi round-trip", + mdata2.get("external_doi") == "10.5281/zenodo.1234567"), + ("Test 2: external_url round-trip", + mdata2.get("external_url") == "https://zenodo.org/record/1234567"), + ("Test 2: external_source round-trip", + mdata2.get("external_source") == "Zenodo"), + ("Test 3: combined domains", + mdata3.get("domains") == ["biology"]), + ("Test 3: combined external_doi", + mdata3.get("external_doi") == "10.5061/dryad.abc123"), + ("Test 3: combined external_source", + mdata3.get("external_source") == "Dryad"), + ] + + for label, passed in checks: + mark = "PASS" if passed else "FAIL" + print(f" [{mark}] {label}") + + passed_count = sum(1 for _, p in checks if p) + print(f"\n {passed_count}/{len(checks)} checks passed\n") + + +if __name__ == "__main__": + main() diff --git a/examples/test_globus_direct_upload.py b/examples/test_globus_direct_upload.py new file mode 100644 index 0000000..303333f --- /dev/null +++ b/examples/test_globus_direct_upload.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +"""Direct Globus HTTPS upload test — no server in the middle. + +Authenticates with Globus, gets a data token for data.materialsdatafacility.org, +and tries a simple HTTPS PUT to /tmp/staging/ on the NCSA endpoint. + +Usage: + python examples/test_globus_direct_upload.py +""" + +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "src")) + +import httpx +from mdf_agent.auth.globus import ( + DATA_MDF_SCOPE, + MDF_CONNECT_SCOPE, + NCSA_MDF_COLLECTION_UUID, + get_authorizer_for_scopes, +) + +HTTPS_SERVER = "data.materialsdatafacility.org" +BASE_PATH = "/tmp/staging" +TEST_FILENAME = "mdf_upload_test.txt" +TEST_CONTENT = b"Hello from MDF Agent direct upload test!\n" + + +def extract_token(authorizer) -> str: + h = authorizer.get_authorization_header() + return h.removeprefix("Bearer ") + + +def main(): + print("1. Authenticating with Globus...") + authorizers = get_authorizer_for_scopes([MDF_CONNECT_SCOPE, DATA_MDF_SCOPE]) + + print(f" Authorizers obtained for: {list(authorizers.keys())}") + + data_auth = authorizers.get(NCSA_MDF_COLLECTION_UUID) + if not data_auth: + print(" ERROR: No authorizer for data.materialsdatafacility.org") + print(" Try: rm ~/.config/mdf_agent/tokens.json && mdf login --service prod") + sys.exit(1) + + data_token = extract_token(data_auth) + print(f" Data token: {data_token[:20]}...{data_token[-10:]}") + + # Also show auth.globus.org token for comparison + auth_auth = authorizers.get("auth.globus.org") + if auth_auth: + auth_token = extract_token(auth_auth) + print(f" Auth token: {auth_token[:20]}...{auth_token[-10:]}") + + url = f"https://{HTTPS_SERVER}{BASE_PATH}/{TEST_FILENAME}" + print(f"\n2. Uploading to: {url}") + + with httpx.Client(timeout=30.0, follow_redirects=True) as client: + resp = client.put( + url, + content=TEST_CONTENT, + headers={ + "Authorization": f"Bearer {data_token}", + "Content-Type": "text/plain", + }, + ) + print(f" PUT response: {resp.status_code}") + if resp.status_code >= 400: + print(f" Response body: {resp.text[:500]}") + + if resp.status_code < 300: + print("\n3. Verifying with GET...") + resp2 = client.get( + url, + headers={"Authorization": f"Bearer {data_token}"}, + ) + print(f" GET response: {resp2.status_code}") + if resp2.status_code == 200: + print(f" Content: {resp2.text}") + print("\n SUCCESS — upload and download both work!") + else: + print(f" GET failed: {resp2.text[:300]}") + + print("\n4. Cleaning up...") + resp3 = client.delete( + url, + headers={"Authorization": f"Bearer {data_token}"}, + ) + print(f" DELETE response: {resp3.status_code}") + + +if __name__ == "__main__": + main() diff --git a/examples/test_staging_e2e.py b/examples/test_staging_e2e.py new file mode 100644 index 0000000..4d9e50f --- /dev/null +++ b/examples/test_staging_e2e.py @@ -0,0 +1,113 @@ +#!/usr/bin/env python3 +"""E2E test of the v2 publication pipeline on staging. + +Runs: submit → verify pending_curation → approve (mint_doi=true) → verify published + DOI + +Usage: + python examples/test_staging_e2e.py +""" + +import sys +from pathlib import Path + +# Add src to path +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) + +from mdf_agent.core.backend_client import BackendClient + +STAGING_URL = "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging" + + +def heading(title): + print(f"\n{'=' * 60}") + print(f" {title}") + print(f"{'=' * 60}\n") + + +def main(): + heading("MDF v2 Staging E2E — Publication Pipeline") + + # Authenticate via Globus (will open browser if needed) + print("Authenticating with Globus...") + client = BackendClient.authenticated(base_url=STAGING_URL, service_instance="prod") + print(" Authenticated.\n") + + # Step 1: Health check + print("[1] Health check...") + health = client._request("GET", "/health") + print(f" {health}\n") + + # Step 2: Submit + print("[2] Submitting dataset...") + result = client.submit({ + "title": "E2E Pipeline Test — Real DOI Minting", + "authors": [ + {"name": "MDF Test Suite", "given_name": "MDF", "family_name": "Test Suite", "affiliation": "Materials Data Facility"}, + ], + "description": "Automated E2E test of the v2 publication pipeline with real DataCite DOI minting on staging.", + "keywords": ["test", "e2e", "publication-pipeline", "datacite"], + "data_sources": ["https://data.materialsdatafacility.org/test/e2e/sample.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "license": {"name": "CC-BY-4.0", "identifier": "CC-BY-4.0"}, + }) + print(f" Result: {result}") + + if not result.get("success"): + print(f"\n Submit failed: {result}") + sys.exit(1) + + source_id = result["source_id"] + version = result["version"] + print(f" Source ID: {source_id}") + print(f" Version: {version}\n") + + # Step 3: Check status — should be pending_curation + print("[3] Checking status...") + status = client.status(source_id) + current_status = status.get("submission", {}).get("status") + print(f" Status: {current_status}") + assert current_status == "pending_curation", f"Expected pending_curation, got {current_status}" + print(" OK: status is pending_curation\n") + + # Step 4: Approve with DOI minting + print("[4] Approving submission (mint_doi=true)...") + approve_result = client._request("POST", f"/curation/{source_id}/approve", json_data={ + "mint_doi": True, + "notes": "Automated E2E test approval", + }) + print(f" Result: {approve_result}\n") + + # Step 5: Check final status — should be published with DOI + print("[5] Checking final status...") + final_status = client.status(source_id) + submission = final_status.get("submission", {}) + print(f" Status: {submission.get('status')}") + print(f" DOI: {submission.get('doi')}") + print(f" Published at: {submission.get('published_at')}") + + # Step 6: Get citation + print("\n[6] Getting citation...") + citation = client.get_citation(source_id, format="bibtex") + if citation.get("success"): + print(f" BibTeX:\n {citation.get('bibtex', 'N/A')[:200]}") + else: + print(f" Citation: {citation}") + + # Step 7: Search + print("\n[7] Searching for dataset...") + search = client.search("E2E Pipeline Test") + print(f" Total results: {search.get('total', 0)}") + for r in search.get("results", [])[:3]: + print(f" - {r.get('title')} (status: {r.get('status')})") + + heading("Summary") + print(f" Source ID: {source_id}") + print(f" Status: {submission.get('status')}") + print(f" DOI: {submission.get('doi')}") + print(f" Published: {submission.get('published_at')}") + print() + + +if __name__ == "__main__": + main() diff --git a/examples/test_staging_search_e2e.py b/examples/test_staging_search_e2e.py new file mode 100644 index 0000000..2e7829f --- /dev/null +++ b/examples/test_staging_search_e2e.py @@ -0,0 +1,232 @@ +#!/usr/bin/env python3 +"""E2E test: submit → approve → poll for publish → verify Globus Search ingest. + +Tests the full v2 pipeline including real DataCite DOI minting, real +Globus Search index ingestion, and domains / external import fields on staging. + +Usage: + python examples/test_staging_search_e2e.py +""" + +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) + +from mdf_agent.core.backend_client import BackendClient + +STAGING_URL = "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging" + +# Unique tag so we can find this exact submission in search +RUN_TAG = f"search-e2e-{int(time.time())}" + + +def heading(title): + print(f"\n{'=' * 60}") + print(f" {title}") + print(f"{'=' * 60}\n") + + +def poll_status(client, source_id, target, timeout=90, interval=5): + """Poll until submission reaches target status or timeout.""" + deadline = time.time() + timeout + while time.time() < deadline: + resp = client.status(source_id) + sub = resp.get("submission", resp) + current = sub.get("status") + print(f" status={current}") + if current == target: + return sub + time.sleep(interval) + print(f" TIMEOUT waiting for status={target}") + return None + + +def main(): + heading("MDF v2 Staging E2E — Full Search Pipeline") + ok = True + + # -- Auth --------------------------------------------------------------- + print("Authenticating with Globus...") + client = BackendClient.authenticated( + base_url=STAGING_URL, service_instance="prod" + ) + print(" Authenticated.\n") + + # -- 1. Health ---------------------------------------------------------- + print("[1] Health check...") + health = client.health() + print(f" {health}") + assert health.get("status") == "ok", f"Health check failed: {health}" + print(" PASS\n") + + # -- 2. Submit ---------------------------------------------------------- + print("[2] Submitting dataset...") + payload = { + "title": f"Search Integration Test ({RUN_TAG})", + "authors": [ + { + "name": "Search Test Bot", + "given_name": "Search", + "family_name": "Test Bot", + "affiliations": ["Materials Data Facility"], + } + ], + "description": "Automated test verifying Globus Search index ingest on staging.", + "keywords": ["e2e", "search-test", RUN_TAG], + "data_sources": ["https://data.materialsdatafacility.org/test/search-e2e/sample.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "license": {"name": "CC-BY-4.0", "identifier": "CC-BY-4.0"}, + "domains": ["materials", "chemistry"], + "external_doi": "10.5281/zenodo.9999999", + "external_url": "https://zenodo.org/record/9999999", + "external_source": "Zenodo", + "test": True, + } + result = client.submit(payload) + print(f" {result}") + + if not result.get("source_id"): + print(f"\n FAIL: submit did not return source_id") + sys.exit(1) + + source_id = result["source_id"] + version = result["version"] + print(f" source_id = {source_id}") + print(f" version = {version}\n") + + # -- 3. Verify pending_curation ----------------------------------------- + print("[3] Checking initial status...") + status_resp = client.status(source_id) + sub = status_resp.get("submission", status_resp) + initial_status = sub.get("status") + print(f" status = {initial_status}") + assert initial_status == "pending_curation", f"Expected pending_curation, got {initial_status}" + print(" PASS\n") + + # -- 4. Approve with DOI minting ---------------------------------------- + print("[4] Approving (mint_doi=true)...") + approve = client.curation_approve( + source_id, mint_doi=True, notes="Automated search E2E test" + ) + print(f" {approve}") + approved_status = approve.get("status") + print(f" immediate status = {approved_status}") + # Staging uses SQS — approve returns "approved", publish happens async + assert approved_status in ("approved", "published"), f"Unexpected status: {approved_status}" + print(" PASS\n") + + # -- 5. Poll for published ---------------------------------------------- + print("[5] Polling for published status (SQS async, up to 90s)...") + final = poll_status(client, source_id, "published", timeout=90, interval=5) + if final: + doi = final.get("doi") + published_at = final.get("published_at") + print(f" PASS: published") + print(f" doi = {doi}") + print(f" published_at = {published_at}\n") + else: + print(" FAIL: never reached published status") + ok = False + + # -- 6. Verify domains & external import fields in status ---------------- + print("[6] Verifying domains & external import in dataset_mdata...") + mdata_resp = client.status(source_id) + mdata = mdata_resp.get("submission", {}).get("dataset_mdata", {}) + print(f" domains: {mdata.get('domains')}") + print(f" external_doi: {mdata.get('external_doi')}") + print(f" external_url: {mdata.get('external_url')}") + print(f" external_source: {mdata.get('external_source')}") + + if mdata.get("domains") == ["materials", "chemistry"]: + print(" PASS: domains round-trip") + else: + print(" FAIL: domains mismatch") + ok = False + + if ( + mdata.get("external_doi") == "10.5281/zenodo.9999999" + and mdata.get("external_url") == "https://zenodo.org/record/9999999" + and mdata.get("external_source") == "Zenodo" + ): + print(" PASS: external import fields round-trip") + else: + print(" FAIL: external import fields mismatch") + ok = False + + # MDF should have minted its own DOI, distinct from the external one + mdf_doi = mdata_resp.get("submission", {}).get("doi") + if mdf_doi and mdf_doi != "10.5281/zenodo.9999999": + print(f" PASS: MDF DOI ({mdf_doi}) != external DOI") + elif mdf_doi: + print(f" FAIL: MDF DOI equals external DOI") + ok = False + else: + print(" SKIP: no MDF DOI to compare (may be async)") + print() + + # -- 7. Search for the dataset ------------------------------------------ + # Globus Search ingest is async too — give it a moment + print("[7] Searching for dataset...") + time.sleep(3) + + search_result = client.search(RUN_TAG, limit=10) + total = search_result.get("total", 0) + results = search_result.get("results", []) + print(f" query = {RUN_TAG}") + print(f" total = {total}") + for r in results: + print(f" - {r.get('title', r.get('source_id', '?'))}") + + if total > 0: + print(" PASS: dataset found in search\n") + else: + # Search may be local fallback — try broader query + print(" Trying broader search (source_id)...") + search2 = client.search(source_id, limit=5) + total2 = search2.get("total", 0) + for r in search2.get("results", []): + print(f" - {r.get('title', r.get('source_id', '?'))}") + if total2 > 0: + print(" PASS: found via source_id search\n") + else: + print(" WARN: not found in search yet (Globus Search ingest may still be processing)\n") + ok = False + + # -- 8. Citation -------------------------------------------------------- + print("[8] Getting citation...") + cite = client.get_citation(source_id, format="bibtex") + bibtex = cite.get("bibtex", "") + if bibtex: + print(f" BibTeX (first 200 chars):") + print(f" {bibtex[:200]}") + print(" PASS\n") + else: + print(f" {cite}") + print(" WARN: no bibtex returned\n") + + # -- Summary ------------------------------------------------------------ + heading("Summary") + print(f" source_id {source_id}") + print(f" version {version}") + print(f" doi {final.get('doi') if final else 'N/A'}") + print(f" published_at {final.get('published_at') if final else 'N/A'}") + print(f" domains {mdata.get('domains')}") + print(f" external_doi {mdata.get('external_doi')}") + print(f" external_source {mdata.get('external_source')}") + print(f" search hit {total > 0}") + print(f" run tag {RUN_TAG}") + print() + + if ok: + print(" ALL CHECKS PASSED") + else: + print(" SOME CHECKS FAILED (see above)") + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/examples/test_versioning_e2e.py b/examples/test_versioning_e2e.py new file mode 100644 index 0000000..806c94e --- /dev/null +++ b/examples/test_versioning_e2e.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python3 +"""E2E test of dataset versioning on staging. + +Characterizes the versioning lifecycle: + [1] Submit v1.0 → approve with mint_doi=True → record DOI, status + [2] Submit v1.1 (update=True) → approve with mint_doi=False → inherits dataset DOI + [3] Submit v1.2 (update=True) → approve with mint_doi=True → version-specific DOI + [4] Query all 3 versions via status endpoint — compare doi fields + [5] Search for dataset — check what version appears, what DOI is shown + [6] Query DataCite test API for the DOI — check metadata + [7] Print summary + +Usage: + python examples/test_versioning_e2e.py +""" + +import json +import sys +from pathlib import Path +from urllib.parse import quote + +import httpx + +# Add src to path +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) + +from mdf_agent.core.backend_client import BackendClient + +STAGING_URL = "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging" +DATACITE_TEST_API = "https://api.test.datacite.org" + + +def heading(title): + print(f"\n{'=' * 60}") + print(f" {title}") + print(f"{'=' * 60}\n") + + +def step(num, title): + print(f"[{num}] {title}") + + +def check_datacite(doi): + """Query DataCite test API for a DOI's metadata.""" + try: + resp = httpx.get( + f"{DATACITE_TEST_API}/dois/{quote(doi, safe='')}", + timeout=15.0, + ) + if resp.status_code == 200: + data = resp.json().get("data", {}) + attrs = data.get("attributes", {}) + return { + "found": True, + "doi": data.get("id"), + "state": attrs.get("state"), + "title": (attrs.get("titles") or [{}])[0].get("title"), + "creators": [c.get("name") for c in attrs.get("creators", [])], + "relatedIdentifiers": attrs.get("relatedIdentifiers", []), + "version": attrs.get("version"), + } + return {"found": False, "status_code": resp.status_code} + except Exception as exc: + return {"found": False, "error": str(exc)} + + +def main(): + heading("MDF v2 Staging E2E — Dataset Versioning") + + print("Authenticating with Globus...") + client = BackendClient.authenticated(base_url=STAGING_URL, service_instance="prod") + print(" Authenticated.\n") + + # ── Step 1: Submit v1.0 ────────────────────────────────────────────── + step(1, "Submit v1.0 → approve with mint_doi=True") + + result = client.submit({ + "title": "Versioning E2E Test Dataset", + "authors": [ + {"name": "Test Author v1.0", "given_name": "Test", "family_name": "Author v1.0"}, + ], + "description": "v1.0 of the versioning E2E test dataset.", + "keywords": ["test", "versioning", "e2e"], + "data_sources": ["https://data.materialsdatafacility.org/test/versioning/v1.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "license": {"name": "CC-BY-4.0", "identifier": "CC-BY-4.0"}, + }) + if not result.get("success"): + print(f" FAIL: Submit v1.0 failed: {result}") + sys.exit(1) + + source_id = result["source_id"] + print(f" Source ID: {source_id}") + print(f" Version: {result['version']}") + + approve = client._request("POST", f"/curation/{source_id}/approve", json_data={ + "mint_doi": True, + "notes": "E2E versioning test — v1.0 with DOI", + }) + print(f" Approve result: status={approve.get('status')}") + + v1_status = client.status(source_id, version="1.0") + v1_sub = v1_status.get("submission", {}) + v1_doi = v1_sub.get("doi") + v1_dataset_doi = v1_sub.get("dataset_doi") + print(f" v1.0 doi: {v1_doi}") + print(f" v1.0 dataset_doi: {v1_dataset_doi}") + print(f" v1.0 status: {v1_sub.get('status')}") + print() + + # ── Step 2: Submit v1.1 (update, no new DOI) ──────────────────────── + step(2, "Submit v1.1 (update=True) → approve with mint_doi=False") + + result2 = client.submit({ + "title": "Versioning E2E Test Dataset — Updated", + "authors": [ + {"name": "Test Author v1.1", "given_name": "Test", "family_name": "Author v1.1"}, + ], + "description": "v1.1 of the versioning E2E test — updated metadata, inherits DOI.", + "keywords": ["test", "versioning", "e2e", "updated"], + "data_sources": ["https://data.materialsdatafacility.org/test/versioning/v1.1.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "update": True, + "extensions": {"mdf_source_id": source_id}, + }) + if not result2.get("success"): + print(f" FAIL: Submit v1.1 failed: {result2}") + sys.exit(1) + print(f" Version: {result2['version']}") + + approve2 = client._request("POST", f"/curation/{source_id}/approve", json_data={ + "mint_doi": False, + "version": result2["version"], + "notes": "E2E versioning test — v1.1, inherit DOI", + }) + print(f" Approve result: status={approve2.get('status')}") + + v11_status = client.status(source_id, version=result2["version"]) + v11_sub = v11_status.get("submission", {}) + v11_doi = v11_sub.get("doi") + v11_dataset_doi = v11_sub.get("dataset_doi") + print(f" v1.1 doi: {v11_doi}") + print(f" v1.1 dataset_doi: {v11_dataset_doi}") + print(f" v1.1 status: {v11_sub.get('status')}") + print() + + # ── Step 3: Submit v1.2 (update, mint version DOI) ────────────────── + step(3, "Submit v1.2 (update=True) → approve with mint_doi=True") + + result3 = client.submit({ + "title": "Versioning E2E Test Dataset — v1.2 Release", + "authors": [ + {"name": "Test Author v1.2", "given_name": "Test", "family_name": "Author v1.2"}, + {"name": "New Collaborator", "given_name": "New", "family_name": "Collaborator"}, + ], + "description": "v1.2 of the versioning E2E test — new version DOI.", + "keywords": ["test", "versioning", "e2e", "v1.2"], + "data_sources": ["https://data.materialsdatafacility.org/test/versioning/v1.2.csv"], + "publisher": "Materials Data Facility", + "publication_year": 2026, + "update": True, + "extensions": {"mdf_source_id": source_id}, + }) + if not result3.get("success"): + print(f" FAIL: Submit v1.2 failed: {result3}") + sys.exit(1) + print(f" Version: {result3['version']}") + + approve3 = client._request("POST", f"/curation/{source_id}/approve", json_data={ + "mint_doi": True, + "version": result3["version"], + "notes": "E2E versioning test — v1.2, version-specific DOI", + }) + print(f" Approve result: status={approve3.get('status')}") + + v12_status = client.status(source_id, version=result3["version"]) + v12_sub = v12_status.get("submission", {}) + v12_doi = v12_sub.get("doi") + v12_dataset_doi = v12_sub.get("dataset_doi") + print(f" v1.2 doi: {v12_doi}") + print(f" v1.2 dataset_doi: {v12_dataset_doi}") + print(f" v1.2 status: {v12_sub.get('status')}") + print() + + # ── Step 4: Query all versions ────────────────────────────────────── + step(4, "Query all versions via status endpoint") + + for ver_label, ver in [("v1.0", "1.0"), ("v1.1", result2["version"]), ("v1.2", result3["version"])]: + s = client.status(source_id, version=ver) + sub = s.get("submission", {}) + print(f" {ver_label}: status={sub.get('status')}, doi={sub.get('doi')}, dataset_doi={sub.get('dataset_doi')}") + print() + + # ── Step 5: Search ────────────────────────────────────────────────── + step(5, "Search for dataset") + search = client.search("Versioning E2E Test Dataset") + print(f" Total results: {search.get('total', 0)}") + for r in search.get("results", [])[:5]: + print(f" - {r.get('title')} (source_id={r.get('source_id')}, version={r.get('version')})") + print() + + # ── Step 6: Check DataCite ────────────────────────────────────────── + step(6, "Query DataCite test API") + + if v1_doi: + dc_dataset = check_datacite(v1_doi) + print(f" Dataset DOI ({v1_doi}):") + print(f" found: {dc_dataset.get('found')}") + print(f" title: {dc_dataset.get('title')}") + print(f" creators: {dc_dataset.get('creators')}") + print(f" relatedIdentifiers: {dc_dataset.get('relatedIdentifiers')}") + else: + print(" No dataset DOI to check.") + + if v12_doi and v12_doi != v1_doi: + dc_version = check_datacite(v12_doi) + print(f" Version DOI ({v12_doi}):") + print(f" found: {dc_version.get('found')}") + print(f" title: {dc_version.get('title')}") + print(f" relatedIdentifiers: {dc_version.get('relatedIdentifiers')}") + else: + print(f" v1.2 DOI same as dataset DOI or not set ({v12_doi})") + print() + + # ── Step 7: Summary ───────────────────────────────────────────────── + heading("Summary") + print(f" Source ID: {source_id}") + print(f" v1.0 doi: {v1_doi}") + print(f" v1.0 dataset_doi:{v1_dataset_doi}") + print(f" v1.1 doi: {v11_doi}") + print(f" v1.1 dataset_doi:{v11_dataset_doi}") + print(f" v1.2 doi: {v12_doi}") + print(f" v1.2 dataset_doi:{v12_dataset_doi}") + print() + + # Assertions for post-implementation verification + heading("Assertions (post-implementation)") + checks = [] + + # v1.0: doi and dataset_doi should both be set and equal + if v1_doi and v1_dataset_doi and v1_doi == v1_dataset_doi: + checks.append(("v1.0: doi == dataset_doi", True)) + elif v1_doi and not v1_dataset_doi: + checks.append(("v1.0: doi set but dataset_doi missing (pre-implementation)", False)) + else: + checks.append(("v1.0: doi/dataset_doi mismatch", False)) + + # v1.1: no version doi, but dataset_doi inherited + if not v11_doi and v11_dataset_doi: + checks.append(("v1.1: no doi, dataset_doi inherited", True)) + elif v11_doi: + checks.append(("v1.1: unexpected doi set (pre-implementation may do this)", False)) + else: + checks.append(("v1.1: no doi and no dataset_doi", False)) + + # v1.2: version-specific doi different from dataset_doi + if v12_doi and v12_dataset_doi and v12_doi != v12_dataset_doi: + checks.append(("v1.2: version doi != dataset_doi", True)) + elif v12_doi and v12_doi == v12_dataset_doi: + checks.append(("v1.2: version doi == dataset_doi (pre-implementation)", False)) + else: + checks.append(("v1.2: missing doi or dataset_doi", False)) + + for label, passed in checks: + status_mark = "PASS" if passed else "FAIL" + print(f" [{status_mark}] {label}") + print() + + passed_count = sum(1 for _, p in checks if p) + print(f" {passed_count}/{len(checks)} checks passed") + print() + + +if __name__ == "__main__": + main() diff --git a/examples/xrd-patterns/.mdf/state.json b/examples/xrd-patterns/.mdf/state.json new file mode 100644 index 0000000..fd19fd3 --- /dev/null +++ b/examples/xrd-patterns/.mdf/state.json @@ -0,0 +1,6 @@ +{ + "version": "1", + "root": "/Users/ben/Desktop/git/mdf_client/examples/xrd-patterns", + "staged_files": [], + "commits": [] +} \ No newline at end of file diff --git a/examples/xrd-patterns/instrument.yaml b/examples/xrd-patterns/instrument.yaml new file mode 100644 index 0000000..bf734fe --- /dev/null +++ b/examples/xrd-patterns/instrument.yaml @@ -0,0 +1,31 @@ +instrument: + name: "Rigaku SmartLab" + type: "X-ray Diffractometer" + location: "MIT Materials Characterization Lab" + +source: + type: "Cu K-alpha" + wavelength_A: 1.5406 + voltage_kV: 40 + current_mA: 30 + +detector: + type: "D/teX Ultra 250" + mode: "1D" + +scan_parameters: + start_2theta_deg: 20 + end_2theta_deg: 90 + step_size_deg: 0.02 + scan_speed_deg_min: 2.0 + +samples: + - id: "sample_001" + composition: "Fe2O3" + preparation: "Ball milled 4 hours" + temperature_C: 25 + + - id: "sample_002" + composition: "Fe3O4" + preparation: "Hydrothermal synthesis" + temperature_C: 25 diff --git a/examples/xrd-patterns/mdf.yaml b/examples/xrd-patterns/mdf.yaml new file mode 100644 index 0000000..e38391e --- /dev/null +++ b/examples/xrd-patterns/mdf.yaml @@ -0,0 +1,10 @@ +title: XRD Patterns of Iron Oxide Nanoparticles +authors: +- Ben Johnson +- Sarah Williams +description: X-ray diffraction patterns for Fe2O3 and Fe3O4 nanoparticles +data_sources: [] + +data_sources: + - "./sample_001.csv" + - "./sample_002.csv" diff --git a/examples/xrd-patterns/sample_001.csv b/examples/xrd-patterns/sample_001.csv new file mode 100644 index 0000000..26ef705 --- /dev/null +++ b/examples/xrd-patterns/sample_001.csv @@ -0,0 +1,16 @@ +two_theta_deg,intensity_counts,d_spacing_A +20.5,1234,4.328 +25.3,5678,3.517 +30.1,2345,2.966 +35.8,8901,2.506 +40.2,3456,2.241 +45.6,6789,1.988 +50.3,4567,1.813 +55.1,2345,1.665 +60.8,5678,1.522 +65.2,3456,1.430 +70.5,7890,1.335 +75.3,2345,1.261 +80.1,4567,1.197 +85.6,1234,1.134 +90.2,3456,1.087 diff --git a/examples/xrd-patterns/sample_002.csv b/examples/xrd-patterns/sample_002.csv new file mode 100644 index 0000000..f725221 --- /dev/null +++ b/examples/xrd-patterns/sample_002.csv @@ -0,0 +1,16 @@ +two_theta_deg,intensity_counts,d_spacing_A +21.2,2345,4.186 +26.1,6789,3.411 +31.5,3456,2.837 +36.2,9012,2.479 +41.8,4567,2.159 +46.3,7890,1.959 +51.9,5678,1.760 +56.7,3456,1.622 +61.2,6789,1.513 +66.8,4567,1.399 +71.3,8901,1.321 +76.9,2345,1.239 +81.5,5678,1.181 +86.2,3456,1.127 +91.8,2345,1.071 diff --git a/legacy/.github/workflows/python-publish.yml b/legacy/.github/workflows/python-publish.yml new file mode 100644 index 0000000..c42bd06 --- /dev/null +++ b/legacy/.github/workflows/python-publish.yml @@ -0,0 +1,30 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries +name: Upload Python Package to PYPI + +on: + release: + types: [created] + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* \ No newline at end of file diff --git a/legacy/.github/workflows/testing-work.yml b/legacy/.github/workflows/testing-work.yml new file mode 100644 index 0000000..5e686a3 --- /dev/null +++ b/legacy/.github/workflows/testing-work.yml @@ -0,0 +1,39 @@ +name: Package Testing + +on: + pull_request: + branches: + - dev + - master + - update-test + +jobs: + + build: + runs-on: ubuntu-latest + timeout-minutes: 20 + strategy: + matrix: + python-version: [3.7, 3.8, 3.9] + + env: + CLIENT_ID: ${{ secrets.CLIENT_ID }} + CLIENT_SECRET: ${{ secrets.CLIENT_SECRET }} + name: build + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e . + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install -r test_requirements.txt + + - name: Test with pytest + run: | + pytest -s tests/test_connect_client.py diff --git a/legacy/.gitignore b/legacy/.gitignore new file mode 100644 index 0000000..3f0628f --- /dev/null +++ b/legacy/.gitignore @@ -0,0 +1,19 @@ +*.pyc +*.swp +*.swo +*login* +*.ipynb_checkpoints +.venv +Untitled*.ipynb +.idea +*.cache* +*pytest_cache* + +*temp/* + +**/build/ +**/dist/ +*.egg* + +travis.tar +.coverage diff --git a/legacy/LICENSE b/legacy/LICENSE new file mode 100644 index 0000000..16b6481 --- /dev/null +++ b/legacy/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2017 The University of Chicago + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/legacy/NOTICE b/legacy/NOTICE new file mode 100644 index 0000000..7869c77 --- /dev/null +++ b/legacy/NOTICE @@ -0,0 +1,6 @@ +Materials Data Facility Forge (MDF Forge) +Copyright 2017 The University of Chicago. + +This product includes software developed at Globus (http://www.globus.org/). + +This software was developed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD). This work was also supported by the National Science Foundation as part of the Midwest Big Data Hub under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate". diff --git a/legacy/README.md b/legacy/README.md new file mode 100644 index 0000000..c7bc715 --- /dev/null +++ b/legacy/README.md @@ -0,0 +1,31 @@ +# MDF Connect Client +[![PyPI](https://img.shields.io/pypi/v/mdf_connect_client.svg)](https://pypi.python.org/pypi/mdf-connect-client) +[![Coverage Status](https://coveralls.io/repos/github/materials-data-facility/connect_client/badge.svg?branch=master)](https://coveralls.io/github/materials-data-facility/connect_client?branch=master) +![GHA](https://github.com/materials-data-facility/connect_client/actions/workflows/testing-work.yml/badge.svg) + +The MDF Connect Client is the Python client to easily submit datasets to MDF Connect. + +# Installation + +``` +pip install mdf_connect_client +``` + +### For Developers +``` +git clone https://github.com/materials-data-facility/connect_client.git +cd connect_client +pip install -e . +``` + +# Documentation and examples +Documentation is available on [Read the Docs](https://mdf-connect-client.readthedocs.io/) and [GitHub](https://github.com/materials-data-facility/connect_client/tree/master/docs/). [Examples and tutorials](https://github.com/materials-data-facility/connect_client/tree/master/docs/tutorials) are provided as Jupyter notebooks, which can optionally be run interactively with [Jupyter](http://jupyter.org/). + + +# Requirements +* The Connect Client requires Python 3.5 or greater. +* To submit data to MDF Connect, you must have an account recognized by Globus Auth (including Google, ORCiD, many academic institutions, or a [free Globus ID](https://www.globusid.org/create)), and be a member of the [MDF Connect Convert Globus Group](https://app.globus.org/groups/cc192dca-3751-11e8-90c1-0a7c735d220a). + + +# Support +This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the [Center for Hierarchical Material Design (CHiMaD)](http://chimad.northwestern.edu). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the [Midwest Big Data Hub](http://midwestbigdatahub.org) under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate". diff --git a/legacy/docs/sphinx/Makefile b/legacy/docs/sphinx/Makefile new file mode 100644 index 0000000..d4dc0eb --- /dev/null +++ b/legacy/docs/sphinx/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = mdf-connect-client +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/legacy/docs/sphinx/source/conf.py b/legacy/docs/sphinx/source/conf.py new file mode 100644 index 0000000..689dc04 --- /dev/null +++ b/legacy/docs/sphinx/source/conf.py @@ -0,0 +1,180 @@ +# -*- coding: utf-8 -*- +# +# Configuration file for the Sphinx documentation builder. +# +# This file does only contain a selection of the most common options. For a +# full list see the documentation: +# http://www.sphinx-doc.org/en/master/config + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) +import sphinx_bootstrap_theme + + +# -- Project information ----------------------------------------------------- + +project = 'MDF Connect Client' +copyright = '2018, The University of Chicago' +author = 'The University of Chicago' + +# The short X.Y version +version = '' +# The full version, including alpha/beta/rc tags +release = '' + + +# -- General configuration --------------------------------------------------- + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.coverage', + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', + 'sphinx.ext.mathjax', + 'm2r', + 'nbsphinx' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +# source_suffix = ['.rst', '.md'] +source_suffix = ['.rst', '.md', '.ipynb'] + +# The master toctree document. +master_doc = 'index' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path . +exclude_patterns = ['_build', '**.ipynb_checkpoints'] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'bootstrap' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} +html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = [] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# The default sidebars (for documents that don't match any pattern) are +# defined by theme itself. Builtin themes are using these templates by +# default: ``['localtoc.html', 'relations.html', 'sourcelink.html', +# 'searchbox.html']``. +# +# html_sidebars = {} + + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'mdf-connect-clientdoc' + + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'mdf-connect-client.tex', 'mdf-connect-client Documentation', + 'jgaff', 'manual'), +] + + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'mdf-connect-client', 'mdf-connect-client Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'mdf-connect-client', 'mdf-connect-client Documentation', + author, 'mdf-connect-client', 'One line description of project.', + 'Miscellaneous'), +] + + +# -- Extension configuration ------------------------------------------------- +# Napoleon settings +# see http://www.sphinx-doc.org/en/stable/ext/napoleon.html +napoleon_google_docstring = True +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = True +napoleon_use_admonition_for_examples = False +napoleon_use_admonition_for_notes = True +napoleon_use_admonition_for_references = False +napoleon_use_ivar = False +napoleon_use_param = True +napoleon_use_rtype = True diff --git a/legacy/docs/sphinx/source/index.rst b/legacy/docs/sphinx/source/index.rst new file mode 100644 index 0000000..8a590de --- /dev/null +++ b/legacy/docs/sphinx/source/index.rst @@ -0,0 +1,17 @@ +.. mdf-connect-client documentation master file, created by + sphinx-quickstart on Tue Dec 11 10:36:57 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + + +Index +===== + +.. toctree:: + :maxdepth: 2 + + MDF Connect Client + Tutorials + + +.. mdinclude:: ../../../README.md diff --git a/legacy/docs/sphinx/source/mdf_connect_client.rst b/legacy/docs/sphinx/source/mdf_connect_client.rst new file mode 100644 index 0000000..bb474a9 --- /dev/null +++ b/legacy/docs/sphinx/source/mdf_connect_client.rst @@ -0,0 +1,6 @@ + +MDF Connect Client +================== +.. autoclass:: mdf_connect_client.MDFConnectClient + :members: + diff --git a/legacy/docs/sphinx/source/tutorial_list.rst b/legacy/docs/sphinx/source/tutorial_list.rst new file mode 100644 index 0000000..69b9112 --- /dev/null +++ b/legacy/docs/sphinx/source/tutorial_list.rst @@ -0,0 +1,9 @@ +Connect Tutorials +================= + + +.. toctree:: + + tutorials/Basic_MDF_Connect_Submission_Example + tutorials/MDF_Connect_Client_Tutorial + diff --git a/legacy/docs/sphinx/source/tutorials b/legacy/docs/sphinx/source/tutorials new file mode 120000 index 0000000..8b8f5e2 --- /dev/null +++ b/legacy/docs/sphinx/source/tutorials @@ -0,0 +1 @@ +../../tutorials \ No newline at end of file diff --git a/legacy/docs/tutorials/MDF_Connect_Client_Tutorial.ipynb b/legacy/docs/tutorials/MDF_Connect_Client_Tutorial.ipynb new file mode 100644 index 0000000..dd5b316 --- /dev/null +++ b/legacy/docs/tutorials/MDF_Connect_Client_Tutorial.ipynb @@ -0,0 +1,2564 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MDF Connect Client Tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from mdf_connect_client import MDFConnectClient\n", + "\n", + "# These imports are only necessary for the examples in this notebook;\n", + "# they are not necessary for regular use of the client.\n", + "from datetime import datetime\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of contents\n", + "\n", + "- [Overview/instantiation](#MDF-Connect-Client)\n", + "- [Mandatory inputs](#Mandatory-inputs)\n", + "- [Recommended inputs](#Recommended-inputs)\n", + "- [Optional inputs](#Optional-inputs)\n", + "- [Advanced inputs](#Advanced-inputs)\n", + "- [Submitting a dataset](#Submitting-a-dataset)\n", + "- [Checking submission status](#Checking-submission-status)\n", + "- [Curating a submission](#Curating-a-submission)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MDF Connect Client\n", + "The MDF Connect Client (`MDFConnectClient`) is a class designed to help you submit datasets to MDF Connect using Python. When you instantiate the Client, it will attempt to authenticate you with Globus automatically. You cannot use MDF Connect anonymously.\n", + "\n", + "Note: While you can access and modify the internal variables of a client (for example, `mdfcc.mdf`), it is recommended that you instead only use the helper functions. This tutorial accesses those variables only for display purposes.\n", + "\n", + "**IMPORTANT**: To submit data to MDF Connect, you must have an account recognized by Globus Auth (including Google, ORCiD, many academic institutions, or a [free Globus ID](https://www.globusid.org/create)). Additionally, you must be in the [MDF Connect](https://www.globus.org/app/groups/cc192dca-3751-11e8-90c1-0a7c735d220a/about) Globus Group." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** You will see the comment `# NBVAL_SKIP` in some of these examples. It is an administrative flag. It does not affect the content of the example and can be safely ignored." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MDFCC Constructor (`MDFConnectClient`)\n", + "It is recommended that you use the helper functions (detailed below) to construct your MDF Connect submission. However, if you have already assembled all or part of your submission, you can pre-load your client with the appropriate metadata.\n", + "\n", + "Note: If you have the entire submission already prepared, you can skip to [Submitting a dataset](#Submitting-a-dataset) and pass your submission directly to `submit_dataset()`.\n", + "\n", + "#### Optional arguments:\n", + "- `test` (boolean): When `True`, enables test mode. When `False`, disables test mode. For more information about test mode, see `set_test()`.\n", + "\n", + "#### Advanced optional arguments (developer use only):\n", + "- `service_instance` (string): The instance of the MDF Connect service to use. Normal users should not alter this value.\n", + "- `authorizer` (Authorizer): A valid, authenticated Authorizer from the Globus SDK. Normal users do not need to change this value. Accepted Authorizers are:\n", + " - globus_sdk.RefreshTokenAuthorizer\n", + " - globus_sdk.ClientCredentialsAuthorizer\n", + " - globus_sdk.NullAuthorizer (This Authorizer will fail authentication.)\n", + "\n", + "The advanced arguments may cause issues, and are not recommended for normal users." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mdfcc = MDFConnectClient(test=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `logout`\n", + "If you want to log out and invalidate your current login, you can call `logout()`. This method clears your current submission and invalidates your current authentication tokens.\n", + "\n", + "Once this method is called, you must create a new MDF Connect Client in order to interact with MDF Connect." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "# mdfcc.logout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Mandatory inputs\n", + "\n", + "- [`create_dc_block`](#create_dc_block)\n", + "- [`add_data_source`](#add_data_source)\n", + "\n", + "These helpers are for inputs that are mandatory to provide. All required arguments for these required inputs must be supplied. Your submission will be rejected if you do not provide this information." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `create_dc_block`\n", + "The `dc` (DataCite) block is mandatory for all submissions. `create_dc_block()` helps create the `dc` block for you.\n", + "\n", + "#### Required arguments:\n", + "- `title` (string): The title of the dataset.\n", + "- `authors` (string or list of strings): The authors of the dataset, in one of these forms:\n", + " - \"Givenname Familyname\"\n", + " - \"Familyname, Givenname\"\n", + " - \"Familyname; Givenname\"\n", + "\n", + "#### Arguments with defaults:\n", + "- `publisher` (string): The publisher of this dataset (*not* an associated paper). The default is `\"Materials Data Facility\"`.\n", + "- `publication_year` (integer or string): The year the dataset was published. The default is the current year.\n", + "- `resource_type` (string): The type of resource. Except in unusual cases, this should be `\"Dataset\"`. The default is `\"Dataset\"`. Unless you know that your submission needs a different value, please leave it as the default.\n", + "\n", + "#### Optional arguments (not present by default):\n", + "- `affiliations` (string or list of strings): The affiliations of the authors, in the same order as the authors. If a different number of affiliations are given, all affiliations will be applied to all authors. Multiple author affiliations can be given as a list. (See examples below for more details.)\n", + "- `description` (string): A description of the dataset.\n", + "- `dataset_doi` (string): The DOI for this dataset (*not* an associated paper).\n", + "- `related_dois` (string or list of strings): DOIs related to this dataset, such as an associated paper's DOI. This *does not* include a DOI for the dataset itself. \n", + "- `subjects` (string or list of strings): Subjects (in Datacite terminology) or tags related to the dataset.\n", + "\n", + "\n", + "If you understand the DataCite schema, you can also add other keyword arguments corresponding to DataCite fields. Additional information on DataCite fields is available from the [official DataCite website](https://schema.datacite.org/meta/kernel-4.1/).\n", + "\n", + "You cannot clear the `dc` block. You can overwrite the `dc` block by calling this method again." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "# Extra affiliations examples\n", + "# Assume we have three authors: Alice, Bob, and Cathy\n", + "authors = [\"Fromnist, Alice\", \"Fromnist; Bob\", \"Cathy Multiples\"]\n", + "\n", + "# If all authors are from NIST:\n", + "affiliations = \"NIST\"\n", + "# Equivalent to [\"NIST\", \"NIST\", \"NIST\"]\n", + "\n", + "# If all authors are from both NIST and UChicago:\n", + "affiliations = [\"NIST\", \"UChicago\"]\n", + "# Equivalent to [[\"NIST\", \"UChicago\"], [\"NIST\", \"UChicago\"], [\"NIST\", \"UChicago\"]]\n", + "\n", + "# If Alice and Bob are from NIST, Cathy is from NIST and UChicago:\n", + "affliliations = [\"NIST\", \"NIST\", [\"NIST\", \"UChicago\"]]\n", + "# This is the only way to express these affiliations\n", + "\n", + "# This is incorrect! If applying affiliations to all authors, lists must not be nested.\n", + "# These apply to all authors because there are 4 affiliations for 3 authors.\n", + "affiliations = [\"NIST\", [\"NIST\", \"UChicago\"], \"Argonne\", \"Oak Ridge\"]\n", + "# Do not use this format, it is incorrect for this number of authors." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'titles': [{'title': 'Sample Submission for Tutorial'}],\n", + " 'creators': [{'creatorName': 'Smith, Foo',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Foo',\n", + " 'affiliations': ['The International Institute of Data']},\n", + " {'creatorName': 'Smith, Bar',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Bar',\n", + " 'affiliations': ['The International Institute of Data']},\n", + " {'creatorName': 'Baz, Smith;',\n", + " 'familyName': 'Baz',\n", + " 'givenName': 'Smith;',\n", + " 'affiliations': ['The International Institute of Data']}],\n", + " 'publisher': 'The Journal of Datasets',\n", + " 'publicationYear': '2000',\n", + " 'resourceType': {'resourceTypeGeneral': 'Dataset', 'resourceType': 'Dataset'},\n", + " 'descriptions': [{'description': 'This is an example submission.',\n", + " 'descriptionType': 'Other'}],\n", + " 'identifier': {'identifier': '10.1234/dataset', 'identifierType': 'DOI'},\n", + " 'relatedIdentifiers': [{'relatedIdentifier': '10.1234/paper1',\n", + " 'relatedIdentifierType': 'DOI',\n", + " 'relationType': 'IsPartOf'},\n", + " {'relatedIdentifier': '10.1234/paper2',\n", + " 'relatedIdentifierType': 'DOI',\n", + " 'relationType': 'IsPartOf'}],\n", + " 'subjects': [{'subject': 'Examples'}, {'subject': 'Datasets'}]}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.create_dc_block(title=\"Sample Submission for Tutorial\",\n", + " authors=[\"Foo Smith\", \"Smith, Bar\", \"Smith; Baz\"],\n", + " affiliations=[\"The International Institute of Data\"],\n", + " publisher=\"The Journal of Datasets\",\n", + " publication_year=2000,\n", + " description=\"This is an example submission.\",\n", + " dataset_doi=\"10.1234/dataset\",\n", + " related_dois=[\"10.1234/paper1\", \"10.1234/paper2\"],\n", + " subjects=[\"Examples\", \"Datasets\"])\n", + "mdfcc.dc" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'titles': [{'title': 'Tutorial Example Two'}],\n", + " 'creators': [{'creatorName': 'Smith, Foo',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Foo',\n", + " 'affiliations': ['Foo University']},\n", + " {'creatorName': 'Smith, Bar',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Bar',\n", + " 'affiliations': ['The Bureau of Bar']}],\n", + " 'publisher': 'Materials Data Facility',\n", + " 'publicationYear': '2020',\n", + " 'resourceType': {'resourceTypeGeneral': 'Dataset', 'resourceType': 'Dataset'}}" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.create_dc_block(title=\"Tutorial Example Two\",\n", + " authors=[\"Foo Smith\", \"Smith, Bar\"],\n", + " affiliations=[\"Foo University\", \"The Bureau of Bar\"])\n", + "\n", + "mdfcc.dc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_data_source`\n", + "Some kind of data is mandatory for all submissions. `add_data_source()` will add a data location to your dataset. This action is cumulative, so each calls adds more data. Subsequent calls do not overwrite.\n", + "\n", + "#### Required arguments:\n", + "- `data_source` (string): The location of the data.\n", + "\n", + "You can add data located at a Globus endpoint, HTTP(S) link, or on Google Drive. MDF Connect will extract data located in archives, including zip files.\n", + "\n", + "- Globus endpoint: `globus://endpoint_id/path/to/data` or you can copy the \"Get link\" link from the Globus Web App. Your Globus account must have permission to read the data.\n", + "- HTTP(S): Copy the link to the data file (NOT a landing page) exactly. The data must be accessible without authentication.\n", + "- Google Drive: `googledrive:///path/from/shared/location`. You must share the data with materialsdatafacility@gmail.com.\n", + "\n", + "To clear all data from the submission, call `clear_data_sources()`." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://dl.dropboxusercontent.com/u/12345/abcdef',\n", + " 'googledrive:///mydata.zip',\n", + " 'globus://1a2b3c/data/']" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_data_source(\"https://dl.dropboxusercontent.com/u/12345/abcdef\")\n", + "mdfcc.add_data_source([\"googledrive:///mydata.zip\", \"globus://1a2b3c/data/\"])\n", + "mdfcc.data_sources" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_data_sources()\n", + "mdfcc.data_sources" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['https://www.globus.org/app/transfer?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAlFe%2F']" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This is the actual test data, using a Globus Web App link\n", + "mdfcc.add_data_source(\"https://www.globus.org/app/transfer?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAlFe%2F\")\n", + "mdfcc.data_sources" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Recommended inputs\n", + "\n", + "- [`add_tag`](#add_tag)\n", + "- [`add_index`](#add_index)\n", + "- [`add_service`](#add_service)\n", + "- [`set_test`](#set_test)\n", + "- [`add_organization`](#add_organization)\n", + "\n", + "These helpers are for inputs that MDF recommends you provide or consider, but are not required." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_tag`\n", + "`add_tag()` will add tags (also known as \"subjects\" in the DataCite schema, or \"keywords\") to your dataset.\n", + "\n", + "`add_tag(\"tag\")` is equivalent to `create_dc_block(..., subjects=[\"tag\"])`. This method exists for convenience of managing tags.\n", + "\n", + "#### Required arguments:\n", + "- `tag` (string or list of strings): The tag to add.\n", + "\n", + "To clear all of the tags added with `add_tag()`, call `clear_tags()`. Note that `clear_tags()` does not remove tags set through `create_dc_block()`." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['example']" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_tag(\"example\")\n", + "mdfcc.tags" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_tags()\n", + "mdfcc.tags" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_index`\n", + "To extract JSON, CSV, YAML, XML, or Excel files, MDF Connect requires a mapping that translates the file into MDF schema format. `add_index()` will add a mapping for a specific data type to your submission.\n", + "\n", + "#### Required arguments:\n", + "- `data_type` (string): The type of data being mapped. Supported types include:\n", + " - `json`\n", + " - `csv`\n", + " - `yaml`\n", + " - `xml`\n", + " - `excel`\n", + " - `filename` (This type is special; see below.)\n", + "- `mapping` (dictionary of strings): The mapping of MDF fields to your data type's fields (see below).\n", + "\n", + "#### Arguments with defaults:\n", + "- `delimiter` (string): For tabular data (ex. CSV), the column delimiter. The default is \",\" (comma).\n", + "- `na_values` (string or list of strings): Values to treat as \"data missing\" entries. The default for tabular data (ex. CSV), v is blank and space, while the default for other types (ex. JSON) is nothing (no values will be discarded).\n", + "\n", + "#### About `mapping`:\n", + "Mappings must be dictionaries, where the key is the MDF schema field (expressed in dot notation) and the value is the data's field or column name. \"Dot notation\" means one string that uses a period between dictionary levels. For example, `block.field.subfield` is the dot notation equivalent of `my_dict[\"block\"][\"field\"][\"subfield\"]` in Python.\n", + "The exception to this is `filename` mapping. To extract data from a file's name, create a regular expression that returns the correct information. The mapping field is still the associated MDF field in dot notation, but the mapping value is the regular expression you created.\n", + "\n", + "Fields with missing data will be ignored. If you have multiple schemas for one data type in one dataset, you can combine the mappings safely.\n", + "\n", + "Each data type can only have one associated mapping, so multiple calls with the same data type will overwrite. Calls with different data types will not overwrite. To clear all the mappings, call `clear_index()`.\n", + "\n", + "For more information on the MDF schemas, see the [official schema repository](https://github.com/materials-data-facility/data-schemas)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the following example, assume we're submitting a dataset that contains a JSON file structured like this:\n", + "```json\n", + "{\n", + " \"my_data\": {\n", + " \"mat\": {\n", + " \"comp\": \"H\"\n", + " },\n", + " \"atom_num\": 1\n", + " },\n", + " \"space_grp\": 10\n", + "}\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "# This is the mapping we would use to get the JSON file into MDF format.\n", + "mapping = {\n", + " \"material.composition\": \"my_data.mat.comp\",\n", + " \"crystal_structure.number_of_atoms\": \"my_data.atom_num\",\n", + " \"crystal_structure.space_group_number\": \"space_grp\",\n", + " # We could add another field here, if we had multiple JSON schemas.\n", + " \"dft.converged\": \"dft_info.conv\"\n", + " # This field would be ignored by MDF Connect in this submission because the field doesn't exist in the data.\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'json': {'mapping': {'material.composition': 'my_data.mat.comp',\n", + " 'crystal_structure.number_of_atoms': 'my_data.atom_num',\n", + " 'crystal_structure.space_group_number': 'space_grp',\n", + " 'dft.converged': 'dft_info.conv'}}}" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_index(\"json\", mapping)\n", + "mdfcc.index" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_index()\n", + "mdfcc.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_service`\n", + "MDF Connect has integrations to submit data to other community services, as well as additional MDF-related options. To automatically submit your dataset to an integrated service, use `add_service()`.\n", + "\n", + "#### Required arguments:\n", + "- `service` (string): One service to push your dataset to. Integrated services include:\n", + " - `mdf_publish`, the MDF publication service with DOI minting\n", + " - `citrine`, industry-partnered machine-learning specialists\n", + " - `mrr`, the NIST Materials Resource Registry\n", + "\n", + "#### Arguments with defaults:\n", + "- `parameters` (dictionary): Optional, service-specific parameters. Fields include:\n", + " - For `mdf_publish`:\n", + " - publication_location (string): The Globus Endpoint and path on which to save the published files. The default will publish onto MDF resources.\n", + " - For `citrine`:\n", + " - public (boolean): When `True`, the data will be made public. Otherwise, the data will be inaccessible. The default is `True`.\n", + "\n", + "This action is cumulative, so subsequent calls will add more services, not overwrite previous.\n", + "\n", + "To clear all the service selections from your submission, call `clear_services()`." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'citrine': True}" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_service(\"citrine\")\n", + "mdfcc.services" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_services()\n", + "mdfcc.services" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_test`\n", + "You can use `set_test()` to create a test submission as a dry-run for MDF Connect. The submission will go through the normal processing, but the results will not be submitted to the normal locations. This flag is a great way to tell if your submission will process the way you want it to.\n", + "\n", + "#### Required arguments:\n", + "- `test` (boolean): When `True`, enables test mode. When `False`, disables test mode.\n", + "\n", + "#### About test mode:\n", + "Test datasets are submitted to test/sandbox/temporary resources instead of \"production\" resources, including the following. These setting override all other parameters.\n", + "- Tests are ingested into the `mdf-test` search index\n", + "- Tests are given a sandbox DOI, which is not permanent (if `mdf_publish` is a requested service)\n", + "- Tests are not made public on Citrination (if `citrine` is a requested service)\n", + "- Tests are given a special `source_id` by prepending `_test_`" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_test(False)\n", + "mdfcc.test" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_test(True)\n", + "mdfcc.test" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_organization`\n", + "`add_organization()` marks your submission for an organization. This action is cumulative, so each call adds more organizations. Subsequent calls do not overwrite. Organizations may modify the parameters of your submission, such as mandating curation. More information about specific organizations can be found using [MDF Forge](https://mdf-forge.readthedocs.io/en/master/mdf_forge.html#mdf_forge.Forge.describe_organization).\n", + "\n", + "#### Required arguments:\n", + "- `organization` (string or list of strings): The organization to add.\n", + "\n", + "Organizations automatically add their parent organizations. Organizations not registered with MDF will be discarded.\n", + "\n", + "To clear your organizations, call `clear_organizations()`." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'organizations': ['CHiMaD']}" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_organization(\"CHiMaD\")\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_organizations()\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optional inputs\n", + "\n", + "- [`set_custom_block`](#set_custom_block)\n", + "- [`set_custom_descriptions`](#set_custom_descriptions)\n", + "- [`set_base_acl`](#set_base_acl)\n", + "- [`set_dataset_acl`](#set_dataset_acl)\n", + "- [`set_source_name`](#set_source_name)\n", + "- [`set_incremental_update`](#set_incremental_update)\n", + "- [`add_data_destination`](#add_data_destination)\n", + "- [`set_external_uri`](#set_external_uri)\n", + "- [`create_mrr_block`](#create_mrr_block)\n", + "\n", + "These helpers are for inputs that are optional and can be skipped if you aren't interested in providing them." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_custom_block`\n", + "The `custom` block is an area for you to add your own custom metadata, if it isn't covered by the MDF schema. It can be set by calling `set_custom_block()`.\n", + "\n", + "#### Required arguments:\n", + "- `custom_fields` (dictionary): Custom field-value pairs for your dataset.\n", + "\n", + "You are allowed ten keys in your custom dictionary. You may additionally add descriptions of your fields by creating a new field called \"\\[field\\]\\_desc\" with the string description inside. You can also add descriptions by calling `set_custom_descriptions()`.\n", + "\n", + "Note that, unlike the `index` mappings, you supply the actual values for the dataset-level `custom` block.\n", + "\n", + "Subsequent calls will overwrite your `custom` block. You can clear the `custom` block by passing in an empty dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'quench_method': 'water', 'quench_method_desc': 'The method of quenching'}" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_values = {\n", + " \"quench_method\": \"water\",\n", + " \"quench_method_desc\": \"The method of quenching\"\n", + "}\n", + "mdfcc.set_custom_block(custom_values)\n", + "mdfcc.custom" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_custom_block({})\n", + "mdfcc.custom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_custom_descriptions`\n", + "To add descriptions for your `custom` block fields, you can call `set_custom_descriptions()`.\n", + "\n", + "#### Required arguments:\n", + "- `custom_descriptions` (dictionary): The custom fields and descriptions. The dictionary fields must be the same as your `custom` block fields, and the values must be their descriptions.\n", + "\n", + "Every field in `custom` can have a description, but descriptions are not allowed without a corresponding field.\n", + "\n", + "Subsequent calls will overwrite the descriptions you provide. To clear descriptions, you have to use `set_custom_block()`." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'quench_method': 'water'}" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_values = {\n", + " \"quench_method\": \"water\"\n", + "}\n", + "mdfcc.set_custom_block(custom_values)\n", + "mdfcc.custom" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'quench_method': 'water', 'quench_method_desc': 'The method of quenching'}" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_desc = {\n", + " \"quench_method\": \"The method of quenching\"\n", + "}\n", + "mdfcc.set_custom_descriptions(custom_desc)\n", + "mdfcc.custom" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_custom_block({})\n", + "mdfcc.custom" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_base_acl`\n", + "`set_base_acl()` sets the Access Control List for all the data in this submission. Anyone in this list can read the dataset entry, record entries, and files in the dataset.\n", + "\n", + "#### Required arguments:\n", + "- acl (string or list of strings): The Access Control List. The ACL must contain either the Globus UUIDs of users and/or groups allowed to access the submission, or `\"public\"` to make the submission open to everyone. The default ACL is `\"public\"`.\n", + "\n", + "You can reset the ACL to the default with `clear_base_acl()`.\n", + "\n", + "#### *Warning*:\n", + "The identities listed in the `base_acl` of your submission can always see your submission, including dataset entry, even if they are not listed in the `dataset_acl`. This means that **if you do not specify a `base_acl`**, because it defaults to `\"public\"`, **your entire dataset will be public.**\n", + "\n", + "MDF encourages you to make your data public, but if you do not want it public you must specify this value." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'acl': ['UUID1', 'UUID2']}" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_base_acl([\"UUID1\", \"UUID2\"])\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_base_acl()\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_dataset_acl`\n", + "`set_dataset_acl()` sets the Access Control List for just the dataset entry in MDF Search. Anyone in this list can see that dataset entry but _not_ the record entries or files (unless they are also in the `base_acl`, see above).\n", + "\n", + "#### Required arguments:\n", + "- acl (string or list of strings): The Access Control List. The ACL must contain either the Globus UUIDs of users and/or groups allowed to access the dataset entry, or `\"public\"` to make the dataset entry open to everyone. By default, the dataset ACL is empty (which means that the base ACL is the only effective permission list).\n", + "\n", + "You can reset the ACL to the default with `clear_dataset_acl()`." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['public']" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_dataset_acl([\"public\"])\n", + "mdfcc.dataset_acl" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "mdfcc.clear_dataset_acl()\n", + "mdfcc.dataset_acl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_source_name`\n", + "`set_source_name()` sets the `source_name` of your dataset. By default, the `source_name` is generated based on the title of your dataset (as set in the `dc` block). If your title is long or otherwise unwieldy to type or remember, setting a custom `source_name` can help.\n", + "\n", + "#### Required arguments:\n", + "- `source_name` (string): The desired `source_name`, which must be unique for new datasets.\n", + "\n", + "Please note that your source name will be cleaned when submitted to MDF Connect, so the actual source_name may differ from this value. Additionally, the `source_id` (which is the `source_name` plus version) is required to fetch the status of a submission. `check_status()` can handle this for you.\n", + "\n", + "You can reset the `source_name` to the default by calling `clear_source_name()`." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source_name': 'my_foobar_dataset'}" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_source_name(\"my_foobar_dataset\")\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_source_name()\n", + "mdfcc.mdf" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# Here we're setting a unique source_name, so the submission will create a new dataset.\n", + "mdfcc.set_source_name(\"tutorial_submission_{}\".format(int(time.time())))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_incremental_update`\n", + "`set_incremental_update()` makes this submission an incremental update of a previous submission. Incremental updates use the same submission metadata, except for whatever you specify in the new submission.\n", + "\n", + "For example, if you submit an incremental update and only include a ``data_source``, the submission will run as if you copied the DC block and other metadata into the submission, but used the new ``data_source``. The new submission is processed normally, including data download and metadata extraction. (To update only the dataset metadata, use [`submit_dataset_metadata_update()`](#submit_dataset_metadata_update) when submitting.)\n", + "\n", + "**Note:**\n", + "You must still set ``update=True`` when submitting an incremental update.\n", + "\n", + "#### Required arguments:\n", + "- `source_id` (string): The ``source_id`` of the previous submission to update and resubmit.\n", + "\n", + "You can unmark an incremental update by calling this method with the `source_id` set to `False`." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'old_submission_v4.2'" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_incremental_update(\"old_submission_v4.2\")\n", + "mdfcc.incremental_update" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_incremental_update(False)\n", + "mdfcc.incremental_update" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `add_data_destination`\n", + "`add_data_destination()` will add secondary storage locations for your dataset. MDF Connect will automatically use Globus Transfer to send your data to all of the data destinations you list. Destinations must be Globus Endpoints and the path must be writable by `mdf_dataset_submission`, which is the MDF Connect Globus account. (This account will show up as `c17f27bb-f200-486a-b785-2a25e82af505@clients.auth.globus.org`.)\n", + "\n", + "#### Required arguments:\n", + "- `data_destination` (string or list of strings): The Globus Endpoint for backing up the dataset. The destinations must be formatted as `globus://endpoint_id/path/to/destination` or you can copy the \"Get link\" link from the Globus Web App.\n", + "\n", + "You can clear all data destinations by calling `clear_data_destinations()`." + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/my_data/mdf_submissions/']" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.add_data_destination(\"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/my_data/mdf_submissions/\")\n", + "mdfcc.data_destinations" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.clear_data_destinations()\n", + "mdfcc.data_destinations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_external_uri`\n", + "If your dataset is already hosted at another data repository, you can use `set_external_uri()` to point at it. This link will be added to the dataset entry in MDF Search.\n", + "\n", + "#### Required arguments:\n", + "- `uri` (string): The link to the external landing page for this dataset.\n", + "\n", + "You can clear the external URI by calling `clear_external_uri()`." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://example.com'" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_external_uri(\"https://example.com\")\n", + "mdfcc.external_uri" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "mdfcc.clear_external_uri()\n", + "mdfcc.external_uri" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `create_mrr_block`\n", + "`create_mrr_block()` adds data for the NIST Materials Resource Registry into your submission.\n", + "\n", + "Currently, you must build a dictionary with the appropriate fields yourself in order to attach MRR metadata. Helpful arguments, in line with `create_dc_block()`, are intended to be added in the future.\n", + "\n", + "#### Required arguments:\n", + "- `mrr_data` (dictionary): The Materials Resource Registry metadata.\n", + "\n", + "You can clear the `mrr` block by passing in an empty dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dataOrigin': 'experiment'}" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.create_mrr_block({\"dataOrigin\": \"experiment\"})\n", + "mdfcc.mrr" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.create_mrr_block({})\n", + "mdfcc.mrr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced inputs\n", + "\n", + "- [`set_passthrough`](#set_passthrough)\n", + "- [`set_project_block`](#set_project_block)\n", + "- [`set_curation`](#set_curation)\n", + "- [`set_conversion_config`](#set_conversion_config)\n", + "\n", + "These helpers are for advanced inputs that most users don't need to worry about." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_passthrough`\n", + "`set_passthrough()` sets the pass-through (or no-extract) flag for your submission.\n", + "\n", + "Caution: The pass-through flag will cause metadata from your dataset's files to not be extracted by MDF Connect, so only high-level dataset metadata will be available in MDF Search. _This flag is only intended for datasets that cannot be extracted._\n", + "\n", + "HTTP(S) data sources are not supported when the pass-through flag is set.\n", + "\n", + "#### Required arguments:\n", + "- `passthrough` (boolean): When `False`, the dataset will be processed normally. When `True`, the metadata in the dataset files will not be extracted." + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_passthrough(True)\n", + "mdfcc.no_extract" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_passthrough(False)\n", + "mdfcc.no_extract" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_project_block`\n", + "`set_project_block()` sets project-specific metadata on your dataset entry. The project block is a special area for specific metadata, that must be registered with the MDF. If you have a project block defined in the MDF schema, you can set that metadata for your dataset entry in this way.\n", + "\n", + "#### Required arguments:\n", + "- `project` (string): The name of the project block in MDF.\n", + "- `data` (dictionary): The metadata for the project block.\n", + "\n", + "You can clear this block by passing in an empty `data` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'example_project': {'field': 'value'}}" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_project_block(\"example_project\", {\"field\": \"value\"})\n", + "mdfcc.projects" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_project_block(\"example_project\", None)\n", + "mdfcc.projects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_curation`\n", + "To trigger curation of your submission, use `set_curation()`. An approved curator (see below) must accept your submission before it will be indexed in MDF Search (and published with MDF Publish or sent to any other services, if applicable). Normally, this flag is set by an organization's rules, and not by an end-user, but you can set it yourself if you like.\n", + "\n", + "##### About approved curators:\n", + "If your organization has set the curation flag, the approved curators are the managers and admins of the organization's permission groups. If you manually set the curation flag, the approved curators are based on your Access Control List (see [`set_acl()`](#set_acl)); anyone you list directly in your ACL can curate, as well as managers and admins of any group you list.\n", + "If you set your ACL to \"public\" then anyone can curate your submission.\n", + "\n", + "#### Required arguments:\n", + "- `curation` (boolean): When `False`, the dataset will be fully processed automatically and not require approval. When `True`, the dataset will go through metadata extraction and then require an approved curator to accept it before ingesting to any service (including MDF Search and MDF Publish).\n", + "\n", + "Remember that your organization (set with [`add_organization()`](#add_organization)) may force curation. Setting the curation flag to `False` does not override your organization's rules." + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_curation(True)\n", + "mdfcc.curation" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_curation(False)\n", + "mdfcc.curation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `set_extraction_config`\n", + "`set_extraction_config()` sets advanced configuration parameters for your submission in the `extraction_config` block. These options are intended for advanced users and/or special-case datasets. Most submissions do not need to worry about these parameters.\n", + "\n", + "#### Required arguments:\n", + "- `config` (dictionary): The extraction configuration options.\n", + "\n", + "You can clear this block by passing in an empty dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'group_by_dir': True}" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_extraction_config({\"group_by_dir\": True})\n", + "mdfcc.extraction_config" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.set_extraction_config({})\n", + "mdfcc.extraction_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submitting a dataset\n", + "- [`get_submission`](#get_submission)\n", + "- [`reset_submission`](#reset_submission)\n", + "- [`submit_dataset`](#submit_dataset)\n", + "- [`submit_dataset_metadata_update`](#submit_dataset_metadata_update)\n", + "\n", + "After you have created your submission with the above helpers, you can submit and check your submission with these helpers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `get_submission`\n", + "`get_submission()` shows you your current submission, as it will be sent to MDF Connect. This method is a great way to check for any errors.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the current submission." + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dc': {'titles': [{'title': 'Tutorial Example Two'}],\n", + " 'creators': [{'creatorName': 'Smith, Foo',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Foo',\n", + " 'affiliations': ['Foo University']},\n", + " {'creatorName': 'Smith, Bar',\n", + " 'familyName': 'Smith',\n", + " 'givenName': 'Bar',\n", + " 'affiliations': ['The Bureau of Bar']}],\n", + " 'publisher': 'Materials Data Facility',\n", + " 'publicationYear': '2020',\n", + " 'resourceType': {'resourceTypeGeneral': 'Dataset',\n", + " 'resourceType': 'Dataset'}},\n", + " 'data_sources': ['https://www.globus.org/app/transfer?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAlFe%2F'],\n", + " 'test': True,\n", + " 'update': False,\n", + " 'mdf': {'source_name': 'tutorial_submission_1580155419'}}" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.get_submission()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `reset_submission`\n", + "If you need to clear away your entire submission, call `reset_submission()`. This is irreversible.\n", + "\n", + "Caution: This method will clear the current `source_id`, which means that you will have to keep track of any previous `source_id`s from other submissions to see their statuses." + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "# mdfcc.reset_submission()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `submit_dataset`\n", + "`submit_dataset()` will send your dataset to MDF Connect for indexing. You will get back the `source_id` if the submission is successful. The `source_id` is the unique identifier for your specific submission, and can be used to check the status of your submission later. The `source_id` is also saved to the client.\n", + "\n", + "#### Optional arguments:\n", + "- `update` (boolean): If you wish to submit this dataset after submitting it previously, set this to `True`. If this is the first submission, leave this `False`. The default is `False`.\n", + "- `submission` (dictionary): If you have assembled your own MDF Connect submission without this client, you can submit it by passing the dictionary in here. By default, the submission made in the client will be used.\n", + "- `reset` (boolean): If True, the submission will be cleared after the submission attempt, with `reset_submission()`. The test flag will be preserved. The default is `False`. Caution: This flag will clear your `source_id`, which means that you will have to keep track of it manually to check your submission's status.\n", + "\n", + "#### Return value:\n", + "- A dictionary with the following submission information:\n", + " - `success` (boolean): `True` if the submission was successfully sent to MDF Connect. `False` otherwise.\n", + " - `source_id` (string): The `source_id` of the submission, from MDF Connect. This value may be `None` or an old ID if the submission failed.\n", + " - `error` (string): If the submission failed, the reason for failure. If the submission suceeded, this will be `None` instead." + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source_id': '_test_tutorial_submission_1580155419_v1.1',\n", + " 'success': True,\n", + " 'error': None,\n", + " 'status_code': 202}" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "mdfcc.submit_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `submit_dataset_metadata_update`\n", + "To submit only updates to an existing submission's dataset entry, use `submit_dataset_metadata_update()`. This includes updates to the DC block, such as the author list. You can create the metadata using the same helpers, but any helpers that set non-dataset metadata (such as [`add_data_source()`](#add_data_source)) will be ignored.\n", + "\n", + "To be clear, this method submits an update to a dataset entry only, and NOT the data or record entries. The submission you are updating must have completed processing successfully at the time you submit the update - you are not allowed to update a submission that is still processing, or failed processing.\n", + "\n", + "#### Required arguments:\n", + "- `source_id` (string): The `source_id` of the dataset you wish to update. You must be the owner of the dataset.\n", + "\n", + "#### Optional arguments:\n", + "- `metadata_update` (dictionary): If you have assembled the dataset metadata yourself, you can submit it here. This argument supersedes any data set through other methods. The default is `None`, to use the method-assembled data.\n", + "- `reset` (boolean): If `True`, will clear the old metadata from the client. The `test` flag will be preserved. If `False`, the metadata will be preserved. The default is `False`." + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'success': True, 'error': None, 'status_code': 200}" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "update_source_id = \"author_updatable_example_submission_v1.1\"\n", + "new_metadata = {\n", + " \"dc\": {\n", + " \"titles\": [{\n", + " \"title\": \"Updated Title\"\n", + " }]\n", + " }\n", + "}\n", + "mdfcc.submit_dataset_metadata_update(update_source_id, metadata_update=new_metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checking submission status\n", + "- [`check_status`](#check_status)\n", + "- [`check_all_submissions`](#check_all_submissions)\n", + "\n", + "After submitting a dataset to MDF Connect, you can see the status of the submission's processing with these helpers." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `check_status`\n", + "To see the progress your submission is making, use `check_status()`. If you haven't cleared the submission from the client, you can use it without arguments to check the most recent submission status.\n", + "\n", + "#### Optional arguments:\n", + "- `source_id` (string): The `source_id` of the submission you want to check on. If you don't supply a `source_id`, the ID of the last submission you made with the client will be used instead (an error will result if you have not submitted a dataset with the client yet and also don't supply an ID).\n", + "- `short` (boolean): When `False`, a status summary will be printed for the submission. When `True`, an abbreviated summary containing only the minimum information will be printed (this is useful for checking many submissions at once). The default is `False`.\n", + "- `raw` (boolean): When `False`, a nicely-formatted status summary will be printed to standard output. When `True`, the full status result will be returned instead (the full result is not recommended for direct human consumption). The default is `False`.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the full submission status (only when `raw` is `True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Status of TEST submission _test_tutorial_submission_1580155419_v1.1 (Tutorial Example Two)\n", + "Submitted by Jonathon Gaff at 2020-01-27T20:03:52.207133Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 21 files will be grouped and extracted (from 0 archives).\n", + "Data transfer to primary destination is in progress.\n", + "Metadata extraction has not started yet.\n", + "Dataset curation has not started yet.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "\n", + "This submission is still processing.\n", + "\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "mdfcc.check_status()" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Status of TEST submission _test_name_status_checking_example_v1.1 (Status Checking Example)\n", + "Submitted by Jonathon Gaff at 2019-08-08T20:26:36.263611Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 12 files will be converted (0 archives extracted).\n", + "Data transfer to primary destination was successful.\n", + "Metadata extraction was successful: 4 records parsed out of 4 groups.\n", + "Dataset curation was not requested or required.\n", + "MDF Search ingestion was successful.\n", + "Data transfer to secondary destinations was not requested or required.\n", + "MDF Publish publication was not requested or required.\n", + "Citrine upload was not requested or required.\n", + "Materials Resource Registration was not requested or required.\n", + "Post-processing cleanup was successful.\n", + "\n", + "This submission is no longer processing.\n", + "\n" + ] + } + ], + "source": [ + "mdfcc.check_status(\"_test_name_status_checking_example_v1.1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_test_name_status_checking_example_v1.1: This submission is no longer processing.\n" + ] + } + ], + "source": [ + "mdfcc.check_status(\"_test_name_status_checking_example_v1.1\", short=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'status': {'active': False,\n", + " 'source_id': '_test_name_status_checking_example_v1.1',\n", + " 'status_code': 'SMSMNSNNNNS',\n", + " 'status_list': [{'signal': 'success',\n", + " 'text': 'Submission initialization was successful.'},\n", + " {'signal': 'success',\n", + " 'text': 'Connect data download was successful: 12 files will be converted (0 archives extracted).'},\n", + " {'signal': 'success',\n", + " 'text': 'Data transfer to primary destination was successful.'},\n", + " {'signal': 'success',\n", + " 'text': 'Metadata extraction was successful: 4 records parsed out of 4 groups.'},\n", + " {'signal': 'idle',\n", + " 'text': 'Dataset curation was not requested or required.'},\n", + " {'signal': 'success', 'text': 'MDF Search ingestion was successful.'},\n", + " {'signal': 'idle',\n", + " 'text': 'Data transfer to secondary destinations was not requested or required.'},\n", + " {'signal': 'idle',\n", + " 'text': 'MDF Publish publication was not requested or required.'},\n", + " {'signal': 'idle', 'text': 'Citrine upload was not requested or required.'},\n", + " {'signal': 'idle',\n", + " 'text': 'Materials Resource Registration was not requested or required.'},\n", + " {'signal': 'success', 'text': 'Post-processing cleanup was successful.'}],\n", + " 'status_message': 'Status of TEST submission _test_name_status_checking_example_v1.1 (Status Checking Example)\\nSubmitted by Jonathon Gaff at 2019-08-08T20:26:36.263611Z\\n\\nSubmission initialization was successful.\\nConnect data download was successful: 12 files will be converted (0 archives extracted).\\nData transfer to primary destination was successful.\\nMetadata extraction was successful: 4 records parsed out of 4 groups.\\nDataset curation was not requested or required.\\nMDF Search ingestion was successful.\\nData transfer to secondary destinations was not requested or required.\\nMDF Publish publication was not requested or required.\\nCitrine upload was not requested or required.\\nMaterials Resource Registration was not requested or required.\\nPost-processing cleanup was successful.\\n',\n", + " 'submission_time': '2019-08-08T20:26:36.263611Z',\n", + " 'submitter': 'Jonathon Gaff',\n", + " 'test': True,\n", + " 'title': 'Status Checking Example'},\n", + " 'success': True,\n", + " 'status_code': 200}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This is not recommended for human consumption\n", + "mdfcc.check_status(\"_test_name_status_checking_example_v1.1\", raw=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `check_all_submissions`\n", + "If you want to see the status of all submissions you've made to MDF Connect, use `check_all_submissions()`. This method is helpful if you forget a submission's `source_id`, or you have multiple submissions processing at once.\n", + "\n", + "#### Optional arguments:\n", + "- `verbose` (boolean): When `False`, a basic summary of your submissions will be printed. When `True`, the full status summary of each submission will be printed, in the same form as `check_status()`. The default is `False`. (This argument has no effect if `raw` is `True`.)\n", + "- `active_only` (boolean): When `True`, only active submissions will be printed. The default is `False`.\n", + "- `include_tests` (boolean): When `False`, only non-test submissions will be printed. The default is `True`.\n", + "- `newer_than_date` (datetime or tuple of integers): Excludes submissions made before this date. Accepts a `datetime` object or a tuple of `(year, month, day)` integers. The default is `None`, to set no maximum age.\n", + "- `older_than_date`: (datetime or tuple of ints): Excludes submissions made after this date. Accepts a `datetime` object or a tuple of `(year, month, day)` integers. The default is `None`, to set no minimum age.\n", + "- `raw` (boolean): When `False`, the summary selected by `verbose` will be printed. When `True`, the full status result will be returned instead (the full result is not recommended for direct human consumption). The default is `False`.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the full submission statuses (only when `raw` is `True`).\n", + "\n", + "#### Note about date filtering:\n", + "Days are compared in UTC, at exactly 0:00 (12:00am). This means that the two dates cannot be the same, as they would filter out all submissions not made at exactly 0:00:00 on the chosen date. To see submissions made on a specific date, set the older_than filter one day away from the date in question. For example, to see submissions made on Feb 11, 2020, use `newer_than_date=(2020, 2, 11), older_than_date=(2020, 2, 12)`." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "_test_tutorial_submission_1579021617_v1.1: Processing - Not started\n", + "_test_abrehabiruk_virtual_db_v1.1: Processing - Processing\n", + "_test_smaller_example_dataset_submission_v3-2: Processing - Processing\n", + "_test_smaller_example_dataset_submission_v3-1: Processing - Processing\n", + "_test_name_curation_task_example_v1.4: Processing - Processing\n", + "_test_name_curation_rejecting_example_v1.4: Processing - Processing\n", + "_test_name_curation_accepting_example_v4.2: Processing - Processing\n" + ] + } + ], + "source": [ + "mdfcc.check_all_submissions(active_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "_test_gaff_email_testing_v1.2: Not processing - Succeeded\n", + "_test_gaff_email_testing_v1.1: Not processing - Cancelled\n", + "_test_gaff_gdrive_verification_v2.1: Not processing - Failed\n" + ] + } + ], + "source": [ + "# Filtering for submissions made between Jan 1, 2020 and Feb 10, 2020\n", + "mdfcc.check_all_submissions(newer_than_date=datetime(2020, 5, 30), older_than_date=(2020, 7, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Status of TEST submission _test_tutorial_submission_1579021617_v1.1 (Tutorial Example Two)\n", + "Submitted by Jonathon Gaff at 2020-01-14T17:07:00.063623Z\n", + "\n", + "Submission initialization has not started yet.\n", + "Connect data download has not started yet.\n", + "Data transfer to primary destination has not started yet.\n", + "Metadata extraction has not started yet.\n", + "Dataset curation has not started yet.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_abrehabiruk_virtual_db_v1.1 (Virtual Excited State Reference for the Discovery of Electronic Materials (VERDE Materials DB))\n", + "Submitted by Jonathon Gaff at 2019-09-12T18:51:41.927599Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 8417 files will be converted (1 archives extracted).\n", + "Data transfer to primary destination was successful.\n", + "Metadata extraction is in progress.\n", + "Dataset curation has not started yet.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_smaller_example_dataset_submission_v3-2 (Smaller Example Dataset Submission)\n", + "Submitted by Jonathon Gaff at 2018-12-05T20:56:45.927284Z\n", + "\n", + "Submission initialization is in progress.\n", + "Connect data download has not started yet.\n", + "Data transfer to primary destination has not started yet.\n", + "Metadata extraction has not started yet.\n", + "Dataset curation has not started yet.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_smaller_example_dataset_submission_v3-1 (Smaller Example Dataset Submission)\n", + "Submitted by Jonathon Gaff at 2018-12-05T20:55:14.066883Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 21 files will be processed (0 archives extracted).\n", + "Data transfer to primary destination was successful: 9 records parsed out of 7 groups.\n", + "Metadata extraction was successful.\n", + "Dataset curation is in progress.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_name_curation_task_example_v1.4 (Curation Task Example)\n", + "Submitted by Jonathon Gaff at 2020-01-14T16:48:27.414911Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 12 files will be grouped and extracted (from 0 archives).\n", + "Data transfer to primary destination was successful.\n", + "Metadata extraction was successful: 4 metadata records extracted out of 4 file groups.\n", + "Dataset curation is in progress.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_name_curation_rejecting_example_v1.4 (Curation Rejecting Example)\n", + "Submitted by Jonathon Gaff at 2020-01-14T16:47:56.995810Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 12 files will be grouped and extracted (from 0 archives).\n", + "Data transfer to primary destination was successful.\n", + "Metadata extraction was successful: 4 metadata records extracted out of 4 file groups.\n", + "Dataset curation is in progress.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n", + "\n", + "\n", + "Status of TEST submission _test_name_curation_accepting_example_v4.2 (Curation Accepting Example)\n", + "Submitted by Jonathon Gaff at 2020-01-14T16:48:14.775396Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download was successful: 12 files will be grouped and extracted (from 0 archives).\n", + "Data transfer to primary destination was successful.\n", + "Metadata extraction was successful: 4 metadata records extracted out of 4 file groups.\n", + "Dataset curation is in progress.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "This submission is still processing.\n" + ] + } + ], + "source": [ + "mdfcc.check_all_submissions(verbose=True, include_tests=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Curating a submission\n", + "- [`get_curation_task`](#get_curation_task)\n", + "- [`get_available_curation_tasks`](#get_available_curation_tasks)\n", + "- [`accept_curation_submission`](#accept_curation_submission)\n", + "- [`reject_curation_submission`](#reject_curation_submission)\n", + "\n", + "When a submission has the curation flag set through [`set_curation()`](#set_curation) or through an organization's rules (see [`add_organization()`](#add_organization)), the dataset goes through metadata extraction but is temporarily stopped before ingestion to any other service (including MDF Search and MDF Publish, when applicable). An approved curator must review and accept the submission before it can proceed (see below). These helpers allow an approved curator to view and approve or reject submissions waiting for curation.\n", + "\n", + "##### About approved curators:\n", + "If the dataset submitter's organization has set the curation flag, the approved curators are the managers and admins of the organization's permission groups. If the curation flag was manually set, the approved curators are based on the Access Control List (see [`set_acl()`](#set_acl)); anyone listed directly in the ACL can curate, as well as managers and admins of any group listed.\n", + "If the ACL is \"public\" then anyone can curate the submission, but it will not show up in `get_available_curation_tasks()`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `get_curation_task`\n", + "To see the details of a specific dataset that needs curation, use `get_curation_task()`. You must have permission to curate any submission you wish to view.\n", + "\n", + "#### Required arguments:\n", + "- `source_id` (string): The `source_id` of the submission you want to view. If you don't know the `source_id`, you can use [`get_available_curation_tasks`](#get_available_curation_tasks) or ask the dataset submitter.\n", + "\n", + "#### Optional arguments:\n", + "- `summary` (boolean): When `False`, the entire curation task, including the dataset entry and sample records, will be printed. When `True`, only a summary of the curation task will be printed. The default is `False`.\n", + "- `raw` (boolean): When `False`, the curation task information selected by `summary` will be printed. When `True`, a dictionary containing the full curation task will be returned, regardless of `summary`. The default is `False`, which is recommended for direct human consumption.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the full curation task (only when `raw` is `True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"allowed_curators\": [\n", + " \"public\"\n", + " ],\n", + " \"curation_start_date\": \"2020-01-14 16:51:04.459251\",\n", + " \"dataset\": {\n", + " \"data\": {\n", + " \"endpoint_path\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/\",\n", + " \"link\": \"https://app.globus.org/file-manager?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/\"\n", + " },\n", + " \"dc\": {\n", + " \"creators\": [\n", + " {\n", + " \"creatorName\": \"Name\",\n", + " \"familyName\": \"Name\",\n", + " \"givenName\": \"\"\n", + " }\n", + " ],\n", + " \"publicationYear\": \"2020\",\n", + " \"publisher\": \"Materials Data Facility\",\n", + " \"resourceType\": {\n", + " \"resourceType\": \"Dataset\",\n", + " \"resourceTypeGeneral\": \"Dataset\"\n", + " },\n", + " \"titles\": [\n", + " {\n", + " \"title\": \"Curation Task Example\"\n", + " }\n", + " ]\n", + " },\n", + " \"mdf\": {\n", + " \"acl\": [\n", + " \"public\"\n", + " ],\n", + " \"ingest_date\": \"2020-01-14T16:50:55.265890Z\",\n", + " \"resource_type\": \"dataset\",\n", + " \"scroll_id\": 0,\n", + " \"source_id\": \"_test_name_curation_task_example_v1.4\",\n", + " \"source_name\": \"_test_name_curation_task_example\",\n", + " \"version\": 1\n", + " },\n", + " \"services\": {}\n", + " },\n", + " \"extraction_summary\": \"4 records were extracted out of 4 groups from 12 files\",\n", + " \"sample_records\": [\n", + " {\n", + " \"crystal_structure\": {\n", + " \"number_of_atoms\": 4.0,\n", + " \"space_group_number\": 221,\n", + " \"stoichiometry\": \"AB3\",\n", + " \"volume\": 66.87455107336443\n", + " },\n", + " \"dft\": {\n", + " \"converged\": true,\n", + " \"cutoff_energy\": 249.8,\n", + " \"exchange_correlation_functional\": \"PAW_PBE\"\n", + " },\n", + " \"files\": [\n", + " {\n", + " \"data_type\": \"ASCII text\",\n", + " \"filename\": \"OUTCAR\",\n", + " \"globus\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/OUTCAR\",\n", + " \"length\": 98631,\n", + " \"mime_type\": \"text/plain\",\n", + " \"sha512\": \"0926ad16c90f477345254945938003f3e946c0d05c766802a28f57b71aba66bd9746987ea6d0c98aeeb454b0b6a28fd38ae9a5c4ce10f2d3172f40ec0af9964c\",\n", + " \"url\": \"https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/OUTCAR\"\n", + " },\n", + " {\n", + " \"data_type\": \"ASCII text\",\n", + " \"filename\": \"INCAR\",\n", + " \"globus\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/INCAR\",\n", + " \"length\": 476,\n", + " \"mime_type\": \"text/plain\",\n", + " \"sha512\": \"b9d2bb98b4df98fd9b8583fa8135db7a4ef28d984b7ab4cf07ed11901564d8eed748568d0faa00ffe9674581a8c932012afe75cddc320c5657ae7503403ef255\",\n", + " \"url\": \"https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/INCAR\"\n", + " },\n", + " {\n", + " \"data_type\": \"ASCII text\",\n", + " \"filename\": \"POSCAR\",\n", + " \"globus\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/POSCAR\",\n", + " \"length\": 743,\n", + " \"mime_type\": \"text/plain\",\n", + " \"sha512\": \"2118d83f6f1c29216d92fd2f0791da9d17d845fd49ac60df8648333296d8f6ec7b5f01b215c289459d5a2d63f4636549b96fb76b32c293d087dd9782bd90f92c\",\n", + " \"url\": \"https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cF4/POSCAR\"\n", + " }\n", + " ],\n", + " \"material\": {\n", + " \"composition\": \"Al1Ag3\",\n", + " \"elements\": [\n", + " \"Ag\",\n", + " \"Al\"\n", + " ]\n", + " },\n", + " \"mdf\": {\n", + " \"acl\": [\n", + " \"public\"\n", + " ],\n", + " \"ingest_date\": \"2020-01-14T16:50:55.265890Z\",\n", + " \"resource_type\": \"record\",\n", + " \"scroll_id\": 1,\n", + " \"source_id\": \"_test_name_curation_task_example_v1.4\",\n", + " \"source_name\": \"_test_name_curation_task_example\",\n", + " \"version\": 1\n", + " }\n", + " },\n", + " {\n", + " \"crystal_structure\": {\n", + " \"number_of_atoms\": 4.0,\n", + " \"space_group_number\": 123,\n", + " \"stoichiometry\": \"AB3\",\n", + " \"volume\": 67.20758519915091\n", + " },\n", + " \"dft\": {\n", + " \"converged\": true,\n", + " \"cutoff_energy\": 249.8,\n", + " \"exchange_correlation_functional\": \"PAW_PBE\"\n", + " },\n", + " \"files\": [\n", + " {\n", + " \"data_type\": \"ASCII text\",\n", + " \"filename\": \"OUTCAR\",\n", + " \"globus\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cI2/OUTCAR\",\n", + " \"length\": 631529,\n", + " \"mime_type\": \"text/plain\",\n", + " \"sha512\": \"04b4236e7bbdcc0688fa213ee65154a291c885f8fd11e77758d24066a3b0908c600b6ba97388e8ddef75f61f008e7d55150cf05ef99126392fc3602a04a2d384\",\n", + " \"url\": \"https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cI2/OUTCAR\"\n", + " },\n", + " {\n", + " \"data_type\": \"ASCII text\",\n", + " \"filename\": \"INCAR\",\n", + " \"globus\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/Ag3Al.cI2/INCAR\",\n", + " \"length\": 476,\n", + " \"mime_type\": \"text/plain\",\n", + " \"sha512\": 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\"_test_name_curation_task_example\",\n", + " \"version\": 1\n", + " }\n", + " }\n", + " ],\n", + " \"source_id\": \"_test_name_curation_task_example_v1.4\",\n", + " \"submission_info\": {\n", + " \"acl\": [\n", + " \"public\"\n", + " ],\n", + " \"canon_destination\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/\",\n", + " \"curation\": true,\n", + " \"data_destinations\": [],\n", + " \"data_sources\": [\n", + " \"https://app.globus.org/file-manager?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAgAl%2F\"\n", + " ],\n", + " \"dataset_acl\": [\n", + " \"public\"\n", + " ],\n", + " \"extraction_config\": {},\n", + " \"index\": {\n", + " \"file\": {\n", + " \"globus_host\": \"globus://e38ee745-6d04-11e5-ba46-22000b92c6ec/MDF/mdf_connect/prod/data/_test_name_curation_task_example_v1.4/\",\n", + " \"http_host\": \"https://e38ee745-6d04-11e5-ba46-22000b92c6ec.e.globus.org\",\n", + " \"local_path\": \"/home/ubuntu/data/_test_name_curation_task_example_v1.4/\"\n", + " }\n", + " },\n", + " \"no_extract\": false,\n", + " \"services\": {\n", + " \"mdf_search\": {\n", + " \"index\": \"mdf-test\"\n", + " }\n", + " },\n", + " \"submitter\": \"Jonathon Gaff\",\n", + " \"test\": true,\n", + " \"update\": true\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "mdfcc.get_curation_task(\"_test_name_curation_task_example_v1.4\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_test_name_curation_task_example_v1.4 by Jonathon Gaff\n", + "Waiting since 2020-01-14 16:51:04.459251\n", + "4 records were extracted out of 4 groups from 12 files\n", + "\n" + ] + } + ], + "source": [ + "mdfcc.get_curation_task(\"_test_name_curation_task_example_v1.4\", summary=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `get_available_curation_tasks`\n", + "To see all of the submissions you have permission to curate (excluding \"public\" submissions, which anyone can curate), use `get_available_curation_tasks()`.\n", + "\n", + "#### Optional arguments:\n", + "- `summary` (boolean): When `False`, the entiretly of each curation task available to you, including the dataset entry and sample records, will be printed. When `True`, only a summary of each curation task will be printed. The default is `True`. The summaries are very useful to get an overview of tasks, but it is recommended to then use [`get_curation_task`](#get_curation_task) to view the details.\n", + "- `raw` (boolean): When `False`, the curation task information selected by `summary` will be printed. When `True`, a dictionary containing the full curation tasks will be returned, regardless of `summary`. The default is `False`, which is recommended for direct human consumption.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the full curation tasks (only when `raw` is `True`).\n", + "\n", + "**Note:** This helper does not show curation tasks that are \"public\", which anyone can curate, to minimize irrelevant results." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have no open curation tasks.\n" + ] + } + ], + "source": [ + "mdfcc.get_available_curation_tasks()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `accept_curation_submission`\n", + "After reviewing a curation task, you can accept the submission with `accept_curation_submission()`. You must have permission to curate any submission you wish to accept.\n", + "\n", + "It is strongly recommended that you view submissions with [`get_curation_task`](#get_curation_task) before accepting or rejecting them.\n", + "\n", + "After a submission is accepted, it will continue processing and ingest to MDF Search (and any other applicable services, such as MDF Publish).\n", + "\n", + "#### Required arguments:\n", + "- `source_id` (string): The `source_id` of the submission you wish to accept.\n", + "\n", + "#### Optional arguments:\n", + "- `reason` (string): The reason for accepting this submission. If a reason is not provided, a generic acceptance message will be used instead.\n", + "- `prompt` (boolean): When `True`, you will be prompted to confirm acceptance of the submission with a task summary. When `False`, confirmation will not be required. The default is `True`, and it is recommended to review this summary to avoid errors.\n", + "- `raw` (boolean): When `False`, the completion result will be printed. When `True`, a dictionary of the completion result will be returned. The default is `False`.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the completion result (only when `raw` is `True`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** This sample curation task was submitted outside of this notebook, to make a simple example of accepting a submission. It cannot be re-accepted." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Are you sure you want to accept the following submission?\n", + "_test_name_curation_accepting_example_v4.2 by Jonathon Gaff\n", + "Waiting since 2020-01-14 16:51:05.598132\n", + "4 records were extracted out of 4 groups from 12 files\n", + "\n", + "\n", + "Confirm accepting submission [yes/no]: yes\n", + "\n", + "What is the reason for accepting this submission?\n", + "\tThis dataset shows substantive results.\n", + "\n", + "Submission accepted with reason: This dataset shows substantive results.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "mdfcc.accept_curation_submission(\"_test_name_curation_accepting_example_v4.2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `reject_curation_submission`\n", + "After reviewing a curation task, you can reject the submission with `reject_curation_submission()`. You must have permission to curate any submission you wish to reject.\n", + "\n", + "It is strongly recommended that you view submissions with [`get_curation_task`](#get_curation_task) before accepting or rejecting them.\n", + "\n", + "After a submission is rejected, it will fail and permanently stop processing. It will not be ingested to MDF Search or any other services (such as MDF Publish).\n", + "\n", + "#### Required arguments:\n", + "- `source_id` (string): The `source_id` of the submission you wish to reject.\n", + "\n", + "#### Optional arguments:\n", + "- `reason` (string): The reason for rejecting this submission. If a reason is not provided, a generic rejection message will be used instead. It is recommended to provide this argument to help the submitter understand why you rejected the submission.\n", + "- `prompt` (boolean): When `True`, you will be prompted to confirm rejection of the submission with a task summary. When `False`, confirmation will not be required. The default is `True`, and it is recommended to review this summary to avoid errors.\n", + "- `raw` (boolean): When `False`, the completion result will be printed. When `True`, a dictionary of the completion result will be returned. The default is `False`.\n", + "\n", + "#### Return value:\n", + "- A dictionary containing the completion result (only when `raw` is `True`)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** This sample curation task was submitted outside of this notebook, to make a simple example of rejecting a submission. It cannot be re-rejected." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Are you sure you want to reject the following submission?\n", + "_test_name_curation_rejecting_example_v1.4 by Jonathon Gaff\n", + "Waiting since 2020-01-14 16:50:25.421009\n", + "4 records were extracted out of 4 groups from 12 files\n", + "\n", + "\n", + "Confirm rejecting submission [yes/no]: yes\n", + "\n", + "What is the reason for rejecting this submission?\n", + "\tThis submission misused the analysis technique.\n", + "\n", + "Submission rejected with reason: This submission misused the analysis technique.\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "\n", + "mdfcc.reject_curation_submission(\"_test_name_curation_rejecting_example_v1.4\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/legacy/docs/tutorials/tutorial_MDF_submission.ipynb b/legacy/docs/tutorials/tutorial_MDF_submission.ipynb new file mode 100644 index 0000000..da5b73a --- /dev/null +++ b/legacy/docs/tutorials/tutorial_MDF_submission.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "VmzUYbD8L-xO" + }, + "source": [ + "# MDF Connect Submission Tutorial\n", + "This notebook walks through publishing your data to MDF with MDF Connect. You can run this notebook locally as a Jupyter notebook or you can run it on Google Colab.\n", + "\n", + "First, we'll need to install MDF connect and import it." + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install mdf-connect-client" + ], + "metadata": { + "id": "-7EVVT9cNPMO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GR4UvisRL-xP" + }, + "outputs": [], + "source": [ + "from mdf_connect_client import MDFConnectClient" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RNk9prRGL-xQ" + }, + "source": [ + "## Simple Example\n", + "To get started, let's instantiate the client." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jkxXsQATL-xQ" + }, + "outputs": [], + "source": [ + "mdfcc = MDFConnectClient()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_uDiLmeaL-xQ" + }, + "source": [ + "### Creating the submission\n", + "Then we set the required metadata (the DataCite block and the data location)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ag1JFkdeL-xQ" + }, + "outputs": [], + "source": [ + "# The DC block only requires a title and author.\n", + "mdfcc.create_dc_block(title=\"Connect Sample Dataset\", authors=\"A. Person\")\n", + "\n", + "# The data field requires one data location.\n", + "mdfcc.add_data_source(\"https://app.globus.org/file-manager?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAgAl%2F\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Next we'll add some optional settings. The `mdf_publish` service will get us a publication and a DOI." + ], + "metadata": { + "id": "Rg2kx8OPN-E8" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i1w33XTtL-xR" + }, + "outputs": [], + "source": [ + "mdfcc.add_service(\"mdf_publish\")\n", + "\n", + "# Since this is just an example and not a real submission, we'll set the 'test' flag to True.\n", + "# The DOI we will get with a test submission is a sandbox DOI, which is not permanent.\n", + "mdfcc.set_test(True)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can use `get_submission` to take a look at the submission." + ], + "metadata": { + "id": "8qhh0VkeOZxX" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0nC3yla-L-xR", + "outputId": "2b972732-9119-4ffb-8c6a-d22bf03f67d5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dc': {'titles': [{'title': 'Connect Sample Dataset'}],\n", + " 'creators': [{'creatorName': 'Person, A.',\n", + " 'familyName': 'Person',\n", + " 'givenName': 'A.'}],\n", + " 'publisher': 'Materials Data Facility',\n", + " 'publicationYear': '2020',\n", + " 'resourceType': {'resourceTypeGeneral': 'Dataset',\n", + " 'resourceType': 'Dataset'}},\n", + " 'data_sources': ['https://app.globus.org/file-manager?origin_id=e38ee745-6d04-11e5-ba46-22000b92c6ec&origin_path=%2Fcitrine_mdf_demo%2Falloy.pbe%2FAgAl%2F'],\n", + " 'test': True,\n", + " 'update': False,\n", + " 'services': {'mdf_publish': True}}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.get_submission()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jfIyotczL-xR" + }, + "source": [ + "### Submitting the dataset\n", + "We're ready to submit the dataset! You can submit with the line below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9MvBx-bVL-xS", + "outputId": "bb98feea-173b-4b85-ba4f-1647daa5c17f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source_id': '_test_person_connect_sample_v1.1',\n", + " 'success': True,\n", + " 'error': None,\n", + " 'status_code': 202}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mdfcc.submit_dataset()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Once we've submitted, we can check the status." + ], + "metadata": { + "id": "H5JL9RDoPGTF" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GWpiSEOVL-xS", + "outputId": "51ab1712-372c-4dc4-f31a-63deb4be017c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Status of TEST submission _test_person_connect_sample_v1.1 (Connect Sample Dataset)\n", + "Submitted by Jonathon Gaff at 2020-01-14T16:40:37.823519Z\n", + "\n", + "Submission initialization was successful.\n", + "Connect data download is in progress.\n", + "Data transfer to primary destination has not started yet.\n", + "Metadata extraction has not started yet.\n", + "Dataset curation has not started yet.\n", + "MDF Search ingestion has not started yet.\n", + "Data transfer to secondary destinations has not started yet.\n", + "MDF Publish publication has not started yet.\n", + "Citrine upload has not started yet.\n", + "Materials Resource Registration has not started yet.\n", + "Post-processing cleanup has not started yet.\n", + "\n", + "This submission is still processing.\n", + "\n" + ] + } + ], + "source": [ + "mdfcc.check_status()" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### You did it!\n", + "You've submitted your dataset to MDF. Keep checking back on the status to see when it's ready to share with others. " + ], + "metadata": { + "id": "xARCPwNLPVJl" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1mCHkdILL-xS" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.1" + }, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/legacy/mdf_connect_client/__init__.py b/legacy/mdf_connect_client/__init__.py new file mode 100644 index 0000000..f0a10b1 --- /dev/null +++ b/legacy/mdf_connect_client/__init__.py @@ -0,0 +1,2 @@ +from .mdfcc import MDFConnectClient # noqa: F401 +from .version import __version__ # noqa: F401 diff --git a/legacy/mdf_connect_client/mdfcc.py b/legacy/mdf_connect_client/mdfcc.py new file mode 100644 index 0000000..2a68524 --- /dev/null +++ b/legacy/mdf_connect_client/mdfcc.py @@ -0,0 +1,1467 @@ +from datetime import datetime +import json + +import globus_sdk +import mdf_toolbox +from nameparser import HumanName +import requests + +from .version import __version__ + +CONNECT_SERVICE_LOC = "https://publish-prod.materialsdatafacility.org" +CONNECT_DEV_LOC = "https://publish-dev.materialsdatafacility.org" + +CONNECT_EXTRACT_ROUTE = "/submit" +CONNECT_STATUS_ROUTE = "/status/" +CONNECT_ALL_STATUS_ROUTE = "/submissions" +CONNECT_CURATION_ROUTE = "/curate/" +CONNECT_ALL_CURATION_ROUTE = "/curation/" +CONNECT_MD_UPDATE_ROUTE = "/update/" +CURATION_SUMMARY_STR = ("{source_id} by {submitter}\nWaiting since {waiting_since}" + "\n{extraction_summary}\n") +DEFAULT_CURATION_REASONS = { + "accept": "This submission has been accepted because it meets the appropriate standards", + "reject": ("This submission has been rejected because it does not meet the " + "appropriate standards") +} + + +class MDFConnectClient: + """The MDF Connect Client is the Python client to easily submit datasets to MDF Connect.""" + __app_name = "MDF_Connect_Client" + __client_id = "fcb9bf5a-4492-4e25-970f-510b69abc964" + __login_services = ["mdf_connect", "mdf_connect_dev"] + __allowed_authorizers = [ + globus_sdk.AccessTokenAuthorizer, + globus_sdk.RefreshTokenAuthorizer, + globus_sdk.ClientCredentialsAuthorizer, + globus_sdk.NullAuthorizer + ] + + def __init__(self, test=False, service_instance=None, authorizer=None): + """Create an MDF Connect Client. + + Arguments: + test (bool): When ``False``, the dataset will be processed normally. + When ``True``, the dataset will be processed, but submitted to + test/sandbox/temporary resources instead of live resources. + This includes the ``mdf-test`` Search index and test DOIs minted + with MDF Publish. + **Default:** ``False`` + service_instance (str): The instance of the MDF Connect API to use. + This value should not normally be changed from the default. + **Default:** ``None``, to use the default API instance. + authorizer (globus_sdk.GlobusAuthorizer): The authorizer to use for authentication. + This value should not normally be changed from the default. + **Default:** ``None``, to run the standard authentication flow. + + Returns: + *MDFConnectClient*: An initialized, authenticated MDF Connect Client. + """ + self.test = test + self.update = False + if (service_instance == "prod" or service_instance == "production" + or service_instance is None): + self.service_loc = CONNECT_SERVICE_LOC + elif service_instance == "dev" or service_instance == "development": + self.service_loc = CONNECT_DEV_LOC + else: + raise ValueError("'service_instance' must be 'prod' or 'dev', not '{}'" + .format(service_instance)) + self.extract_route = CONNECT_EXTRACT_ROUTE + self.status_route = CONNECT_STATUS_ROUTE + self.all_status_route = CONNECT_ALL_STATUS_ROUTE + self.curation_route = CONNECT_CURATION_ROUTE + self.all_curation_route = CONNECT_ALL_CURATION_ROUTE + self.curation_summary_template = CURATION_SUMMARY_STR + self.default_curation_reasons = DEFAULT_CURATION_REASONS + self.md_update_route = CONNECT_MD_UPDATE_ROUTE + + self.reset_submission() + login_service = "mdf_connect" if self.service_loc == CONNECT_SERVICE_LOC else "mdf_connect_dev" + + if any([isinstance(authorizer, allowed) for allowed in self.__allowed_authorizers]): + self.__authorizer = authorizer + else: + self.__authorizer = mdf_toolbox.login(services=self.__login_services, + client_id=self.__client_id, + app_name=self.__app_name).get(login_service) + if not self.__authorizer: + raise ValueError("Unable to authenticate") + + def logout(self): + """Log out by removing cached tokens and discarding the client's authorizer. + Also clear the current submission, as it cannot be interacted with. + """ + self.reset_submission() + self.__authorizer = None + mdf_toolbox.logout(client_id=self.__client_id, app_name=self.__app_name) + return "Logged out. You must create a new MDF Connect Client to log back in." + + @property + def version(self): + return __version__ + + # *********************************************** + # * Mandatory inputs + # *********************************************** + + def create_dc_block(self, title, authors, + affiliations=None, publisher=None, publication_year=None, + resource_type=None, description=None, dataset_doi=None, + related_dois=None, subjects=None, + **kwargs): + """Create your submission's dc block. + This block is the DataCite block. Additional information on DataCite fields + is available from the official DataCite website: + https://schema.datacite.org/meta/kernel-4.1/ + + Arguments: + title (str or list of str): The title(s) of the dataset. + authors (str or list of str): The author(s) of the dataset. + The name will be automatically parsed into given name and family name. + publisher (str): The publisher of the dataset (not an associated paper). + **Default:** The Materials Data Facility. + publication_year (int or str): The year of dataset publication. + **Default:** The current year. + resource_type (str): The type of resource. Except in unusual cases, this should be + ``"Dataset"``. **Default:** ``"Dataset"`` + affiliations (str or list of str or list of list of str): + The affiliations of the authors, in the same order. + If a different number of affiliations are given, + all affiliations will be applied to all authors. + Multiple affiliations can be given as a list. + **Default:** ``None`` for no affiliations for any author. + + Examples:: + + authors = ["Fromnist, Alice", "Fromnist; Bob", "Cathy Multiples"] + # All authors are from NIST + affiliations = "NIST" + # All authors are from both NIST and UChicago + affiliations = ["NIST", "UChicago"] + # Alice and Bob are from NIST, Cathy is from NIST and UChicago + affliliations = ["NIST", "NIST", ["NIST", "UChicago"]] + + # This is incorrect! If applying affiliations to all authors, + # lists must not be nested. + affiliations = ["NIST", ["NIST", "UChicago"], "Argonne", "Oak Ridge"] + description (str): A description of the dataset. + **Default:** ``None`` for no description. + dataset_doi (str): The DOI for this dataset (not an associated paper). + **Default:** ``None`` + related_dois (str or list of str): DOIs related to this dataset, + not including the dataset's own DOI (for example, an associated paper's DOI). + **Default:** ``None`` + subjects (str or list of str): Subjects (in Datacite terminology) or tags related + to the dataset. **Sefault:** ``None`` + Any further keyword arguments will be added to the DataCite metadata (the dc block). + These arguments should be valid DataCite, as listed in the MDF Connect documentation. + This is completely optional. + """ + if not title and not authors: + raise TypeError("'title' and 'authors' are required arguments.") + if not title: + raise TypeError("'title' is a required arguments.") + if not authors: + raise TypeError("'authors' is a required argument.") + # titles + if not isinstance(title, list): + title = [title] + titles = [{"title": t} for t in title] + + # creators + if not isinstance(authors, list): + authors = [authors] + if not affiliations: + affiliations = [] + elif not isinstance(affiliations, list): + affiliations = [affiliations] + if not len(authors) == len(affiliations): + affiliations = [affiliations] * len(authors) + creators = [] + for auth, affs in zip(authors, affiliations): + name = HumanName(auth) + given = "{} {}".format(name.first, name.middle).strip() + family = "{} {}".format(name.last, name.suffix).strip() + creator = { + "creatorName": "{}, {}".format(family, given).strip(" ,"), + "familyName": family, + "givenName": given + } + if not isinstance(affs, list): + affs = [affs] + if affs: + creator["affiliations"] = affs + creators.append(creator) + + # publisher + if not publisher: + publisher = "Materials Data Facility" + + # publicationYear + try: + publication_year = str(int(publication_year)) + except (ValueError, TypeError): + publication_year = str(datetime.now().year) + + # resourceType + if not resource_type: + resource_type = "Dataset" + + dc = { + "titles": titles, + "creators": creators, + "publisher": publisher, + "publicationYear": publication_year, + "resourceType": { + "resourceTypeGeneral": "Dataset", + "resourceType": resource_type + } + } + + # descriptions + if description: + dc["descriptions"] = [{ + "description": description, + "descriptionType": "Other" + }] + + # identifier + if dataset_doi: + dc["identifier"] = { + "identifier": dataset_doi, + "identifierType": "DOI" + } + + # relatedIdentifiers + if related_dois: + if not isinstance(related_dois, list): + related_dois = [related_dois] + dc["relatedIdentifiers"] = [{ + "relatedIdentifier": doi, + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf" + } for doi in related_dois] + + # subjects + if subjects: + if not isinstance(subjects, list): + subjects = [subjects] + dc["subjects"] = [{ + "subject": sub + } for sub in subjects] + + # misc + if kwargs: + dc = mdf_toolbox.dict_merge(dc, kwargs) + + self.dc = dc + + def add_data_source(self, data_source): + """Add a data source to your submission. + Note that this method is cumulative, so calls do not overwrite previous ones. + + Arguments: + data_source (str or list of str): The location(s) of the data. + These should be formatted with protocol. + + Examples: + ``"https://example.com/path/data.zip"`` + ``"https://www.globus.org/app/transfer?..."`` + ``"globus://endpoint123/path/data.out"`` + + """ + if not isinstance(data_source, list): + data_source = [data_source] + self.data_sources.extend(data_source) + + def clear_data_sources(self): + """Clear all data sources added so far to your dataset.""" + self.data_sources = [] + + # *********************************************** + # * Recommended inputs + # *********************************************** + + def add_tag(self, tag): + """Add a tag or keyword to your dataset. + Note that this method is cumulative, so calls do not overwrite previous ones. + + Note: + Setting tags here is equivalent to setting tags in ``create_dc_block(subjects=...)``. + This method exists only for convenience. + + Arguments: + tag (str or list of str): The tag(s) to add. + """ + if not isinstance(tag, list): + tag = [tag] + self.tags.extend(tag) + + def clear_tags(self): + """Clear all tags added so far to your dataset.""" + self.tags = [] + + def add_index(self, data_type, mapping, delimiter=None, na_values=None): + """Add indexing instructions for your dataset. + This method can be called multiple times for multiple data types, + but multiple calls with the same data type will overwrite each other. + + Arguments: + data_type (str): The type of data to apply to. Supported types are: ``json``, ``csv``, + ``yaml``, ``xml``, ``excel``, and ``filename``. + mapping (dict): The mapping of MDF fields to your data type's fields. + It is strongly recommended that you use "dot notation", + where nested JSON objects are represented with a period. + + Examples:: + + { + "material.composition": "my_json.data.stuff.comp", + "dft.converged": "my_json.data.dft.convgd" + } + { + "material.composition": "csv_header_1", + "crystal_structure.space_group_number": "csv_header_2" + } + + delimiter (str): The character that delimits cells in a table. + Only applicable to tabular data. + **Default:** comma. + na_values (str or list of str): Values to treat as N/A (not applicable/available). + Applies to all values. + **Default:** For tabular data, blank and space. + For other data, ``None`` (no N/A values). + + """ + # TODO: Additional validation + try: + json.dumps(mapping, allow_nan=False) + except Exception as e: + return "Error: Your mapping is invalid: {}".format(repr(e)) + index = { + "mapping": mapping + } + if delimiter is not None: + index["delimiter"] = delimiter + if na_values is not None: + if not isinstance(na_values, list): + na_values = [na_values] + index["na_values"] = na_values + + self.index[data_type] = index + + def clear_index(self): + """Clear all indexing instructions set so far.""" + self.index = {} + + def add_service(self, service, parameters=None): + """Add a service for data submission. + + Arguments: + service (str): The integrated service to submit your dataset to. + Connected services include: + + * ``mdf_publish`` (publication with DOI minting) + * ``citrine`` (industry-partnered machine-learning specialists) + * ``mrr`` (NIST Materials Resource Registry) + + parameters (dict): Optional, service-specific parameters. + + * For ``mdf_publish``: + * **publication_location** (*str*) - The Globus Endpoint + and path on which to save the published files. + It is recommended to not specify this parameter, + which causes the dataset to be published on MDF resources. + + * For ``citrine``: + * **public** (*bool*) - When ``True``, will make data public. + Otherwise, it is inaccessible. + """ + if parameters is None: + parameters = True + self.services[service] = parameters + + def clear_services(self): + """Clear all services added so far.""" + self.services = {} + + def set_test(self, test): + """Set the test flag for this dataset. + + Arguments: + test (bool): When ``False``, the dataset will be processed normally. + When ``True``, the dataset will be processed, but submitted to + test/sandbox/temporary resources instead of live resources. + This includes the ``mdf-test`` Search index and test DOIs minted + with MDF Publish. + **Default:** ``False`` + """ + self.test = test + + def set_organization(self, organization): + """Set the organization that governs the dataset. + + Arguments: + organization (str): The organization to add. + If the organization is not registered with MDF, it will be discarded. + """ + + self.mdf["organization"] = organization + + def clear_organization(self): + """Clear the added organizations from the submission.""" + self.mdf.pop("organization", None) + + def add_links(self, links): + """Add links to a dataset. + + Arguments: + link (str or list of str): The link(s) to add. + Should be of the form {"type":str, "doi":str, "url":str, "description":str, "bibtex":str} + """ + if not isinstance(links, list): + links = [links] + if not self.mdf.get("links"): + self.mdf["links"] = links + + def clear_links(self): + """Clear all tags added so far to your dataset.""" + self.links = [] + + # *********************************************** + # * Optional inputs + # *********************************************** + + def set_custom_block(self, custom_fields): + """Set the custom block for your dataset. + + Arguments: + custom_fields (dict): Custom field-value pairs for your dataset. + You may add descriptions of your fields by creating a new field + called ``[field]_desc`` with the string description inside, or by + calling ``set_custom_descriptions()``. + """ + try: + json.dumps(custom_fields, allow_nan=False) + except Exception as e: + return "Error: Your custom block is invalid: {}".format(repr(e)) + self.custom = custom_fields + + def set_custom_descriptions(self, custom_descriptions): + """Add descriptions to your custom block. + + Arguments: + custom_descriptions (dict): Custom field-description pairs for your dataset. + Field names in this argument must match field names added by + calling ``set_custom_block()``. + """ + try: + json.dumps(custom_descriptions, allow_nan=False) + except Exception as e: + return "Error: Your custom descriptions are invalid: {}".format(repr(e)) + for field, desc in custom_descriptions.items(): + self.custom[field+"_desc"] = desc + + def set_base_acl(self, acl): + """Set the Access Control List for your entire dataset. + + Arguments: + acl (str or list of str): The Globus UUIDs of users or groups that + should be granted full read access to the dataset, including records and files. + **Default:** The special keyword ``"public"``, which makes the dataset + visible to everyone. + + Warning: + The identities listed in the `base_acl` of your submission can always see + your submission, including dataset entry, even if they are not listed in + the ``dataset_acl``. This means that **if you do not specify a ``base_acl``**, + because it defaults to `"public"`, **your entire dataset will be public.** + MDF encourages you to make your data public, but if you do not want it public + you must specify this value. + """ + if not isinstance(acl, list): + acl = [acl] + self.mdf["acl"] = acl + + def clear_base_acl(self): + """Reset the base ACL of your dataset to the default value ``["public"]``.""" + self.mdf.pop("acl", None) + + def set_dataset_acl(self, acl): + """Set the Access Control List for just the dataset entry of your dataset. + + Arguments: + acl (str or list of str): The Globus UUIDs of users or groups that + should be granted read access only to the dataset entry for your dataset + in MDF Search (this includes the author list, title, etc. but + does not include extracted metadata in records or files). + Anyone listed in the base ACL already has this permission. + """ + if not isinstance(acl, list): + acl = [acl] + self.dataset_acl = acl + + def clear_dataset_acl(self): + """Remove all Globus UUIDs from the dataset ACL for your dataset.""" + self.dataset_acl = None + + def set_source_name(self, source_name): + """Set the source name for your dataset. + + Arguments: + source_name (str): The desired source name. Must be unique for new datasets. + Please note that your source name will be cleaned when submitted to Connect, + so the actual ``source_name`` may differ from this value. + Additionally, the ``source_id`` (which is the ``source_name`` plus + version information) is required to fetch the status of a submission. + ``check_status()`` can handle this for you. + """ + self.mdf["source_name"] = source_name + + def clear_source_name(self): + """Remove a previously set source_name.""" + self.mdf.pop("source_name", None) + + def set_update_metadata_only(self, metadata_only): + """Make this submission an update on the metadata only of a previous submission. + Incremental updates use the same submission metadata, except for whatever you + specify in the new submission. For example, if you submit an metadata only update + and only include a ``data_source``, the submission will run as if you copied the + DC block and other metadata into the submission, but with the new ``data_source``. + + Note: + You must still set ``update=True`` when submitting an incremental update. + + Arguments: + metadata_only (boolean): If true then flow performs an update on the metadata only + """ + self.update_metadata_only = metadata_only + + def add_data_destination(self, data_destination): + """Add a data destination to your submission. + Note that this method is cumulative, so calls do not overwrite previous ones. + + Arguments: + data_destination (str or list of str): The destination for the data. + Destinations must be Globus Endpoints, and formatted with protocol. + + Example: + ``"globus://endpoint123/path/data.out"`` + """ + if not isinstance(data_destination, list): + data_destination = [data_destination] + self.data_destinations.extend(data_destination) + + def clear_data_destinations(self): + """Clear all data destinations added so far to your dataset.""" + self.data_destinations = [] + + def set_external_uri(self, uri): + """Set an external URI for your dataset. This is used to point at + a landing page outside of MDF that also hosts the dataset. + + Arguments: + uri (str): The external URI. + """ + self.external_uri = uri + + def clear_external_uri(self): + """Remove any set external URI from your submission.""" + self.external_uri = None + + def create_mrr_block(self, mrr_data): + """Create the mrr block for your dataset. + This helper should be more helpful in the future. + + Arguments: + mrr_data (dict): The MRR schema-compliant metadata. + """ + self.mrr = mrr_data + + # *********************************************** + # * Advanced inputs + # *********************************************** + + def set_passthrough(self, passthrough): + """Set the dataset pass-through flag for your submission. + + Caution: + This flag will cause metadata from your dataset's files to not be extracted by + MDF Connect, so only high-level dataset metadata will be available in MDF Search. + *This flag is only intended for datasets that cannot be extracted.* + + Arguments: + passthrough (bool): When ``False``, the dataset will be processed normally. + When ``True``, the metadata in the files will not be extracted. + **Default:** ``False`` + """ + self.no_extract = passthrough + + def set_project_block(self, project, data): + """Set the project block for your dataset. + Intended only for use by members of an approved project. + To delete a project block, call this method with ``data=None``. + + Arguments: + project (str): The name of the project block. + data (dict): The data for the project block. + """ + try: + json.dumps(data, allow_nan=False) + except Exception as e: + return "Your project block is invalid: {}".format(repr(e)) + if data: + self.projects[project] = data + else: + self.projects.pop(project, None) + + def set_curation(self, curation): + """Set the curation flag for this submission. + + Note: + Normally, this flag is set automatically by an organization, and is not set + manually by the dataset submitter. + + Arguments: + curation (bool): When ``False``, the dataset will be processed normally. + When ``True``, the dataset must be approved in curation + before it will be ingested to MDF Search or any other service. + **Default:** ``False`` + """ + self.curation = curation + + def set_extraction_config(self, config): + """Set advanced configuration parameters for dataset extraction. + These parameters are intended for advanced users and/or special-case datasets. + + Arguments: + config (dict): The extraction configuration parameters. + """ + try: + json.dumps(config, allow_nan=False) + except Exception as e: + return "Error: Your extraction config is invalid: {}".format(repr(e)) + self.extraction_config = config + + # *********************************************** + # * Dataset submission + # *********************************************** + + def get_submission(self): + """Fetch the current state of your submission. + + Returns: + *dict*: Your submission. + """ + submission = { + "dc": self.dc, + "data_sources": self.data_sources, + "test": self.test, + "update": self.update + } + if self.mdf: + submission["mdf"] = self.mdf + else: + submission["mdf"] = {} + if self.mrr: + submission["mrr"] = self.mrr + if self.custom: + submission["custom"] = self.custom + if self.projects: + submission["projects"] = self.projects + if self.data_destinations: + submission["data_destinations"] = self.data_destinations + if self.external_uri: + submission["external_uri"] = self.external_uri + if self.index: + submission["index"] = self.index + if self.extraction_config: + submission["extraction_config"] = self.extraction_config + if self.services: + submission["services"] = self.services + if self.tags: + submission["tags"] = self.tags + if self.links: + submission["links"] = self.links + if self.curation: + submission["curation"] = self.curation + if self.no_extract: + submission["no_extract"] = self.no_extract + if self.dataset_acl: + submission["dataset_acl"] = self.dataset_acl + submission["update_metadata_only"] = self.update_metadata_only + return submission + + def reset_submission(self): + """Reset and clear metadata from your submission. + + Warning: + **This action cannot be undone.** + + The last submission's source_id will also be cleared. If you want to use ``check_status``, + you will be required to input the ``source_id`` manually. + + Returns: + *dict*: The variables that are NOT cleared, which includes: + * **test**: (*bool*) - If the submission is a test submission or not. + * **service_location** (*str*) - The URL of the MDF Connect server in use. + """ + self.dc = {} + self.mdf = {} + self.mrr = {} + + self.projects = {} + + self.set_custom_block({}) + self.set_extraction_config({}) + self.set_curation(False) + self.set_passthrough(False) + self.set_update_metadata_only(False) + + self.clear_data_sources() + self.clear_external_uri() + self.clear_data_destinations() + self.clear_index() + self.clear_services() + self.clear_tags() + self.clear_links() + self.clear_dataset_acl() + + self.source_id = None + + return { + "test": self.test, + "service_location": self.service_loc + } + + def submit_dataset(self, update=False, submission=None, reset=False): + """Submit your dataset to MDF Connect for processing. + + Arguments: + update (bool): If you wish to submit this dataset again, set this to ``True``. + If this is the first submission, leave this ``False``. + **Default:** ``False`` + submission (dict): If you have assembled the Connect metadata yourself, + you can submit it here. This argument supersedes any data + set through other methods. + **Default:** ``None``, to use method-assembled data. + reset (bool): If True, will clear the old submission. The test flag will be preserved. + **IMPORTANT**: The ``source_id`` of the submission will not be saved if + this argument is ``True``. ``check_status`` will require you to + pass the ``source_id`` as an argument. + If ``False``, the submission will be preserved. + **Default:** ``False`` + + Returns: + *dict*: The submission information. + * **success** (*bool*) - Whether the submission was successful. + * **source_id** (*string*) - The ``source_id`` of your dataset, which is also saved + in ``self.source_id``. The ``source_id`` is the ``source_name`` + plus version information. In other words, the ``source_name`` is unique + to your dataset, and the ``source_id`` is unique to + your submission of the dataset. + * **error** (*string*) - Error message, if applicable. + """ + # If submission not supplied, get from stored values + if not submission: + # Ensure update set if known resubmission + if not update and self.source_id: + return { + 'source_id': None, + 'success': False, + 'error': ("You have already submitted this dataset." + " Set update=True to resubmit it") + } + self.update = update + submission = self.get_submission() + + # Check for required data + if ((not submission["dc"] or not submission["data_sources"]) + and not submission["update_metadata_only"]): + return { + 'source_id': None, + 'success': False, + 'error': "You must populate the dc and data blocks before submission." + } + # Validate JSON + try: + json.dumps(submission, allow_nan=False) + except Exception as e: + return { + 'source_id': None, + 'success': False, + 'error': "The submission JSON is invalid: {}".format(repr(e)) + } + + # Make the request + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(self.service_loc+self.extract_route, + json=submission, headers=headers) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(self.service_loc+self.extract_route, + json=submission, headers=headers) + + # Check for success + error = None + try: + json_res = res.json() + except Exception: + if res.status_code < 300: + error = "Error decoding {} response: {}".format(res.status_code, res.content) + else: + error = ("Error {}. MDF Connect may be experiencing technical" + " difficulties.").format(res.status_code) + else: + if res.status_code < 300: + self.source_id = json_res["source_id"] + else: + error = ("Error {} submitting dataset: {}" + .format(res.status_code, json_res.get("error", json_res))) + + # Prepare the output + source_id = self.source_id + if reset: + self.reset_submission() + + # Return results + return { + "source_id": source_id, + "success": error is None, + "error": error, + "status_code": res.status_code + } + + def submit_dataset_metadata_update(self, source_id, metadata_update=None, reset=False): + """Submit an update to a dataset entry (and NOT the data or record entries). + + Arguments: + source_id (str): The ``source_id`` of the dataset you wish to update. + You must be the owner of the dataset. + metadata_update (dict): If you have assembled the dataset metadata yourself, + you can submit it here. This argument supersedes any data + set through other methods. + **Default:** ``None``, to use method-assembled data. + reset (bool): If True, will clear the old metadata from the client. + The test flag will be preserved. + If ``False``, the metadata will be preserved. + **Default:** ``False`` + """ + if not metadata_update: + metadata_update = self.get_submission() + # Strip off submission pieces not used in update + metadata_update.pop("data_sources", None) + metadata_update.pop("test", None) + metadata_update.pop("update", None) + metadata_update.pop("data_destinations", None) + metadata_update.pop("index", None) + metadata_update.pop("extraction_config", None) + metadata_update.pop("services", None) + metadata_update.pop("curation", None) + metadata_update.pop("no_extract", None) + metadata_update.pop("update_metadata_only", None) + + # Validate JSON + try: + json.dumps(metadata_update, allow_nan=False) + except Exception as e: + return { + 'source_id': None, + 'success': False, + 'error': "The metadata update JSON is invalid: {}".format(repr(e)) + } + + # Make the request + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(self.service_loc+self.md_update_route+source_id, + json=metadata_update, headers=headers) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(self.service_loc+self.md_update_route+source_id, + json=metadata_update, headers=headers) + + # Check for success + error = None + try: + json_res = res.json() + except Exception: + if res.status_code < 300: + error = "Error decoding {} response: {}".format(res.status_code, res.content) + else: + error = ("Error {}. MDF Connect may be experiencing technical" + " difficulties.").format(res.status_code) + else: + if res.status_code >= 300: + error = ("Error {} submitting dataset: {}" + .format(res.status_code, json_res.get("error", json_res))) + + if reset: + self.reset_submission() + + # Return results + return { + "success": error is None, + "error": error, + "status_code": res.status_code + } + + # *********************************************** + # * Status checking + # *********************************************** + + def check_status(self, source_id=None, short=False, raw=False): + """Check the status of your submission. + You may only check the status of your own submissions. + + Arguments: + source_id (str): The ``source_id`` (``source_name`` + version information) of the + submission to check. + **Default:** ``self.source_id`` + short (bool): When ``False``, will print a status summary containing + all of the status steps for the dataset. + When ``True``, will print a short finished/processing message, + useful for checking many datasets' status at once. + **Default:** ``False`` + raw (bool): When ``False``, will print a nicely-formatted status summary. + When ``True``, will return the full status result. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + + Returns: + If ``raw`` is ``True``, *dict*: The full status result. + """ + if not source_id and not self.source_id: + print("Error: No dataset submitted") + return None + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.status_route+(source_id or self.source_id), + headers=headers) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.status_route+(source_id or self.source_id), + headers=headers) + + try: + json_res = res.json() + except Exception as e: + if raw: + return { + "success": False, + "error": "{}: {}".format(e, res.content), + "status_code": res.status_code + } + elif res.status_code < 300: + print("Error decoding {} response: {}".format(res.status_code, res.content)) + else: + print("Error {}. MDF Connect may be experiencing technical" + " difficulties.".format(res.status_code)) + else: + if 'status' not in json_res['flow_status']: + print("Error: No status found for this submission.") + return json_res + + if json_res['flow_status']['status'] == 'ACTIVE': + active_msg = "This submission is still processing." + else: + active_msg = "This submission is no longer processing." + if raw: + json_res['flow_status']['status_code'] = res.status_code + return json_res + elif res.status_code >= 300: + print("Error {} fetching status: {}".format(res.status_code, + json_res.get("error", json_res))) + elif short: + print("{}: {}".format((source_id or self.source_id), active_msg)) + else: + print("\n{}\n{}\n".format(json_res["display_status"], active_msg)) + + def check_all_submissions(self, verbose=False, active_only=False, include_tests=True, + newer_than_date=None, older_than_date=None, raw=False, + filters=None, _admin_code=None): + """Check the status of all of your submissions. + + Arguments: + verbose (bool): When ``False``, will print a basic summary of your submissions. + When ``True``, will print the full status summary of each submission, + as if you called ``check_status()`` on each. Has no effect if raw is ``True``. + **Default:** ``False`` + active_only (bool): When ``True``, will only print active submissions. + **Default:** ``False`` + include_tests (bool): When ``False``, will only print non-test submissions. + **Default:** ``True`` + newer_than_date (datetime or tuple of ints): Exclude submissions made before + this date. Accepts a ``datetime`` object or ``(year, month, day)`` + as integers. Comparisons are made in UTC. + **Default:**: ``None``, to set no maximum age. + older_than_date: (datetime or tuple of ints): Exclude submissions made after + this date. Accepts a ``datetime`` object or ``(year, month, day)`` + as integers. Comparisons are made in UTC. + **Default:**: ``None``, to set no minimum age. + raw (bool): When ``False``, will print your submissions' summaries. + When ``True``, will return the full status results. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + filters (list of tuples): **Advanced users only** + Filters to apply to the status database scan. + For a submission to be returned, all filters must match. + **Default:** ``None``. + Format: (field, operator, value) + field: The status field to filter on. + operator: The relation of field to value. Valid operators: + ^: Begins with + *: Contains + ==: Equal to (or field does not exist, if value is None) + !=: Not equal to (or field exists, if value is None) + >: Greater than + >=: Greater than or equal to + <: Less than + <=: Less than or equal to + []: Between, inclusive (requires a list of two values) + in: Is one of the values (requires a list of values) + This operator effectively allows OR-ing '==' + value: The value of the field. + _admin_code (str): *For MDF Connect administrators only,* a special function code. + Valid codes: + + * ``all``: All submission statuses + * ``active``: All active submission statuses + + Only MDF Connect administrators are allowed to use these codes. + **Default:** ``None``, the only valid value for non-admins. + + Note about date filtering: + Days are compared in UTC, at exactly 0:00 (12:00am). This means that the two dates + cannot be the same, as they would filter out all submissions not made at exactly + 0:00:00 on the chosen date. To see submissions made on a specific date, set the + older_than filter one day away from the date in question. + For example, to see submissions from Feb 11, 2020, use + ``newer_than_date=(2020, 2, 11), older_than_date=(2020, 2, 12)``. + + Returns: + if raw is ``True``, *dict*: The full status results. + """ + if filters is None: + filters = [] + if active_only: + filters.append(("active", "==", True)) + if not include_tests: + filters.append(("test", "==", False)) + + # Date filters + if newer_than_date is not None and not isinstance(newer_than_date, datetime): + newer_than_date = datetime(*newer_than_date) + if older_than_date is not None and not isinstance(older_than_date, datetime): + older_than_date = datetime(*older_than_date) + # Validate date filters if both present + if newer_than_date is not None and older_than_date is not None: + # Cannot be the same + if newer_than_date == older_than_date: + raise ValueError("Date filters cannot be the identical. To see submissions " + "made on a specific date, set the older_than filter one day " + "away from the date in question.\nFor example, to see " + "submissions from Feb 11, 2020, use " + "'newer_than_date=(2020, 2, 11), older_than_date=(2020, 2, 12)'.") + elif newer_than_date > older_than_date: + raise ValueError("newer_than_date must be before older_than_date") + if newer_than_date: + filters.append(("submission_time", ">=", newer_than_date.isoformat("T") + "Z")) + if older_than_date: + filters.append(("submission_time", "<=", older_than_date.isoformat("T") + "Z")) + + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + body = { + "filters": filters + } + url = self.service_loc + self.all_status_route + (_admin_code or "") + res = requests.post(url, headers=headers, json=body) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(url, headers=headers, json=body) + + try: + json_res = res.json() + except Exception as e: + if raw: + return { + "success": False, + "error": "{}: {}".format(e, res.content), + "status_code": res.status_code + } + elif res.status_code < 300: + print("Error decoding {} response: {}".format(res.status_code, res.content)) + else: + print("Error {}. MDF Connect may be experiencing technical" + " difficulties.".format(res.status_code)) + else: + if raw: + json_res["status_code"] = res.status_code + return json_res + elif res.status_code >= 300: + print("Error {} fetching status: {}".format(res.status_code, + json_res.get("error", json_res))) + else: + if not verbose: + print() # Newline, because non-verbose won't include one + for sub in json_res["submissions"]: + if verbose: + # Same message as check_status() with extra spacing + if sub["active"]: + active_msg = "This submission is still processing." + else: + active_msg = "This submission is no longer processing." + print("\n\n", sub["status_message"], active_msg, sep="") + else: + # Decide if submission failed/succeeded/in processing/etc. + if "F" in sub["status_code"]: + status_word = "Failed" + elif "P" in sub["status_code"]: + status_word = "Processing" + elif sub["status_code"][-1] == "S": + status_word = "Succeeded" + elif sub["status_code"][-1] == "X": + status_word = "Cancelled" + elif sub["status_code"][0] == "z": + status_word = "Not started" + elif "R" in sub["status_code"]: + status_word = "Retrying error" + else: + status_word = "Unknown" + print("{}: {} - {}".format(sub["source_id"], + ("Processing" if sub["active"] + else "Not processing"), status_word)) + + # *********************************************** + # * Curation + # *********************************************** + + def get_curation_task(self, source_id, summary=False, raw=False): + """Get the content of a curation task. + You must have curation permissions on the selected submission. + + Arguments: + source_id (str): The ``source_id`` (``source_name`` + version information) of the + curation task. You can acquire this through + ``get_available_curation_tasks()``. + summary (bool): When ``False``, will print the entire curation task, + including the verbose dataset entry and sample records. + When ``True``, will only print a summary of the task. + **Default:** ``False`` + raw (bool): When ``False``, will print the curation task. + When ``True``, will return a dictionary of the full result. + Overrides the value of ``summary``. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + + Returns: + if raw is ``True``, *dict*: The full task results. + """ + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.curation_route+source_id, headers=headers) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.curation_route+source_id, headers=headers) + + try: + json_res = res.json() + except Exception as e: + if raw: + return { + "success": False, + "error": "{}: {}".format(e, res.content), + "status_code": res.status_code + } + elif res.status_code < 300: + print("Error decoding {} response: {}".format(res.status_code, res.content)) + else: + print("Error {}. MDF Connect may be experiencing technical" + " difficulties.".format(res.status_code)) + else: + if raw: + json_res["status_code"] = res.status_code + return json_res + elif res.status_code >= 300: + print("Error {} fetching curation task: {}" + .format(res.status_code, json_res.get("error", json_res))) + elif summary: + task = json_res["curation_task"] + print(self.curation_summary_template.format( + source_id=task["source_id"], + submitter=task["submission_info"]["submitter"], + waiting_since=task["curation_start_date"], + extraction_summary=task["extraction_summary"])) + else: + task = json_res["curation_task"] + # TODO: Are the dataset and record entries human-useful? + # task.pop("dataset") + # task.pop("sample_records") + print(json.dumps(task, indent=4, sort_keys=True)) + + def get_available_curation_tasks(self, summary=True, raw=False, _admin_code=None): + """Get all curation tasks available to you. + + Arguments: + summary (bool): When ``False``, will print the entire curation task, + including dataset entry and sample records. + When ``True``, will only print a summary of the task. + Using the summary is recommended to find specific tasks to + get full task information on using ``get_curation_task()``. + **Default:** ``True`` + raw (bool): When ``False``, will print out summaries of your available + curation tasks. When ``True``, will return a dictionary containing + the results. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + _admin_code (str): *For MDF Connect administrators only,* a special function code. + Valid codes: + + * ``all``: All waiting curation tasks. + + Only MDF Connect administrators are allowed to use these codes. + **Default:** ``None``, the only valid value for non-admins. + + Returns: + if raw is ``True``, *dict*: The full task results. + """ + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.all_curation_route+(_admin_code or ""), + headers=headers) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.all_curation_route+(_admin_code or ""), + headers=headers) + try: + json_res = res.json() + except Exception as e: + if raw: + return { + "success": False, + "error": "{}: {}".format(e, res.content), + "status_code": res.status_code + } + elif res.status_code < 300: + print("Error decoding {} response: {}".format(res.status_code, res.content)) + else: + print("Error {}. MDF Connect may be experiencing technical" + " difficulties.".format(res.status_code)) + else: + if raw: + json_res["status_code"] = res.status_code + return json_res + elif res.status_code >= 300: + print("Error {} fetching curation tasks: {}" + .format(res.status_code, json_res.get("error", json_res))) + # Check that results were returned + elif len(json_res["curation_tasks"]) < 1: + print("You have no open curation tasks.") + elif summary: + print() # Newline for spacing + for task in json_res["curation_tasks"]: + print(self.curation_summary_template.format( + source_id=task["source_id"], + submitter=task["submission_info"]["submitter"], + waiting_since=task["curation_start_date"], + extraction_summary=task["extraction_summary"])) + else: + for task in json_res["curation_tasks"]: + # TODO: Are the dataset and record entries human-useful? + # task.pop("dataset") + # task.pop("sample_records") + print("========== {} ==========".format(task["source_id"])) + print(json.dumps(task, indent=4, sort_keys=True)) + print("\n") # Double newline + + def _complete_curation_task(self, source_id, verdict, reason, prompt=True, raw=False): + """Complete a curation task by accepting or rejecting it. + You must have curation permissions on the selected submission. + + Note: + This method is intended to be used through ``accept_curation_submission()`` + and ``reject_curation_submission()``, as those methods are more explicit, + although the internal logic is almost identical. + + Arguments: + source_id (str): The ``source_id`` (``source_name`` + version information) of the + curation task. You can acquire this through + ``get_available_curation_tasks()``. + verdict (str): "accept" or "reject" to accept or reject the submission. + reason (str): The reason for accepting/rejecting this submission. + **Default:** ``None``, to use a generic reason. + prompt (bool): When ``True``, will prompt the user to confirm action selection, + with a summary of the selected task. + When ``False``, will not require confirmation. + **Default:** ``True``. + raw (bool): When ``False``, will print the result. + When ``True``, will return a dictionary of the full result. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + + Returns: + if raw is ``True``, *dict*: The full task results. + """ + # Validate verdict + verdict = verdict.strip().lower() + if verdict not in self.default_curation_reasons.keys(): + error = ("Verdict '{}' is invalid. Valid verdicts are: {}" + .format(verdict, self.default_curation_reasons.keys())) + if raw: + return { + "success": False, + "error": error + } + else: + print(error) + return + # Check that curation task exists + task_json = self.get_curation_task(source_id, raw=True) + if task_json["status_code"] == 404: + error = task_json.get("error", "Curation task not found") + if raw: + return { + "success": False, + "error": error + } + else: + print(error) + return + elif task_json["status_code"] >= 300: + default_error = "MDF Connect may be experiencing technical difficulties." + error = ("Error {} fetching curation task: {}" + .format(task_json["status_code"], task_json.get("error", default_error))) + if raw: + return { + "success": False, + "error": error + } + else: + print(error) + return + + # Prompt user to confirm, if requested + if prompt: + print("Are you sure you want to {} the following submission?".format(verdict)) + self.get_curation_task(source_id, summary=True) + prompt_response = input("\nConfirm {}ing submission [yes/no]: ".format(verdict)) + if prompt_response.strip().lower() != "yes": + error = "Curation cancelled" + if raw: + return { + "success": False, + "error": error + } + else: + print(error) + return + elif not reason: + reason = input("\nWhat is the reason for {}ing this submission?\n\t" + .format(verdict)).strip() + + if not reason: + reason = self.default_curation_reasons[verdict] + + # Submit verdict + command = { + "action": verdict, + "reason": reason + } + headers = {} + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.post(self.service_loc+self.curation_route+source_id, headers=headers, + json=command) + # Handle first 401/403 by regenerating auth headers + if res.status_code == 401 or res.status_code == 403: + self.__authorizer.handle_missing_authorization() + headers["Authorization"] = self.__authorizer.get_authorization_header() + res = requests.get(self.service_loc+self.curation_route+source_id, headers=headers, + json=command) + + try: + json_res = res.json() + except Exception as e: + if raw: + return { + "success": False, + "error": "{}: {}".format(e, res.content), + "status_code": res.status_code + } + elif res.status_code < 300: + print("Error decoding {} response: {}".format(res.status_code, res.content)) + else: + print("Error {}. MDF Connect may be experiencing technical" + " difficulties.".format(res.status_code)) + else: + if raw: + json_res["status_code"] = res.status_code + return json_res + elif res.status_code >= 300: + print("Error {} fetching curation task: {}" + .format(res.status_code, json_res.get("error", json_res))) + else: + print("\n", json_res["message"], sep="") + + def accept_curation_submission(self, source_id, reason=None, prompt=True, raw=False): + """Complete a curation task by accepting the submission. + You must have curation permissions on the selected submission. + + Arguments: + source_id (str): The ``source_id`` (``source_name`` + version information) of the + curation task. You can acquire this through + ``get_available_curation_tasks()``. + reason (str): The reason for accepting this submission. + **Default:** ``None``, to use a generic acceptance reason. + prompt (bool): When ``True``, will prompt the user to confirm action selection, + with a summary of the selected task. + When ``False``, will not require confirmation. + **Default:** ``True``. + raw (bool): When ``False``, will print the result. + When ``True``, will return a dictionary of the full result. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + + Returns: + if raw is ``True``, *dict*: The full task results. + """ + return self._complete_curation_task(source_id, "accept", reason, prompt, raw) + + def reject_curation_submission(self, source_id, reason=None, prompt=True, raw=False): + """Complete a curation task by rejecting the submission. + You must have curation permissions on the selected submission. + + Arguments: + source_id (str): The ``source_id`` (``source_name`` + version information) of the + curation task. You can acquire this through + ``get_available_curation_tasks()``. + reason (str): The reason for rejecting this submission. + **Default:** ``None``, to use a generic rejection reason. + prompt (bool): When ``True``, will prompt the user to confirm action selection, + with a summary of the selected task. + When ``False``, will not require confirmation. + **Default:** ``True``. + raw (bool): When ``False``, will print the result. + When ``True``, will return a dictionary of the full result. + For direct human consumption, ``False`` is recommended. + **Default:** ``False`` + + Returns: + if raw is ``True``, *dict*: The full task results. + """ + return self._complete_curation_task(source_id, "reject", reason, prompt, raw) diff --git a/legacy/mdf_connect_client/version.py b/legacy/mdf_connect_client/version.py new file mode 100644 index 0000000..f0c5b44 --- /dev/null +++ b/legacy/mdf_connect_client/version.py @@ -0,0 +1,2 @@ +# Single source of truth for package version +__version__ = "0.5.0" diff --git a/legacy/setup.cfg b/legacy/setup.cfg new file mode 100644 index 0000000..af02e77 --- /dev/null +++ b/legacy/setup.cfg @@ -0,0 +1,6 @@ +[bdist_wheel] +universal=1 + +[flake8] +exclude = .git,*.egg* +max-line-length = 100 diff --git a/legacy/setup.py b/legacy/setup.py new file mode 100644 index 0000000..5c9e9b9 --- /dev/null +++ b/legacy/setup.py @@ -0,0 +1,39 @@ +import os +from setuptools import setup + +# Single source of truth for version +version_ns = {} +with open(os.path.join("mdf_connect_client", "version.py")) as f: + exec(f.read(), version_ns) +version = version_ns['__version__'] + +setup( + name='mdf_connect_client', + version=version, + packages=['mdf_connect_client'], + description='Materials Data Facility Connect Client', + long_description=("The MDF Connect Client is the Python client to easily submit" + " datasets to MDF Connect."), + install_requires=[ + "mdf-toolbox>=0.7.1", + "nameparser>=1.0.4", + "requests>=2.18.4" + ], + python_requires=">=3.5", + classifiers=[ + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: Apache Software License", + "Natural Language :: English", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + ], + keywords=[ + "MDF", + "Materials Data Facility", + "materials science", + "utility", + "Connect Client" + ], + license="Apache License, Version 2.0", + url="https://github.com/materials-data-facility/connect_client" +) diff --git a/legacy/test_requirements.txt b/legacy/test_requirements.txt new file mode 100644 index 0000000..a63d3f0 --- /dev/null +++ b/legacy/test_requirements.txt @@ -0,0 +1,6 @@ +coveralls>=1.10.0 +nbval>=0.9.4 +pytest>=5.3.1 +pytest-cov>=2.5.1 +pytest-mock>=1.10.4 +mdf_toolbox \ No newline at end of file diff --git a/legacy/tests/test_connect_client.py b/legacy/tests/test_connect_client.py new file mode 100644 index 0000000..5309a74 --- /dev/null +++ b/legacy/tests/test_connect_client.py @@ -0,0 +1,434 @@ +from datetime import datetime + +from mdf_toolbox import insensitive_comparison +import pytest + +from mdf_connect_client import MDFConnectClient +from mdf_connect_client.mdfcc import CONNECT_SERVICE_LOC, CONNECT_DEV_LOC +from globus_sdk import NullAuthorizer + +@pytest.fixture +def auths(mocker): + return {"mdf_connect": NullAuthorizer(), "mdf_connect_dev": NullAuthorizer()} + + +def test_service_loc(auths): + mdf1 = MDFConnectClient(authorizer=auths["mdf_connect"]) + assert mdf1.service_loc == CONNECT_SERVICE_LOC + mdf2 = MDFConnectClient(service_instance="prod", authorizer=auths["mdf_connect"]) + assert mdf2.service_loc == CONNECT_SERVICE_LOC + mdf3 = MDFConnectClient(service_instance="dev", authorizer=auths["mdf_connect_dev"]) + assert mdf3.service_loc == CONNECT_DEV_LOC + + with pytest.raises(ValueError): + MDFConnectClient(service_instance="foobar", authorizer=auths["mdf_connect"]) + + +def test_create_dc_block(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + # Full test, no lists + mdf.create_dc_block( + title="Connect Title", + authors="Data Facility, Materials", + affiliations="UChicago", + publisher="Globus", + publication_year="2017", + resource_type="Dataset", + description="This is a test", + dataset_doi="10.555", + related_dois="10.5555", + subjects="Science", + other=5, + ) + assert mdf.dc == { + "creators": [ + { + "affiliations": ["UChicago"], + "creatorName": "Data Facility, Materials", + "familyName": "Data Facility", + "givenName": "Materials", + } + ], + "descriptions": [{"description": "This is a test", "descriptionType": "Other"}], + "identifier": {"identifier": "10.555", "identifierType": "DOI"}, + "other": 5, + "publicationYear": "2017", + "publisher": "Globus", + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.5555", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf", + } + ], + "resourceType": {"resourceType": "Dataset", "resourceTypeGeneral": "Dataset"}, + "titles": [{"title": "Connect Title"}], + "subjects": [{"subject": "Science"}], + } + # Full test, all lists + mdf.create_dc_block( + title=["Connect Title", "Other Title"], + authors=["Data Facility, Materials", "Blaiszik, Ben", "Jonathon Gaff"], + affiliations=["UChicago", "Argonne"], + publisher="Globus", + publication_year="2017", + resource_type="Dataset", + description="This is a test", + dataset_doi="10.555", + related_dois=["10.5555", "10.555-5555"], + subjects=["Science", "Math"], + other=5, + list_other=["a", "b"], + ) + assert mdf.dc == { + "creators": [ + { + "affiliations": ["UChicago", "Argonne"], + "creatorName": "Data Facility, Materials", + "familyName": "Data Facility", + "givenName": "Materials", + }, + { + "affiliations": ["UChicago", "Argonne"], + "creatorName": "Blaiszik, Ben", + "familyName": "Blaiszik", + "givenName": "Ben", + }, + { + "affiliations": ["UChicago", "Argonne"], + "creatorName": "Gaff, Jonathon", + "familyName": "Gaff", + "givenName": "Jonathon", + }, + ], + "descriptions": [{"description": "This is a test", "descriptionType": "Other"}], + "identifier": {"identifier": "10.555", "identifierType": "DOI"}, + "list_other": ["a", "b"], + "other": 5, + "publicationYear": "2017", + "publisher": "Globus", + "relatedIdentifiers": [ + { + "relatedIdentifier": "10.5555", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf", + }, + { + "relatedIdentifier": "10.555-5555", + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf", + }, + ], + "resourceType": {"resourceType": "Dataset", "resourceTypeGeneral": "Dataset"}, + "titles": [{"title": "Connect Title"}, {"title": "Other Title"}], + "subjects": [{"subject": "Science"}, {"subject": "Math"}], + } + # Minimum test + mdf.create_dc_block( + title="Project One", authors=["Artemis Moonshot", "Landing, Apollo"] + ) + assert mdf.dc == { + "creators": [ + { + "creatorName": "Moonshot, Artemis", + "familyName": "Moonshot", + "givenName": "Artemis", + }, + { + "creatorName": "Landing, Apollo", + "familyName": "Landing", + "givenName": "Apollo", + }, + ], + "publicationYear": str(datetime.now().year), + "publisher": "Materials Data Facility", + "resourceType": {"resourceType": "Dataset", "resourceTypeGeneral": "Dataset"}, + "titles": [{"title": "Project One"}], + } + + +def test_acl(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_base_acl("12345abc") + assert mdf.mdf == {"acl": ["12345abc"]} + mdf.set_base_acl(["12345abc", "6789def"]) + assert mdf.mdf == {"acl": ["12345abc", "6789def"]} + mdf.set_base_acl("public") + assert mdf.mdf == {"acl": ["public"]} + mdf.clear_base_acl() + assert mdf.mdf.get("acl", None) is None + + mdf.set_dataset_acl("12345abc") + assert mdf.dataset_acl == ["12345abc"] + mdf.set_dataset_acl(["12345abc", "6789def"]) + assert mdf.dataset_acl == ["12345abc", "6789def"] + mdf.set_dataset_acl("public") + assert mdf.dataset_acl == ["public"] + mdf.clear_dataset_acl() + assert mdf.dataset_acl is None + + +def test_source_name(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_source_name("foo") + assert mdf.mdf == {"source_name": "foo"} + mdf.clear_source_name() + assert mdf.mdf.get("source_name", None) is None + + +def test_organizations(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_organization("ANL") + assert mdf.mdf["organization"] == "ANL" + + +def test_create_mrr_block(auths): + # TODO: Update after helper is helpful + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.create_mrr_block({"a": "b"}) + assert mdf.mrr == {"a": "b"} + + +def test_set_custom_block(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_custom_block({"foo": "bar"}) + assert mdf.custom == {"foo": "bar"} + # OOR floats not allowed + res = mdf.set_custom_block({"foo": float("nan")}) + assert "Out of range float values are not JSON compliant" in res + assert mdf.custom == {"foo": "bar"} + # Clear block + mdf.set_custom_block({}) + assert mdf.custom == {} + + +def test_set_custom_descriptions(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_custom_block({"foo": "bar"}) + mdf.set_custom_descriptions({"foo": "This is a foo"}) + assert mdf.custom == {"foo": "bar", "foo_desc": "This is a foo"} + # OOR floats not allowed + res = mdf.set_custom_descriptions({"foo": float("nan")}) + assert "Out of range float values are not JSON compliant" in res + assert mdf.custom == {"foo": "bar", "foo_desc": "This is a foo"} + + +def test_set_project_block(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_project_block("proj1", {"foo": "bar"}) + assert mdf.projects == {"proj1": {"foo": "bar"}} + # OOR floats not allowed + res = mdf.set_project_block("proj2", {"foo": float("nan")}) + assert "Out of range float values are not JSON compliant" in res + assert mdf.projects == {"proj1": {"foo": "bar"}} + # Pop project + mdf.set_project_block("proj1", None) + assert mdf.projects == {} + + +def test_data(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + # data_sources + mdf.add_data_source("https://example.com/path/data.zip") + assert mdf.data_sources == ["https://example.com/path/data.zip"] + mdf.add_data_source( + [ + "https://www.globus.org/app/transfer?123", + "globus://endpoint123/path/data.out", + ] + ) + assert mdf.data_sources == [ + "https://example.com/path/data.zip", + "https://www.globus.org/app/transfer?123", + "globus://endpoint123/path/data.out", + ] + mdf.clear_data_sources() + assert mdf.data_sources == [] + + # data_destinations + mdf.add_data_destination("https://example.com/path/data.zip") + assert mdf.data_destinations == ["https://example.com/path/data.zip"] + mdf.add_data_destination( + [ + "https://www.globus.org/app/transfer?123", + "globus://endpoint123/path/data.out", + ] + ) + assert mdf.data_destinations == [ + "https://example.com/path/data.zip", + "https://www.globus.org/app/transfer?123", + "globus://endpoint123/path/data.out", + ] + mdf.clear_data_destinations() + assert mdf.data_destinations == [] + + +def test_index(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + # Mapping only + mdf.add_index("json", mapping={"materials.composition": "my_json.data.stuff.comp"}) + assert mdf.index == { + "json": {"mapping": {"materials.composition": "my_json.data.stuff.comp"}} + } + # With delim/na + mdf.add_index( + "csv", + mapping={"materials.composition": "header1"}, + delimiter="#", + na_values="zero", + ) + assert mdf.index == { + "json": {"mapping": {"materials.composition": "my_json.data.stuff.comp"}}, + "csv": { + "mapping": {"materials.composition": "header1"}, + "delimiter": "#", + "na_values": ["zero"], + }, + } + # Overwrite + mdf.add_index( + "csv", mapping={"crystal_structure.space_group_number": "csv_header_2"} + ) + assert mdf.index == { + "json": {"mapping": {"materials.composition": "my_json.data.stuff.comp"}}, + "csv": {"mapping": {"crystal_structure.space_group_number": "csv_header_2"}}, + } + # Bad input + res = mdf.add_index( + "json", mapping={"crystal_structure.space_group_number": float("nan")} + ) + assert "Out of range float values are not JSON compliant" in res + assert mdf.index == { + "json": {"mapping": {"materials.composition": "my_json.data.stuff.comp"}}, + "csv": {"mapping": {"crystal_structure.space_group_number": "csv_header_2"}}, + } + # Clear + mdf.clear_index() + assert mdf.index == {} + + +def test_extraction_config(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.set_extraction_config({"group_by_dir": True}) + assert mdf.extraction_config == {"group_by_dir": True} + # OOR floats not allowed + res = mdf.set_extraction_config({"dirs": float("nan")}) + assert "Error: Your extraction config is invalid" in res + assert mdf.extraction_config == {"group_by_dir": True} + # Clear block + mdf.set_extraction_config({}) + assert mdf.extraction_config == {} + + +def test_services(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + # No parameters + mdf.add_service("citrine") + assert mdf.services == {"citrine": True} + # With parameters + mdf.add_service("globus_publish", parameters={"collection_id": 5555}) + assert mdf.services == {"citrine": True, "globus_publish": {"collection_id": 5555}} + # Cancelling + mdf.add_service("citrine", False) + assert mdf.services == {"citrine": False, "globus_publish": {"collection_id": 5555}} + # Removing + mdf.clear_services() + assert mdf.services == {} + + +def test_tags(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + mdf.add_tag("foo") + assert mdf.tags == ["foo"] + mdf.add_tag(["bar", "baz"]) + assert mdf.tags == ["foo", "bar", "baz"] + mdf.clear_tags() + assert mdf.tags == [] + + +def test_curation(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + assert mdf.curation is False + mdf.set_curation(True) + assert mdf.curation is True + mdf.set_curation(False) + assert mdf.curation is False + + +def test_set_test(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + assert mdf.test is False + mdf.set_test(True) + assert mdf.test is True + mdf.set_test(False) + assert mdf.test is False + mdf2 = MDFConnectClient(test=True, authorizer=auths["mdf_connect"]) + assert mdf2.test is True + + +def test_passthrough(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + assert mdf.no_extract is False + mdf.set_passthrough(True) + assert mdf.no_extract is True + mdf.set_passthrough(False) + assert mdf.no_extract is False + + +def test_submission(auths): + mdf = MDFConnectClient(authorizer=auths["mdf_connect"]) + assert insensitive_comparison( + mdf.get_submission(), + { + "dc": {}, + "data_sources": [], + "mdf": {}, + "test": False, + "update": False, + "update_metadata_only": False, + }, + ) + mdf.dc = {"a": "a"} + mdf.mdf = {"b": "b"} + mdf.services = {"c": "c"} + mdf.projects = {"foo": {"bar": "baz"}} + assert insensitive_comparison( + mdf.get_submission(), + { + "dc": {"a": "a"}, + "mdf": {"b": "b"}, + "projects": {"foo": {"bar": "baz"}}, + "services": {"c": "c"}, + "data_sources": [], + "test": False, + "update": False, + "update_metadata_only": False, + }, + ) + + mdf.reset_submission() + assert insensitive_comparison( + mdf.get_submission(), + {"dc": {}, "mdf": {}, "data_sources": [], "test": False, "update": False, "update_metadata_only": False}, + ) + mdf.set_test(True) + mdf.reset_submission() + assert insensitive_comparison( + mdf.get_submission(), + {"dc": {}, "mdf": {},"data_sources": [], "test": True, "update": False, "update_metadata_only": False}, + ) + + +# def test_submit_dataset(): +# # TODO +# pass + + +# def test_check_status(): +# # TODO +# pass + + +# def test_check_all_submissions(): +# # TODO +# pass diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..92ddbac --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,47 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "mdf_agent" +version = "0.2.0" +description = "Modern MDF Connect client with CLI, Python API, and Claude skill integration." +readme = "README.md" +requires-python = ">=3.10" +license = { text = "MIT" } +authors = [ + { name = "Materials Data Facility" } +] +classifiers = [ + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "License :: OSI Approved :: MIT License", +] + +dependencies = [ + "pydantic>=2.0", + "globus-sdk>=3.0", + "typer[all]>=0.9.0", + "rich>=13.0", + "pyyaml>=6.0", + "httpx>=0.24.0", + "nameparser>=1.1.0", +] + +[project.optional-dependencies] +extractors = [ + "pypdf>=3.0", + "pandas>=2.0", + "openpyxl>=3.1", +] + +[project.scripts] +mdf = "mdf_agent.cli.main:app" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/scratch.md b/scratch.md new file mode 100644 index 0000000..a5e2311 --- /dev/null +++ b/scratch.md @@ -0,0 +1,183 @@ +# MDF Development Notes + +## Repository Structure + +Two separate git repos living in one directory: + +- **`mdf_client/`** — `connect_client` repo on GitHub (`materials-data-facility/connect_client`) + - Branch: `mdf-agent` + - Remote tracks `origin/master` (not `main`) + - Client-side code: `src/mdf_agent/` + - Tests: `tests/` + +- **`mdf_client/cs/`** — `connect_server` repo on GitHub (`materials-data-facility/connect_server`) + - Branch: `v2-backend-curation` + - Separate `.git`, separate remotes + - Backend code: `aws/v2/` + - Tests: `aws/v2/test_v2_*.py` (colocated with source, not in a tests/ dir) + - SAM template + deploy: `aws/template.yaml`, `aws/deploy.sh`, `aws/samconfig.toml` + +**Gotcha:** `git status` in the root shows `cs/` as untracked — it's a nested repo, not a submodule. Commits must be done separately in each directory. + +## Deployment + +- **Dev:** Self-contained, no Globus needed. `AUTH_MODE=dev` uses `X-User-Id` headers. +- **Staging:** `AUTH_MODE=production`, Globus token introspection. Globus creds from SSM (`/mdf/globus-client-id`, `/mdf/globus-client-secret`). DataCite creds from `samconfig.toml` (not SSM yet). `AllowAllCurators=true`. +- **Prod:** Same as staging but with real credentials from SSM. + +Deploy flow: `cd cs/aws && sam build && ./deploy.sh staging` + +Quick code-only deploy (skips CloudFormation): `./deploy.sh quick staging` + +Staging API URL: `https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging` + +## DataCite Credentials + +**Working test credentials:** +- Username: `Globus.TEST` +- Password: `NTroFAzElE` +- API: `https://api.test.datacite.org` +- Prefix: **`10.23677`** (NOT `10.26311` — that prefix is not assigned to this repository) + +**How we found the right prefix:** The plan had `globus` / `fhy77$g3` — that repo doesn't exist on the test API (404). The correct repo is `Globus.TEST`. We then got 403 because the plan's prefix `10.26311` wasn't assigned to it. Queried the DataCite API to discover the actual prefix: +``` +GET https://api.test.datacite.org/prefixes?client-id=globus.test +→ prefix: 10.23677 +``` + +**Lesson:** Always verify DataCite prefix assignment before assuming credentials work. The repository ID, password, AND prefix must all be correct together. + +## Auth / Backend Auth Flow + +The staging backend validates tokens by calling `globus_sdk.AuthClient.userinfo()` with the bearer token. It accepts **any valid Globus access token** — no scope restriction. So the native app's `auth.globus.org` openid token works fine. + +The `BackendClient.authenticated()` method handles the full flow: +1. Checks for explicit token / env var +2. Falls back to interactive Globus OAuth login (opens browser) +3. Caches tokens at `~/.config/mdf_agent/tokens.json` + +For testing against staging, use `BackendClient.authenticated(base_url=STAGING_URL, service_instance="prod")`. The `service_instance="prod"` is needed because staging uses the same Globus auth as prod. + +## Metadata Model Gotchas + +- `license` field in `DatasetMetadata` is a dict (`{"name": "CC-BY-4.0", "identifier": "CC-BY-4.0"}`), NOT a string. Pydantic will reject a plain string. +- `authors` expects list of dicts with `name` key, not plain strings. +- The metadata model is at `cs/aws/v2/metadata.py` — check `DatasetMetadata` for the canonical schema. + +## Status Lifecycle (v2) + +``` +submit → pending_curation → approved → published + ↘ rejected +``` + +Submissions now land directly as `pending_curation` (changed from `submitted`). +`ALLOWED_STATUSES` for manual updates: `{pending_curation, approved, published, rejected}`. + +## Async Job Dispatch + +Three modes controlled by `ASYNC_DISPATCH_MODE`: +- `inline` — synchronous, runs immediately (dev default, also used in API Lambda) +- `sqs` — queues to SQS, processed by AsyncWorkerFunction Lambda +- `sqlite` — local SQLite queue, processed by `run_sqlite_worker_once()` (tests) + +Staging uses `sqs` for the API Lambda. The async worker Lambda always uses `inline` (it processes jobs directly from SQS events). + +**Timing note:** When dispatch is `sqs`, the publish job is queued and the approve endpoint returns `status: "approved"`. The status changes to `published` after the async worker processes it (~15-20 seconds on staging). Tests that need to verify the final state should either use `inline` dispatch or poll. + +## Testing Patterns + +### Client tests (`tests/`) +- `conftest.py` adds `src/` to sys.path +- Pure pytest, class-based organization (`TestFoo`) +- Uses `tmp_path` fixture for temp dirs +- No mocking framework needed for URL normalization tests + +### Server tests (`cs/aws/v2/test_v2_*.py`) +- Files colocated with source code (not in a separate tests/ dir) +- `sys.path.insert(0, ...)` to parent dir at top of file +- `FastAPI TestClient` for API tests +- Fixtures set up SQLite store + env vars via `monkeypatch` +- Must call `reset_storage_backend()` and `reset_middleware_state()` in fixtures +- `USE_MOCK_DATACITE=true` and `USE_MOCK_SEARCH=true` for tests + +### Running tests +```bash +# Client tests +cd mdf_client && python -m pytest tests/test_submission_normalize.py -v + +# Server tests +cd mdf_client/cs/aws && python -m pytest v2/test_v2_publish_pipeline.py -v + +# All server v2 tests +cd mdf_client/cs/aws && python -m pytest v2/test_v2_*.py -v +``` + +## Ben's Preferences + +- Commit and push both repos separately (different commit messages per repo) +- Include all pre-existing changes when committing (don't cherry-pick just session changes) +- Exclude build artifacts (`.aws-sam/`, `.DS_Store`) +- Test with real deployed infrastructure when possible (staging E2E) +- Use the mdf_agent client for testing against staging (not raw curl with manually obtained tokens) +- Fix forward: when credentials are wrong, debug systematically (check repo exists, check prefix assignment) rather than guessing +- Practical over theoretical: validate things work against real APIs before wiring into infrastructure + +## Globus Search Integration (completed 2026-02-07) + +### Search Index +- Test index UUID: `ab19b80b-0887-4337-b9f8-b8cc7feb1fdc` +- Both `SearchIndexUUID` and `TestSearchIndexUUID` in samconfig.toml point to this index for staging +- Writer role granted to confidential app via `cs/aws/v2/scripts/grant_search_index_role.py` +- `USE_MOCK_SEARCH` is NOT a CloudFormation parameter — it's auto-derived from `AuthMode` via `!If [IsDevAuth, "true", "false"]`. For staging (`AuthMode=production`), it's already `"false"`. Do NOT add `UseMockSearch` to samconfig — it would break the deploy. + +### Bugs Fixed in Search Ingest +1. **Missing `requested_scopes`:** `oauth2_client_credentials_tokens()` must explicitly request `urn:globus:auth:scope:search.api.globus.org:all`. Without it, `by_resource_server` has no `search.api.globus.org` entry and `access_token` is `None`. +2. **Pydantic `License` not JSON-serializable:** `meta.license` is a `License(BaseModel)`, not a string. The `simplejson` serializer in globus_sdk chokes on it. Extract `.identifier` or `.name` before putting it in the GMetaEntry. + +### Search Fallback Behavior +- `search.py:search_datasets()` tries Globus Search first, silently falls back to DynamoDB scan on any exception. This means the `/search` endpoint can return results even when Globus Search ingest is broken — the results just come from DynamoDB instead. +- To verify entries are actually in the Globus Search index: use `cs/aws/v2/scripts/test_search_token.py` or query the index directly with `globus_sdk.SearchClient`. + +### Debugging Lambda Logs +```bash +# List recent async worker log streams +aws logs describe-log-streams \ + --log-group-name "/aws/lambda/mdf-connect-v2-staging-AsyncWorkerFunction-3t7JsGf6A6Ci" \ + --region us-east-1 --order-by LastEventTime --descending --limit 3 + +# Read events from a specific stream +aws logs get-log-events \ + --log-group-name "/aws/lambda/mdf-connect-v2-staging-AsyncWorkerFunction-3t7JsGf6A6Ci" \ + --log-stream-name '2026/02/07/[$LATEST]' \ + --region us-east-1 +``` + +The API function log group is `mdf-connect-v2-staging-ApiFunction-UciD01YjsOEN`. Search ingest errors appear in the **async worker** logs (not the API logs), since publish runs via SQS. + +## v1 Search Index Interaction + +The v1 code (`submit.py`, `automate_manager.py`) also reads `SEARCH_INDEX_UUID` and passes it into Globus Automate flows. Setting both `SearchIndexUUID` and `TestSearchIndexUUID` to the same test index ensures all code paths — v1 Flows and v2 async worker — converge on the same index. + +## Operational Scripts + +- `cs/aws/v2/scripts/grant_search_index_role.py` — One-shot: grant writer role on search index to the confidential app. Uses interactive Globus login, resolves client ID from SSM. +- `cs/aws/v2/scripts/setup_datacite_ssm.sh` — Interactive: create SSM params for production DataCite credentials. `deploy.sh` reads these automatically. +- `cs/aws/v2/scripts/test_search_token.py` — Diagnostic: verify the confidential app can obtain a search token and query the index. + +## What's Left (as of 2026-02-07) + +- PRs for both repos +- DataCite SSM parameters not yet created for prod (staging uses samconfig.toml values directly) +- Production search index (separate from test index) — create when ready for prod +- Production CORS is configured (`template.yaml`) but not yet deployed to a prod stack + +## Ben's Preferences (updated) + +- **Always use `mdf_agent` / `BackendClient` for scripts and testing** — it handles auth automatically. Don't use raw `globus_sdk`, raw `curl`, or manually-obtained tokens. If a capability is missing from the client, add it there rather than working around it. +- When something fails silently (like search ingest), **check the Lambda logs first** — the async worker logs show the actual exception. The API logs won't show async job failures. +- **Audit for serialization safety** when building dicts that will be JSON-serialized — Pydantic models, datetime objects, and custom classes all need explicit conversion. +- **Test against real infrastructure** (staging E2E with real Globus Search, real DataCite) rather than assuming mock behavior matches. The mock search client silently succeeds while real Globus Search has auth, scope, and serialization requirements. +- **Deploy frequently and check logs** — don't batch multiple untested changes. Fix one thing, deploy, verify, then move on. +- Prefer diagnostic scripts that can be re-run (`test_search_token.py`) over one-off debug commands. +- When debugging token/auth issues: the scope must be (1) configured on the Globus app registration, (2) explicitly requested in `requested_scopes`, and (3) the app must have the right role on the target resource (e.g., writer on the search index). diff --git a/simple_walkthrough.md b/simple_walkthrough.md new file mode 100644 index 0000000..fa8e45f --- /dev/null +++ b/simple_walkthrough.md @@ -0,0 +1,75 @@ +# MDF Quick Start: Publish & Approve + +## Publish a dataset + +```bash +# 1. Log in +mdf login + +# 2. Publish a local directory +mdf publish ./my_data/ \ + --title "My Dataset" \ + --author "Jane Doe" \ + --submit +``` + +Files upload with a progress bar, then the dataset enters the curation queue. + +## Check status + +```bash +mdf status +``` + +## Approve the dataset (curator) + +```bash +# See what's waiting +mdf pending + +# Approve it +mdf approve my_dataset_v1 +``` + +After approval the backend mints a DOI and indexes the dataset for search. + +## Find it + +```bash +mdf list # your datasets +mdf show my_dataset_v1 # dataset card with DOI +mdf search "my dataset" # full-text search +``` + +## Update it later + +### Add new data (major version bump) + +```bash +mdf update --data ./new_data/ --submit +``` + +New data → **major** version bump (1.0 → 2.0). The new data_sources replace the prior version's. + +### Update metadata only (minor version bump) + +```bash +mdf update --title "Better Title" --submit +``` + +No `--data` → **minor** version bump (2.0 → 2.1). The prior version's `data_sources` are inherited automatically. + +### Version chain example + +``` +1.0 → 2.0 → 2.1 → 3.0 + ↑ ↑ ↑ ↑ +init new data metadata new data + only +``` + +### Check versions + +```bash +mdf versions +``` diff --git a/src/mdf_agent/__init__.py b/src/mdf_agent/__init__.py new file mode 100644 index 0000000..e7a1291 --- /dev/null +++ b/src/mdf_agent/__init__.py @@ -0,0 +1,7 @@ +"""MDF Agent package.""" + +from mdf_agent.version import __version__ +from mdf_agent.core.agent import MDFAgent +from mdf_agent.core.backend_client import BackendClient + +__all__ = ["MDFAgent", "BackendClient", "__version__"] diff --git a/src/mdf_agent/__main__.py b/src/mdf_agent/__main__.py new file mode 100644 index 0000000..279a99f --- /dev/null +++ b/src/mdf_agent/__main__.py @@ -0,0 +1,4 @@ +from mdf_agent.cli.main import app + +if __name__ == "__main__": + app() diff --git a/src/mdf_agent/auth/__init__.py b/src/mdf_agent/auth/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/auth/globus.py b/src/mdf_agent/auth/globus.py new file mode 100644 index 0000000..f4da51b --- /dev/null +++ b/src/mdf_agent/auth/globus.py @@ -0,0 +1,271 @@ +"""Globus authentication for MDF Agent. + +This module provides authentication utilities for connecting to MDF Connect +and related Globus services. It supports both token-based authentication +(for automation) and interactive OAuth2 login (for human users). +""" + +from __future__ import annotations + +import os +from pathlib import Path +from typing import Optional + +from globus_sdk import ( + AccessTokenAuthorizer, + GlobusAppConfig, + NativeAppAuthClient, + RefreshTokenAuthorizer, + UserApp, +) +from globus_sdk.tokenstorage import JSONTokenStorage + +# ============================================================================= +# MDF Agent Credentials +# ============================================================================= + +# Native app client ID (registered at developers.globus.org) +MDF_AGENT_CLIENT_ID = "074cebcc-19ad-4332-bbf2-78402291b659" +REDIRECT_URI = "https://auth.globus.org/v2/web/auth-code" +APP_NAME = "MDF_Agent" + +# Default token storage location +DEFAULT_TOKEN_PATH = Path("~/.config/mdf_agent/tokens.json").expanduser() + +# ============================================================================= +# MDF Connect Scopes +# ============================================================================= + +# Production MDF Connect +MDF_CONNECT_SCOPE = "https://auth.globus.org/scopes/4d5f8e8b-a61d-40d8-bb58-5a3f5d1d200a/connect" +MDF_CONNECT_RESOURCE_SERVER = "4d5f8e8b-a61d-40d8-bb58-5a3f5d1d200a" + +# Development MDF Connect +MDF_CONNECT_DEV_SCOPE = "https://auth.globus.org/scopes/0e0a9538-ce45-43c1-998a-d3a7031a83f0/connect" +MDF_CONNECT_DEV_RESOURCE_SERVER = "0e0a9538-ce45-43c1-998a-d3a7031a83f0" + +# ============================================================================= +# Related Service Scopes (for clone, transfer, search) +# ============================================================================= + +TRANSFER_SCOPE = "urn:globus:auth:scope:transfer.api.globus.org:all" +SEARCH_SCOPE = "urn:globus:auth:scope:search.api.globus.org:search" +SEARCH_INGEST_SCOPE = "urn:globus:auth:scope:search.api.globus.org:all" +# NCSA MDF collection — the HTTPS endpoint requires a scope tied to the +# collection UUID, not the hostname. +NCSA_MDF_COLLECTION_UUID = "82f1b5c6-6e9b-11e5-ba47-22000b92c6ec" +DATA_MDF_SCOPE = f"https://auth.globus.org/scopes/{NCSA_MDF_COLLECTION_UUID}/https" + +# Scope sets for different operations +PUBLISH_SCOPES = [MDF_CONNECT_SCOPE] +PUBLISH_DEV_SCOPES = [MDF_CONNECT_DEV_SCOPE] +CLONE_SCOPES = [SEARCH_SCOPE] +TRANSFER_SCOPES = [TRANSFER_SCOPE, DATA_MDF_SCOPE] + + +def get_scopes_for_service(service_instance: str = "prod") -> tuple[str, str]: + """Get the appropriate scope and resource server for a service instance. + + Args: + service_instance: Either "prod" or "dev" + + Returns: + Tuple of (scope, resource_server_id) + """ + if service_instance in ("dev", "development"): + return MDF_CONNECT_DEV_SCOPE, MDF_CONNECT_DEV_RESOURCE_SERVER + # staging uses prod auth (same Globus introspection) + return MDF_CONNECT_SCOPE, MDF_CONNECT_RESOURCE_SERVER + + +def get_authorizer( + token: Optional[str] = None, + client_id: Optional[str] = None, + scope: Optional[str] = None, + service_instance: str = "prod", + app_name: str = APP_NAME, + token_path: Path = DEFAULT_TOKEN_PATH, +) -> AccessTokenAuthorizer | RefreshTokenAuthorizer: + """Get a Globus authorizer for MDF Connect. + + This function supports three authentication methods: + 1. Direct token (passed as argument or via MDF_CONNECT_TOKEN env var) + 2. Interactive OAuth2 login (opens browser, user authenticates) + 3. Cached tokens from previous login (stored in ~/.config/mdf_agent/) + + Args: + token: Optional access token. If not provided, checks MDF_CONNECT_TOKEN env var. + client_id: Optional Globus client ID. Defaults to MDF_AGENT_CLIENT_ID. + scope: Optional scope. If not provided, uses appropriate MDF Connect scope. + service_instance: "prod" or "dev" - determines which MDF Connect instance to use. + app_name: Application name for OAuth2 flow. + token_path: Path to store cached tokens. + + Returns: + A Globus authorizer ready for API calls. + + Raises: + RuntimeError: If authentication fails or no valid auth method available. + + Examples: + # Using environment variable + >>> os.environ["MDF_CONNECT_TOKEN"] = "my-token" + >>> auth = get_authorizer() + + # Using interactive login + >>> auth = get_authorizer(service_instance="dev") + + # Using specific token + >>> auth = get_authorizer(token="my-access-token") + """ + # Check for direct token (argument or environment variable) + token = token or os.getenv("MDF_CONNECT_TOKEN") + if token: + return AccessTokenAuthorizer(token) + + # Use defaults if not provided + client_id = client_id or MDF_AGENT_CLIENT_ID + + # Get appropriate scope for service instance + if scope is None: + scope, resource_server = get_scopes_for_service(service_instance) + else: + resource_server = scope.split("/scopes/")[1].split("/")[0] if "/scopes/" in scope else scope + + # Ensure token directory exists + token_path.parent.mkdir(parents=True, exist_ok=True) + + # Set up token storage and app config + storage = JSONTokenStorage(str(token_path)) + config = GlobusAppConfig( + token_storage=storage, + request_refresh_tokens=True, + ) + + # Create UserApp with scope requirements + app = UserApp( + app_name, + client_id=client_id, + config=config, + ) + + # Add the required scope + app.add_scope_requirements({resource_server: [scope]}) + + # Login if needed (opens browser for user authentication) + if app.login_required(): + print(f"Opening browser for Globus authentication...") + print(f"If browser doesn't open, visit: {REDIRECT_URI}") + app.login() + + return app.get_authorizer(resource_server) + + +def get_authorizer_for_scopes( + scopes: list[str], + client_id: Optional[str] = None, + app_name: str = APP_NAME, + token_path: Path = DEFAULT_TOKEN_PATH, +) -> dict[str, AccessTokenAuthorizer | RefreshTokenAuthorizer]: + """Get authorizers for multiple scopes (e.g., for clone + transfer). + + Args: + scopes: List of scope URIs to request. + client_id: Optional Globus client ID. + app_name: Application name for OAuth2 flow. + token_path: Path to store cached tokens. + + Returns: + Dictionary mapping resource server IDs to authorizers. + """ + client_id = client_id or MDF_AGENT_CLIENT_ID + + token_path.parent.mkdir(parents=True, exist_ok=True) + storage = JSONTokenStorage(str(token_path)) + config = GlobusAppConfig( + token_storage=storage, + request_refresh_tokens=True, + ) + + app = UserApp(app_name, client_id=client_id, config=config) + + # Build scope requirements + scope_reqs = {} + for scope in scopes: + if "/scopes/" in scope: + resource_server = scope.split("/scopes/")[1].split("/")[0] + elif ":" in scope: + # URN format: urn:globus:auth:scope:server:scope_name + parts = scope.split(":") + resource_server = parts[4] if len(parts) > 4 else scope + else: + resource_server = scope + + if resource_server not in scope_reqs: + scope_reqs[resource_server] = [] + scope_reqs[resource_server].append(scope) + + app.add_scope_requirements(scope_reqs) + + if app.login_required(): + print("Opening browser for Globus authentication...") + app.login() + + # UserApp always adds openid/profile/email for auth.globus.org; + # include that authorizer so callers can use it for identity. + all_servers = list(scope_reqs.keys()) + if "auth.globus.org" not in all_servers: + all_servers.append("auth.globus.org") + + result = {} + for rs in all_servers: + try: + result[rs] = app.get_authorizer(rs) + except Exception: + pass + return result + + +def logout(token_path: Path = DEFAULT_TOKEN_PATH) -> bool: + """Clear cached tokens and log out. + + Args: + token_path: Path to token storage file. + + Returns: + True if tokens were cleared, False if no tokens existed. + """ + if token_path.exists(): + token_path.unlink() + print("Logged out and cleared cached tokens.") + return True + return False + + +def is_logged_in( + service_instance: str = "prod", + client_id: Optional[str] = None, + token_path: Path = DEFAULT_TOKEN_PATH, +) -> bool: + """Check if user has valid cached tokens. + + Args: + service_instance: "prod" or "dev" + client_id: Optional Globus client ID. + token_path: Path to token storage file. + + Returns: + True if valid cached tokens exist. + """ + if not token_path.exists(): + return False + + client_id = client_id or MDF_AGENT_CLIENT_ID + scope, resource_server = get_scopes_for_service(service_instance) + + storage = JSONTokenStorage(str(token_path)) + config = GlobusAppConfig(token_storage=storage, request_refresh_tokens=True) + app = UserApp(APP_NAME, client_id=client_id, config=config) + app.add_scope_requirements({resource_server: [scope]}) + + return not app.login_required() diff --git a/src/mdf_agent/cli/__init__.py b/src/mdf_agent/cli/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/cli/add.py b/src/mdf_agent/cli/add.py new file mode 100644 index 0000000..18757a6 --- /dev/null +++ b/src/mdf_agent/cli/add.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +from typing import List, Optional + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Stage files for MDF submission") + + +@app.command() +def add( + paths: List[str] = typer.Argument(..., help="Files or globs to add"), + discover: Optional[bool] = typer.Option( + None, "--discover/--no-discover", help="Auto-discover metadata" + ), +): + agent = MDFAgent.from_repo(".") + staged = agent.add(*paths, discover=discover) + typer.echo("Staged:") + for path in staged: + typer.echo(f" {path}") diff --git a/src/mdf_agent/cli/backend.py b/src/mdf_agent/cli/backend.py new file mode 100644 index 0000000..826eb84 --- /dev/null +++ b/src/mdf_agent/cli/backend.py @@ -0,0 +1,484 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Dict, Optional + +import typer +from rich.console import Console +from rich.panel import Panel +from rich.table import Table +from rich import box + +from mdf_agent.core.backend_client import BackendClient +from mdf_agent.core.config import resolve_service +from mdf_agent.cli.formatting import format_result_or_json, format_status_badge, handle_api_result + +app = typer.Typer(help="Interact with MDF backend (v2) API") +console = Console() +_auth_opts: Dict[str, Optional[str]] = {"service": None, "token": None, "dev_user": None} + + +@app.callback() +def backend_callback( + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev/local)"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id (X-User-Id)"), +): + """Interact with MDF backend (v2) API.""" + _auth_opts.update(service=service, token=token, dev_user=dev_user) + + +def _client(api_url: Optional[str]) -> BackendClient: + return BackendClient.authenticated( + base_url=api_url, + token=_auth_opts.get("token"), + service_instance=resolve_service(_auth_opts.get("service")), + dev_user_id=_auth_opts.get("dev_user"), + ) + + +def _print(result): + typer.echo(json.dumps(result, indent=2)) + + +@app.command("health") +def health( + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.health() + client.close() + format_result_or_json(result, json_output, success_msg="Backend is healthy") + + +@app.command("submit") +def submit( + payload: Path = typer.Option(..., "--payload", help="Path to submission JSON"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + data = json.loads(payload.read_text(encoding="utf-8")) + result = client.submit(data) + client.close() + if json_output: + _print(result) + else: + if result.get("success"): + console.print(f"[green]Submitted:[/green] {result.get('source_id')} v{result.get('version')}") + else: + handle_api_result(result, error_prefix="Submit failed") + + +@app.command("status") +def status( + source_id: str = typer.Option(..., "--source-id"), + version: Optional[str] = typer.Option(None, "--version"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.status(source_id, version=version) + client.close() + if json_output: + _print(result) + else: + sub = result.get("submission") or result + if sub.get("source_id"): + console.print(f"[cyan]{sub.get('source_id')}[/cyan] v{sub.get('version', '?')}") + console.print(f" Status: {format_status_badge(sub.get('status', 'unknown'))}") + else: + handle_api_result(result, error_prefix="Status lookup") + + +@app.command("submissions") +def submissions( + organization: Optional[str] = typer.Option(None, "--organization"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.submissions(organization=organization) + client.close() + if json_output: + _print(result) + else: + subs = result.get("submissions", []) + if subs: + console.print(f"\n[bold]{len(subs)} submission(s):[/bold]") + for s in subs: + console.print(f" {s.get('source_id', '')} v{s.get('version', '?')} — {format_status_badge(s.get('status', ''))}") + else: + handle_api_result(result, error_prefix="Submissions") + + +@app.command("curation-pending") +def curation_pending( + limit: int = typer.Option(50, "--limit"), + offset: int = typer.Option(0, "--offset"), + organization: Optional[str] = typer.Option(None, "--organization"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.curation_pending(limit=limit, offset=offset, organization=organization) + client.close() + if json_output: + _print(result) + else: + subs = result.get("submissions", []) + if subs: + console.print(f"\n[bold]Pending curation ({len(subs)}):[/bold]") + for s in subs: + console.print(f" {s.get('source_id', '')} — {s.get('title', 'Untitled')}") + else: + console.print("[dim]No datasets pending curation.[/dim]") + + +@app.command("curation-detail") +def curation_detail( + source_id: str = typer.Option(..., "--source-id"), + version: Optional[str] = typer.Option(None, "--version"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.curation_detail(source_id, version=version) + client.close() + format_result_or_json(result, json_output, error_prefix="Curation detail") + + +@app.command("curation-approve") +def curation_approve( + source_id: str = typer.Option(..., "--source-id"), + mint_doi: bool = typer.Option(True, "--mint-doi/--no-mint-doi"), + notes: Optional[str] = typer.Option(None, "--notes"), + metadata_updates: Optional[Path] = typer.Option( + None, + "--metadata-updates", + help="Path to JSON metadata updates", + ), + version: Optional[str] = typer.Option(None, "--version"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + metadata_payload = None + if metadata_updates: + metadata_payload = json.loads(metadata_updates.read_text(encoding="utf-8")) + + client = _client(api_url) + result = client.curation_approve( + source_id=source_id, + mint_doi=mint_doi, + notes=notes, + metadata_updates=metadata_payload, + version=version, + ) + client.close() + format_result_or_json(result, json_output, success_msg=f"Approved: {source_id}", error_prefix="Approve failed") + + +@app.command("curation-reject") +def curation_reject( + source_id: str = typer.Option(..., "--source-id"), + reason: str = typer.Option(..., "--reason"), + suggestions: Optional[str] = typer.Option(None, "--suggestions"), + version: Optional[str] = typer.Option(None, "--version"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.curation_reject( + source_id=source_id, + reason=reason, + suggestions=suggestions, + version=version, + ) + client.close() + format_result_or_json(result, json_output, success_msg=f"Rejected: {source_id}", error_prefix="Reject failed") + + +@app.command("update-status") +def update_status( + source_id: str = typer.Option(..., "--source-id"), + version: str = typer.Option(..., "--version"), + status: str = typer.Option(..., "--status"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.update_status(source_id, version, status) + client.close() + format_result_or_json(result, json_output, success_msg=f"Status updated: {source_id} → {status}", error_prefix="Update failed") + + +@app.command("stream-create") +def stream_create( + title: str = typer.Option(..., "--title"), + lab_id: Optional[str] = typer.Option(None, "--lab-id"), + organization: Optional[str] = typer.Option(None, "--organization"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_create(title, lab_id=lab_id, organization=organization) + client.close() + if json_output: + _print(result) + else: + if result.get("success"): + console.print(f"[green]Stream created:[/green] {result.get('stream_id')}") + else: + handle_api_result(result, error_prefix="Stream create failed") + + +@app.command("stream-append") +def stream_append( + stream_id: str = typer.Option(..., "--stream-id"), + files: Optional[Path] = typer.Option(None, "--files", help="JSON list of files or dict with files"), + file_count: Optional[int] = typer.Option(None, "--file-count"), + total_bytes: Optional[int] = typer.Option(None, "--total-bytes"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + files_payload = None + if files: + files_payload = json.loads(files.read_text(encoding="utf-8")) + if isinstance(files_payload, dict) and "files" in files_payload: + files_payload = files_payload["files"] + result = client.stream_append( + stream_id, + files=files_payload, + file_count=file_count, + total_bytes=total_bytes, + ) + client.close() + format_result_or_json(result, json_output, success_msg="Files appended", error_prefix="Append failed") + + +@app.command("stream-status") +def stream_status( + stream_id: str = typer.Option(..., "--stream-id"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_status(stream_id) + client.close() + if json_output: + _print(result) + else: + if result.get("success"): + s = result + console.print(f"[cyan]{s.get('stream_id', stream_id)}[/cyan]") + console.print(f" Status: {format_status_badge(s.get('status', 'unknown'))}") + console.print(f" Files: {s.get('file_count', 0)}") + else: + handle_api_result(result, error_prefix="Stream status") + + +@app.command("stream-close") +def stream_close( + stream_id: str = typer.Option(..., "--stream-id"), + mint_doi: Optional[bool] = typer.Option(None, "--mint-doi/--no-mint-doi"), + title: Optional[str] = typer.Option(None, "--title"), + description: Optional[str] = typer.Option(None, "--description"), + authors: Optional[Path] = typer.Option(None, "--authors", help="Path to JSON author list"), + keywords: Optional[Path] = typer.Option(None, "--keywords", help="Path to JSON keywords list"), + license: Optional[str] = typer.Option(None, "--license"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + authors_payload = None + if authors: + authors_payload = json.loads(authors.read_text(encoding="utf-8")) + + keywords_payload = None + if keywords: + keywords_payload = json.loads(keywords.read_text(encoding="utf-8")) + + client = _client(api_url) + result = client.stream_close( + stream_id=stream_id, + mint_doi=mint_doi, + title=title, + description=description, + authors=authors_payload, + keywords=keywords_payload, + license=license, + ) + client.close() + format_result_or_json(result, json_output, success_msg=f"Stream closed: {stream_id}", error_prefix="Stream close failed") + + +@app.command("stream-snapshot") +def stream_snapshot( + stream_id: str = typer.Option(..., "--stream-id"), + title: Optional[str] = typer.Option(None, "--title"), + update: bool = typer.Option(False, "--update", help="Update existing dataset if present"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_snapshot(stream_id, title=title, update=update) + client.close() + format_result_or_json(result, json_output, success_msg="Snapshot created", error_prefix="Snapshot failed") + + +@app.command("search") +def search( + query: str = typer.Argument(..., help="Search query"), + search_type: str = typer.Option("all", "--type", "-t", help="all, datasets, or streams"), + limit: int = typer.Option(20, "--limit", "-n", help="Max results"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Search datasets and streams.""" + client = _client(api_url) + result = client.search(query, search_type=search_type, limit=limit) + client.close() + + # Pretty-print search results + if result.get("results"): + typer.echo(f"\nFound {result.get('total', 0)} results for '{result.get('query')}':\n") + for i, item in enumerate(result["results"], 1): + if item.get("type") == "dataset": + typer.echo(f" {i}. [DATASET] {item.get('title', 'Untitled')}") + typer.echo(f" source_id: {item.get('source_id')} v{item.get('version')}") + typer.echo(f" status: {item.get('status')} | authors: {', '.join(item.get('authors', []))}") + else: + typer.echo(f" {i}. [STREAM] {item.get('title', 'Untitled')}") + typer.echo(f" stream_id: {item.get('stream_id')}") + typer.echo(f" lab_id: {item.get('lab_id')} | files: {item.get('file_count', 0)}") + typer.echo() + else: + typer.echo(f"No results found for '{query}'") + + +@app.command("card") +def card( + source_id: str = typer.Argument(..., help="Dataset source ID"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Show a dataset preview card.""" + client = _client(api_url) + result = client.get_card(source_id, version=version) + client.close() + + if not result.get("success"): + console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}") + raise typer.Exit(1) + + c = result.get("card", {}) + + # Title and basic info + console.print() + console.print(Panel( + f"[bold]{c.get('title', 'Untitled')}[/bold]\n" + f"[dim]{c.get('description', 'No description')}[/dim]", + title=f"[cyan]{c.get('source_id')}[/cyan] v{c.get('version', '1.0')}", + border_style="blue", + )) + + # Metadata table + table = Table(show_header=False, box=box.SIMPLE, padding=(0, 2)) + table.add_column("Field", style="dim", width=15) + table.add_column("Value") + + if c.get("authors"): + table.add_row("Authors", ", ".join(c["authors"])) + if c.get("publisher"): + table.add_row("Publisher", c["publisher"]) + if c.get("publication_year"): + table.add_row("Year", str(c["publication_year"])) + if c.get("organization"): + table.add_row("Organization", c["organization"]) + if c.get("lab_id"): + table.add_row("Lab ID", c["lab_id"]) + if c.get("keywords"): + table.add_row("Keywords", ", ".join(c["keywords"])) + if c.get("methods"): + table.add_row("Methods", ", ".join(c["methods"])) + if c.get("doi"): + table.add_row("DOI", f"https://doi.org/{c['doi']}") + table.add_row("Status", f"[green]{c.get('status', 'unknown')}[/green]") + + console.print(table) + + # Stats + stats = c.get("stats", {}) + if stats: + console.print(f"\n[dim]Files:[/dim] {stats.get('data_sources_count', 0)} sources | Types: {', '.join(stats.get('file_types', []))}") + + console.print() + + +@app.command("cite") +def cite( + source_id: str = typer.Argument(..., help="Dataset source ID"), + format: str = typer.Option("apa", "--format", "-f", help="Citation format: bibtex, ris, apa, datacite"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + output: Optional[Path] = typer.Option(None, "--output", "-o", help="Output file"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Get citation for a dataset. + + Examples: + mdf backend cite my_dataset # APA format + mdf backend cite my_dataset -f bibtex # BibTeX for papers + mdf backend cite my_dataset -f ris -o ref.ris # Export to file + """ + client = _client(api_url) + result = client.get_citation(source_id, format=format, version=version) + client.close() + + if not result.get("success"): + console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}") + raise typer.Exit(1) + + # Get the citation text + citation_text = result.get(format) or result.get("apa", "") + + if output: + output.write_text(citation_text) + console.print(f"[green]Citation saved to:[/green] {output}") + else: + console.print() + if format == "bibtex": + console.print(Panel(citation_text, title="BibTeX", border_style="green")) + elif format == "ris": + console.print(Panel(citation_text, title="RIS", border_style="green")) + elif format == "datacite": + console.print(Panel(citation_text, title="DataCite XML", border_style="green")) + else: + console.print(Panel(citation_text, title="APA Citation", border_style="green")) + console.print() + + +@app.command("preview") +def preview( + source_id: str = typer.Argument(..., help="Dataset source ID"), + files: bool = typer.Option(False, "--files", help="List files in profile"), + sample: bool = typer.Option(False, "--sample", help="Show dataset sample"), + file_path: Optional[str] = typer.Option(None, "--file-path", help="Show details for a specific profile file"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + if file_path and (files or sample): + raise typer.BadParameter("--file-path cannot be combined with --files or --sample") + if files and sample: + raise typer.BadParameter("--files and --sample cannot be combined") + + client = _client(api_url) + if file_path: + result = client.dataset_file_detail(source_id, file_path) + elif files: + result = client.dataset_files(source_id) + elif sample: + result = client.dataset_sample(source_id) + else: + result = client.dataset_preview(source_id) + client.close() + _print(result) diff --git a/src/mdf_agent/cli/clone.py b/src/mdf_agent/cli/clone.py new file mode 100644 index 0000000..16a6f37 --- /dev/null +++ b/src/mdf_agent/cli/clone.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from typing import List + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Create a new dataset derived from an MDF source") + + +@app.command() +def clone( + source_id: str = typer.Argument(..., help="Source dataset ID"), + path: str = typer.Argument(".", help="Destination path"), + title: str = typer.Option(None, "--title", help="Title for derived dataset"), + author: List[str] = typer.Option([], "--author", help="Author name (repeatable)"), +): + derived_title = title or f"Derived from {source_id}" + authors = author if author else ["Unknown"] + agent = MDFAgent.init(path=path, title=derived_title, authors=authors) + agent.manifest.derived_from = [{"source_id": source_id, "relationship": "derived"}] + agent.save_manifest() + typer.echo(f"Initialized derived dataset in {path}") diff --git a/src/mdf_agent/cli/commit.py b/src/mdf_agent/cli/commit.py new file mode 100644 index 0000000..9fd46aa --- /dev/null +++ b/src/mdf_agent/cli/commit.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Record staged files") + + +@app.command() +def commit( + message: str = typer.Option(..., "-m", "--message", help="Commit message") +): + agent = MDFAgent.from_repo(".") + commit_info = agent.commit(message) + typer.echo(f"Committed {len(commit_info['staged_files'])} files") diff --git a/src/mdf_agent/cli/config_cmd.py b/src/mdf_agent/cli/config_cmd.py new file mode 100644 index 0000000..dea9c3d --- /dev/null +++ b/src/mdf_agent/cli/config_cmd.py @@ -0,0 +1,66 @@ +"""CLI commands for mdf config show/set/get/path.""" + +from __future__ import annotations + +import json + +import typer +from rich.console import Console +from rich.syntax import Syntax + +from mdf_agent.core.config import GlobalConfig + +app = typer.Typer(help="Manage MDF Agent configuration") +console = Console() + + +@app.command("show") +def show(): + """Show current configuration.""" + cfg = GlobalConfig() + if cfg.data: + syntax = Syntax(json.dumps(cfg.data, indent=2), "json", theme="monokai") + console.print(syntax) + else: + console.print("[dim]No configuration set yet.[/dim]") + console.print(f"[dim]Config file:[/dim] {cfg.path}") + + +@app.command("set") +def set_value( + key: str = typer.Argument(..., help="Dotted key (e.g. defaults.service)"), + value: str = typer.Argument(..., help="Value to set"), +): + """Set a configuration value.""" + from mdf_agent.core.config import validate_config_value + + warning = validate_config_value(key, value) + if warning: + console.print(f"[yellow]Warning:[/yellow] {warning}") + + cfg = GlobalConfig() + cfg.set(key, value) + console.print(f"[green]Set[/green] {key} = {value}") + + +@app.command("get") +def get_value( + key: str = typer.Argument(..., help="Dotted key (e.g. defaults.service)"), +): + """Get a configuration value.""" + cfg = GlobalConfig() + val = cfg.get(key) + if val is None: + console.print(f"[dim]{key} is not set[/dim]") + raise typer.Exit(code=1) + if isinstance(val, (dict, list)): + console.print(json.dumps(val, indent=2)) + else: + console.print(str(val)) + + +@app.command("path") +def path(): + """Show configuration file path.""" + cfg = GlobalConfig() + console.print(str(cfg.path)) diff --git a/src/mdf_agent/cli/formatting.py b/src/mdf_agent/cli/formatting.py new file mode 100644 index 0000000..c8ba3a1 --- /dev/null +++ b/src/mdf_agent/cli/formatting.py @@ -0,0 +1,87 @@ +"""Shared output formatting for MDF CLI commands. + +Provides consistent Rich-formatted output for API results, status badges, +and error interpretation across all CLI commands. +""" + +from __future__ import annotations + +from typing import Any, Dict, Optional + +from rich.console import Console + +console = Console() + +_STATUS_STYLES = { + "pending_curation": "[yellow]pending_curation[/yellow]", + "pending": "[yellow]pending[/yellow]", + "approved": "[blue]approved[/blue]", + "published": "[green]published[/green]", + "rejected": "[red]rejected[/red]", + "failed": "[red]failed[/red]", + "active": "[cyan]active[/cyan]", + "closed": "[dim]closed[/dim]", +} + + +def format_status_badge(status: str) -> str: + """Return Rich-markup colored string for a submission status.""" + return _STATUS_STYLES.get(status, f"[dim]{status}[/dim]") + + +def handle_api_result( + result: Dict[str, Any], + success_msg: Optional[str] = None, + error_prefix: str = "Error", +) -> bool: + """Interpret a standard API result envelope and print formatted output. + + Returns True on success, False on error. + """ + if result.get("success"): + if success_msg: + console.print(f"[green]{success_msg}[/green]") + return True + + error = result.get("error") or result.get("detail") or "Unknown error" + + # Map common HTTP-level errors to actionable messages + error_str = str(error).lower() + if "401" in error_str or "unauthorized" in error_str or "not authenticated" in error_str: + console.print(f"[red]{error_prefix}: Authentication required[/red]") + console.print("[dim]Run:[/dim] [cyan]mdf login[/cyan]") + return False + + if "403" in error_str or "forbidden" in error_str: + console.print(f"[red]{error_prefix}: Permission denied[/red]") + return False + + if "429" in error_str or "rate limit" in error_str: + console.print(f"[yellow]{error_prefix}: Rate limited — try again shortly[/yellow]") + return False + + if "404" in error_str or "not found" in error_str: + console.print(f"[yellow]{error_prefix}: Not found[/yellow]") + return False + + if "validation" in error_str: + console.print(f"[red]{error_prefix}: Validation error[/red]") + console.print(f" [dim]{error}[/dim]") + return False + + console.print(f"[red]{error_prefix}:[/red] {error}") + return False + + +def format_result_or_json( + result: Dict[str, Any], + json_mode: bool, + success_msg: Optional[str] = None, + error_prefix: str = "Error", +) -> bool: + """If json_mode, dump raw JSON. Otherwise use handle_api_result.""" + if json_mode: + import json + console.print(json.dumps(result, indent=2)) + return result.get("success", False) + return handle_api_result(result, success_msg=success_msg, error_prefix=error_prefix) diff --git a/src/mdf_agent/cli/init.py b/src/mdf_agent/cli/init.py new file mode 100644 index 0000000..35568d2 --- /dev/null +++ b/src/mdf_agent/cli/init.py @@ -0,0 +1,29 @@ +from __future__ import annotations + +from typing import List + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Initialize an MDF dataset repository") + + +@app.command() +def init( + path: str = typer.Argument(".", help="Repository path"), + title: str = typer.Option(..., "--title", help="Dataset title"), + author: List[str] = typer.Option(..., "--author", help="Author name (repeatable)"), + description: str | None = typer.Option(None, "--description", help="Dataset description"), + publisher: str | None = typer.Option(None, "--publisher", help="Dataset publisher"), + publication_year: int | None = typer.Option(None, "--year", help="Publication year"), +): + MDFAgent.init( + path=path, + title=title, + authors=author, + description=description, + publisher=publisher, + publication_year=publication_year, + ) + typer.echo(f"Initialized MDF repository at {path}") diff --git a/src/mdf_agent/cli/main.py b/src/mdf_agent/cli/main.py new file mode 100644 index 0000000..cf6285d --- /dev/null +++ b/src/mdf_agent/cli/main.py @@ -0,0 +1,1233 @@ +"""MDF Agent CLI - Main entry point. + +This module provides the command-line interface for MDF Agent. +Commands are organized as direct subcommands of the main `mdf` command. + +Usage: + mdf init ./my_dataset --title "My Dataset" --author "Jane Doe" + mdf add *.csv + mdf commit -m "Add experimental data" + mdf publish --test + mdf publish ./data/ --title "Test" --author "Jane" --submit +""" + +from __future__ import annotations + +from functools import wraps +import os +import sys +from typing import List, Optional + +import typer +from rich.console import Console +from rich.table import Table +from rich.panel import Panel +from rich import print as rprint + +from mdf_agent.core.agent import MDFAgent +from mdf_agent.core.backend_client import BackendClient, _api_url_for_service +from mdf_agent.core.config import GlobalConfig, resolve_service +from mdf_agent.core.exceptions import NotARepositoryError +from mdf_agent.cli.backend import app as backend_app +from mdf_agent.cli.stream import app as stream_app +from mdf_agent.cli.config_cmd import app as config_app + +console = Console() + + +def handle_repo_error(func): + """Decorator to catch NotARepositoryError and display a clean message.""" + @wraps(func) + def wrapper(*args, **kwargs): + try: + return func(*args, **kwargs) + except NotARepositoryError: + console.print("\n[red]Not an MDF repository[/red]") + console.print("[dim]Run [/dim][cyan]mdf init[/cyan][dim] to create one here, or cd to an existing repository.[/dim]\n") + raise typer.Exit(code=1) + return wrapper + + +app = typer.Typer( + help="MDF Agent CLI - Materials Data Facility dataset management", + add_completion=False, + no_args_is_help=True, +) + +app.add_typer(backend_app, name="backend") +app.add_typer(stream_app, name="stream") +app.add_typer(config_app, name="config") + + +@app.command() +def login( + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev)"), + token: Optional[str] = typer.Option(None, "--token", help="Use an explicit access token"), +): + """Authenticate with Globus for MDF Connect.""" + from mdf_agent.auth.globus import ( + DEFAULT_TOKEN_PATH, + DATA_MDF_SCOPE, + TRANSFER_SCOPE, + get_authorizer_for_scopes, + get_scopes_for_service, + ) + + resolved = resolve_service(service) + + if token: + # Explicit token: just use get_authorizer (no multi-scope needed) + from mdf_agent.auth.globus import get_authorizer + get_authorizer(token=token, service_instance=resolved) + else: + scope, _rs = get_scopes_for_service(resolved) + # Request scopes upfront so one login covers publish, upload, + # and transfer operations. + get_authorizer_for_scopes([scope, DATA_MDF_SCOPE, TRANSFER_SCOPE]) + console.print("[green]Authentication ready[/green]") + console.print(f"[dim]Token store:[/dim] {DEFAULT_TOKEN_PATH}") + if token: + console.print("[dim]Using token from --token for this invocation.[/dim]") + + +@app.command() +def logout(): + """Clear cached Globus credentials.""" + from mdf_agent.auth.globus import DEFAULT_TOKEN_PATH, logout as clear_cached_tokens + + removed = clear_cached_tokens() + if removed: + console.print("[green]Logged out[/green]") + else: + console.print(f"[yellow]No cached token file found[/yellow] ({DEFAULT_TOKEN_PATH})") + + +@app.command() +def whoami( + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev)"), +): + """Show current authentication status.""" + from mdf_agent.auth.globus import DEFAULT_TOKEN_PATH, is_logged_in + + resolved = resolve_service(service) + cached = is_logged_in(service_instance=resolved) + env_token = bool(os.environ.get("MDF_CONNECT_TOKEN")) + status = "authenticated" if (cached or env_token) else "not authenticated" + console.print(f"[bold]Service:[/bold] {resolved}") + console.print(f"[bold]Status:[/bold] {status}") + console.print(f"[bold]Token store:[/bold] {DEFAULT_TOKEN_PATH}") + if env_token: + console.print("[dim]MDF_CONNECT_TOKEN is set in environment[/dim]") + + +@app.command() +def init( + path: str = typer.Argument(".", help="Repository path"), + title: Optional[str] = typer.Option(None, "--title", "-t", help="Dataset title"), + author: Optional[List[str]] = typer.Option(None, "--author", "-a", help="Author name (repeatable)"), + description: Optional[str] = typer.Option(None, "--description", "-d", help="Dataset description"), + publisher: Optional[str] = typer.Option(None, "--publisher", help="Dataset publisher"), + publication_year: Optional[int] = typer.Option(None, "--year", "-y", help="Publication year"), +): + """Initialize an MDF dataset repository. + + Creates a git-backed repository with an mdf.yaml manifest. + When --title and --author are not provided and running interactively, + prompts for required metadata. + """ + resolved_title = title + resolved_authors = list(author) if author else [] + + if not resolved_title or not resolved_authors: + if sys.stdin.isatty(): + console.print("[bold]Initialize MDF dataset[/bold]\n") + if not resolved_title: + resolved_title = typer.prompt("Dataset title") + if not resolved_authors: + console.print("[dim]Enter author names one per line. Empty line to finish.[/dim]") + while True: + name = typer.prompt("Author", default="", show_default=False) + if not name: + break + resolved_authors.append(name) + if not description: + description = typer.prompt("Description (optional)", default="", show_default=False) or None + else: + if not resolved_title: + console.print("[red]--title is required[/red]") + raise typer.Exit(code=1) + if not resolved_authors: + console.print("[red]--author is required[/red]") + raise typer.Exit(code=1) + + if not resolved_title: + console.print("[red]Title is required[/red]") + raise typer.Exit(code=1) + if not resolved_authors: + console.print("[red]At least one author is required[/red]") + raise typer.Exit(code=1) + + MDFAgent.init( + path=path, + title=resolved_title, + authors=resolved_authors, + description=description, + publisher=publisher, + publication_year=publication_year, + ) + console.print(f"[green]Initialized MDF repository at[/green] [bold]{path}[/bold]") + console.print(f" [dim]Title:[/dim] {resolved_title}") + console.print(f" [dim]Authors:[/dim] {', '.join(resolved_authors)}") + + +@app.command() +@handle_repo_error +def add( + paths: List[str] = typer.Argument(..., help="Files or globs to stage"), + discover: Optional[bool] = typer.Option( + None, "--discover/--no-discover", help="Auto-discover metadata from files" + ), +): + """Stage files for the next commit (git add). + + Supports glob patterns like *.csv or data/**/*.json. + Use --discover to automatically extract metadata from PDFs and data files. + """ + agent = MDFAgent.from_repo(".") + staged = agent.add(*paths, discover=discover) + console.print("[green]Staged:[/green]") + for file_path in staged: + console.print(f" [cyan]+[/cyan] {file_path}") + + +@app.command() +@handle_repo_error +def commit( + message: str = typer.Option(..., "--message", "-m", help="Commit message"), +): + """Record staged files as a git commit. + + Creates a local checkpoint that can later be published to MDF Connect. + """ + agent = MDFAgent.from_repo(".") + commit_data = agent.commit(message) + file_count = len(commit_data.get('staged_files', [])) + short_hash = commit_data.get('hash', '')[:7] + console.print(f"[green]Committed:[/green] {commit_data.get('message', '')} [dim]({short_hash})[/dim]") + console.print(f" [dim]{file_count} file{'s' if file_count != 1 else ''} recorded[/dim]") + + +@app.command() +def status( + source_id: Optional[str] = typer.Argument(None, help="Source ID to check (default: last published)"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Show dataset status. + + With no args inside a repo: shows local repo status + backend status. + With no args outside a repo: shows backend status of last published dataset. + With source_id: shows backend status for that dataset. + """ + resolved = resolve_service(service) + + # Try to show repo status if we're in a repo + in_repo = False + try: + agent = MDFAgent.from_repo(".") + in_repo = True + state = agent.status() + + staged = state.get("staged_files", []) + modified = state.get("modified_files", []) + untracked = state.get("untracked_files", []) + + if staged: + console.print("\n[bold]Staged files:[/bold]") + for f in staged: + console.print(f" [green]+[/green] {f}") + + if modified: + console.print("\n[bold]Modified (unstaged):[/bold]") + for f in modified: + console.print(f" [yellow]~[/yellow] {f}") + + if untracked: + console.print("\n[bold]Untracked files:[/bold]") + for f in untracked: + console.print(f" [dim]?[/dim] {f}") + + if not staged and not modified and not untracked: + console.print("\n[dim]Working tree clean[/dim]") + + commits = state.get("commits", []) + if commits: + console.print(f"\n[bold]Recent commits ({len(commits)}):[/bold]") + table = Table(show_header=True, header_style="bold") + table.add_column("Hash", style="dim", width=8) + table.add_column("Message") + table.add_column("Time", style="dim") + + for c in commits: + table.add_row( + c.get("hash", "")[:7], + c.get("message", ""), + c.get("timestamp", "")[:19] if c.get("timestamp") else "", + ) + console.print(table) + else: + console.print("\n[dim]No commits yet[/dim]") + except NotARepositoryError: + pass + + # Resolve source_id for backend lookup + lookup_id = source_id + if not lookup_id: + cfg = GlobalConfig() + lookup_id = cfg.last_source_id + if not lookup_id: + if not in_repo: + console.print("\n[dim]No source_id provided and no last published dataset.[/dim]") + console.print("[dim]Usage: mdf status [/dim]") + return + + # Backend status lookup + try: + from mdf_agent.cli.formatting import format_status_badge + + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.status(lookup_id, version=version) + client.close() + + sub = result.get("submission") or result + sid = sub.get("source_id") + if sid: + ver = sub.get("version", "?") + st = sub.get("status", "unknown") + console.print(f"\n[bold]Backend status:[/bold] [cyan]{sid}[/cyan] v{ver}") + console.print(f" [dim]Status:[/dim] {format_status_badge(st)}") + # Parse dataset_mdata for extra fields + mdata = sub.get("dataset_mdata", {}) + if isinstance(mdata, str): + import json as _json + try: + mdata = _json.loads(mdata) + except Exception: + mdata = {} + if isinstance(mdata, dict): + if mdata.get("title"): + console.print(f" [dim]Title:[/dim] {mdata['title']}") + if sub.get("dataset_doi") or sub.get("doi"): + doi = sub.get("dataset_doi") or sub.get("doi") + console.print(f" [dim]DOI:[/dim] https://doi.org/{doi}") + + # Transfer progress + transfer = result.get("transfer", {}) + if transfer and transfer.get("status"): + t_status = transfer["status"] + t_bytes = transfer.get("bytes_transferred", 0) + t_files = transfer.get("files_transferred", 0) + if t_status == "active": + console.print(f" [dim]Transfer:[/dim] [cyan]active[/cyan] — {t_files} files, {t_bytes:,} bytes transferred") + elif t_status == "succeeded": + console.print(f" [dim]Transfer:[/dim] [green]complete[/green] — {t_files} files, {t_bytes:,} bytes") + elif t_status == "failed": + console.print(f" [dim]Transfer:[/dim] [red]failed[/red]") + + # Version chain context + if isinstance(mdata, dict): + is_latest = mdata.get("latest", True) + root_version = mdata.get("root_version", "") + prev_version = mdata.get("previous_version", "") + if prev_version: + console.print(f" [dim]Previous version:[/dim] {prev_version}") + if root_version and root_version != f"{sid}-{ver}": + console.print(f" [dim]Root version:[/dim] {root_version}") + if not is_latest: + console.print(" [yellow]This is not the latest version[/yellow]") + + # Next action hint + if st == "pending_curation": + console.print(" [dim]Next:[/dim] Waiting for curation review") + elif st == "approved": + console.print(" [dim]Next:[/dim] Publishing in progress...") + elif st == "published": + console.print(" [dim]Next:[/dim] Dataset is live!") + elif st == "rejected": + console.print(" [dim]Next:[/dim] Review feedback and resubmit with --update") + + elif result.get("error"): + console.print(f"\n[yellow]Backend:[/yellow] {result.get('error')}") + else: + console.print(f"\n[dim]No backend record for {lookup_id}[/dim]") + except Exception as e: + console.print(f"\n[yellow]Could not reach backend:[/yellow] {e}") + + +@app.command() +@handle_repo_error +def validate(): + """Validate manifest before publishing. + + Checks for required fields and common issues. + Returns exit code 1 if validation fails. + """ + agent = MDFAgent.from_repo(".") + results = agent.validate() + errors = results.get("errors", []) + warnings = results.get("warnings", []) + + if errors: + console.print("\n[bold red]Errors:[/bold red]") + for error in errors: + console.print(f" [red]x[/red] {error}") + + if warnings: + console.print("\n[bold yellow]Warnings:[/bold yellow]") + for warning in warnings: + console.print(f" [yellow]![/yellow] {warning}") + + if errors: + console.print("\n[red]Validation failed[/red]") + raise typer.Exit(code=1) + + console.print("\n[bold green]Validation passed[/bold green]") + + +@app.command() +def publish( + data: Optional[List[str]] = typer.Argument(None, help="Data paths/URIs to publish (direct mode)"), + title: Optional[str] = typer.Option(None, "--title", "-t", help="Dataset title"), + author: Optional[List[str]] = typer.Option(None, "--author", "-a", help="Author name (repeatable)"), + description: Optional[str] = typer.Option(None, "--description", "-d", help="Dataset description"), + dry_run: bool = typer.Option(True, "--dry-run/--submit", help="Preview without submitting"), + test: bool = typer.Option(False, "--test", help="Submit to test environment"), + update: bool = typer.Option(False, "--update", "-u", help="Update existing dataset"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev/local)"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL for local backend"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id (X-User-Id)"), +): + """Publish dataset to MDF Connect. + + Direct mode (data args provided): + mdf publish ./data/ --title "My Dataset" --author "Jane" --submit + + Repo mode (inside an MDF repository): + mdf publish --submit + + By default, performs a dry run showing the payload. + Use --submit to actually send to MDF Connect. + """ + import json + from rich.syntax import Syntax + from mdf_agent.models.config import ManifestConfig + + resolved = resolve_service(service) + + # Mode detection + if data: + # Direct mode: build manifest on the fly + if not title: + console.print("[red]Direct mode requires --title[/red]") + console.print("[dim]Example: mdf publish ./data/ --title \"My Dataset\" --author \"Jane\"[/dim]") + raise typer.Exit(code=1) + if not author: + console.print("[red]Direct mode requires --author[/red]") + console.print("[dim]Example: mdf publish ./data/ --title \"My Dataset\" --author \"Jane\"[/dim]") + raise typer.Exit(code=1) + + cfg = GlobalConfig() + manifest = ManifestConfig( + title=title, + authors=author, + description=description, + data_sources=list(data), + publisher=cfg.publisher, + organization=cfg.organization, + ) + agent = MDFAgent(root=None, manifest=manifest) + else: + # Repo mode + try: + agent = MDFAgent.from_repo(".") + except NotARepositoryError: + console.print("\n[red]No data paths provided and not in an MDF repository[/red]") + console.print("[dim]Direct mode:[/dim] mdf publish ./data/ --title \"My Dataset\" --author \"Jane\" --submit") + console.print("[dim]Repo mode:[/dim] cd my_repo && mdf publish --submit") + raise typer.Exit(code=1) + + # In repo mode, --title and --author are optional overrides + if title: + agent.manifest.title = title + if author: + agent.manifest.authors = author + if description: + agent.manifest.description = description + + payload = agent.build_submission(test=test, update=update) + + if dry_run: + console.print("\n[bold cyan]Dry run - would submit:[/bold cyan]") + target = api_url or _api_url_for_service(resolved) + console.print(f"[dim]Target: {target} ({resolved})[/dim]") + syntax = Syntax(json.dumps(payload, indent=2), "json", theme="monokai") + console.print(syntax) + return + + # Build a rich progress callback for file uploads + from rich.progress import Progress, BarColumn, DownloadColumn, TransferSpeedColumn + + progress_callback = None + progress_ctx = None + file_tasks: dict = {} + + if sys.stderr.isatty(): + progress_ctx = Progress( + "[progress.description]{task.description}", + BarColumn(), + DownloadColumn(), + TransferSpeedColumn(), + console=console, + transient=True, + ) + progress_ctx.start() + + def _progress_cb(filename: str, bytes_sent: int, total_bytes: int) -> None: + if filename not in file_tasks: + file_tasks[filename] = progress_ctx.add_task(filename, total=total_bytes) + progress_ctx.update(file_tasks[filename], completed=bytes_sent) + + progress_callback = _progress_cb + + try: + result = agent.publish( + test=test, + update=update, + dry_run=False, + token=token, + service_instance=resolved, + api_url=api_url, + dev_user_id=dev_user, + progress_callback=progress_callback, + ) + finally: + if progress_ctx is not None: + progress_ctx.stop() + + if result.get("success"): + console.print("\n[bold green]Published successfully![/bold green]") + source_id = result.get("source_id") + version = result.get("version") + console.print(f" [dim]Source ID:[/dim] [cyan]{source_id}[/cyan]") + if version: + console.print(f" [dim]Version:[/dim] [cyan]{version}[/cyan]") + + # Save to global config + if source_id: + cfg = GlobalConfig() + cfg.record_publish(source_id, version, resolved) + else: + console.print(f"\n[bold red]Publish failed:[/bold red] {result.get('error')}") + raise typer.Exit(code=1) + + +@app.command("list") +def list_datasets( + limit: int = typer.Option(50, "--limit", "-n", help="Max results"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + """List your submitted datasets. + + Examples: + mdf list + mdf list --limit 50 + """ + import json as json_mod + from mdf_agent.cli.formatting import format_status_badge + + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.submissions() + client.close() + + if json_output: + console.print(json_mod.dumps(result, indent=2)) + return + + submissions = result.get("submissions", []) + if not submissions: + console.print("\n[dim]No datasets found.[/dim]") + return + + console.print(f"\n[bold]Your datasets ({len(submissions)}):[/bold]\n") + table = Table(show_header=True, header_style="bold") + table.add_column("Source ID", no_wrap=True) + table.add_column("Title", max_width=40) + table.add_column("Version", style="dim", no_wrap=True) + table.add_column("Status", no_wrap=True) + table.add_column("Updated", style="dim", no_wrap=True) + + for sub in submissions[:limit]: + mdata = sub.get("dataset_mdata", {}) + if isinstance(mdata, str): + try: + mdata = json_mod.loads(mdata) + except Exception: + mdata = {} + title = mdata.get("title") or sub.get("title") or "Untitled" + status_str = format_status_badge(sub.get("status", "")) + updated = (sub.get("updated_at") or sub.get("created_at") or "")[:19] + table.add_row( + sub.get("source_id", ""), + title, + sub.get("version", ""), + status_str, + updated, + ) + console.print(table) + + +@app.command() +def show( + source_id: str = typer.Argument(..., help="Dataset source ID"), + cite: bool = typer.Option(False, "--cite", "-c", help="Include citation"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Show dataset detail view. + + Examples: + mdf show my_dataset_v1 + mdf show my_dataset_v1 --cite + mdf show my_dataset_v1 --json + """ + import json as json_mod + from rich import box + from mdf_agent.cli.formatting import format_status_badge + + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.get_card(source_id, version=version) + + citation_result = None + if cite and result.get("success"): + citation_result = client.get_citation(source_id, format="apa", version=version) + + client.close() + + if json_output: + output = {"card": result} + if citation_result: + output["citation"] = citation_result + console.print(json_mod.dumps(output, indent=2)) + return + + if not result.get("success"): + console.print(f"[red]Error:[/red] {result.get('error', 'Dataset not found')}") + raise typer.Exit(1) + + c = result.get("card", {}) + + # Title panel + console.print() + console.print(Panel( + f"[bold]{c.get('title', 'Untitled')}[/bold]\n" + f"[dim]{c.get('description', 'No description')}[/dim]", + title=f"[cyan]{c.get('source_id')}[/cyan] v{c.get('version', '1.0')}", + border_style="blue", + )) + + # Metadata table + table = Table(show_header=False, box=box.SIMPLE, padding=(0, 2)) + table.add_column("Field", style="dim", width=15) + table.add_column("Value") + + if c.get("authors"): + table.add_row("Authors", ", ".join(c["authors"])) + if c.get("publisher"): + table.add_row("Publisher", c["publisher"]) + if c.get("publication_year"): + table.add_row("Year", str(c["publication_year"])) + if c.get("organization"): + table.add_row("Organization", c["organization"]) + if c.get("keywords"): + table.add_row("Keywords", ", ".join(c["keywords"])) + if c.get("doi"): + table.add_row("DOI", f"https://doi.org/{c['doi']}") + if c.get("license"): + table.add_row("License", c["license"]) + table.add_row("Status", format_status_badge(c.get("status", "unknown"))) + + # ML metadata + ml = c.get("ml") or {} + if ml: + if ml.get("task_type"): + table.add_row("ML Task", ml["task_type"]) + if ml.get("data_format"): + table.add_row("ML Format", ml["data_format"]) + + console.print(table) + + # File stats + stats = c.get("stats", {}) + if stats: + console.print(f"\n[dim]Files:[/dim] {stats.get('data_sources_count', 0)} sources | Types: {', '.join(stats.get('file_types', []))}") + + # Citation + if citation_result and citation_result.get("success"): + apa = citation_result.get("apa", "") + if apa: + console.print(f"\n[bold]Citation:[/bold]\n {apa}") + + console.print() + + +@app.command() +def versions( + source_id: str = typer.Argument(..., help="Dataset source ID"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Show version history for a dataset. + + Examples: + mdf versions my_dataset_v1 + """ + import json as json_mod + from mdf_agent.cli.formatting import format_status_badge + + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.versions(source_id) + client.close() + + if json_output: + console.print(json_mod.dumps(result, indent=2)) + return + + if not result.get("success"): + console.print(f"[yellow]{result.get('error', 'No versions found')}[/yellow]") + return + + version_list = result.get("versions", []) + if not version_list: + console.print("[dim]No versions found.[/dim]") + return + + console.print(f"\n[bold]Versions for[/bold] [cyan]{source_id}[/cyan]\n") + + table = Table(show_header=True, header_style="bold") + table.add_column("Version", no_wrap=True) + table.add_column("Title", max_width=40) + table.add_column("Status", no_wrap=True) + table.add_column("DOI", style="dim") + table.add_column("Updated", style="dim", no_wrap=True) + + for v in version_list: + doi = v.get("doi") or "" + if doi: + doi = f"https://doi.org/{doi}" + table.add_row( + v.get("version", ""), + v.get("title", ""), + format_status_badge(v.get("status", "")), + doi, + (v.get("updated_at") or v.get("created_at") or "")[:19], + ) + console.print(table) + + dataset_doi = result.get("dataset_doi") + if dataset_doi: + console.print(f"\n[dim]Dataset DOI:[/dim] https://doi.org/{dataset_doi}") + console.print() + + +@app.command() +def clone( + source_id: str = typer.Argument(..., help="Source dataset ID to download"), + output_dir: str = typer.Argument(".", help="Output directory"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Specific version to clone"), + transfer: bool = typer.Option(False, "--transfer", help="Use Globus Transfer (requires GCP)"), + derive: bool = typer.Option(False, "--derive", help="Also initialize an MDF repo with derived-from lineage"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Download a dataset's files from MDF. + + Downloads using the fastest available method: zip archive (if available), + HTTPS file-by-file, or Globus Transfer (with --transfer flag). + + Examples: + mdf clone my_dataset_v1.1 + mdf clone my_dataset_v1.1 ./local_copy + mdf clone my_dataset_v1.1 --transfer + mdf clone my_dataset_v1.1 --derive + """ + from rich.progress import Progress, BarColumn, DownloadColumn, TransferSpeedColumn + + resolved = resolve_service(service) + method = "transfer" if transfer else "auto" + + agent = MDFAgent() + + # Fetch card first to show dataset info + resolved_token = token or os.environ.get("MDF_CONNECT_TOKEN") + resolved_dev_user = dev_user or os.environ.get("MDF_DEV_USER_ID") + + console.print(f"\n[bold]Cloning[/bold] [cyan]{source_id}[/cyan]", end="") + if version: + console.print(f" [dim]v{version}[/dim]", end="") + console.print() + + # Build progress callback + progress_callback = None + progress_ctx = None + file_tasks: dict = {} + + if sys.stderr.isatty(): + progress_ctx = Progress( + "[progress.description]{task.description}", + BarColumn(), + DownloadColumn(), + TransferSpeedColumn(), + console=console, + transient=True, + ) + progress_ctx.start() + + def _progress_cb(filename: str, bytes_sent: int, total_bytes: int) -> None: + if filename not in file_tasks: + file_tasks[filename] = progress_ctx.add_task(filename, total=total_bytes) + progress_ctx.update(file_tasks[filename], completed=bytes_sent) + + progress_callback = _progress_cb + + try: + result = agent.clone( + source_id=source_id, + output_dir=output_dir, + version=version, + method=method, + progress_callback=progress_callback, + api_url=api_url, + token=resolved_token, + service_instance=resolved, + dev_user_id=resolved_dev_user, + ) + except RuntimeError as exc: + if progress_ctx is not None: + progress_ctx.stop() + console.print(f"\n[red]Error:[/red] {exc}") + raise typer.Exit(code=1) + finally: + if progress_ctx is not None: + progress_ctx.stop() + + if not result.get("success"): + console.print(f"\n[red]Clone failed:[/red] {result.get('error', 'Unknown error')}") + raise typer.Exit(code=1) + + console.print(f"\n[bold green]Clone complete![/bold green]") + if result.get("title"): + console.print(f" [dim]Title:[/dim] {result['title']}") + console.print(f" [dim]Method:[/dim] {result['method']}") + if result.get("files_count"): + console.print(f" [dim]Files:[/dim] {result['files_count']}") + console.print(f" [dim]Path:[/dim] {result['path']}") + if result.get("task_id"): + console.print(f" [dim]Transfer task:[/dim] {result['task_id']}") + console.print(f" [dim]Monitor:[/dim] {result['monitor_url']}") + + # Optionally initialize a derived MDF repo + if derive: + from pathlib import Path as _Path + from mdf_agent.models.config import DerivedFrom + + derive_root = _Path(output_dir).resolve() + derive_agent = MDFAgent.init( + path=str(derive_root), + title=f"Derived from {source_id}", + authors=["Unknown"], + ) + derive_agent.manifest.derived_from = [ + DerivedFrom(source_id=source_id, relationship="derived") + ] + derive_agent.save_manifest() + console.print(f"\n [green]Initialized git-backed MDF repo with derived-from lineage[/green]") + console.print() + + +@app.command() +def search( + query: str = typer.Argument(..., help="Search query"), + search_type: str = typer.Option("all", "--type", "-t", help="all, datasets, or streams"), + limit: int = typer.Option(20, "--limit", "-n", help="Max results"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev/local)"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id (X-User-Id)"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Search datasets and streams in MDF. + + Examples: + mdf search "perovskite" + mdf search "XRD" --type streams + mdf search "iron oxide" --limit 5 + """ + resolved = resolve_service(service) + resolved_token = token or os.environ.get("MDF_CONNECT_TOKEN") + resolved_dev_user = dev_user or os.environ.get("MDF_DEV_USER_ID") + if resolved_token or resolved_dev_user: + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + else: + client = BackendClient(base_url=api_url or _api_url_for_service(resolved)) + result = client.search(query, search_type=search_type, limit=limit) + client.close() + + if result.get("results"): + total = result.get("total", 0) + console.print(f"\n[bold]Found {total} results for[/bold] [cyan]'{result.get('query')}'[/cyan]\n") + + table = Table(show_header=True, header_style="bold") + table.add_column("#", style="dim", width=3) + table.add_column("Type", width=8) + table.add_column("Title", max_width=40) + table.add_column("ID", no_wrap=True) + table.add_column("Status", style="dim", no_wrap=True) + + for i, item in enumerate(result["results"], 1): + if item.get("type") == "dataset": + table.add_row( + str(i), + "[blue]dataset[/blue]", + item.get("title", "Untitled"), + f"{item.get('source_id')} v{item.get('version')}", + item.get("status", ""), + ) + else: + table.add_row( + str(i), + "[green]stream[/green]", + item.get("title", "Untitled"), + item.get("stream_id", ""), + f"{item.get('file_count', 0)} files", + ) + + console.print(table) + else: + console.print(f"\n[dim]No results found for '{query}'[/dim]") + + +@app.command() +def pending( + limit: int = typer.Option(20, "--limit", "-n", help="Max results"), + organization: Optional[str] = typer.Option(None, "--organization", "-o", help="Filter by organization"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """List datasets awaiting curation. + + Examples: + mdf pending + mdf pending --organization argonne + """ + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.curation_pending(limit=limit, organization=organization) + client.close() + + submissions = result.get("submissions", []) + if not submissions: + console.print("\n[dim]No datasets pending curation.[/dim]") + return + + console.print(f"\n[bold]Pending curation ({len(submissions)}):[/bold]\n") + table = Table(show_header=True, header_style="bold") + table.add_column("#", style="dim", width=3) + table.add_column("Source ID", no_wrap=True) + table.add_column("Title", max_width=40) + table.add_column("Version", style="dim", no_wrap=True) + table.add_column("Submitted", style="dim", no_wrap=True) + + for i, sub in enumerate(submissions, 1): + table.add_row( + str(i), + sub.get("source_id", ""), + sub.get("title") or "Untitled", + sub.get("version", ""), + (sub.get("submitted_at") or sub.get("created_at") or "")[:19], + ) + console.print(table) + + +@app.command() +def approve( + source_id: str = typer.Argument(..., help="Source ID of dataset to approve"), + mint_doi: bool = typer.Option(True, "--mint-doi/--no-mint-doi", help="Mint a DOI"), + notes: Optional[str] = typer.Option(None, "--notes", "-n", help="Curator notes"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Approve a dataset for publication. + + Examples: + mdf approve my_dataset_v1 + mdf approve my_dataset_v1 --notes "LGTM" --no-mint-doi + """ + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.curation_approve( + source_id=source_id, + mint_doi=mint_doi, + notes=notes, + version=version, + ) + client.close() + + if result.get("success"): + console.print(f"\n[bold green]Approved:[/bold green] [cyan]{source_id}[/cyan]") + if result.get("doi"): + console.print(f" [dim]DOI:[/dim] https://doi.org/{result.get('doi')}") + else: + console.print(f"\n[bold red]Approve failed:[/bold red] {result.get('error', 'Unknown error')}") + raise typer.Exit(code=1) + + +@app.command() +def reject( + source_id: str = typer.Argument(..., help="Source ID of dataset to reject"), + reason: str = typer.Option(..., "--reason", "-r", help="Rejection reason"), + suggestions: Optional[str] = typer.Option(None, "--suggestions", help="Suggestions for improvement"), + version: Optional[str] = typer.Option(None, "--version", "-v", help="Dataset version"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Reject a dataset and return to submitter. + + Examples: + mdf reject my_dataset_v1 --reason "Missing methods section" + mdf reject my_dataset_v1 --reason "Bad data" --suggestions "Re-run experiment" + """ + resolved = resolve_service(service) + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=resolved, + dev_user_id=dev_user, + ) + result = client.curation_reject( + source_id=source_id, + reason=reason, + suggestions=suggestions, + version=version, + ) + client.close() + + if result.get("success"): + console.print(f"\n[bold yellow]Rejected:[/bold yellow] [cyan]{source_id}[/cyan]") + console.print(f" [dim]Reason:[/dim] {reason}") + else: + console.print(f"\n[bold red]Reject failed:[/bold red] {result.get('error', 'Unknown error')}") + raise typer.Exit(code=1) + + +@app.command() +def update( + source_id: Optional[str] = typer.Argument(None, help="Source ID of dataset to update (default: last published)"), + data: Optional[List[str]] = typer.Option(None, "--data", "-D", help="Data paths/URIs"), + title: Optional[str] = typer.Option(None, "--title", "-t", help="Updated title"), + author: Optional[List[str]] = typer.Option(None, "--author", "-a", help="Author name (repeatable)"), + description: Optional[str] = typer.Option(None, "--description", "-d", help="Updated description"), + dry_run: bool = typer.Option(True, "--dry-run/--submit", help="Preview without submitting"), + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API URL"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id"), +): + """Update an existing published dataset. + + Creates a new version of an existing dataset. Defaults to the last published + dataset from config if source_id is not given. + + Examples: + mdf update --data ./new_data/ --submit + mdf update my_dataset_v1 --title "Updated Title" --submit + """ + import json as json_mod + from rich.syntax import Syntax + from mdf_agent.models.config import ManifestConfig + + resolved = resolve_service(service) + + # Resolve source_id + resolved_source_id = source_id + if not resolved_source_id: + cfg = GlobalConfig() + resolved_source_id = cfg.last_source_id + if not resolved_source_id: + console.print("[red]No source_id provided and no last published dataset.[/red]") + console.print("[dim]Usage: mdf update --data ./new_data/ --submit[/dim]") + raise typer.Exit(code=1) + + # Fetch existing metadata so user doesn't have to re-supply title/authors + cfg = GlobalConfig() + existing_title = resolved_source_id + existing_authors: List[str] = [] + try: + from mdf_agent.core.backend_client import BackendClient as _BC, _api_url_for_service + _base = api_url or _api_url_for_service(resolved) + _client = _BC.authenticated( + base_url=_base, token=token, service_instance=resolved, dev_user_id=dev_user, + ) + try: + card = _client.get_card(resolved_source_id) + if card.get("success") and card.get("card"): + c = card["card"] + existing_title = c.get("title", resolved_source_id) + existing_authors = c.get("authors", []) + finally: + _client.close() + except Exception: + pass + + # Build manifest + data_sources = list(data) if data else [] + manifest = ManifestConfig( + title=title or existing_title, + authors=author or existing_authors or [resolved_source_id], + description=description, + data_sources=data_sources, + publisher=cfg.publisher, + organization=cfg.organization, + ) + manifest.custom = {"mdf_source_id": resolved_source_id} + + agent = MDFAgent(root=None, manifest=manifest) + payload = agent.build_submission(test=False, update=True) + + if dry_run: + console.print("\n[bold cyan]Dry run — would update:[/bold cyan]") + target = api_url or _api_url_for_service(resolved) + console.print(f"[dim]Target: {target} ({resolved})[/dim]") + console.print(f"[dim]Source ID: {resolved_source_id}[/dim]") + syntax = Syntax(json_mod.dumps(payload, indent=2), "json", theme="monokai") + console.print(syntax) + return + + # Build progress callback for uploads + from rich.progress import Progress, BarColumn, DownloadColumn, TransferSpeedColumn + + progress_callback = None + progress_ctx = None + file_tasks: dict = {} + + if sys.stderr.isatty(): + progress_ctx = Progress( + "[progress.description]{task.description}", + BarColumn(), + DownloadColumn(), + TransferSpeedColumn(), + console=console, + transient=True, + ) + progress_ctx.start() + + def _progress_cb(filename: str, bytes_sent: int, total_bytes: int) -> None: + if filename not in file_tasks: + file_tasks[filename] = progress_ctx.add_task(filename, total=total_bytes) + progress_ctx.update(file_tasks[filename], completed=bytes_sent) + + progress_callback = _progress_cb + + try: + result = agent.publish( + test=False, + update=True, + dry_run=False, + token=token, + service_instance=resolved, + api_url=api_url, + dev_user_id=dev_user, + progress_callback=progress_callback, + ) + finally: + if progress_ctx is not None: + progress_ctx.stop() + + if result.get("success"): + new_version = result.get("version") + console.print(f"\n[bold green]Updated successfully![/bold green]") + console.print(f" [dim]Source ID:[/dim] [cyan]{result.get('source_id')}[/cyan]") + if new_version: + console.print(f" [dim]Version:[/dim] [cyan]{new_version}[/cyan]") + + if result.get("source_id"): + cfg = GlobalConfig() + cfg.record_publish(result.get("source_id"), new_version, resolved) + else: + error = result.get("error") or result.get("detail") or "Unknown error" + console.print(f"\n[bold red]Update failed:[/bold red] {error}") + raise typer.Exit(code=1) + + +if __name__ == "__main__": + app() diff --git a/src/mdf_agent/cli/publish.py b/src/mdf_agent/cli/publish.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/cli/status.py b/src/mdf_agent/cli/status.py new file mode 100644 index 0000000..372d768 --- /dev/null +++ b/src/mdf_agent/cli/status.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +import json + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Show MDF repository status") + + +@app.command() +def status(): + agent = MDFAgent.from_repo(".") + status_info = agent.status() + typer.echo(json.dumps(status_info, indent=2)) diff --git a/src/mdf_agent/cli/stream.py b/src/mdf_agent/cli/stream.py new file mode 100644 index 0000000..d729cc6 --- /dev/null +++ b/src/mdf_agent/cli/stream.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +import json +import sys +from pathlib import Path +from typing import Dict, List, Optional + +import typer +from rich.console import Console +from rich.table import Table + +from mdf_agent.core.backend_client import BackendClient +from mdf_agent.core.config import resolve_service +from mdf_agent.cli.formatting import format_result_or_json, handle_api_result + +console = Console() + +app = typer.Typer(help="MDF Streaming commands") +_auth_opts: Dict[str, Optional[str]] = {"service": None, "token": None, "dev_user": None} + + +@app.callback() +def stream_callback( + service: Optional[str] = typer.Option(None, "--service", "-s", help="Service instance (staging/prod/dev/local)"), + token: Optional[str] = typer.Option(None, "--token", help="Globus access token"), + dev_user: Optional[str] = typer.Option(None, "--dev-user", help="Dev-mode user id (X-User-Id)"), +): + """MDF Streaming commands.""" + _auth_opts.update(service=service, token=token, dev_user=dev_user) + + +def _client(api_url: Optional[str]) -> BackendClient: + return BackendClient.authenticated( + base_url=api_url, + token=_auth_opts.get("token"), + service_instance=resolve_service(_auth_opts.get("service")), + dev_user_id=_auth_opts.get("dev_user"), + ) + + +def _print(result): + typer.echo(json.dumps(result, indent=2)) + + +@app.command("create") +def create( + title: str = typer.Option(..., "--title"), + lab_id: Optional[str] = typer.Option(None, "--lab-id"), + organization: Optional[str] = typer.Option(None, "--organization"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_create(title, lab_id=lab_id, organization=organization) + client.close() + if json_output: + _print(result) + else: + if result.get("success"): + console.print(f"[green]Stream created:[/green] {result.get('stream_id')}") + else: + handle_api_result(result, error_prefix="Stream create failed") + + +@app.command("append") +def append( + stream_id: str = typer.Option(..., "--stream-id"), + files: Optional[Path] = typer.Option(None, "--files", help="JSON list of files or dict with files"), + file_count: Optional[int] = typer.Option(None, "--file-count"), + total_bytes: Optional[int] = typer.Option(None, "--total-bytes"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + files_payload = None + if files: + files_payload = json.loads(files.read_text(encoding="utf-8")) + if isinstance(files_payload, dict) and "files" in files_payload: + files_payload = files_payload["files"] + result = client.stream_append( + stream_id, + files=files_payload, + file_count=file_count, + total_bytes=total_bytes, + ) + client.close() + format_result_or_json(result, json_output, success_msg="Files appended", error_prefix="Append failed") + + +@app.command("status") +def status( + stream_id: str = typer.Option(..., "--stream-id"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_status(stream_id) + client.close() + format_result_or_json(result, json_output, error_prefix="Stream status") + + +@app.command("close") +def close( + stream_id: str = typer.Option(..., "--stream-id"), + mint_doi: Optional[bool] = typer.Option(None, "--mint-doi/--no-mint-doi"), + title: Optional[str] = typer.Option(None, "--title"), + description: Optional[str] = typer.Option(None, "--description"), + authors: Optional[Path] = typer.Option(None, "--authors", help="Path to JSON author list"), + keywords: Optional[Path] = typer.Option(None, "--keywords", help="Path to JSON keywords list"), + license: Optional[str] = typer.Option(None, "--license"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + authors_payload = None + if authors: + authors_payload = json.loads(authors.read_text(encoding="utf-8")) + + keywords_payload = None + if keywords: + keywords_payload = json.loads(keywords.read_text(encoding="utf-8")) + + client = _client(api_url) + result = client.stream_close( + stream_id=stream_id, + mint_doi=mint_doi, + title=title, + description=description, + authors=authors_payload, + keywords=keywords_payload, + license=license, + ) + client.close() + format_result_or_json(result, json_output, success_msg=f"Stream closed: {stream_id}", error_prefix="Stream close failed") + + +@app.command("snapshot") +def snapshot( + stream_id: str = typer.Option(..., "--stream-id"), + title: Optional[str] = typer.Option(None, "--title"), + update: bool = typer.Option(False, "--update"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), + json_output: bool = typer.Option(False, "--json", help="Raw JSON output"), +): + client = _client(api_url) + result = client.stream_snapshot(stream_id, title=title, update=update) + client.close() + format_result_or_json(result, json_output, success_msg="Snapshot created", error_prefix="Snapshot failed") + + +_PRESIGNED_THRESHOLD = 6 * 1024 * 1024 # 6 MB + + +@app.command("upload") +def upload( + stream_id: str = typer.Option(..., "--stream-id"), + files: List[Path] = typer.Argument(..., help="Files to upload"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """Upload files to a stream. + + Files > 6 MB are uploaded via pre-signed URL with a progress bar. + + Examples: + mdf stream upload --stream-id abc123 data.csv + mdf stream upload --stream-id abc123 *.csv + """ + import hashlib + import httpx + from rich.progress import Progress, BarColumn, DownloadColumn, TransferSpeedColumn + + client = _client(api_url) + + uploaded = [] + errors = [] + + use_progress = sys.stderr.isatty() + progress_ctx = None + if use_progress: + progress_ctx = Progress( + "[progress.description]{task.description}", + BarColumn(), + DownloadColumn(), + TransferSpeedColumn(), + console=console, + transient=True, + ) + progress_ctx.start() + + try: + for file_path in files: + if not file_path.exists(): + errors.append({"file": str(file_path), "error": "File not found"}) + continue + + file_size = file_path.stat().st_size + + try: + if file_size > _PRESIGNED_THRESHOLD: + # Use presigned URL path for large files + url_result = client.stream_get_upload_url(stream_id, file_path.name) + if not url_result.get("success"): + errors.append({"file": str(file_path), "error": url_result.get("error", "Failed to get upload URL")}) + continue + + upload_url = url_result.get("url", "") + upload_headers = url_result.get("headers", {}) + storage_path = url_result.get("path", "") + + task_id = None + if progress_ctx: + task_id = progress_ctx.add_task(file_path.name, total=file_size) + + # Stream the file via PUT + bytes_sent = 0 + md5 = hashlib.md5() + + def _file_stream(): + nonlocal bytes_sent + with open(file_path, "rb") as f: + while True: + chunk = f.read(8 * 1024 * 1024) + if not chunk: + break + md5.update(chunk) + bytes_sent += len(chunk) + if progress_ctx and task_id is not None: + progress_ctx.update(task_id, completed=bytes_sent) + yield chunk + + timeout = httpx.Timeout(connect=30, read=300, write=300, pool=30) + with httpx.Client(timeout=timeout) as http: + resp = http.put(upload_url, content=_file_stream(), headers=upload_headers) + resp.raise_for_status() + + # Confirm the upload + confirm = client.stream_confirm_upload( + stream_id, + path=storage_path, + size_bytes=file_size, + checksum_md5=md5.hexdigest(), + ) + if confirm.get("success"): + uploaded.append({ + "filename": file_path.name, + "size_bytes": file_size, + "checksum_md5": md5.hexdigest(), + }) + else: + errors.append({"file": str(file_path), "error": confirm.get("error", "Confirm failed")}) + else: + # Small file: base64 upload + content = file_path.read_bytes() + result = client.stream_upload(stream_id, file_path.name, content) + if result.get("success"): + uploaded.extend(result.get("files", [])) + else: + errors.append({"file": str(file_path), "error": result.get("error", "Unknown error")}) + except Exception as e: + errors.append({"file": str(file_path), "error": str(e)}) + finally: + if progress_ctx: + progress_ctx.stop() + + client.close() + + if uploaded: + console.print(f"\n[green]Uploaded {len(uploaded)} file(s)[/green]") + table = Table(show_header=True, header_style="bold") + table.add_column("Filename") + table.add_column("Size", justify="right") + table.add_column("Checksum", style="dim") + + for f in uploaded: + size_kb = f.get("size_bytes", 0) / 1024 + table.add_row( + f.get("filename", ""), + f"{size_kb:.1f} KB", + (f.get("checksum_md5", "") or "")[:12] + "...", + ) + console.print(table) + + if errors: + console.print(f"\n[red]Errors ({len(errors)}):[/red]") + for err in errors: + console.print(f" [red]x[/red] {err['file']}: {err['error']}") + + +@app.command("files") +def list_files( + stream_id: str = typer.Option(..., "--stream-id"), + api_url: Optional[str] = typer.Option(None, "--api-url", help="Override API base URL"), +): + """List files in a stream.""" + client = _client(api_url) + result = client.stream_list_files(stream_id) + client.close() + + if result.get("success"): + files = result.get("files", []) + console.print(f"\n[bold]Stream {stream_id}[/bold] - {len(files)} file(s)\n") + + if files: + table = Table(show_header=True, header_style="bold") + table.add_column("Filename") + table.add_column("Size", justify="right") + table.add_column("Uploaded", style="dim") + table.add_column("Checksum", style="dim") + + for f in files: + size_kb = f.get("size_bytes", 0) / 1024 + stored_at = f.get("stored_at", "")[:19] if f.get("stored_at") else "" + table.add_row( + f.get("filename", ""), + f"{size_kb:.1f} KB", + stored_at, + f.get("checksum_md5", "")[:12] + "...", + ) + console.print(table) + else: + console.print("[dim]No files uploaded yet[/dim]") + else: + console.print(f"[red]Error:[/red] {result.get('error', 'Unknown error')}") diff --git a/src/mdf_agent/cli/validate.py b/src/mdf_agent/cli/validate.py new file mode 100644 index 0000000..1bfef14 --- /dev/null +++ b/src/mdf_agent/cli/validate.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +import typer + +from mdf_agent.core.agent import MDFAgent + +app = typer.Typer(help="Validate manifest and data before publishing") + + +@app.command() +def validate(): + agent = MDFAgent.from_repo(".") + results = agent.validate() + errors = results.get("errors", []) + warnings = results.get("warnings", []) + + if errors: + typer.echo("Errors:") + for error in errors: + typer.echo(f" - {error}") + if warnings: + typer.echo("Warnings:") + for warning in warnings: + typer.echo(f" - {warning}") + + if errors: + raise typer.Exit(code=1) + typer.echo("Validation passed") diff --git a/src/mdf_agent/core/__init__.py b/src/mdf_agent/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/core/agent.py b/src/mdf_agent/core/agent.py new file mode 100644 index 0000000..abb2e81 --- /dev/null +++ b/src/mdf_agent/core/agent.py @@ -0,0 +1,1092 @@ +"""MDF Agent - Primary Python API. + +This module provides the main MDFAgent class, which is the primary interface +for interacting with MDF datasets programmatically. It supports two modes: + +1. Repository mode: Work with a git-style .mdf/ repository for tracking changes +2. Direct mode: Build and submit datasets without local state + +Examples: + Repository mode:: + + agent = MDFAgent.init("./my_dataset", title="My Dataset", authors=["Jane Doe"]) + agent.add("data/*.csv", discover=True) + agent.commit("Add experimental data") + result = agent.publish(test=True) + + Direct mode:: + + agent = MDFAgent() + agent.set_title("My Dataset") + agent.add_author("Jane Doe", affiliations=["MIT"]) + agent.add_data_source("globus://endpoint/path") + result = agent.publish() +""" + +from __future__ import annotations + +import os +import sys +import tempfile +import zipfile +from pathlib import Path +from typing import Any, Callable, Dict, List, NamedTuple, Optional + +from mdf_agent.core.manifest import load_manifest, save_manifest +from mdf_agent.core.repository import Repository +from mdf_agent.core.submission import build_submission +from mdf_agent.core.validation import validate_manifest +from mdf_agent.core.backend_client import BackendClient +from mdf_agent.extractors.registry import discover_metadata +from mdf_agent.models.config import Author, ManifestConfig + + +_MDF_HTTPS_BASE = "https://data.materialsdatafacility.org" +_NCSA_MDF_COLLECTION_UUID = "82f1b5c6-6e9b-11e5-ba47-22000b92c6ec" + + +_UPLOAD_CHUNK_SIZE = 8 * 1024 * 1024 # 8 MB +_ZIP_MAX_TOTAL_BYTES = 12 * 1024 * 1024 * 1024 # 12 GB +_INSECURE_SSL_WARNING_EMITTED = False + + +class UploadResult(NamedTuple): + data_sources: List[str] + archive_url: Optional[str] + archive_size: Optional[int] + + +def _resolve_upload_tls_verify() -> bool | str: + raw_value = os.environ.get("MDF_SSL_VERIFY") + if raw_value is None or not raw_value.strip(): + return True + + normalized = raw_value.strip().lower() + if normalized in ("true", "1", "yes"): + return True + if normalized in ("false", "0", "no"): + global _INSECURE_SSL_WARNING_EMITTED + if not _INSECURE_SSL_WARNING_EMITTED: + print( + "Warning: MDF_SSL_VERIFY=false disables TLS certificate verification for MDF uploads.", + file=sys.stderr, + ) + _INSECURE_SSL_WARNING_EMITTED = True + return False + return raw_value + + +def _mkdir_on_collection( + path: str, + transfer_token: str, +) -> None: + """Create a directory on the MDF Globus collection via the Transfer API.""" + import httpx + + url = f"https://transfer.api.globus.org/v0.10/operation/endpoint/{_NCSA_MDF_COLLECTION_UUID}/mkdir" + with httpx.Client(timeout=30.0) as client: + resp = client.post( + url, + json={"DATA_TYPE": "mkdir", "path": path}, + headers={"Authorization": f"Bearer {transfer_token}"}, + ) + # 502 "already exists" is fine + if resp.status_code in (200, 201, 202) or "already exists" in resp.text.lower(): + return + resp.raise_for_status() + + +def _create_zip_archive( + local_dirs: List[tuple[Path, str]], + progress_callback: Optional[Callable[[str, int, int], None]] = None, +) -> Optional[Path]: + """Create a zip archive from one or more local directories. + + Args: + local_dirs: List of (resolved_dir_path, prefix_in_zip) pairs. + progress_callback: Optional progress callback. + + Returns: + Path to temp zip file, or None if skipped (too large / empty). + """ + # Collect all files and compute total size + file_entries: List[tuple[Path, str]] = [] # (absolute_path, arcname) + total_size = 0 + + for dir_path, prefix in local_dirs: + for file_path in sorted(dir_path.rglob("*")): + if not file_path.is_file(): + continue + # Symlink safety: skip if target resolves outside the source dir + if file_path.is_symlink(): + try: + resolved = file_path.resolve() + if not str(resolved).startswith(str(dir_path.resolve())): + continue + except (OSError, ValueError): + continue + relative = file_path.relative_to(dir_path) + arcname = f"{prefix}/{relative}" if prefix else str(relative) + total_size += file_path.stat().st_size + file_entries.append((file_path, arcname)) + + if not file_entries: + return None + + if total_size > _ZIP_MAX_TOTAL_BYTES: + print( + f"Warning: Skipping zip archive — total size ({total_size / (1024**3):.1f} GB) " + f"exceeds {_ZIP_MAX_TOTAL_BYTES / (1024**3):.0f} GB limit.", + file=sys.stderr, + ) + return None + + tmp = tempfile.NamedTemporaryFile(suffix=".zip", delete=False) + tmp.close() + tmp_path = Path(tmp.name) + + bytes_written = 0 + with zipfile.ZipFile(tmp_path, "w", zipfile.ZIP_DEFLATED) as zf: + for file_path, arcname in file_entries: + zf.write(file_path, arcname) + bytes_written += file_path.stat().st_size + if progress_callback: + progress_callback("Creating archive", bytes_written, total_size) + + return tmp_path + + +def _upload_local_files( + data_sources: List[str], + data_token: str, + source_id: Optional[str] = None, + progress_callback: Optional[Callable[[str, int, int], None]] = None, + transfer_token: Optional[str] = None, +) -> UploadResult: + """Upload local file paths to MDF HTTPS storage, returning updated source list. + + Local paths are uploaded via HTTPS PUT to the MDF Globus collection and + replaced with ``globus://`` URIs. Non-local sources pass through unchanged. + When directories are uploaded, a zip archive is also created and uploaded + to ``{upload_prefix}/.mdf/data.zip``. + + Args: + data_sources: List of data source paths/URIs. + data_token: Globus HTTPS bearer token. + source_id: If available, upload to ``/mdf_open/{source_id}/`` for + deterministic paths. Falls back to ``/tmp/_uploads/{uuid}/``. + progress_callback: Optional ``(filename, bytes_sent, total_bytes)`` callback. + transfer_token: Globus Transfer API token, used to mkdir before upload. + """ + import uuid + + updated: List[str] = [] + local_dirs: List[tuple[Path, str]] = [] + + if source_id: + upload_prefix = f"/mdf_open/{source_id}" + else: + upload_id = uuid.uuid4().hex[:8] + upload_prefix = f"/tmp/_uploads/{upload_id}" + + # Create the upload directory on the collection via Transfer API + if transfer_token: + _mkdir_on_collection(upload_prefix + "/", transfer_token) + + # Count directories to determine prefix logic + dir_count = sum(1 for s in data_sources if not s.startswith(("globus://", "https://", "http://", "stream://")) and Path(s).is_dir()) + + for source in data_sources: + # Skip anything that's already a URL + if source.startswith(("globus://", "https://", "http://", "stream://")): + updated.append(source) + continue + + local_path = Path(source) + if not local_path.exists(): + # Keep as-is — server will validate + updated.append(source) + continue + + if local_path.is_dir(): + # Track for zip archive creation + prefix = local_path.name if dir_count > 1 else "" + local_dirs.append((local_path.resolve(), prefix)) + # Upload directory contents recursively + for file_path in sorted(local_path.rglob("*")): + if file_path.is_file(): + relative = file_path.relative_to(local_path) + dest_path = f"{upload_prefix}/{relative}" + uri = _https_put_file(file_path, dest_path, data_token, progress_callback=progress_callback) + if uri: + updated.append(uri) + else: + dest_path = f"{upload_prefix}/{local_path.name}" + uri = _https_put_file(local_path, dest_path, data_token, progress_callback=progress_callback) + if uri: + updated.append(uri) + + # Create and upload zip archive for local directories + archive_url: Optional[str] = None + archive_size: Optional[int] = None + if local_dirs: + zip_path = _create_zip_archive(local_dirs, progress_callback=progress_callback) + if zip_path is not None: + try: + archive_size = zip_path.stat().st_size + mdf_dir = f"{upload_prefix}/.mdf" + if transfer_token: + _mkdir_on_collection(mdf_dir + "/", transfer_token) + dest = f"{mdf_dir}/data.zip" + uri = _https_put_file(zip_path, dest, data_token, progress_callback=progress_callback) + if uri: + archive_url = uri + finally: + zip_path.unlink(missing_ok=True) + + sources = updated if updated else data_sources + return UploadResult(data_sources=sources, archive_url=archive_url, archive_size=archive_size) + + +_UPLOAD_MAX_RETRIES = 3 +_UPLOAD_RETRY_STATUSES = {502, 503, 504} + + +def _https_put_file( + local_path: Path, + dest_path: str, + data_token: str, + progress_callback: Optional[Callable[[str, int, int], None]] = None, +) -> Optional[str]: + """Upload a single file via streaming HTTPS PUT and return its globus:// URI. + + Reads the file in 8 MB chunks to avoid loading entire files into memory. + Retries on 502/503/504 and connection errors up to 3 times. + """ + import time + import httpx + + url = f"{_MDF_HTTPS_BASE}{dest_path}" + file_size = local_path.stat().st_size + ssl_verify = _resolve_upload_tls_verify() + + def file_stream(): + bytes_sent = 0 + with open(local_path, "rb") as f: + while True: + chunk = f.read(_UPLOAD_CHUNK_SIZE) + if not chunk: + break + bytes_sent += len(chunk) + if progress_callback: + progress_callback(local_path.name, bytes_sent, file_size) + yield chunk + + timeout = httpx.Timeout(connect=30, read=300, write=300, pool=30) + + for attempt in range(_UPLOAD_MAX_RETRIES + 1): + try: + with httpx.Client(timeout=timeout, verify=ssl_verify) as client: + resp = client.put( + url, + content=file_stream(), + headers={ + "Authorization": f"Bearer {data_token}", + "Content-Type": "application/octet-stream", + "Content-Length": str(file_size), + }, + ) + if resp.status_code in (200, 201, 204): + return f"globus://{_NCSA_MDF_COLLECTION_UUID}{dest_path}" + if resp.status_code in _UPLOAD_RETRY_STATUSES and attempt < _UPLOAD_MAX_RETRIES: + time.sleep(2 ** attempt) + continue + raise RuntimeError( + f"Failed to upload {local_path.name}: HTTP {resp.status_code} {resp.text}" + ) + except (httpx.ConnectError, httpx.ConnectTimeout) as exc: + if attempt < _UPLOAD_MAX_RETRIES: + time.sleep(2 ** attempt) + continue + raise RuntimeError( + f"Failed to upload {local_path.name} after {_UPLOAD_MAX_RETRIES + 1} attempts: {exc}" + ) from exc + return None + + +def _globus_uri_to_https(uri: str) -> Optional[str]: + """Convert a globus:// URI on the NCSA MDF collection to an HTTPS URL. + + Returns None for non-NCSA endpoint URIs (can't HTTPS-download from + arbitrary Globus endpoints). + """ + if not uri.startswith("globus://"): + return None + rest = uri[len("globus://"):] + slash_idx = rest.find("/") + if slash_idx < 0: + return None + endpoint_id = rest[:slash_idx] + path = rest[slash_idx:] + if endpoint_id.lower() != _NCSA_MDF_COLLECTION_UUID: + return None + return f"{_MDF_HTTPS_BASE}{path}" + + +def _resolve_globus_files( + data_sources: List[str], + transfer_token: str, +) -> List[tuple]: + """Resolve globus:// URIs to (https_url, relative_path) pairs for HTTPS download. + + For per-file NCSA URIs: convert directly. + For directory NCSA URIs (ending with /): use operation_ls to list contents recursively. + Non-NCSA globus:// URIs are skipped with a warning. + Non-globus sources (https://, stream://) are skipped. + """ + import globus_sdk + + results: List[tuple] = [] + + for source in data_sources: + if not source.startswith("globus://"): + continue + + rest = source[len("globus://"):] + slash_idx = rest.find("/") + if slash_idx < 0: + continue + endpoint_id = rest[:slash_idx] + path = rest[slash_idx:] + + if endpoint_id.lower() != _NCSA_MDF_COLLECTION_UUID: + print( + f"Warning: Skipping non-NCSA source {source} — " + f"use --transfer for cross-endpoint downloads.", + file=sys.stderr, + ) + continue + + if path.endswith("/"): + # Directory URI — use operation_ls to list files recursively + authorizer = globus_sdk.AccessTokenAuthorizer(transfer_token) + tc = globus_sdk.TransferClient(authorizer=authorizer) + _ls_recursive(tc, endpoint_id, path, path, results) + else: + # Per-file URI + https_url = f"{_MDF_HTTPS_BASE}{path}" + # Extract relative path: strip common upload prefixes + # e.g. /mdf_open/source_id/subdir/file.csv → subdir/file.csv + parts = path.lstrip("/").split("/") + if len(parts) > 2 and parts[0] == "mdf_open": + rel = "/".join(parts[2:]) + else: + rel = parts[-1] if parts else path.lstrip("/") + results.append((https_url, rel)) + + return results + + +def _ls_recursive( + tc: Any, + endpoint_id: str, + dir_path: str, + base_path: str, + results: List[tuple], +) -> None: + """Recursively list a Globus directory, appending (https_url, rel_path) to results.""" + try: + listing = tc.operation_ls(endpoint_id, path=dir_path) + except Exception as exc: + print(f"Warning: Failed to list {dir_path}: {exc}", file=sys.stderr) + return + + for entry in listing: + entry_type = entry.get("type", entry.get("DATA_TYPE", "")) + name = entry.get("name", "") + full_path = f"{dir_path}{name}" if dir_path.endswith("/") else f"{dir_path}/{name}" + + if entry_type == "dir": + _ls_recursive(tc, endpoint_id, full_path + "/", base_path, results) + else: + https_url = f"{_MDF_HTTPS_BASE}{full_path}" + # Relative path from the base directory + rel = full_path[len(base_path):].lstrip("/") + results.append((https_url, rel)) + + +def _download_https_file( + url: str, + dest_path: Path, + token: str, + progress_callback: Optional[Callable[[str, int, int], None]] = None, +) -> Path: + """Download a file via streaming HTTPS GET. + + Creates parent directories as needed. Retries on 502/503/504. + """ + import time + import httpx + + dest_path.parent.mkdir(parents=True, exist_ok=True) + ssl_verify = _resolve_upload_tls_verify() + timeout = httpx.Timeout(connect=30, read=300, write=300, pool=30) + + for attempt in range(_UPLOAD_MAX_RETRIES + 1): + try: + with httpx.Client(timeout=timeout, verify=ssl_verify) as client: + with client.stream( + "GET", + url, + headers={"Authorization": f"Bearer {token}"}, + ) as resp: + if resp.status_code in _UPLOAD_RETRY_STATUSES and attempt < _UPLOAD_MAX_RETRIES: + time.sleep(2 ** attempt) + continue + resp.raise_for_status() + + total = int(resp.headers.get("content-length", 0)) + bytes_received = 0 + with open(dest_path, "wb") as f: + for chunk in resp.iter_bytes(chunk_size=_UPLOAD_CHUNK_SIZE): + f.write(chunk) + bytes_received += len(chunk) + if progress_callback: + progress_callback(dest_path.name, bytes_received, total) + except (httpx.ConnectError, httpx.ConnectTimeout) as exc: + if attempt < _UPLOAD_MAX_RETRIES: + time.sleep(2 ** attempt) + continue + raise RuntimeError( + f"Failed to download {url} after {_UPLOAD_MAX_RETRIES + 1} attempts: {exc}" + ) from exc + else: + return dest_path + + return dest_path + + +def _detect_gcp_endpoint() -> Optional[str]: + """Detect a local Globus Connect Personal endpoint ID. + + Tries the SDK first, then falls back to global config. + """ + try: + import globus_sdk + gcp = globus_sdk.LocalGlobusConnectPersonal() + ep_id = gcp.endpoint_id + if ep_id: + return ep_id + except Exception: + pass + + from mdf_agent.core.config import GlobalConfig + cfg = GlobalConfig() + return cfg.get("globus.local_endpoint_id") + + +class MDFAgent: + """Primary Python API for MDF Agent. + + This class provides a high-level interface for creating, managing, and + publishing datasets to the Materials Data Facility (MDF). + + Attributes: + root: Path to the dataset directory (repository mode) or None (direct mode). + repo: Repository instance for git-style operations, or None. + manifest: The ManifestConfig containing dataset metadata. + """ + + def __init__(self, root: Optional[Path] = None, manifest: Optional[ManifestConfig] = None): + self.root = root + self.repo: Optional[Repository] = None + if root is not None: + self.repo = Repository.load(root) + self.manifest = self.repo.load_manifest() + else: + self.manifest = manifest or ManifestConfig() + + @classmethod + def init( + cls, + path: str, + title: str, + authors: List[str], + description: Optional[str] = None, + publisher: Optional[str] = None, + publication_year: Optional[int] = None, + ) -> "MDFAgent": + root = Path(path).resolve() + repo = Repository.init_repo( + root, + title=title, + authors=authors, + description=description, + publisher=publisher, + publication_year=publication_year, + ) + agent = cls.__new__(cls) + agent.root = root + agent.repo = repo + agent.manifest = repo.load_manifest() + return agent + + @classmethod + def from_repo(cls, path: str) -> "MDFAgent": + root = Path(path).resolve() + return cls(root=root) + + def save_manifest(self) -> None: + if self.repo is None: + raise ValueError("No repository attached") + self.repo.save_manifest(self.manifest) + + def add(self, *paths: str, discover: Optional[bool] = None) -> List[str]: + if self.repo is None: + raise ValueError("Repository mode required for add") + staged = self.repo.stage(paths) + + if discover or (discover is None and self.manifest.auto_discover): + files = [str((self.root / path).resolve()) for path in staged] + extracted = discover_metadata(files) + if extracted: + current = self.manifest.auto_metadata or {} + current.update(extracted) + self.manifest.auto_metadata = current + self.save_manifest() + # Stage the updated manifest + self.repo.stage(["mdf.yaml"]) + return staged + + def commit(self, message: str) -> Dict[str, Any]: + if self.repo is None: + raise ValueError("Repository mode required for commit") + return self.repo.commit(message) + + def status(self) -> Dict[str, Any]: + if self.repo is None: + raise ValueError("Repository mode required for status") + return self.repo.get_status() + + def validate(self) -> Dict[str, List[str]]: + has_tracked_data = bool( + self.repo and self.repo.get_tracked_files() + ) + errors, warnings = validate_manifest(self.manifest, has_committed_files=has_tracked_data) + return {"errors": errors, "warnings": warnings} + + def build_submission(self, test: bool = False, update: bool = False) -> Dict[str, Any]: + root = self.root or Path.cwd() + return build_submission(self.manifest, root, test=test, update=update) + + def publish( + self, + test: bool = False, + update: bool = False, + dry_run: bool = True, + token: Optional[str] = None, + service_instance: str = "prod", + api_url: Optional[str] = None, + dev_user_id: Optional[str] = None, + authorizer: Optional[Any] = None, + progress_callback: Optional[Callable[[str, int, int], None]] = None, + ) -> Dict[str, Any]: + payload = self.build_submission(test=test, update=update) + if dry_run: + return {"success": True, "payload": payload} + + # Backward compatibility: extract a bearer token if a legacy authorizer is passed. + if authorizer is not None and token is None: + header = authorizer.get_authorization_header() + if isinstance(header, str): + bearer_prefix = "Bearer " + token = header[len(bearer_prefix):] if header.startswith(bearer_prefix) else header + + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + try: + # Upload local files to MDF storage via HTTPS and replace + # paths with globus:// URIs before submitting. + data_sources = payload.get("data_sources", []) + if data_sources and client._globus_data_token: + # Use source_id for deterministic upload paths when available + ext = payload.get("extensions", {}) + upload_source_id = ext.get("mdf_source_id") or ext.get("mdf_source_name") + upload_result = _upload_local_files( + data_sources, + client._globus_data_token, + source_id=upload_source_id, + progress_callback=progress_callback, + transfer_token=client._globus_transfer_token, + ) + payload["data_sources"] = upload_result.data_sources + if upload_result.archive_url: + payload["download_url"] = upload_result.archive_url + if upload_result.archive_size: + payload["archive_size"] = upload_result.archive_size + return client.submit(payload) + finally: + client.close() + + def set_title(self, title: str) -> None: + self.manifest.title = title + + def add_author(self, name: str, affiliations: Optional[List[str]] = None, orcid: Optional[str] = None) -> None: + if self.manifest.authors is None: + self.manifest.authors = [] + author = Author(name=name, affiliations=affiliations or [], orcid=orcid) + self.manifest.authors.append(author) + + def add_data_source(self, source: str) -> None: + if self.manifest.data_sources is None: + self.manifest.data_sources = [] + self.manifest.data_sources.append(source) + + # Search, curation, and discovery helpers + + def search( + self, + query: str, + search_type: str = "all", + limit: int = 20, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Search datasets and streams.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.search(query, search_type=search_type, limit=limit) + client.close() + return result + + def pending( + self, + limit: int = 50, + organization: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """List datasets pending curation.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.curation_pending(limit=limit, organization=organization) + client.close() + return result + + def approve( + self, + source_id: str, + mint_doi: bool = True, + notes: Optional[str] = None, + version: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Approve a dataset for publication.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.curation_approve(source_id=source_id, mint_doi=mint_doi, notes=notes, version=version) + client.close() + return result + + def reject( + self, + source_id: str, + reason: str, + suggestions: Optional[str] = None, + version: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Reject a dataset and return to submitter.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.curation_reject(source_id=source_id, reason=reason, suggestions=suggestions, version=version) + client.close() + return result + + def versions( + self, + source_id: str, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Get version history for a dataset.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.versions(source_id) + client.close() + return result + + def show( + self, + source_id: str, + version: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Get dataset preview card.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.get_card(source_id, version=version) + client.close() + return result + + def cite( + self, + source_id: str, + format: str = "all", + version: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Get citation for a dataset.""" + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.get_citation(source_id, format=format, version=version) + client.close() + return result + + # Streaming helpers + def stream_create( + self, + title: str, + lab_id: Optional[str] = None, + organization: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ): + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.stream_create(title, lab_id=lab_id, organization=organization) + client.close() + return result + + def stream_append( + self, + stream_id: str, + files: Optional[Any] = None, + file_count: Optional[int] = None, + total_bytes: Optional[int] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ): + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.stream_append(stream_id, files=files, file_count=file_count, total_bytes=total_bytes) + client.close() + return result + + def stream_status( + self, + stream_id: str, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ): + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.stream_status(stream_id) + client.close() + return result + + def stream_close( + self, + stream_id: str, + mint_doi: Optional[bool] = None, + title: Optional[str] = None, + description: Optional[str] = None, + authors: Optional[list] = None, + keywords: Optional[list] = None, + license: Optional[str] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ): + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.stream_close( + stream_id=stream_id, + mint_doi=mint_doi, + title=title, + description=description, + authors=authors, + keywords=keywords, + license=license, + ) + client.close() + return result + + def stream_snapshot( + self, + stream_id: str, + title: Optional[str] = None, + update: bool = False, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ): + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + result = client.stream_snapshot(stream_id, title=title, update=update) + client.close() + return result + + def clone( + self, + source_id: str, + output_dir: str = ".", + version: Optional[str] = None, + method: str = "auto", + progress_callback: Optional[Callable[[str, int, int], None]] = None, + api_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> Dict[str, Any]: + """Clone (download) a dataset's files. + + Args: + source_id: Dataset source ID. + output_dir: Local directory to download into. + version: Specific version (default: latest). + method: "auto" (zip if available, else HTTPS), "https", or "transfer". + progress_callback: Optional (filename, bytes, total) callback. + api_url: Override API base URL. + token: Explicit bearer token. + service_instance: "prod", "staging", "dev", or "local". + dev_user_id: Dev-mode user ID. + + Returns: + Dict with success, method used, file count, output path, etc. + """ + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + try: + card_resp = client.get_card(source_id, version=version) + card = card_resp.get("card", card_resp) + if not card.get("source_id"): + return {"success": False, "error": f"Dataset '{source_id}' not found"} + + download_url = card.get("download_url") + archive_size = card.get("archive_size") + data_sources = card.get("data_sources", []) + data_token = client._globus_data_token or "" + transfer_token = client._globus_transfer_token or "" + + out = Path(output_dir).resolve() + out.mkdir(parents=True, exist_ok=True) + + # Strategy selection + if method == "transfer": + return self._clone_via_transfer( + data_sources, transfer_token, out, card, + ) + + if method == "auto" and download_url: + use_zip = True + elif method == "https": + use_zip = False + else: + use_zip = bool(download_url) + + if use_zip and download_url: + return self._clone_via_zip( + download_url, data_token, out, card, + progress_callback=progress_callback, + ) + + # HTTPS file-by-file + if not data_sources: + return {"success": False, "error": "No data sources found for this dataset"} + + files = _resolve_globus_files(data_sources, transfer_token) + if not files: + return { + "success": False, + "error": "No HTTPS-downloadable files found. Try --transfer for cross-endpoint downloads.", + } + + downloaded = 0 + for https_url, rel_path in files: + dest = out / rel_path + _download_https_file(https_url, dest, data_token, progress_callback=progress_callback) + downloaded += 1 + + return { + "success": True, + "method": "https", + "files_count": downloaded, + "path": str(out), + "source_id": source_id, + "title": card.get("title"), + } + finally: + client.close() + + def _clone_via_zip( + self, + download_url: str, + data_token: str, + out: Path, + card: Dict[str, Any], + progress_callback: Optional[Callable[[str, int, int], None]] = None, + ) -> Dict[str, Any]: + """Download and extract a zip archive.""" + tmp = tempfile.NamedTemporaryFile(suffix=".zip", delete=False) + tmp.close() + tmp_path = Path(tmp.name) + + try: + _download_https_file( + download_url, tmp_path, data_token, + progress_callback=progress_callback, + ) + with zipfile.ZipFile(tmp_path) as zf: + zf.extractall(out) + file_count = len(zf.namelist()) + finally: + tmp_path.unlink(missing_ok=True) + + return { + "success": True, + "method": "zip", + "files_count": file_count, + "path": str(out), + "source_id": card.get("source_id"), + "title": card.get("title"), + } + + def _clone_via_transfer( + self, + data_sources: List[str], + transfer_token: str, + out: Path, + card: Dict[str, Any], + ) -> Dict[str, Any]: + """Submit a Globus Transfer task.""" + import globus_sdk + + local_ep = _detect_gcp_endpoint() + if not local_ep: + raise RuntimeError( + "Globus Transfer requires a local endpoint. " + "Install Globus Connect Personal or run: " + "mdf config set globus.local_endpoint_id " + ) + + authorizer = globus_sdk.AccessTokenAuthorizer(transfer_token) + tc = globus_sdk.TransferClient(authorizer=authorizer) + + # Group sources by endpoint + for source in data_sources: + if not source.startswith("globus://"): + continue + + rest = source[len("globus://"):] + slash_idx = rest.find("/") + if slash_idx < 0: + continue + src_endpoint = rest[:slash_idx] + src_path = rest[slash_idx:] + + td = globus_sdk.TransferData( + tc, + source_endpoint=src_endpoint, + destination_endpoint=local_ep, + label=f"MDF clone: {card.get('source_id', '')}", + ) + is_dir = src_path.endswith("/") + td.add_item(src_path, str(out) + "/", recursive=is_dir) + + result = tc.submit_transfer(td) + task_id = result.get("task_id", "") + + return { + "success": True, + "method": "transfer", + "task_id": task_id, + "monitor_url": f"https://app.globus.org/activity/{task_id}", + "path": str(out), + "source_id": card.get("source_id"), + "title": card.get("title"), + } + + return {"success": False, "error": "No globus:// sources found for transfer"} diff --git a/src/mdf_agent/core/backend_client.py b/src/mdf_agent/core/backend_client.py new file mode 100644 index 0000000..f5fcf17 --- /dev/null +++ b/src/mdf_agent/core/backend_client.py @@ -0,0 +1,661 @@ +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import Any, Dict, Optional +from urllib.parse import quote, unquote, urlparse + +import httpx + +_V2_API_URLS = { + "prod": "https://api.materialsdatafacility.org", + "dev": "https://api-dev.materialsdatafacility.org", + "staging": "https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging", + "local": "http://127.0.0.1:8080", +} + + +def _api_url_for_service(service_instance: str) -> str: + override_url = os.environ.get("MDF_API_URL") + if override_url: + return override_url + normalized = (service_instance or "prod").strip().lower() + if normalized == "production": + normalized = "prod" + elif normalized == "development": + normalized = "dev" + url = _V2_API_URLS.get(normalized, "") + if not url: + raise ValueError( + f"No API URL configured for service '{normalized}'. " + f"Use --api-url or set MDF_API_URL environment variable." + ) + return url + + +class BackendClient: + def __init__( + self, + base_url: str, + token: Optional[str] = None, + user_id: Optional[str] = None, + globus_data_token: Optional[str] = None, + globus_transfer_token: Optional[str] = None, + ): + self.base_url = base_url.rstrip("/") + self._client = httpx.Client(timeout=30.0) + self._token = token + self._user_id = user_id + self._globus_data_token = globus_data_token + self._globus_transfer_token = globus_transfer_token + + @classmethod + def from_env(cls) -> "BackendClient": + base_url = os.environ.get("MDF_API_URL", "http://127.0.0.1:8080") + token = os.environ.get("MDF_CONNECT_TOKEN") + user_id = os.environ.get("MDF_DEV_USER_ID") + return cls(base_url=base_url, token=token, user_id=user_id) + + @classmethod + def authenticated( + cls, + base_url: Optional[str] = None, + token: Optional[str] = None, + service_instance: str = "prod", + dev_user_id: Optional[str] = None, + ) -> "BackendClient": + """Create an authenticated backend client. + + Resolution order: + 1. Explicit token argument + 2. MDF_CONNECT_TOKEN env var + 3. MDF_CLIENT_ID + MDF_CLIENT_SECRET env vars (confidential client flow) + 4. dev_user_id / MDF_DEV_USER_ID env var (X-User-Id) + 5. Interactive Globus OAuth2 login + """ + url = base_url or _api_url_for_service(service_instance) + normalized_service = (service_instance or "prod").strip().lower() + + resolved_token = token or os.environ.get("MDF_CONNECT_TOKEN") + if resolved_token: + return cls(base_url=url, token=resolved_token) + + confidential_client_id = os.environ.get("MDF_CLIENT_ID") + confidential_client_secret = os.environ.get("MDF_CLIENT_SECRET") + if confidential_client_id and confidential_client_secret: + # Lazy import so token/dev-user workflows do not require globus_sdk. + import globus_sdk + + from mdf_agent.auth.globus import ( + DATA_MDF_SCOPE, + NCSA_MDF_COLLECTION_UUID, + TRANSFER_SCOPE, + get_scopes_for_service, + ) + + scope, resource_server = get_scopes_for_service(service_instance) + confidential_client = globus_sdk.ConfidentialAppAuthClient( + confidential_client_id, + confidential_client_secret, + ) + token_response = confidential_client.oauth2_client_credentials_tokens( + requested_scopes=f"{scope} {DATA_MDF_SCOPE} {TRANSFER_SCOPE}", + ) + by_resource_server = getattr(token_response, "by_resource_server", {}) or {} + + def _extract_access_token(token_entry: Any) -> str: + if not token_entry: + return "" + if isinstance(token_entry, dict): + return token_entry.get("access_token", "") + return getattr(token_entry, "access_token", "") + + service_token = _extract_access_token(by_resource_server.get(resource_server)) + data_token = _extract_access_token(by_resource_server.get(NCSA_MDF_COLLECTION_UUID)) + transfer_token = _extract_access_token(by_resource_server.get("transfer.api.globus.org")) + if service_token: + return cls(base_url=url, token=service_token, globus_data_token=data_token or None, globus_transfer_token=transfer_token or None) + + resolved_user_id = dev_user_id or os.environ.get("MDF_DEV_USER_ID") + if not resolved_user_id and normalized_service == "local": + resolved_user_id = os.environ.get("LOCAL_USER_ID") + if resolved_user_id: + return cls(base_url=url, user_id=resolved_user_id) + + if normalized_service == "local": + # Local dev backends commonly run in AUTH_MODE=dev and can derive a default user. + return cls(base_url=url) + + # Lazy import so token/dev-user workflows do not require globus_sdk. + from mdf_agent.auth.globus import get_authorizer_for_scopes, get_scopes_for_service, DATA_MDF_SCOPE, TRANSFER_SCOPE, NCSA_MDF_COLLECTION_UUID + + scope, _resource_server = get_scopes_for_service(service_instance) + authorizers = get_authorizer_for_scopes( + [scope, DATA_MDF_SCOPE, TRANSFER_SCOPE], + ) + + bearer_prefix = "Bearer " + + def _extract(authorizer): + if not authorizer: + return "" + h = authorizer.get_authorization_header() + return h[len(bearer_prefix):] if h.startswith(bearer_prefix) else h + + # Use the auth.globus.org token as Bearer — it carries the openid + # scope so the backend can call userinfo() for identity. + openid_token = _extract(authorizers.get("auth.globus.org")) + + # Data token for Globus HTTPS file operations (X-Globus-Token header) + # The resource server key is the collection UUID, not the hostname. + data_token = _extract(authorizers.get(NCSA_MDF_COLLECTION_UUID)) + + # Transfer token for mkdir operations before HTTPS uploads + transfer_token = _extract(authorizers.get("transfer.api.globus.org")) + + return cls(base_url=url, token=openid_token, globus_data_token=data_token or None, globus_transfer_token=transfer_token or None) + + def close(self) -> None: + self._client.close() + + def health(self) -> Dict[str, Any]: + return self._request("GET", "/health") + + def submit(self, payload: Dict[str, Any]) -> Dict[str, Any]: + return self._request("POST", "/submit", json_data=payload) + + def status(self, source_id: str, version: Optional[str] = None) -> Dict[str, Any]: + params = {"version": version} if version else None + return self._request("GET", f"/status/{source_id}", params=params) + + def submissions(self, organization: Optional[str] = None) -> Dict[str, Any]: + params = {"organization": organization} if organization else None + return self._request("GET", "/submissions", params=params) + + def curation_pending( + self, + limit: int = 50, + offset: int = 0, + organization: Optional[str] = None, + ) -> Dict[str, Any]: + params: Dict[str, Any] = { + "limit": limit, + "offset": offset, + } + if organization: + params["organization"] = organization + return self._request("GET", "/curation/pending", params=params) + + def curation_detail(self, source_id: str, version: Optional[str] = None) -> Dict[str, Any]: + params = {"version": version} if version else None + return self._request("GET", f"/curation/{source_id}", params=params) + + def curation_approve( + self, + source_id: str, + mint_doi: bool = True, + notes: Optional[str] = None, + metadata_updates: Optional[Dict[str, Any]] = None, + version: Optional[str] = None, + ) -> Dict[str, Any]: + payload: Dict[str, Any] = {"mint_doi": mint_doi} + if notes is not None: + payload["notes"] = notes + if metadata_updates is not None: + payload["metadata_updates"] = metadata_updates + if version: + payload["version"] = version + return self._request("POST", f"/curation/{source_id}/approve", json_data=payload) + + def curation_reject( + self, + source_id: str, + reason: str, + suggestions: Optional[str] = None, + version: Optional[str] = None, + ) -> Dict[str, Any]: + payload: Dict[str, Any] = {"reason": reason} + if suggestions is not None: + payload["suggestions"] = suggestions + if version: + payload["version"] = version + return self._request("POST", f"/curation/{source_id}/reject", json_data=payload) + + def update_status(self, source_id: str, version: str, status: str) -> Dict[str, Any]: + payload = {"source_id": source_id, "version": version, "status": status} + return self._request("POST", "/status/update", json_data=payload) + + def stream_create(self, title: str, lab_id: Optional[str] = None, organization: Optional[str] = None) -> Dict[str, Any]: + payload = {"title": title} + if lab_id: + payload["lab_id"] = lab_id + if organization: + payload["organization"] = organization + return self._request("POST", "/stream/create", json_data=payload) + + def stream_append( + self, + stream_id: str, + files: Optional[Any] = None, + file_count: Optional[int] = None, + total_bytes: Optional[int] = None, + ) -> Dict[str, Any]: + payload: Dict[str, Any] = {"stream_id": stream_id} + if files is not None: + payload["files"] = files + if file_count is not None: + payload["file_count"] = file_count + if total_bytes is not None: + payload["total_bytes"] = total_bytes + return self._request("POST", f"/stream/{stream_id}/append", json_data=payload) + + def stream_status(self, stream_id: str) -> Dict[str, Any]: + return self._request("GET", f"/stream/{stream_id}") + + def stream_close( + self, + stream_id: str, + mint_doi: Optional[bool] = None, + title: Optional[str] = None, + description: Optional[str] = None, + authors: Optional[list] = None, + keywords: Optional[list] = None, + license: Optional[str] = None, + ) -> Dict[str, Any]: + payload: Dict[str, Any] = {"stream_id": stream_id} + if mint_doi is not None: + payload["mint_doi"] = mint_doi + if title: + payload["title"] = title + if description: + payload["description"] = description + if authors is not None: + payload["authors"] = authors + if keywords is not None: + payload["keywords"] = keywords + if license: + payload["license"] = license + return self._request("POST", f"/stream/{stream_id}/close", json_data=payload) + + def stream_snapshot( + self, + stream_id: str, + title: Optional[str] = None, + update: bool = False, + ) -> Dict[str, Any]: + payload: Dict[str, Any] = {"stream_id": stream_id, "update": update} + if title: + payload["title"] = title + return self._request("POST", f"/stream/{stream_id}/snapshot", json_data=payload) + + def stream_upload( + self, + stream_id: str, + filename: str, + content: bytes, + metadata: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + """Upload a file to a stream. + + Args: + stream_id: The stream ID + filename: Name of the file + content: File contents as bytes + metadata: Optional metadata dict + """ + import base64 + payload: Dict[str, Any] = { + "filename": filename, + "content_base64": base64.b64encode(content).decode("ascii"), + } + if metadata: + payload["metadata"] = metadata + return self._request("POST", f"/stream/{stream_id}/upload", json_data=payload) + + def stream_upload_batch( + self, + stream_id: str, + files: list, + ) -> Dict[str, Any]: + """Upload multiple files to a stream. + + Args: + stream_id: The stream ID + files: List of dicts with filename, content (bytes), and optional metadata + """ + import base64 + payload_files = [] + for f in files: + pf: Dict[str, Any] = { + "filename": f["filename"], + "content_base64": base64.b64encode(f["content"]).decode("ascii"), + } + if f.get("metadata"): + pf["metadata"] = f["metadata"] + payload_files.append(pf) + return self._request("POST", f"/stream/{stream_id}/upload", json_data={"files": payload_files}) + + def stream_list_files(self, stream_id: str) -> Dict[str, Any]: + """List all files in a stream.""" + return self._request("GET", f"/stream/{stream_id}/files") + + def stream_get_upload_url( + self, + stream_id: str, + filename: str, + content_type: str = "application/octet-stream", + expires_in: int = 3600, + ) -> Dict[str, Any]: + """Get a pre-signed URL for direct file upload. + + For large files (> 6MB), use this to upload directly to storage + instead of going through the API. + + Args: + stream_id: The stream ID + filename: Name of the file + content_type: MIME type + expires_in: URL expiration time in seconds + + Returns: + Dict with url, method, headers, path, expires_in + """ + payload = { + "filename": filename, + "content_type": content_type, + "expires_in": expires_in, + } + return self._request("POST", f"/stream/{stream_id}/upload-url", json_data=payload) + + def stream_confirm_upload( + self, + stream_id: str, + path: str, + size_bytes: int, + checksum_md5: str = "", + metadata: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + """Confirm a direct upload completed. + + Call this after uploading directly to storage via the pre-signed URL. + + Args: + stream_id: The stream ID + path: The path returned from get_upload_url + size_bytes: File size in bytes + checksum_md5: MD5 checksum of the file + metadata: Optional custom metadata + """ + payload: Dict[str, Any] = { + "path": path, + "size_bytes": size_bytes, + "checksum_md5": checksum_md5, + } + if metadata: + payload["metadata"] = metadata + return self._request("POST", f"/stream/{stream_id}/upload-confirm", json_data=payload) + + def stream_get_download_url( + self, + stream_id: str, + path: str, + ) -> Dict[str, Any]: + """Get a download URL for a file. + + Args: + stream_id: The stream ID + path: Full path to the file + + Returns: + Dict with download_url + """ + return self._request("POST", f"/stream/{stream_id}/download-url", json_data={"path": path}) + + def versions(self, source_id: str) -> Dict[str, Any]: + """Get version history for a dataset.""" + return self._request("GET", f"/versions/{source_id}") + + def get_card(self, source_id: str, version: Optional[str] = None) -> Dict[str, Any]: + """Get a dataset preview card.""" + params = {"version": version} if version else None + return self._request("GET", f"/card/{source_id}", params=params) + + def get_citation( + self, + source_id: str, + format: str = "all", + version: Optional[str] = None, + ) -> Dict[str, Any]: + """Get citation for a dataset. + + Args: + source_id: Dataset identifier + format: Citation format (bibtex, ris, apa, datacite, all) + version: Optional version + """ + params = {"format": format} + if version: + params["version"] = version + return self._request("GET", f"/citation/{source_id}", params=params) + + def search( + self, + query: str, + search_type: str = "all", + limit: int = 20, + ) -> Dict[str, Any]: + """Search datasets and streams. + + Args: + query: Search query string + search_type: "all", "datasets", or "streams" + limit: Max results to return + """ + params = {"q": query, "type": search_type, "limit": str(limit)} + return self._request("GET", "/search", params=params) + + def stream_preview( + self, + stream_id: str, + filename: Optional[str] = None, + ) -> Dict[str, Any]: + """Get preview of files in a stream. + + Args: + stream_id: The stream ID + filename: Optional specific file to preview + + Returns: + Dict with preview data (columns, rows, structure, etc.) + """ + if filename: + return self._request("GET", f"/stream/{stream_id}/files/{filename}/preview") + return self._request("GET", f"/stream/{stream_id}/preview") + + def dataset_preview(self, source_id: str) -> Dict[str, Any]: + return self._request("GET", f"/preview/{source_id}") + + def dataset_files(self, source_id: str) -> Dict[str, Any]: + return self._request("GET", f"/preview/{source_id}/files") + + def dataset_file_detail(self, source_id: str, path: str) -> Dict[str, Any]: + encoded_path = quote(path, safe="/") + return self._request("GET", f"/preview/{source_id}/files/{encoded_path}") + + def dataset_sample(self, source_id: str) -> Dict[str, Any]: + return self._request("GET", f"/preview/{source_id}/sample") + + def stream_clone( + self, + stream_id: str, + dest_dir: str = ".", + file_filter: Optional[str] = None, + ) -> Dict[str, Any]: + """Clone a stream's files to local directory. + + This downloads all files from the stream (from Globus or other storage) + to a local directory. + + Args: + stream_id: The stream ID to clone + dest_dir: Destination directory + file_filter: Optional glob pattern to filter files (e.g., "*.csv") + + Returns: + Dict with clone results + """ + import fnmatch + + files_result = self.stream_list_files(stream_id) + if not files_result.get("success"): + return { + "success": False, + "stream_id": stream_id, + "error": files_result.get("error", "Failed to list stream files"), + } + + destination = Path(dest_dir).expanduser().resolve() + destination.mkdir(parents=True, exist_ok=True) + + downloaded = [] + errors = [] + + for file_info in files_result.get("files", []): + path = str(file_info.get("path") or "") + filename = str(file_info.get("filename") or Path(path).name) + if not path: + errors.append({"file": filename or "", "error": "Missing file path"}) + continue + + if file_filter and not ( + fnmatch.fnmatch(filename, file_filter) or fnmatch.fnmatch(path, file_filter) + ): + continue + + download_url_result = self.stream_get_download_url(stream_id, path) + if not download_url_result.get("success"): + errors.append({ + "file": filename, + "error": download_url_result.get("error", "Failed to get download URL"), + }) + continue + + download_url = str(download_url_result.get("download_url") or "") + if not download_url: + errors.append({"file": filename, "error": "No download URL returned"}) + continue + + try: + content = self._download_stream_file(download_url, file_info) + except Exception as exc: + errors.append({"file": filename, "error": str(exc)}) + continue + + target_path = self._safe_output_path(destination, filename) + if target_path is None: + errors.append({"file": filename, "error": "Unsafe destination filename"}) + continue + + target_path.parent.mkdir(parents=True, exist_ok=True) + target_path.write_bytes(content) + downloaded.append({ + "filename": filename, + "path": str(target_path), + "size_bytes": len(content), + }) + + return { + "success": len(errors) == 0, + "stream_id": stream_id, + "destination": str(destination), + "downloaded": len(downloaded), + "files": downloaded, + "errors": errors if errors else None, + } + + def _safe_output_path(self, destination: Path, filename: str) -> Optional[Path]: + normalized = (filename or "").replace("\\", "/").lstrip("/") + if not normalized: + return None + candidate = (destination / normalized).resolve() + try: + candidate.relative_to(destination) + except ValueError: + return None + return candidate + + def _download_stream_file(self, download_url: str, file_info: Dict[str, Any]) -> bytes: + parsed = urlparse(download_url) + if parsed.scheme == "file": + return Path(unquote(parsed.path)).read_bytes() + + headers: Optional[Dict[str, str]] = None + storage_backend = str(file_info.get("storage_backend") or "").lower() + host = (parsed.hostname or "").lower() + if storage_backend == "globus" or host.endswith("materialsdatafacility.org"): + token = self._globus_data_token or self._token + if token: + headers = {"Authorization": f"Bearer {token}"} + + response = self._client.get(download_url, headers=headers) + response.raise_for_status() + return response.content + + _RETRY_STATUSES = {429, 502, 503, 504} + _MAX_RETRIES = 3 + + def _request( + self, + method: str, + path: str, + json_data: Optional[Dict[str, Any]] = None, + params: Optional[Dict[str, Any]] = None, + ) -> Dict[str, Any]: + import time + + url = f"{self.base_url}{path}" + headers: Dict[str, str] = {} + if self._token: + headers["Authorization"] = f"Bearer {self._token}" + elif self._user_id: + headers["X-User-Id"] = self._user_id + if self._globus_data_token: + headers["X-Globus-Token"] = self._globus_data_token + + last_exc: Optional[Exception] = None + for attempt in range(self._MAX_RETRIES + 1): + try: + response = self._client.request(method, url, json=json_data, params=params, headers=headers) + except (httpx.ConnectError, httpx.ConnectTimeout) as exc: + last_exc = exc + if attempt < self._MAX_RETRIES: + time.sleep(2 ** attempt) + continue + return {"success": False, "error": f"Connection failed after {self._MAX_RETRIES + 1} attempts: {exc}"} + + if response.status_code in self._RETRY_STATUSES and attempt < self._MAX_RETRIES: + retry_after = response.headers.get("Retry-After") + if retry_after: + try: + delay = float(retry_after) + except ValueError: + delay = 2 ** attempt + else: + delay = 2 ** attempt + time.sleep(delay) + continue + + try: + payload = response.json() + except Exception: + return {"success": False, "error": f"Invalid response: {response.text}"} + + if isinstance(payload, dict) and "body" in payload and isinstance(payload["body"], str): + try: + return json.loads(payload["body"]) + except Exception: + return payload + return payload + + return {"success": False, "error": f"Request failed after {self._MAX_RETRIES + 1} attempts"} diff --git a/src/mdf_agent/core/config.py b/src/mdf_agent/core/config.py new file mode 100644 index 0000000..442e68c --- /dev/null +++ b/src/mdf_agent/core/config.py @@ -0,0 +1,128 @@ +"""Global configuration for MDF Agent CLI. + +Persists user defaults and last-publish state to ~/.config/mdf_agent/config.json. +""" + +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Optional + + +_CONFIG_DIR = Path.home() / ".config" / "mdf_agent" +_CONFIG_PATH = _CONFIG_DIR / "config.json" + + +class GlobalConfig: + """JSON-backed global config at ~/.config/mdf_agent/config.json.""" + + def __init__(self, path: Optional[Path] = None): + self._path = path or _CONFIG_PATH + self._data: dict = {} + self._load() + + def _load(self) -> None: + if self._path.exists(): + try: + self._data = json.loads(self._path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError): + self._data = {} + else: + self._data = {} + + def _save(self) -> None: + self._path.parent.mkdir(parents=True, exist_ok=True) + self._path.write_text(json.dumps(self._data, indent=2) + "\n", encoding="utf-8") + + # --- Typed properties --- + + @property + def service(self) -> Optional[str]: + return self.get("defaults.service") + + @property + def last_source_id(self) -> Optional[str]: + return self.get("last_publish.source_id") + + @property + def last_version(self) -> Optional[str]: + return self.get("last_publish.version") + + @property + def organization(self) -> Optional[str]: + return self.get("user.organization") + + @property + def publisher(self) -> Optional[str]: + return self.get("user.publisher") + + # --- Dotted key access --- + + def get(self, dotted_key: str, default: Any = None) -> Any: + keys = dotted_key.split(".") + node = self._data + for k in keys: + if not isinstance(node, dict) or k not in node: + return default + node = node[k] + return node + + def set(self, dotted_key: str, value: Any) -> None: + keys = dotted_key.split(".") + node = self._data + for k in keys[:-1]: + if k not in node or not isinstance(node[k], dict): + node[k] = {} + node = node[k] + node[keys[-1]] = value + self._save() + + # --- Convenience --- + + def record_publish(self, source_id: str, version: Optional[str], service: str) -> None: + self._data.setdefault("last_publish", {}) + self._data["last_publish"].update( + source_id=source_id, + version=version, + service=service, + timestamp=datetime.now(timezone.utc).isoformat(), + ) + self._save() + + @property + def path(self) -> Path: + return self._path + + @property + def data(self) -> dict: + return self._data + + +_VALID_SERVICES = {"prod", "staging", "dev", "local"} + + +def validate_config_value(key: str, value: str) -> Optional[str]: + """Validate a config value for known keys. + + Returns a warning message if the value is suspect, or None if valid. + Unknown keys pass through without validation. + """ + import re + + if key == "defaults.service": + if value.lower() not in _VALID_SERVICES: + return f"Unknown service '{value}'. Expected one of: {', '.join(sorted(_VALID_SERVICES))}" + elif key == "user.email": + if not re.match(r"^[^@\s]+@[^@\s]+\.[^@\s]+$", value): + return f"'{value}' doesn't look like a valid email address" + return None + + +def resolve_service(explicit: Optional[str]) -> str: + """Resolve the service instance: explicit flag > config default > 'staging'.""" + if explicit: + return explicit + cfg = GlobalConfig() + return cfg.service or "staging" diff --git a/src/mdf_agent/core/exceptions.py b/src/mdf_agent/core/exceptions.py new file mode 100644 index 0000000..3fc0652 --- /dev/null +++ b/src/mdf_agent/core/exceptions.py @@ -0,0 +1,15 @@ +"""Custom exceptions for MDF Agent.""" + + +class MDFError(Exception): + """Base exception for MDF Agent errors.""" + + pass + + +class NotARepositoryError(MDFError): + """Raised when a command requires an MDF repository but none exists.""" + + def __init__(self, path: str = "."): + self.path = path + super().__init__(f"Not an MDF repository (no mdf.yaml found in a git repo)") diff --git a/src/mdf_agent/core/manifest.py b/src/mdf_agent/core/manifest.py new file mode 100644 index 0000000..6f107df --- /dev/null +++ b/src/mdf_agent/core/manifest.py @@ -0,0 +1,136 @@ +"""Manifest file handling for mdf.yaml. + +This module provides functions for reading, writing, and initializing +mdf.yaml manifest files. The manifest is a YAML file that declares +dataset metadata in a human-readable format. + +Example mdf.yaml:: + + title: "My Dataset" + authors: + - name: "Jane Doe" + affiliations: ["MIT"] + - "John Smith" + description: "A test dataset" + data_sources: + - "./data" + - "globus://endpoint/path" + acl: ["public"] +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, List, Union + +import yaml + +from mdf_agent.models.config import Author, DataSource, ManifestConfig + + +def _normalize_authors(authors: List[Union[str, Dict[str, Any]]]) -> List[Union[str, Author]]: + normalized: List[Union[str, Author]] = [] + for author in authors: + if isinstance(author, Author) or isinstance(author, str): + normalized.append(author) + elif isinstance(author, dict): + normalized.append(Author(**author)) + else: + normalized.append(str(author)) + return normalized + + +def _normalize_data_sources( + data_sources: List[Union[str, Dict[str, Any]]] +) -> List[Union[str, DataSource]]: + normalized: List[Union[str, DataSource]] = [] + for source in data_sources: + if isinstance(source, DataSource) or isinstance(source, str): + normalized.append(source) + elif isinstance(source, dict): + normalized.append(DataSource(**source)) + else: + normalized.append(str(source)) + return normalized + + +def load_manifest(path: Path) -> ManifestConfig: + """Load a manifest from a YAML file. + + Parses the YAML file and normalizes authors and data_sources + into their proper Pydantic model types. + + Args: + path: Path to the mdf.yaml file. + + Returns: + ManifestConfig populated from the YAML file. + + Raises: + FileNotFoundError: If the file doesn't exist. + yaml.YAMLError: If the file is not valid YAML. + """ + data = yaml.safe_load(path.read_text(encoding="utf-8")) or {} + if "authors" in data and data["authors"] is not None: + authors = data["authors"] + if not isinstance(authors, list): + authors = [authors] + data["authors"] = _normalize_authors(authors) + if "data_sources" in data and data["data_sources"] is not None: + data_sources = data["data_sources"] + if not isinstance(data_sources, list): + data_sources = [data_sources] + data["data_sources"] = _normalize_data_sources(data_sources) + return ManifestConfig(**data) + + +def save_manifest(config: ManifestConfig, path: Path) -> None: + """Save a manifest to a YAML file. + + Serializes the ManifestConfig to YAML format, excluding None values + for cleaner output. + + Args: + config: The ManifestConfig to save. + path: Path where the YAML file will be written. + """ + data = config.model_dump(exclude_none=True) + path.write_text( + yaml.safe_dump(data, sort_keys=False, allow_unicode=False), + encoding="utf-8", + ) + + +def init_manifest( + path: Path, + title: str, + authors: List[str], + description: str | None = None, + publisher: str | None = None, + publication_year: int | str | None = None, +) -> ManifestConfig: + """Create and save a new manifest file. + + Creates a ManifestConfig with the provided metadata and saves it + to the specified path. + + Args: + path: Path where the mdf.yaml file will be created. + title: Dataset title. + authors: List of author names. + description: Optional dataset description. + publisher: Optional publisher name (defaults to MDF). + publication_year: Optional publication year. + + Returns: + The created ManifestConfig. + """ + config = ManifestConfig( + title=title, + authors=authors, + description=description, + publisher=publisher, + publication_year=publication_year, + ) + save_manifest(config, path) + return config diff --git a/src/mdf_agent/core/repository.py b/src/mdf_agent/core/repository.py new file mode 100644 index 0000000..eb5ca86 --- /dev/null +++ b/src/mdf_agent/core/repository.py @@ -0,0 +1,255 @@ +"""Repository management for git-backed MDF workflow. + +This module provides the Repository class which manages a git-backed +dataset directory for tracking staged files, commits, and metadata. + +The repository structure: + my_dataset/ + ├── mdf.yaml # Manifest configuration + ├── .gitignore # Ignores .mdf/ transient state + └── data/ # User's data files +""" + +from __future__ import annotations + +import subprocess +from pathlib import Path +from typing import Iterable, List, Optional + +from mdf_agent.core.exceptions import NotARepositoryError +from mdf_agent.core.manifest import init_manifest, load_manifest, save_manifest + + +def _run_git(args: List[str], cwd: Path, check: bool = True) -> subprocess.CompletedProcess: + """Run a git command and return the result.""" + return subprocess.run( + ["git"] + args, + cwd=str(cwd), + capture_output=True, + text=True, + check=check, + ) + + +class Repository: + """Manages a git-backed MDF repository. + + A repository tracks the state of a dataset directory using git, + including staging, commits, and metadata via mdf.yaml. + + Attributes: + root: The root directory of the repository. + manifest_file: Relative path to mdf.yaml. + """ + + manifest_file = Path("mdf.yaml") + + def __init__(self, root: Path) -> None: + self.root = root + + @classmethod + def init_repo( + cls, + root: Path, + title: str, + authors: List[str], + description: str | None = None, + publisher: str | None = None, + publication_year: int | str | None = None, + ) -> "Repository": + root.mkdir(parents=True, exist_ok=True) + + # Initialize git repo if not already one + if not (root / ".git").is_dir(): + _run_git(["init"], cwd=root) + + # Create mdf.yaml if it doesn't exist + manifest_path = root / cls.manifest_file + if not manifest_path.exists(): + init_manifest( + manifest_path, + title=title, + authors=authors, + description=description, + publisher=publisher, + publication_year=publication_year, + ) + + # Create .gitignore if it doesn't exist + gitignore_path = root / ".gitignore" + if not gitignore_path.exists(): + gitignore_path.write_text(".mdf/\n", encoding="utf-8") + + # Stage and commit initial files + files_to_add = ["mdf.yaml"] + if gitignore_path.exists(): + files_to_add.append(".gitignore") + _run_git(["add"] + files_to_add, cwd=root) + + # Only commit if there are staged changes + status = _run_git(["diff", "--cached", "--quiet"], cwd=root, check=False) + if status.returncode != 0: + _run_git(["commit", "-m", "Initialize MDF dataset"], cwd=root) + + return cls(root=root) + + @classmethod + def load(cls, root: Path) -> "Repository": + """Load an existing MDF repository. + + A valid MDF repository is a git repo containing mdf.yaml. + """ + manifest_path = root / cls.manifest_file + if not manifest_path.exists(): + raise NotARepositoryError(str(root)) + # Verify it's a git repo + result = _run_git(["rev-parse", "--git-dir"], cwd=root, check=False) + if result.returncode != 0: + raise NotARepositoryError(str(root)) + return cls(root=root) + + def load_manifest(self): + return load_manifest(self.root / self.manifest_file) + + def save_manifest(self, config) -> None: + save_manifest(config, self.root / self.manifest_file) + + def stage(self, paths: Iterable[str]) -> List[str]: + """Stage files for the next commit using git add. + + Args: + paths: File paths or glob patterns to stage. + + Returns: + List of resolved file paths that were staged. + + Raises: + FileNotFoundError: If no files match the provided paths. + """ + resolved: List[str] = [] + for pattern in paths: + matches = list(self.root.glob(pattern)) + if not matches: + candidate = self.root / pattern + if candidate.exists(): + matches = [candidate] + for match in matches: + resolved.append(str(match.relative_to(self.root))) + if not resolved: + raise FileNotFoundError("No files matched the provided paths") + + _run_git(["add"] + resolved, cwd=self.root) + return resolved + + def commit(self, message: str) -> dict: + """Commit staged changes using git commit. + + Args: + message: Commit message. + + Returns: + Dict with commit info: message, files, hash. + + Raises: + ValueError: If nothing is staged. + """ + # Check if there are staged changes + status = _run_git(["diff", "--cached", "--quiet"], cwd=self.root, check=False) + if status.returncode == 0: + raise ValueError("No staged files to commit") + + # Get list of staged files before committing + diff_result = _run_git( + ["diff", "--cached", "--name-only"], + cwd=self.root, + ) + staged_files = [f for f in diff_result.stdout.strip().split("\n") if f] + + _run_git(["commit", "-m", message], cwd=self.root) + + # Get the commit hash + log_result = _run_git(["rev-parse", "HEAD"], cwd=self.root) + commit_hash = log_result.stdout.strip() + + return { + "message": message, + "staged_files": staged_files, + "hash": commit_hash, + } + + def get_status(self) -> dict: + """Get repository status using git status. + + Returns: + Dict with staged, modified, untracked file lists and commit log. + """ + # Staged files + staged_result = _run_git( + ["diff", "--cached", "--name-only"], + cwd=self.root, + ) + staged = [f for f in staged_result.stdout.strip().split("\n") if f] + + # Modified (unstaged) + modified_result = _run_git( + ["diff", "--name-only"], + cwd=self.root, + ) + modified = [f for f in modified_result.stdout.strip().split("\n") if f] + + # Untracked files + untracked_result = _run_git( + ["ls-files", "--others", "--exclude-standard"], + cwd=self.root, + ) + untracked = [f for f in untracked_result.stdout.strip().split("\n") if f] + + # Recent commits + log_result = _run_git( + ["log", "--oneline", "-20", "--format=%H\t%s\t%aI"], + cwd=self.root, + check=False, + ) + commits = [] + if log_result.returncode == 0 and log_result.stdout.strip(): + for line in log_result.stdout.strip().split("\n"): + parts = line.split("\t", 2) + if len(parts) == 3: + commits.append({ + "hash": parts[0], + "message": parts[1], + "timestamp": parts[2], + }) + + return { + "staged_files": staged, + "modified_files": modified, + "untracked_files": untracked, + "commits": commits, + } + + def get_tracked_files(self) -> List[str]: + """Get all tracked files using git ls-files. + + Returns files tracked by git, excluding mdf.yaml and .gitignore. + """ + result = _run_git(["ls-files"], cwd=self.root) + all_files = [f for f in result.stdout.strip().split("\n") if f] + # Filter out MDF infrastructure files + return [f for f in all_files if f not in ("mdf.yaml", ".gitignore")] + + def has_commits(self) -> bool: + """Check if the repo has any commits.""" + result = _run_git(["rev-parse", "HEAD"], cwd=self.root, check=False) + return result.returncode == 0 + + def tag(self, tag_name: str) -> None: + """Create a git tag.""" + _run_git(["tag", tag_name], cwd=self.root) + + def get_tags(self) -> List[str]: + """List all git tags.""" + result = _run_git(["tag", "-l"], cwd=self.root, check=False) + if result.returncode != 0 or not result.stdout.strip(): + return [] + return result.stdout.strip().split("\n") diff --git a/src/mdf_agent/core/submission.py b/src/mdf_agent/core/submission.py new file mode 100644 index 0000000..081081c --- /dev/null +++ b/src/mdf_agent/core/submission.py @@ -0,0 +1,185 @@ +"""Submission building and publishing for MDF Connect. + +This module handles: +1. Building submission payloads from manifest configuration +2. Resolving data sources (local paths, globs, remote URLs) +3. Submitting payloads to MDF Connect API + +The submission payload uses the flat v2 metadata format. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional +from urllib.parse import urlparse, parse_qs, unquote + +from mdf_agent.auth.globus import NCSA_MDF_COLLECTION_UUID +from mdf_agent.models.config import DataSource, ManifestConfig +from mdf_agent.models.submission import Submission + + +def _expand_data_source( + source: DataSource, + root: Path, +) -> List[str]: + base = (root / source.path).resolve() + if not source.include: + return [str(base)] + + matches: List[Path] = [] + for pattern in source.include: + matches.extend(base.glob(pattern)) + + if source.exclude: + excluded: List[Path] = [] + for pattern in source.exclude: + excluded.extend(base.glob(pattern)) + matches = [match for match in matches if match not in excluded] + + return [str(match.resolve()) for match in matches] + + +def normalize_data_source(url: str) -> str: + """Normalize a data source URL to canonical format. + + Converts Globus File Manager URLs and MDF data URLs to ``globus://`` URIs. + Other URLs (``globus://``, ``stream://``, external ``https://``) pass through. + + Args: + url: Raw data source URL string. + + Returns: + Normalized URL string. + """ + parsed = urlparse(url) + + # Globus File Manager URL → globus://collection_uuid/path + if parsed.hostname == "app.globus.org" and "/file-manager" in parsed.path: + qs = parse_qs(parsed.query) + origin_id = qs.get("origin_id", [None])[0] + origin_path = qs.get("origin_path", ["/"])[0] + if origin_id: + return f"globus://{origin_id}{unquote(origin_path)}" + + # MDF data domain → globus://NCSA_MDF_COLLECTION_UUID/path + if parsed.hostname == "data.materialsdatafacility.org": + return f"globus://{NCSA_MDF_COLLECTION_UUID}{parsed.path}" + + return url + + +def resolve_data_sources( + data_sources: Iterable[str | DataSource], + root: Path, +) -> List[str]: + """Resolve data sources to absolute paths or URLs. + + Handles: + - String URLs (globus://, https://) - passed through unchanged + - String paths - resolved relative to root + - DataSource objects - expanded with include/exclude patterns + + Args: + data_sources: List of data source strings or DataSource objects. + root: Root directory for resolving relative paths. + + Returns: + List of resolved absolute paths or URLs. + """ + resolved: List[str] = [] + for source in data_sources: + if isinstance(source, DataSource): + resolved.extend(_expand_data_source(source, root)) + continue + if source.startswith(("globus://", "https://", "stream://")): + resolved.append(normalize_data_source(source)) + else: + resolved.append(str((root / source).resolve())) + return resolved + + +def build_submission( + manifest: ManifestConfig, + root: Path, + test: bool = False, + update: bool = False, +) -> Dict[str, Any]: + """Build a submission payload from manifest configuration. + + Constructs the complete flat v2 JSON payload for the MDF Connect API. + + Args: + manifest: The ManifestConfig containing dataset metadata. + root: Root directory for resolving data source paths. + test: If True, submit to test/sandbox environment. + update: If True, this is an update to an existing dataset. + + Returns: + Dict containing the complete submission payload. + + Raises: + ValueError: If manifest is missing required fields (title, authors). + json.JSONDecodeError: If payload contains invalid JSON (NaN, Infinity). + """ + if not manifest.title or not manifest.authors: + raise ValueError("Manifest requires 'title' and 'authors'") + + # Build the flat metadata payload from the manifest + metadata = manifest.to_metadata_payload() + + data_sources = resolve_data_sources(manifest.data_sources, root) + + # Auto-populate data_sources from git-tracked files if empty + if not data_sources and root: + from mdf_agent.core.repository import Repository + try: + repo = Repository.load(root) + tracked = repo.get_tracked_files() + if tracked: + data_sources = [ + str((root / f).resolve()) for f in tracked + ] + except Exception: + pass + + submission = Submission( + title=metadata.get("title"), + authors=metadata.get("authors"), + description=metadata.get("description"), + keywords=metadata.get("keywords", []), + publisher=metadata.get("publisher", "Materials Data Facility"), + publication_year=metadata.get("publication_year"), + resource_type=metadata.get("resource_type", "Dataset"), + data_sources=data_sources, + test=test, + update=update, + organization=metadata.get("organization"), + tags=metadata.get("tags"), + domains=metadata.get("domains"), + acl=metadata.get("acl"), + related_works=metadata.get("related_works"), + external_doi=metadata.get("external_doi"), + external_url=metadata.get("external_url"), + external_source=metadata.get("external_source"), + extensions=metadata.get("extensions"), + ml=metadata.get("ml"), + # Legacy fields that still need to be passed through + mrr=manifest.mrr, + data_destinations=manifest.data_destinations, + external_uri=manifest.external_uri, + index=manifest.index, + extraction_config=manifest.extraction_config, + services=manifest.services, + links=manifest.links, + no_extract=manifest.no_extract, + dataset_acl=manifest.dataset_acl, + update_metadata_only=bool(manifest.update_metadata_only), + ) + + payload = submission.to_payload() + json.dumps(payload, allow_nan=False) + return payload + + diff --git a/src/mdf_agent/core/utils.py b/src/mdf_agent/core/utils.py new file mode 100644 index 0000000..29db34d --- /dev/null +++ b/src/mdf_agent/core/utils.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from typing import Any, Dict + + +def deep_merge(base: Dict[str, Any], overlay: Dict[str, Any]) -> Dict[str, Any]: + """Recursively merge overlay into base (overlay wins).""" + merged: Dict[str, Any] = dict(base) + for key, value in overlay.items(): + if ( + key in merged + and isinstance(merged[key], dict) + and isinstance(value, dict) + ): + merged[key] = deep_merge(merged[key], value) + else: + merged[key] = value + return merged diff --git a/src/mdf_agent/core/validation.py b/src/mdf_agent/core/validation.py new file mode 100644 index 0000000..ab077fd --- /dev/null +++ b/src/mdf_agent/core/validation.py @@ -0,0 +1,62 @@ +"""Manifest validation for MDF Agent. + +This module provides validation functions to check manifest configurations +before submission. Validation catches common errors early, before the +submission is sent to MDF Connect. + +Validation returns two lists: +- errors: Issues that will prevent submission +- warnings: Issues that may cause problems but won't prevent submission +""" + +from __future__ import annotations + +from typing import List, Tuple + +from mdf_agent.models.config import ManifestConfig + + +def validate_manifest( + manifest: ManifestConfig, + has_committed_files: bool = False, +) -> Tuple[List[str], List[str]]: + """Validate a manifest configuration. + + Checks for required fields and common issues. Returns errors (blocking) + and warnings (non-blocking). + + Args: + manifest: The ManifestConfig to validate. + has_committed_files: If True, data_sources will be auto-populated + from committed files at build time, so skip that check. + + Returns: + Tuple of (errors, warnings) where each is a list of message strings. + + Examples: + >>> manifest = ManifestConfig(title="Test") + >>> errors, warnings = validate_manifest(manifest) + >>> if errors: + ... print("Cannot submit:", errors) + """ + errors: List[str] = [] + warnings: List[str] = [] + + if not manifest.title: + errors.append("Missing required field: title") + if not manifest.authors: + errors.append("Missing required field: authors") + + if not manifest.update_metadata_only and not manifest.data_sources and not has_committed_files: + errors.append("Missing data_sources (or set update_metadata_only)") + + if manifest.publication_year is not None: + try: + int(manifest.publication_year) + except (TypeError, ValueError): + warnings.append("publication_year should be an integer year") + + if manifest.dataset_doi and not str(manifest.dataset_doi).startswith("10."): + warnings.append("dataset_doi does not look like a DOI") + + return errors, warnings diff --git a/src/mdf_agent/extractors/__init__.py b/src/mdf_agent/extractors/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/extractors/base.py b/src/mdf_agent/extractors/base.py new file mode 100644 index 0000000..772f358 --- /dev/null +++ b/src/mdf_agent/extractors/base.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Dict, List + + +class BaseExtractor: + extensions: List[str] = [] + + @classmethod + def can_extract(cls, path: Path) -> bool: + return path.suffix.lower() in cls.extensions + + @classmethod + def extract(cls, path: Path) -> Dict: + raise NotImplementedError diff --git a/src/mdf_agent/extractors/json_yaml.py b/src/mdf_agent/extractors/json_yaml.py new file mode 100644 index 0000000..efd503c --- /dev/null +++ b/src/mdf_agent/extractors/json_yaml.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Dict + +import yaml + +from mdf_agent.extractors.base import BaseExtractor + + +class JsonYamlExtractor(BaseExtractor): + extensions = [".json", ".yml", ".yaml"] + + @classmethod + def extract(cls, path: Path) -> Dict: + data = cls._load(path) + if data is None: + return {} + + schema = cls._infer_schema(data) + return {"mdf": {"json_schema": {"file": path.name, "schema": schema}}} + + @staticmethod + def _load(path: Path) -> Any: + try: + content = path.read_text(encoding="utf-8") + except Exception: + return None + + try: + if path.suffix.lower() == ".json": + return json.loads(content) + return yaml.safe_load(content) + except Exception: + return None + + @classmethod + def _infer_schema(cls, data: Any) -> Any: + if isinstance(data, dict): + return {key: cls._infer_schema(value) for key, value in data.items()} + if isinstance(data, list): + if not data: + return [] + return [cls._infer_schema(data[0])] + return type(data).__name__ diff --git a/src/mdf_agent/extractors/pdf.py b/src/mdf_agent/extractors/pdf.py new file mode 100644 index 0000000..a7943a7 --- /dev/null +++ b/src/mdf_agent/extractors/pdf.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import re +from pathlib import Path +from typing import Dict, List + +from mdf_agent.extractors.base import BaseExtractor + +DOI_PATTERN = re.compile(r"10\.\d{4,9}/[^\s\"<>]+") + + +class PDFExtractor(BaseExtractor): + extensions = [".pdf"] + + @classmethod + def extract(cls, path: Path) -> Dict: + try: + from pypdf import PdfReader + except Exception: + return {} + + try: + reader = PdfReader(str(path)) + except Exception: + return {} + + metadata = reader.metadata or {} + title = getattr(metadata, "title", None) or metadata.get("/Title") + author = getattr(metadata, "author", None) or metadata.get("/Author") + + doi = None + try: + first_page = reader.pages[0].extract_text() or "" + match = DOI_PATTERN.search(first_page) + if match: + doi = match.group(0) + except Exception: + doi = None + + dc: Dict = {} + if title: + dc["titles"] = [{"title": title}] + if author: + creators = [] + for name in re.split(r";|,\s*and\s*", author): + clean = name.strip() + if clean: + creators.append({"creatorName": clean}) + if creators: + dc["creators"] = creators + if doi: + dc["relatedIdentifiers"] = [ + { + "relatedIdentifier": doi, + "relatedIdentifierType": "DOI", + "relationType": "IsPartOf", + } + ] + + if not dc: + return {} + return {"dc": dc} diff --git a/src/mdf_agent/extractors/registry.py b/src/mdf_agent/extractors/registry.py new file mode 100644 index 0000000..7a790a6 --- /dev/null +++ b/src/mdf_agent/extractors/registry.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Dict, Iterable + +from mdf_agent.core.utils import deep_merge +from mdf_agent.extractors.json_yaml import JsonYamlExtractor +from mdf_agent.extractors.pdf import PDFExtractor +from mdf_agent.extractors.tabular import TabularExtractor + +EXTRACTORS = [PDFExtractor, TabularExtractor, JsonYamlExtractor] + + +def discover_metadata(paths: Iterable[str]) -> Dict: + aggregated: Dict = {} + for path_str in paths: + path = Path(path_str) + for extractor in EXTRACTORS: + if extractor.can_extract(path): + extracted = extractor.extract(path) + if extracted: + aggregated = deep_merge(aggregated, extracted) + break + return aggregated diff --git a/src/mdf_agent/extractors/tabular.py b/src/mdf_agent/extractors/tabular.py new file mode 100644 index 0000000..9ea6023 --- /dev/null +++ b/src/mdf_agent/extractors/tabular.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Dict, List + +from mdf_agent.extractors.base import BaseExtractor + + +class TabularExtractor(BaseExtractor): + extensions = [".csv", ".tsv", ".xlsx", ".xls"] + + @classmethod + def extract(cls, path: Path) -> Dict: + if path.suffix.lower() in {".xlsx", ".xls"}: + return cls._extract_excel(path) + return cls._extract_csv(path) + + @staticmethod + def _extract_excel(path: Path) -> Dict: + try: + import pandas as pd + except Exception: + return {} + + try: + df = pd.read_excel(path) + except Exception: + return {} + + return { + "mdf": { + "table_schema": { + "file": path.name, + "columns": [{"name": col, "dtype": str(dtype)} for col, dtype in df.dtypes.items()], + "row_count": int(df.shape[0]), + } + } + } + + @staticmethod + def _extract_csv(path: Path) -> Dict: + try: + with path.open("r", encoding="utf-8") as handle: + reader = csv.reader(handle) + headers = next(reader, []) + row_count = sum(1 for _ in reader) + except Exception: + return {} + + columns: List[Dict[str, str]] = [{"name": header} for header in headers if header] + return { + "mdf": { + "table_schema": { + "file": path.name, + "columns": columns, + "row_count": row_count, + } + } + } diff --git a/src/mdf_agent/models/__init__.py b/src/mdf_agent/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/models/config.py b/src/mdf_agent/models/config.py new file mode 100644 index 0000000..6cd1779 --- /dev/null +++ b/src/mdf_agent/models/config.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Union + +from pydantic import BaseModel, ConfigDict, Field + + +class Author(BaseModel): + name: str + given_name: Optional[str] = None + family_name: Optional[str] = None + affiliations: List[str] = Field(default_factory=list) + orcid: Optional[str] = None + + model_config = ConfigDict(extra="allow") + + +class DataSource(BaseModel): + path: str + include: List[str] = Field(default_factory=list) + exclude: List[str] = Field(default_factory=list) + + model_config = ConfigDict(extra="allow") + + +class DerivedFrom(BaseModel): + source_id: str + relationship: Optional[str] = None + description: Optional[str] = None + + model_config = ConfigDict(extra="allow") + + +class ManifestConfig(BaseModel): + title: Optional[Union[str, List[str]]] = None + authors: Optional[List[Union[str, Author]]] = None + description: Optional[str] = None + publisher: Optional[str] = None + publication_year: Optional[Union[int, str]] = None + resource_type: Optional[str] = None + + dataset_doi: Optional[str] = None + related_dois: Optional[List[str]] = None + subjects: Optional[List[str]] = None + + data_sources: List[Union[str, DataSource]] = Field(default_factory=list) + + organization: Optional[str] = None + acl: Optional[List[str]] = None + tags: Optional[List[str]] = None + domains: Optional[List[str]] = None + + external_doi: Optional[str] = None + external_url: Optional[str] = None + external_source: Optional[str] = None + + services: Optional[Dict[str, Any]] = None + projects: Optional[Dict[str, Any]] = None + links: Optional[List[Dict[str, Any]]] = None + index: Optional[Dict[str, Any]] = None + + mdf: Optional[Dict[str, Any]] = None + mrr: Optional[Dict[str, Any]] = None + custom: Optional[Dict[str, Any]] = None + + dataset_acl: Optional[List[str]] = None + data_destinations: Optional[List[str]] = None + external_uri: Optional[str] = None + no_extract: Optional[bool] = None + extraction_config: Optional[Dict[str, Any]] = None + update_metadata_only: Optional[bool] = None + + auto_discover: Optional[bool] = None + derived_from: Optional[List[DerivedFrom]] = None + + dc: Optional[Dict[str, Any]] = None + auto_metadata: Optional[Dict[str, Any]] = None + + model_config = ConfigDict(extra="allow") + + def to_metadata_payload(self) -> Dict[str, Any]: + """Convert ManifestConfig to the flat v2 metadata API format. + + Maps researcher-friendly manifest fields to the DatasetMetadata schema. + """ + payload: Dict[str, Any] = {} + + # Title + if self.title: + payload["title"] = self.title[0] if isinstance(self.title, list) else self.title + + # Authors + if self.authors: + authors = [] + for a in self.authors: + if isinstance(a, Author): + entry: Dict[str, Any] = {"name": a.name} + if a.given_name: + entry["given_name"] = a.given_name + if a.family_name: + entry["family_name"] = a.family_name + if a.affiliations: + entry["affiliations"] = a.affiliations + if a.orcid: + entry["orcid"] = a.orcid + authors.append(entry) + else: + authors.append({"name": a}) + payload["authors"] = authors + + if self.description: + payload["description"] = self.description + if self.publisher: + payload["publisher"] = self.publisher + if self.publication_year: + try: + payload["publication_year"] = int(self.publication_year) + except (ValueError, TypeError): + pass + if self.resource_type: + payload["resource_type"] = self.resource_type + + # Keywords (from subjects) + if self.subjects: + payload["keywords"] = self.subjects + + # Organization + if self.organization: + payload["organization"] = self.organization + if self.acl: + payload["acl"] = self.acl + if self.tags: + payload["tags"] = self.tags + if self.domains: + payload["domains"] = self.domains + + # External import provenance + if self.external_doi: + payload["external_doi"] = self.external_doi + if self.external_url: + payload["external_url"] = self.external_url + if self.external_source: + payload["external_source"] = self.external_source + + # Related works (from related_dois) + if self.related_dois: + payload["related_works"] = [ + {"identifier": doi, "identifier_type": "DOI", "relation_type": "References"} + for doi in self.related_dois + ] + + # Extensions (from custom, projects minus foundry) + extensions: Dict[str, Any] = {} + if self.custom: + extensions.update(self.custom) + if self.projects: + for k, v in self.projects.items(): + if k != "foundry": + extensions[k] = v + if extensions: + payload["extensions"] = extensions + + return payload diff --git a/src/mdf_agent/models/datacite.py b/src/mdf_agent/models/datacite.py new file mode 100644 index 0000000..59cf3f6 --- /dev/null +++ b/src/mdf_agent/models/datacite.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +from datetime import datetime +from typing import Any, Dict, List, Optional + +from nameparser import HumanName +from pydantic import BaseModel, ConfigDict, Field + +from mdf_agent.core.utils import deep_merge +from mdf_agent.models.config import Author, ManifestConfig + + +class Title(BaseModel): + title: str + + +class Creator(BaseModel): + creatorName: str + familyName: Optional[str] = None + givenName: Optional[str] = None + affiliations: Optional[List[str]] = None + nameIdentifiers: Optional[List[Dict[str, Any]]] = None + + model_config = ConfigDict(extra="allow") + + +class Description(BaseModel): + description: str + descriptionType: str = "Other" + + +class Identifier(BaseModel): + identifier: str + identifierType: str = "DOI" + + +class RelatedIdentifier(BaseModel): + relatedIdentifier: str + relatedIdentifierType: str = "DOI" + relationType: str = "IsPartOf" + + +class Subject(BaseModel): + subject: str + + +class ResourceType(BaseModel): + resourceTypeGeneral: str = "Dataset" + resourceType: str + + +class DataCite(BaseModel): + titles: List[Title] + creators: List[Creator] + publisher: str + publicationYear: str = Field(alias="publicationYear") + resourceType: ResourceType + descriptions: Optional[List[Description]] = None + identifier: Optional[Identifier] = None + relatedIdentifiers: Optional[List[RelatedIdentifier]] = None + subjects: Optional[List[Subject]] = None + + model_config = ConfigDict(extra="allow", populate_by_name=True) + + @staticmethod + def _build_creator(author: Author | str, fallback_affiliations: List[str]) -> Creator: + if isinstance(author, Author): + name = author.name + affiliations = author.affiliations or [] + orcid = author.orcid + else: + name = author + affiliations = fallback_affiliations + orcid = None + + parsed = HumanName(name) + given = "{} {}".format(parsed.first, parsed.middle).strip() + family = "{} {}".format(parsed.last, parsed.suffix).strip() + creator_name = "{}, {}".format(family, given).strip(" ,") + if not creator_name: + creator_name = name + + creator = Creator( + creatorName=creator_name, + familyName=family or None, + givenName=given or None, + affiliations=affiliations or None, + ) + if orcid: + creator.nameIdentifiers = [ + { + "nameIdentifier": orcid, + "nameIdentifierScheme": "ORCID", + "schemeUri": "https://orcid.org", + } + ] + return creator + + @classmethod + def from_manifest(cls, manifest: ManifestConfig) -> "DataCite": + if not manifest.title or not manifest.authors: + raise ValueError("Manifest requires 'title' and 'authors' to build DataCite") + + titles_raw = manifest.title if isinstance(manifest.title, list) else [manifest.title] + titles = [Title(title=title) for title in titles_raw if title] + + authors_raw = manifest.authors + if not isinstance(authors_raw, list): + authors_raw = [authors_raw] + + fallback_affiliations: List[str] = [] + creators = [cls._build_creator(author, fallback_affiliations) for author in authors_raw] + + publisher = manifest.publisher or "Materials Data Facility" + try: + publication_year = str(int(manifest.publication_year)) + except (TypeError, ValueError): + publication_year = str(datetime.now().year) + + resource_type = ResourceType(resourceType=manifest.resource_type or "Dataset") + + data = { + "titles": titles, + "creators": creators, + "publisher": publisher, + "publicationYear": publication_year, + "resourceType": resource_type, + } + + if manifest.description: + data["descriptions"] = [Description(description=manifest.description)] + + if manifest.dataset_doi: + data["identifier"] = Identifier(identifier=manifest.dataset_doi) + + if manifest.related_dois: + data["relatedIdentifiers"] = [ + RelatedIdentifier(relatedIdentifier=doi) for doi in manifest.related_dois + ] + + if manifest.subjects: + data["subjects"] = [Subject(subject=sub) for sub in manifest.subjects] + + datacite = cls(**data) + + if manifest.dc: + merged = deep_merge(datacite.model_dump(by_alias=True), manifest.dc) + return cls.model_validate(merged) + + return datacite + + def to_dict(self) -> Dict[str, Any]: + return self.model_dump(by_alias=True, exclude_none=True) diff --git a/src/mdf_agent/models/state.py b/src/mdf_agent/models/state.py new file mode 100644 index 0000000..d3f9ac2 --- /dev/null +++ b/src/mdf_agent/models/state.py @@ -0,0 +1,5 @@ +# This module previously contained Commit and RepositoryState models +# for the custom .mdf/state.json tracking system. +# +# These have been replaced by git-backed repository operations. +# See mdf_agent.core.repository for the new implementation. diff --git a/src/mdf_agent/models/submission.py b/src/mdf_agent/models/submission.py new file mode 100644 index 0000000..7eeab38 --- /dev/null +++ b/src/mdf_agent/models/submission.py @@ -0,0 +1,157 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from pydantic import BaseModel, ConfigDict, Field + + +class Submission(BaseModel): + # v2 flat metadata fields + title: Optional[str] = None + authors: Optional[List[Dict[str, Any]]] = None + description: Optional[str] = None + keywords: List[str] = Field(default_factory=list) + publisher: str = "Materials Data Facility" + publication_year: Optional[int] = None + resource_type: str = "Dataset" + + data_sources: List[str] = Field(default_factory=list) + test: bool = False + update: bool = False + + organization: Optional[str] = None + tags: Optional[List[str]] = None + acl: Optional[List[str]] = None + + license: Optional[Dict[str, Any]] = None + funding: Optional[List[Dict[str, Any]]] = None + related_works: Optional[List[Dict[str, Any]]] = None + + methods: Optional[List[str]] = None + facility: Optional[str] = None + fields_of_science: Optional[List[str]] = None + domains: Optional[List[str]] = None + + external_doi: Optional[str] = None + external_url: Optional[str] = None + external_source: Optional[str] = None + + ml: Optional[Dict[str, Any]] = None + + extensions: Optional[Dict[str, Any]] = None + + # Legacy fields (still supported for backward compat with old clients) + dc: Optional[Dict[str, Any]] = None + mdf: Optional[Dict[str, Any]] = None + mrr: Optional[Dict[str, Any]] = None + custom: Optional[Dict[str, Any]] = None + projects: Optional[Dict[str, Any]] = None + data_destinations: Optional[List[str]] = None + external_uri: Optional[str] = None + index: Optional[Dict[str, Any]] = None + extraction_config: Optional[Dict[str, Any]] = None + services: Optional[Dict[str, Any]] = None + links: Optional[List[Dict[str, Any]]] = None + curation: Optional[bool] = None + no_extract: Optional[bool] = None + dataset_acl: Optional[List[str]] = None + update_metadata_only: bool = False + + model_config = ConfigDict(extra="allow") + + def to_payload(self) -> Dict[str, Any]: + """Build the flat v2 payload for submission.""" + payload: Dict[str, Any] = {} + + # Required fields + if self.title: + payload["title"] = self.title + if self.authors: + payload["authors"] = self.authors + + # Recommended + if self.description: + payload["description"] = self.description + if self.keywords: + payload["keywords"] = self.keywords + payload["publisher"] = self.publisher + if self.publication_year: + payload["publication_year"] = self.publication_year + payload["resource_type"] = self.resource_type + + # Data + payload["data_sources"] = self.data_sources + payload["test"] = self.test + payload["update"] = self.update + + # Platform + if self.organization: + payload["organization"] = self.organization + if self.tags: + payload["tags"] = self.tags + if self.acl: + payload["acl"] = self.acl + + # Attribution + if self.license: + payload["license"] = self.license + if self.funding: + payload["funding"] = self.funding + if self.related_works: + payload["related_works"] = self.related_works + + # Scientific context + if self.methods: + payload["methods"] = self.methods + if self.facility: + payload["facility"] = self.facility + if self.fields_of_science: + payload["fields_of_science"] = self.fields_of_science + if self.domains: + payload["domains"] = self.domains + + # External import provenance + if self.external_doi: + payload["external_doi"] = self.external_doi + if self.external_url: + payload["external_url"] = self.external_url + if self.external_source: + payload["external_source"] = self.external_source + + # ML metadata + if self.ml: + payload["ml"] = self.ml + + # Extensions + if self.extensions: + payload["extensions"] = self.extensions + + # If legacy dc/mdf fields are set (old-style client), pass them through + # so the server can auto-migrate + if self.dc: + payload["dc"] = self.dc + if self.mdf: + payload["mdf"] = self.mdf + + # Legacy optional fields + optional_legacy = { + "mrr": self.mrr, + "custom": self.custom, + "projects": self.projects, + "data_destinations": self.data_destinations, + "external_uri": self.external_uri, + "index": self.index, + "extraction_config": self.extraction_config, + "services": self.services, + "links": self.links, + "curation": self.curation, + "no_extract": self.no_extract, + "dataset_acl": self.dataset_acl, + } + for key, value in optional_legacy.items(): + if value not in (None, {}, [], ""): + payload[key] = value + + payload["update_metadata_only"] = self.update_metadata_only + + return payload diff --git a/src/mdf_agent/skill/__init__.py b/src/mdf_agent/skill/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/mdf_agent/skill/handlers.py b/src/mdf_agent/skill/handlers.py new file mode 100644 index 0000000..c7e2cd5 --- /dev/null +++ b/src/mdf_agent/skill/handlers.py @@ -0,0 +1,478 @@ +from __future__ import annotations + +import functools +from pathlib import Path +from typing import Any, Dict, List, Optional + +from mdf_agent.core.agent import MDFAgent +from mdf_agent.core.backend_client import BackendClient +from mdf_agent.extractors.registry import discover_metadata + + +def agent_safe(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + try: + result = func(*args, **kwargs) + if isinstance(result, dict) and "success" not in result: + result["success"] = True + return result + except Exception as exc: + return { + "success": False, + "error": str(exc), + "error_type": type(exc).__name__, + } + + return wrapper + + +def _run_backend_action( + action: str, + *, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, + **kwargs, +) -> Dict[str, Any]: + client = BackendClient.authenticated( + base_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + try: + method = getattr(client, action) + return method(**kwargs) + finally: + client.close() + + +@agent_safe +def scan_folder(path: str) -> Dict[str, Any]: + root = Path(path) + files = [str(p) for p in root.rglob("*") if p.is_file()] + return discover_metadata(files) + + +@agent_safe +def create_manifest( + path: str, + title: str, + authors: List[str], + description: Optional[str] = None, +) -> Dict[str, Any]: + agent = MDFAgent.init(path=path, title=title, authors=authors, description=description) + return agent.manifest.model_dump() + + +@agent_safe +def suggest_mappings(headers: List[str]) -> Dict[str, str]: + suggestions: Dict[str, str] = {} + for header in headers: + key = header.lower() + if "temp" in key: + suggestions[header] = "measurement.temperature" + elif "energy" in key: + suggestions[header] = "dft.formation_energy" + elif "comp" in key: + suggestions[header] = "material.composition" + return suggestions + + +@agent_safe +def validate_and_preview(path: str) -> Dict[str, Any]: + agent = MDFAgent.from_repo(path) + validation = agent.validate() + submission = agent.build_submission() + return {"validation": validation, "submission": submission} + + +@agent_safe +def publish( + path: str, + test: bool = False, + update: bool = False, + submit: bool = False, + token: Optional[str] = None, + client_id: Optional[str] = None, + scope: Optional[str] = None, + service_instance: str = "prod", + api_url: Optional[str] = None, + dev_user_id: Optional[str] = None, +) -> Dict[str, Any]: + # client_id/scope are retained for backward compatibility with existing skill callers. + _ = (client_id, scope) + agent = MDFAgent.from_repo(path) + return agent.publish( + test=test, + update=update, + dry_run=not submit, + token=token, + service_instance=service_instance, + api_url=api_url, + dev_user_id=dev_user_id, + ) + + +@agent_safe +def stream_create( + title: str, + lab_id: str | None = None, + organization: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "stream_create", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + title=title, + lab_id=lab_id, + organization=organization, + ) + + +@agent_safe +def stream_append( + stream_id: str, + files: list | None = None, + file_count: int | None = None, + total_bytes: int | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "stream_append", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + stream_id=stream_id, + files=files, + file_count=file_count, + total_bytes=total_bytes, + ) + + +@agent_safe +def stream_status( + stream_id: str, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "stream_status", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + stream_id=stream_id, + ) + + +@agent_safe +def stream_close( + stream_id: str, + mint_doi: bool | None = None, + title: str | None = None, + description: str | None = None, + authors: list | None = None, + keywords: list | None = None, + license: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "stream_close", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + stream_id=stream_id, + mint_doi=mint_doi, + title=title, + description=description, + authors=authors, + keywords=keywords, + license=license, + ) + + +@agent_safe +def stream_snapshot( + stream_id: str, + title: str | None = None, + update: bool = False, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "stream_snapshot", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + stream_id=stream_id, + title=title, + update=update, + ) + + +@agent_safe +def curation_list_pending( + limit: int = 50, + offset: int = 0, + organization: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "curation_pending", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + limit=limit, + offset=offset, + organization=organization, + ) + + +@agent_safe +def curation_review( + source_id: str, + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "curation_detail", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + version=version, + ) + + +@agent_safe +def curation_approve( + source_id: str, + mint_doi: bool = True, + notes: str | None = None, + metadata_updates: dict | None = None, + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "curation_approve", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + mint_doi=mint_doi, + notes=notes, + metadata_updates=metadata_updates, + version=version, + ) + + +@agent_safe +def curation_reject( + source_id: str, + reason: str, + suggestions: str | None = None, + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "curation_reject", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + reason=reason, + suggestions=suggestions, + version=version, + ) + + +@agent_safe +def check_status( + source_id: str, + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "status", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + version=version, + ) + + +@agent_safe +def list_submissions( + organization: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "submissions", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + organization=organization, + ) + + +@agent_safe +def search_datasets( + query: str, + search_type: str = "all", + limit: int = 20, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "search", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + query=query, + search_type=search_type, + limit=limit, + ) + + +@agent_safe +def get_citation( + source_id: str, + format: str = "all", + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "get_citation", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + format=format, + version=version, + ) + + +@agent_safe +def get_card( + source_id: str, + version: str | None = None, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "get_card", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + version=version, + ) + + +@agent_safe +def dataset_preview( + source_id: str, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "dataset_preview", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + ) + + +@agent_safe +def dataset_sample( + source_id: str, + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "dataset_sample", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + source_id=source_id, + ) + + +@agent_safe +def health_check( + api_url: str | None = None, + token: str | None = None, + service_instance: str = "prod", + dev_user_id: str | None = None, +) -> Dict[str, Any]: + return _run_backend_action( + "health", + api_url=api_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) diff --git a/src/mdf_agent/skill/mdf_tools.py b/src/mdf_agent/skill/mdf_tools.py new file mode 100644 index 0000000..b0ee8cf --- /dev/null +++ b/src/mdf_agent/skill/mdf_tools.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +from mdf_agent.skill import handlers + +# Flat tool registry for agentic/MCP integration. +TOOLS = { + "mdf_health_check": handlers.health_check, + "mdf_scan_folder": handlers.scan_folder, + "mdf_create_manifest": handlers.create_manifest, + "mdf_suggest_mappings": handlers.suggest_mappings, + "mdf_validate_and_preview": handlers.validate_and_preview, + "mdf_publish": handlers.publish, + "mdf_stream_create": handlers.stream_create, + "mdf_stream_append": handlers.stream_append, + "mdf_stream_status": handlers.stream_status, + "mdf_stream_close": handlers.stream_close, + "mdf_stream_snapshot": handlers.stream_snapshot, + "mdf_check_status": handlers.check_status, + "mdf_list_submissions": handlers.list_submissions, + "mdf_search_datasets": handlers.search_datasets, + "mdf_get_citation": handlers.get_citation, + "mdf_get_card": handlers.get_card, + "mdf_dataset_preview": handlers.dataset_preview, + "mdf_dataset_sample": handlers.dataset_sample, + "mdf_curation_list_pending": handlers.curation_list_pending, + "mdf_curation_review": handlers.curation_review, + "mdf_curation_approve": handlers.curation_approve, + "mdf_curation_reject": handlers.curation_reject, +} + +__all__ = ["TOOLS"] diff --git a/src/mdf_agent/skill/skill.md b/src/mdf_agent/skill/skill.md new file mode 100644 index 0000000..33bfcb2 --- /dev/null +++ b/src/mdf_agent/skill/skill.md @@ -0,0 +1,8 @@ +# MDF Agent Skill + +Actions: +- scan_folder: Analyze directory contents and extract metadata +- create_manifest: Create mdf.yaml from user input +- suggest_mappings: Infer field mappings from data headers +- validate_and_preview: Validate and show submission JSON +- publish: Submit dataset to MDF Connect diff --git a/src/mdf_agent/version.py b/src/mdf_agent/version.py new file mode 100644 index 0000000..d3ec452 --- /dev/null +++ b/src/mdf_agent/version.py @@ -0,0 +1 @@ +__version__ = "0.2.0" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..99f1af1 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,4 @@ +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) diff --git a/tests/test_agent_backend_routing.py b/tests/test_agent_backend_routing.py new file mode 100644 index 0000000..2a4a6bd --- /dev/null +++ b/tests/test_agent_backend_routing.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, Optional + +from mdf_agent.core.agent import MDFAgent +from mdf_agent.skill import handlers as skill_handlers + + +class _FakeClient: + def __init__(self, result: Optional[Dict[str, Any]] = None): + self.result = result or {"success": True, "source_id": "src-test"} + self.closed = False + self.calls: list[tuple[str, tuple[Any, ...], Dict[str, Any]]] = [] + + def close(self) -> None: + self.closed = True + + def submit(self, payload: Dict[str, Any]) -> Dict[str, Any]: + self.calls.append(("submit", (payload,), {})) + return self.result + + def stream_create(self, *args, **kwargs): + self.calls.append(("stream_create", args, kwargs)) + return {"success": True} + + def stream_append(self, *args, **kwargs): + self.calls.append(("stream_append", args, kwargs)) + return {"success": True} + + def stream_status(self, *args, **kwargs): + self.calls.append(("stream_status", args, kwargs)) + return {"success": True} + + def stream_close(self, *args, **kwargs): + self.calls.append(("stream_close", args, kwargs)) + return {"success": True} + + def stream_snapshot(self, *args, **kwargs): + self.calls.append(("stream_snapshot", args, kwargs)) + return {"success": True} + + +def test_agent_publish_uses_authenticated_client(monkeypatch): + captured: Dict[str, Any] = {} + fake_client = _FakeClient({"success": True, "source_id": "abc"}) + + def fake_authenticated(*, base_url=None, token=None, service_instance="prod", dev_user_id=None): + captured.update( + base_url=base_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + return fake_client + + agent = MDFAgent() + monkeypatch.setattr(agent, "build_submission", lambda test=False, update=False: {"title": "x"}) + monkeypatch.setattr("mdf_agent.core.agent.BackendClient.authenticated", fake_authenticated) + + result = agent.publish( + dry_run=False, + token="tok-123", + service_instance="dev", + api_url="https://api.example", + dev_user_id="dev-user", + ) + + assert result["success"] is True + assert captured["token"] == "tok-123" + assert captured["service_instance"] == "dev" + assert captured["base_url"] == "https://api.example" + assert captured["dev_user_id"] == "dev-user" + assert fake_client.closed is True + + +def test_agent_publish_legacy_authorizer_extracts_token(monkeypatch): + captured: Dict[str, Any] = {} + fake_client = _FakeClient({"success": True, "source_id": "xyz"}) + + class _DummyAuthorizer: + def get_authorization_header(self) -> str: + return "Bearer legacy-token" + + def fake_authenticated(*, base_url=None, token=None, service_instance="prod", dev_user_id=None): + captured["token"] = token + return fake_client + + agent = MDFAgent() + monkeypatch.setattr(agent, "build_submission", lambda test=False, update=False: {"title": "x"}) + monkeypatch.setattr("mdf_agent.core.agent.BackendClient.authenticated", fake_authenticated) + + result = agent.publish(dry_run=False, authorizer=_DummyAuthorizer()) + assert result["success"] is True + assert captured["token"] == "legacy-token" + + +def test_agent_stream_helpers_use_authenticated_client(monkeypatch): + fake_client = _FakeClient() + captured: list[Dict[str, Any]] = [] + + def fake_authenticated(*, base_url=None, token=None, service_instance="prod", dev_user_id=None): + captured.append( + { + "base_url": base_url, + "token": token, + "service_instance": service_instance, + "dev_user_id": dev_user_id, + } + ) + return fake_client + + monkeypatch.setattr("mdf_agent.core.agent.BackendClient.authenticated", fake_authenticated) + agent = MDFAgent() + + agent.stream_create("title", api_url="http://localhost", token="t", service_instance="local", dev_user_id="u") + agent.stream_append("s1", file_count=1, total_bytes=10, token="t", service_instance="dev") + agent.stream_status("s1", token="t") + agent.stream_close("s1", token="t") + agent.stream_snapshot("s1", title="snap", token="t") + + assert len(captured) == 5 + assert all(item["token"] == "t" for item in captured) + + +def test_skill_stream_handlers_use_authenticated_client(monkeypatch): + fake_client = _FakeClient() + captured: Dict[str, Any] = {} + + def fake_authenticated(*, base_url=None, token=None, service_instance="prod", dev_user_id=None): + captured.update( + base_url=base_url, + token=token, + service_instance=service_instance, + dev_user_id=dev_user_id, + ) + return fake_client + + monkeypatch.setattr("mdf_agent.skill.handlers.BackendClient.authenticated", fake_authenticated) + result = skill_handlers.stream_create( + "title", + api_url="https://api.example", + token="token-1", + service_instance="dev", + dev_user_id="dev-user", + ) + + assert result["success"] is True + assert captured["base_url"] == "https://api.example" + assert captured["token"] == "token-1" + assert captured["service_instance"] == "dev" + assert captured["dev_user_id"] == "dev-user" + + diff --git a/tests/test_backend_client_auth.py b/tests/test_backend_client_auth.py new file mode 100644 index 0000000..b8463a1 --- /dev/null +++ b/tests/test_backend_client_auth.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import sys +from types import SimpleNamespace +from typing import Any, Dict, Optional + +from mdf_agent.core.backend_client import BackendClient, _api_url_for_service +from mdf_agent.auth.globus import NCSA_MDF_COLLECTION_UUID, get_scopes_for_service + + +class _DummyResponse: + def __init__(self, payload: Dict[str, Any], status_code: int = 200): + self._payload = payload + self.text = "" + self.status_code = status_code + self.headers: Dict[str, str] = {} + + def json(self) -> Dict[str, Any]: + return self._payload + + +class _RecordingHTTPClient: + def __init__(self): + self.last_headers: Optional[Dict[str, str]] = None + + def request(self, method: str, url: str, json: Any = None, params: Any = None, headers: Any = None): + self.last_headers = headers or {} + return _DummyResponse({"success": True}) + + +def test_from_env_reads_auth(monkeypatch): + monkeypatch.setenv("MDF_API_URL", "https://example.org") + monkeypatch.setenv("MDF_CONNECT_TOKEN", "token-123") + monkeypatch.setenv("MDF_DEV_USER_ID", "user-123") + + client = BackendClient.from_env() + assert client.base_url == "https://example.org" + assert client._token == "token-123" + assert client._user_id == "user-123" + + +def test_request_prefers_token_header(): + client = BackendClient(base_url="https://example.org", token="abc", user_id="dev-user") + recorder = _RecordingHTTPClient() + client._client = recorder # type: ignore[assignment] + + client.search("query") + assert recorder.last_headers == {"Authorization": "Bearer abc"} + + +def test_request_uses_dev_user_header(): + client = BackendClient(base_url="https://example.org", user_id="dev-user") + recorder = _RecordingHTTPClient() + client._client = recorder # type: ignore[assignment] + + client.search("query") + assert recorder.last_headers == {"X-User-Id": "dev-user"} + + +def test_authenticated_uses_env_token(monkeypatch): + monkeypatch.setenv("MDF_CONNECT_TOKEN", "env-token") + monkeypatch.delenv("MDF_DEV_USER_ID", raising=False) + + client = BackendClient.authenticated(base_url="https://example.org", service_instance="dev") + assert client._token == "env-token" + assert client._user_id is None + + +def test_authenticated_local_without_credentials_no_oauth(monkeypatch): + monkeypatch.delenv("MDF_CONNECT_TOKEN", raising=False) + monkeypatch.delenv("MDF_DEV_USER_ID", raising=False) + monkeypatch.delenv("LOCAL_USER_ID", raising=False) + + client = BackendClient.authenticated(base_url="http://127.0.0.1:8080", service_instance="local") + assert client._token is None + assert client._user_id is None + + +def test_authenticated_uses_confidential_credentials(monkeypatch): + monkeypatch.delenv("MDF_CONNECT_TOKEN", raising=False) + monkeypatch.delenv("MDF_DEV_USER_ID", raising=False) + monkeypatch.setenv("MDF_CLIENT_ID", "client-id") + monkeypatch.setenv("MDF_CLIENT_SECRET", "client-secret") + + _scope, resource_server = get_scopes_for_service("prod") + captured: Dict[str, Any] = {} + + class _FakeConfidentialClient: + def __init__(self, client_id: str, client_secret: str): + captured["client_id"] = client_id + captured["client_secret"] = client_secret + + def oauth2_client_credentials_tokens(self, requested_scopes: Optional[str] = None): + captured["requested_scopes"] = requested_scopes + return SimpleNamespace( + by_resource_server={ + resource_server: {"access_token": "service-token"}, + NCSA_MDF_COLLECTION_UUID: {"access_token": "data-token"}, + } + ) + + fake_globus_sdk = SimpleNamespace(ConfidentialAppAuthClient=_FakeConfidentialClient) + monkeypatch.setitem(sys.modules, "globus_sdk", fake_globus_sdk) + + client = BackendClient.authenticated(base_url="https://example.org", service_instance="prod") + assert captured["client_id"] == "client-id" + assert captured["client_secret"] == "client-secret" + assert "requested_scopes" in captured + assert client._token == "service-token" + assert client._globus_data_token == "data-token" + assert client._user_id is None + + +def test_api_url_for_service_respects_env_override(monkeypatch): + monkeypatch.setenv("MDF_API_URL", "https://override.example") + assert _api_url_for_service("prod") == "https://override.example" diff --git a/tests/test_backend_client_sync.py b/tests/test_backend_client_sync.py new file mode 100644 index 0000000..645cee8 --- /dev/null +++ b/tests/test_backend_client_sync.py @@ -0,0 +1,229 @@ +from __future__ import annotations + +from typing import Any, Dict, Optional + +from mdf_agent.core.backend_client import BackendClient + + +class _DummyResponse: + def __init__(self, payload: Dict[str, Any], status_code: int = 200): + self._payload = payload + self.text = "" + self.status_code = status_code + self.headers: Dict[str, str] = {} + + def json(self) -> Dict[str, Any]: + return self._payload + + +class _DummyDownloadResponse: + def __init__(self, content: bytes, status_code: int = 200): + self.content = content + self.status_code = status_code + self.text = content.decode("utf-8", errors="ignore") + + def raise_for_status(self) -> None: + if self.status_code >= 400: + raise RuntimeError(f"HTTP {self.status_code}") + + +class _RecordingHTTPClient: + def __init__(self): + self.calls: list[Dict[str, Any]] = [] + self.download_calls: list[Dict[str, Any]] = [] + + def request( + self, + method: str, + url: str, + json: Optional[Dict[str, Any]] = None, + params: Optional[Dict[str, Any]] = None, + headers: Optional[Dict[str, str]] = None, + ) -> _DummyResponse: + self.calls.append( + { + "method": method, + "url": url, + "json": json, + "params": params, + "headers": headers or {}, + } + ) + + if method == "GET" and url.endswith("/stream/stream-1/files"): + return _DummyResponse( + { + "success": True, + "files": [ + { + "filename": "data.csv", + "path": "streams/stream-1/data.csv", + "storage_backend": "globus", + }, + { + "filename": "notes.txt", + "path": "streams/stream-1/notes.txt", + "storage_backend": "globus", + }, + ], + } + ) + + if method == "POST" and url.endswith("/stream/stream-1/download-url"): + path = (json or {}).get("path") + return _DummyResponse( + {"success": True, "download_url": f"https://download.example/{path}"} + ) + + return _DummyResponse({"success": True}) + + def get( + self, + url: str, + headers: Optional[Dict[str, str]] = None, + ) -> _DummyDownloadResponse: + self.download_calls.append({"url": url, "headers": headers}) + return _DummyDownloadResponse(content=f"download:{url}".encode("utf-8")) + + +def _make_client( + token: Optional[str] = None, + globus_data_token: Optional[str] = None, +) -> tuple[BackendClient, _RecordingHTTPClient]: + client = BackendClient( + base_url="https://api.example", + token=token, + globus_data_token=globus_data_token, + ) + recorder = _RecordingHTTPClient() + client._client = recorder # type: ignore[assignment] + return client, recorder + + +def test_health_calls_endpoint(): + client, recorder = _make_client() + client.health() + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/health" + + +def test_curation_pending_calls_endpoint(): + client, recorder = _make_client() + client.curation_pending(limit=10, offset=5, organization="MDF") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/curation/pending" + assert recorder.calls[-1]["params"] == {"limit": 10, "offset": 5, "organization": "MDF"} + + +def test_curation_detail_calls_endpoint(): + client, recorder = _make_client() + client.curation_detail("src-1", version="2.0") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/curation/src-1" + assert recorder.calls[-1]["params"] == {"version": "2.0"} + + +def test_curation_approve_calls_endpoint(): + client, recorder = _make_client() + client.curation_approve( + source_id="src-1", + mint_doi=False, + notes="looks good", + metadata_updates={"title": "Updated"}, + version="1.0", + ) + assert recorder.calls[-1]["method"] == "POST" + assert recorder.calls[-1]["url"] == "https://api.example/curation/src-1/approve" + assert recorder.calls[-1]["json"] == { + "mint_doi": False, + "notes": "looks good", + "metadata_updates": {"title": "Updated"}, + "version": "1.0", + } + + +def test_curation_reject_calls_endpoint(): + client, recorder = _make_client() + client.curation_reject( + source_id="src-2", + reason="missing metadata", + suggestions="add units", + version="1.0", + ) + assert recorder.calls[-1]["method"] == "POST" + assert recorder.calls[-1]["url"] == "https://api.example/curation/src-2/reject" + assert recorder.calls[-1]["json"] == { + "reason": "missing metadata", + "suggestions": "add units", + "version": "1.0", + } + + +def test_dataset_preview_calls_endpoint(): + client, recorder = _make_client() + client.dataset_preview("src-1") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/preview/src-1" + + +def test_dataset_files_calls_endpoint(): + client, recorder = _make_client() + client.dataset_files("src-1") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/preview/src-1/files" + + +def test_dataset_file_detail_encodes_path(): + client, recorder = _make_client() + client.dataset_file_detail("src-1", "folder/a file.csv") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/preview/src-1/files/folder/a%20file.csv" + + +def test_dataset_sample_calls_endpoint(): + client, recorder = _make_client() + client.dataset_sample("src-1") + assert recorder.calls[-1]["method"] == "GET" + assert recorder.calls[-1]["url"] == "https://api.example/preview/src-1/sample" + + +def test_stream_close_accepts_optional_payload(): + client, recorder = _make_client() + client.stream_close( + stream_id="stream-1", + mint_doi=True, + title="Run 42", + description="Published stream", + authors=[{"name": "Jane Doe"}], + keywords=["alloy", "xrd"], + license="CC-BY-4.0", + ) + assert recorder.calls[-1]["method"] == "POST" + assert recorder.calls[-1]["url"] == "https://api.example/stream/stream-1/close" + assert recorder.calls[-1]["json"] == { + "stream_id": "stream-1", + "mint_doi": True, + "title": "Run 42", + "description": "Published stream", + "authors": [{"name": "Jane Doe"}], + "keywords": ["alloy", "xrd"], + "license": "CC-BY-4.0", + } + + +def test_stream_clone_downloads_files(tmp_path): + client, recorder = _make_client(token="openid-token", globus_data_token="data-token") + result = client.stream_clone("stream-1", dest_dir=str(tmp_path), file_filter="*.csv") + + assert result["success"] is True + assert result["downloaded"] == 1 + output_file = tmp_path / "data.csv" + assert output_file.exists() + assert output_file.read_bytes() == b"download:https://download.example/streams/stream-1/data.csv" + + assert recorder.download_calls == [ + { + "url": "https://download.example/streams/stream-1/data.csv", + "headers": {"Authorization": "Bearer data-token"}, + } + ] diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..dee63d7 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,320 @@ +"""Tests for MDF Agent CLI commands.""" + +from pathlib import Path +from tempfile import TemporaryDirectory +import json +import os + +import pytest + +from typer.testing import CliRunner + +from mdf_agent.cli.main import app + +runner = CliRunner() + + +class TestInitCommand: + """Tests for 'mdf init' command.""" + + def test_init_creates_repository(self): + """mdf init creates git repo and mdf.yaml.""" + with TemporaryDirectory() as tmpdir: + result = runner.invoke( + app, + ["init", tmpdir, "--title", "Test Dataset", "--author", "Jane Doe"], + ) + assert result.exit_code == 0 + assert "Initialized" in result.stdout + assert (Path(tmpdir) / ".git").is_dir() + assert (Path(tmpdir) / "mdf.yaml").is_file() + + def test_init_with_multiple_authors(self): + """mdf init with multiple --author options.""" + with TemporaryDirectory() as tmpdir: + result = runner.invoke( + app, + [ + "init", tmpdir, + "--title", "Test Dataset", + "--author", "Jane Doe", + "--author", "John Smith", + ], + ) + assert result.exit_code == 0 + + # Check manifest has both authors + import yaml + manifest = yaml.safe_load((Path(tmpdir) / "mdf.yaml").read_text()) + assert len(manifest["authors"]) == 2 + + def test_init_with_all_options(self): + """mdf init with all options.""" + with TemporaryDirectory() as tmpdir: + result = runner.invoke( + app, + [ + "init", tmpdir, + "--title", "Test Dataset", + "--author", "Jane Doe", + "--description", "A test dataset", + "--publisher", "Test Publisher", + "--year", "2025", + ], + ) + assert result.exit_code == 0 + + import yaml + manifest = yaml.safe_load((Path(tmpdir) / "mdf.yaml").read_text()) + assert manifest["title"] == "Test Dataset" + assert manifest["description"] == "A test dataset" + assert manifest["publisher"] == "Test Publisher" + assert manifest["publication_year"] == 2025 + + def test_init_missing_title_fails(self): + """mdf init without --title fails.""" + with TemporaryDirectory() as tmpdir: + result = runner.invoke( + app, + ["init", tmpdir, "--author", "Jane Doe"], + ) + assert result.exit_code != 0 + + def test_init_missing_author_fails(self): + """mdf init without --author fails.""" + with TemporaryDirectory() as tmpdir: + result = runner.invoke( + app, + ["init", tmpdir, "--title", "Test"], + ) + assert result.exit_code != 0 + + +class TestAddCommand: + """Tests for 'mdf add' command.""" + + def test_add_stages_files(self): + """mdf add stages files.""" + import os + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + # Initialize repo first + runner.invoke( + app, + ["init", str(root), "--title", "Test", "--author", "Jane"], + ) + + # Change to tmpdir for add command (CLI uses from_repo(".")) + original_cwd = os.getcwd() + try: + os.chdir(root) + result = runner.invoke( + app, + ["add", "data.csv"], + catch_exceptions=False, + ) + assert result.exit_code == 0 + assert "Staged:" in result.stdout + assert "data.csv" in result.stdout + finally: + os.chdir(original_cwd) + + def test_add_with_discover(self): + """mdf add with --discover option.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c\n1,2,3") + + runner.invoke( + app, + ["init", str(root), "--title", "Test", "--author", "Jane"], + ) + + # The discover flag should extract metadata + # Actual behavior depends on implementation + + +class TestCommitCommand: + """Tests for 'mdf commit' command.""" + + def test_commit_requires_staged_files(self): + """mdf commit fails if nothing staged.""" + with TemporaryDirectory() as tmpdir: + runner.invoke( + app, + ["init", tmpdir, "--title", "Test", "--author", "Jane"], + ) + + result = runner.invoke( + app, + ["commit", "-m", "Test commit"], + ) + # Should fail because no files are staged + # (depends on current directory context) + + +class TestStatusCommand: + """Tests for 'mdf status' command.""" + + def test_status_shows_state(self): + """mdf status shows repository state.""" + with TemporaryDirectory() as tmpdir: + runner.invoke( + app, + ["init", tmpdir, "--title", "Test", "--author", "Jane"], + ) + + # Status command should work after init + + +class TestValidateCommand: + """Tests for 'mdf validate' command.""" + + def test_validate_valid_manifest(self): + """mdf validate passes for valid manifest.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + runner.invoke( + app, + ["init", str(root), "--title", "Test", "--author", "Jane"], + ) + + # Add data source to manifest + import yaml + manifest_path = root / "mdf.yaml" + manifest = yaml.safe_load(manifest_path.read_text()) + manifest["data_sources"] = ["./data.csv"] + manifest_path.write_text(yaml.safe_dump(manifest)) + + # Note: validate runs in cwd, need to handle path context + + def test_validate_missing_title_fails(self): + """mdf validate fails when title is missing.""" + import subprocess + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + subprocess.run(["git", "init"], cwd=str(root), capture_output=True, check=True) + (root / "mdf.yaml").write_text("authors:\n - Jane Doe\n") + subprocess.run(["git", "add", "mdf.yaml"], cwd=str(root), capture_output=True, check=True) + subprocess.run(["git", "commit", "-m", "init"], cwd=str(root), capture_output=True, check=True) + + # Validate should fail + + +class TestPublishCommand: + """Tests for 'mdf publish' command.""" + + def test_publish_dry_run(self): + """mdf publish --dry-run shows payload.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + runner.invoke( + app, + ["init", str(root), "--title", "Test", "--author", "Jane"], + ) + + # Add data source + import yaml + manifest_path = root / "mdf.yaml" + manifest = yaml.safe_load(manifest_path.read_text()) + manifest["data_sources"] = ["./data.csv"] + manifest_path.write_text(yaml.safe_dump(manifest)) + + # Dry run should not submit + + +class TestCloneCommand: + """Tests for 'mdf clone' command.""" + + def test_clone_creates_manifest(self): + """mdf clone --help shows download options.""" + result = runner.invoke(app, ["clone", "--help"]) + assert result.exit_code == 0 + assert "--transfer" in result.stdout + assert "--derive" in result.stdout + + +class TestCLIHelp: + """Tests for CLI help output.""" + + def test_main_help(self): + """mdf --help shows usage.""" + result = runner.invoke(app, ["--help"]) + assert result.exit_code == 0 + assert "MDF Agent CLI" in result.stdout + + def test_init_help(self): + """mdf init --help shows options.""" + result = runner.invoke(app, ["init", "--help"]) + assert result.exit_code == 0 + assert "--title" in result.stdout + assert "--author" in result.stdout + + def test_add_help(self): + """mdf add --help shows options.""" + result = runner.invoke(app, ["add", "--help"]) + assert result.exit_code == 0 + + def test_commit_help(self): + """mdf commit --help shows options.""" + result = runner.invoke(app, ["commit", "--help"]) + assert result.exit_code == 0 + assert "-m" in result.stdout or "--message" in result.stdout + + def test_publish_help(self): + """mdf publish --help shows options.""" + result = runner.invoke(app, ["publish", "--help"]) + assert result.exit_code == 0 + assert "--test" in result.stdout or "--dry-run" in result.stdout + + def test_validate_help(self): + """mdf validate --help shows usage.""" + result = runner.invoke(app, ["validate", "--help"]) + assert result.exit_code == 0 + + def test_clone_help(self): + """mdf clone --help shows options.""" + result = runner.invoke(app, ["clone", "--help"]) + assert result.exit_code == 0 + + +class TestAuthCommands: + """Tests for login/logout/whoami commands.""" + + def test_login_invokes_auth(self, monkeypatch): + called = {"ok": False, "service": None, "token": None} + + def fake_get_authorizer(*, token=None, service_instance="prod", **kwargs): + called["ok"] = True + called["service"] = service_instance + called["token"] = token + return object() + + monkeypatch.setattr("mdf_agent.auth.globus.get_authorizer", fake_get_authorizer) + + result = runner.invoke(app, ["login", "--service", "dev", "--token", "abc123"]) + assert result.exit_code == 0 + assert "Authentication ready" in result.stdout + assert called["ok"] is True + assert called["service"] == "dev" + assert called["token"] == "abc123" + + def test_logout_reports_success(self, monkeypatch): + monkeypatch.setattr("mdf_agent.auth.globus.logout", lambda: True) + result = runner.invoke(app, ["logout"]) + assert result.exit_code == 0 + assert "Logged out" in result.stdout + + def test_whoami_uses_env_token_status(self, monkeypatch): + monkeypatch.setattr("mdf_agent.auth.globus.is_logged_in", lambda service_instance="prod": False) + monkeypatch.setenv("MDF_CONNECT_TOKEN", "env-token") + result = runner.invoke(app, ["whoami", "--service", "prod"]) + assert result.exit_code == 0 + assert "authenticated" in result.stdout + assert "MDF_CONNECT_TOKEN is set in environment" in result.stdout diff --git a/tests/test_extractors.py b/tests/test_extractors.py new file mode 100644 index 0000000..8ea45ba --- /dev/null +++ b/tests/test_extractors.py @@ -0,0 +1,399 @@ +"""Tests for metadata extractors.""" + +import json +import pytest +from pathlib import Path +from tempfile import TemporaryDirectory + +from mdf_agent.extractors.base import BaseExtractor +from mdf_agent.extractors.tabular import TabularExtractor +from mdf_agent.extractors.json_yaml import JsonYamlExtractor +from mdf_agent.extractors.registry import discover_metadata + + +class TestBaseExtractor: + """Tests for BaseExtractor interface.""" + + def test_can_extract_matches_extension(self): + """can_extract returns True for matching extensions.""" + + class TestExtractor(BaseExtractor): + extensions = [".txt", ".md"] + + assert TestExtractor.can_extract(Path("file.txt")) is True + assert TestExtractor.can_extract(Path("file.md")) is True + assert TestExtractor.can_extract(Path("file.csv")) is False + + def test_can_extract_case_insensitive(self): + """can_extract is case insensitive.""" + + class TestExtractor(BaseExtractor): + extensions = [".txt"] + + assert TestExtractor.can_extract(Path("file.TXT")) is True + assert TestExtractor.can_extract(Path("file.Txt")) is True + + def test_extract_not_implemented(self): + """BaseExtractor.extract raises NotImplementedError.""" + with pytest.raises(NotImplementedError): + BaseExtractor.extract(Path("test.txt")) + + +class TestTabularExtractor: + """Tests for TabularExtractor (CSV/Excel).""" + + def test_can_extract_csv(self): + """TabularExtractor handles .csv files.""" + assert TabularExtractor.can_extract(Path("data.csv")) is True + + def test_can_extract_tsv(self): + """TabularExtractor handles .tsv files.""" + assert TabularExtractor.can_extract(Path("data.tsv")) is True + + def test_can_extract_excel(self): + """TabularExtractor handles .xlsx files.""" + assert TabularExtractor.can_extract(Path("data.xlsx")) is True + assert TabularExtractor.can_extract(Path("data.xls")) is True + + def test_cannot_extract_other(self): + """TabularExtractor rejects non-tabular files.""" + assert TabularExtractor.can_extract(Path("data.json")) is False + assert TabularExtractor.can_extract(Path("data.pdf")) is False + + def test_extract_csv_basic(self): + """Extract CSV with headers and row count.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "data.csv" + csv_path.write_text("name,age,score\nAlice,30,95\nBob,25,88\n") + + result = TabularExtractor.extract(csv_path) + + assert "mdf" in result + assert "table_schema" in result["mdf"] + schema = result["mdf"]["table_schema"] + assert schema["file"] == "data.csv" + assert schema["row_count"] == 2 + assert len(schema["columns"]) == 3 + column_names = [c["name"] for c in schema["columns"]] + assert column_names == ["name", "age", "score"] + + def test_extract_csv_empty_file(self): + """Extract empty CSV returns empty or minimal result.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "empty.csv" + csv_path.write_text("") + + result = TabularExtractor.extract(csv_path) + # Empty file should return something (or empty dict) + if result: + assert result["mdf"]["table_schema"]["row_count"] == 0 + + def test_extract_csv_header_only(self): + """Extract CSV with only headers.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "headers.csv" + csv_path.write_text("col1,col2,col3\n") + + result = TabularExtractor.extract(csv_path) + + assert "mdf" in result + schema = result["mdf"]["table_schema"] + assert schema["row_count"] == 0 + assert len(schema["columns"]) == 3 + + def test_extract_csv_with_empty_headers(self): + """Extract CSV handles empty header columns.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "sparse.csv" + csv_path.write_text("name,,score\n1,2,3\n") + + result = TabularExtractor.extract(csv_path) + + schema = result["mdf"]["table_schema"] + # Empty headers are filtered out + column_names = [c["name"] for c in schema["columns"]] + assert "name" in column_names + assert "score" in column_names + + def test_extract_tsv(self): + """Extract TSV (tab-separated) file.""" + with TemporaryDirectory() as tmpdir: + tsv_path = Path(tmpdir) / "data.tsv" + tsv_path.write_text("a\tb\tc\n1\t2\t3\n") + + result = TabularExtractor.extract(tsv_path) + + # TSV parsing may differ based on implementation + assert "mdf" in result + + +class TestJsonYamlExtractor: + """Tests for JsonYamlExtractor.""" + + def test_can_extract_json(self): + """JsonYamlExtractor handles .json files.""" + assert JsonYamlExtractor.can_extract(Path("data.json")) is True + + def test_can_extract_yaml(self): + """JsonYamlExtractor handles .yaml and .yml files.""" + assert JsonYamlExtractor.can_extract(Path("config.yaml")) is True + assert JsonYamlExtractor.can_extract(Path("config.yml")) is True + + def test_cannot_extract_other(self): + """JsonYamlExtractor rejects non-JSON/YAML files.""" + assert JsonYamlExtractor.can_extract(Path("data.csv")) is False + assert JsonYamlExtractor.can_extract(Path("data.txt")) is False + + def test_extract_json_flat(self): + """Extract flat JSON object.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "data.json" + json_path.write_text(json.dumps({"name": "test", "count": 42})) + + result = JsonYamlExtractor.extract(json_path) + + assert "mdf" in result + assert "json_schema" in result["mdf"] + schema = result["mdf"]["json_schema"] + assert schema["file"] == "data.json" + assert schema["schema"]["name"] == "str" + assert schema["schema"]["count"] == "int" + + def test_extract_json_nested(self): + """Extract nested JSON structure.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "nested.json" + data = {"metadata": {"author": "Jane", "version": 1}, "values": [1, 2, 3]} + json_path.write_text(json.dumps(data)) + + result = JsonYamlExtractor.extract(json_path) + + schema = result["mdf"]["json_schema"]["schema"] + assert "metadata" in schema + assert schema["metadata"]["author"] == "str" + assert schema["metadata"]["version"] == "int" + assert "values" in schema + # Arrays infer from first element + assert schema["values"] == ["int"] + + def test_extract_json_array_root(self): + """Extract JSON with array at root.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "array.json" + json_path.write_text(json.dumps([{"id": 1}, {"id": 2}])) + + result = JsonYamlExtractor.extract(json_path) + + schema = result["mdf"]["json_schema"]["schema"] + # Root array, first element schema + assert schema == [{"id": "int"}] + + def test_extract_json_empty_array(self): + """Extract JSON with empty array.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "empty_array.json" + json_path.write_text(json.dumps({"items": []})) + + result = JsonYamlExtractor.extract(json_path) + + schema = result["mdf"]["json_schema"]["schema"] + assert schema["items"] == [] + + def test_extract_yaml(self): + """Extract YAML file.""" + with TemporaryDirectory() as tmpdir: + yaml_path = Path(tmpdir) / "config.yaml" + yaml_path.write_text("title: Test\nauthor: Jane\nyear: 2025\n") + + result = JsonYamlExtractor.extract(yaml_path) + + assert "mdf" in result + schema = result["mdf"]["json_schema"]["schema"] + assert schema["title"] == "str" + assert schema["author"] == "str" + assert schema["year"] == "int" + + def test_extract_yml_extension(self): + """Extract .yml extension.""" + with TemporaryDirectory() as tmpdir: + yml_path = Path(tmpdir) / "config.yml" + yml_path.write_text("key: value\n") + + result = JsonYamlExtractor.extract(yml_path) + + assert "mdf" in result + assert result["mdf"]["json_schema"]["schema"]["key"] == "str" + + def test_extract_invalid_json(self): + """Extract invalid JSON returns empty dict.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "invalid.json" + json_path.write_text("not valid json {") + + result = JsonYamlExtractor.extract(json_path) + assert result == {} + + def test_extract_boolean_and_null(self): + """Extract JSON with boolean and null types.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "types.json" + json_path.write_text(json.dumps({"active": True, "value": None})) + + result = JsonYamlExtractor.extract(json_path) + + schema = result["mdf"]["json_schema"]["schema"] + assert schema["active"] == "bool" + assert schema["value"] == "NoneType" + + +class TestPDFExtractor: + """Tests for PDFExtractor.""" + + def test_can_extract_pdf(self): + """PDFExtractor handles .pdf files.""" + from mdf_agent.extractors.pdf import PDFExtractor + + assert PDFExtractor.can_extract(Path("paper.pdf")) is True + assert PDFExtractor.can_extract(Path("PAPER.PDF")) is True + + def test_cannot_extract_other(self): + """PDFExtractor rejects non-PDF files.""" + from mdf_agent.extractors.pdf import PDFExtractor + + assert PDFExtractor.can_extract(Path("data.csv")) is False + assert PDFExtractor.can_extract(Path("doc.docx")) is False + + def test_extract_nonexistent_file(self): + """Extract nonexistent PDF returns empty dict.""" + from mdf_agent.extractors.pdf import PDFExtractor + + result = PDFExtractor.extract(Path("/nonexistent/file.pdf")) + assert result == {} + + def test_doi_pattern(self): + """DOI regex pattern matches valid DOIs.""" + from mdf_agent.extractors.pdf import DOI_PATTERN + + # Valid DOIs + assert DOI_PATTERN.search("10.1234/abc123") + assert DOI_PATTERN.search("See DOI: 10.5678/xyz-999") + assert DOI_PATTERN.search("10.12345/long.doi.path/v1") + + # Should not match + assert DOI_PATTERN.search("9.1234/abc") is None # wrong prefix + assert DOI_PATTERN.search("10.12/short") is None # too short after 10. + + +class TestDiscoverMetadata: + """Tests for the registry's discover_metadata function.""" + + def test_discover_single_csv(self): + """Discover metadata from single CSV.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "data.csv" + csv_path.write_text("a,b\n1,2\n") + + result = discover_metadata([str(csv_path)]) + + assert "mdf" in result + assert "table_schema" in result["mdf"] + + def test_discover_single_json(self): + """Discover metadata from single JSON.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "data.json" + json_path.write_text('{"key": "value"}') + + result = discover_metadata([str(json_path)]) + + assert "mdf" in result + assert "json_schema" in result["mdf"] + + def test_discover_multiple_files(self): + """Discover metadata from multiple files merges results.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "data.csv" + csv_path.write_text("a,b\n1,2\n") + + json_path = Path(tmpdir) / "config.json" + json_path.write_text('{"key": "value"}') + + result = discover_metadata([str(csv_path), str(json_path)]) + + # Should have both table_schema and json_schema + assert "mdf" in result + # Depending on deep_merge behavior, may have both + mdf = result["mdf"] + assert "table_schema" in mdf or "json_schema" in mdf + + def test_discover_empty_list(self): + """Discover metadata from empty list returns empty dict.""" + result = discover_metadata([]) + assert result == {} + + def test_discover_unsupported_extension(self): + """Discover metadata skips unsupported file types.""" + with TemporaryDirectory() as tmpdir: + txt_path = Path(tmpdir) / "notes.txt" + txt_path.write_text("Just some text") + + result = discover_metadata([str(txt_path)]) + assert result == {} + + def test_discover_mixed_supported_unsupported(self): + """Discover handles mix of supported and unsupported files.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "data.csv" + csv_path.write_text("col\n1\n") + + txt_path = Path(tmpdir) / "readme.txt" + txt_path.write_text("readme") + + result = discover_metadata([str(csv_path), str(txt_path)]) + + # Should have CSV metadata, ignore txt + assert "mdf" in result + assert "table_schema" in result["mdf"] + + +class TestExtractorEdgeCases: + """Edge case tests for extractors.""" + + def test_csv_unicode(self): + """CSV with unicode characters.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "unicode.csv" + csv_path.write_text("name,city\nAlice,Munchen\nBob,Tokyo\n", encoding="utf-8") + + result = TabularExtractor.extract(csv_path) + assert result["mdf"]["table_schema"]["row_count"] == 2 + + def test_json_unicode(self): + """JSON with unicode characters.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "unicode.json" + json_path.write_text('{"city": "Tokyo", "greeting": "Hello"}', encoding="utf-8") + + result = JsonYamlExtractor.extract(json_path) + assert result["mdf"]["json_schema"]["schema"]["city"] == "str" + + def test_csv_large_file_simulation(self): + """CSV extraction handles files with many rows.""" + with TemporaryDirectory() as tmpdir: + csv_path = Path(tmpdir) / "large.csv" + lines = ["id,value"] + [f"{i},{i*2}" for i in range(1000)] + csv_path.write_text("\n".join(lines)) + + result = TabularExtractor.extract(csv_path) + assert result["mdf"]["table_schema"]["row_count"] == 1000 + + def test_json_deeply_nested(self): + """JSON with deeply nested structure.""" + with TemporaryDirectory() as tmpdir: + json_path = Path(tmpdir) / "deep.json" + data = {"a": {"b": {"c": {"d": {"e": "value"}}}}} + json_path.write_text(json.dumps(data)) + + result = JsonYamlExtractor.extract(json_path) + schema = result["mdf"]["json_schema"]["schema"] + assert schema["a"]["b"]["c"]["d"]["e"] == "str" diff --git a/tests/test_mdf_agent_basic.py b/tests/test_mdf_agent_basic.py new file mode 100644 index 0000000..6f93b89 --- /dev/null +++ b/tests/test_mdf_agent_basic.py @@ -0,0 +1,564 @@ +import zipfile +from pathlib import Path + +import pytest + +import mdf_agent.core.agent as agent_module +from mdf_agent.core.agent import ( + MDFAgent, + UploadResult, + _create_zip_archive, + _detect_gcp_endpoint, + _download_https_file, + _globus_uri_to_https, + _resolve_globus_files, +) + + +def test_build_submission_shape(tmp_path: Path): + agent = MDFAgent.init( + path=str(tmp_path), + title="Test Dataset", + authors=["Doe, Jane"], + ) + agent.manifest.data_sources = ["./data"] + agent.save_manifest() + + payload = agent.build_submission(test=True, update=False) + + assert payload["title"] == "Test Dataset" + assert payload["data_sources"] + assert payload["test"] is True + assert payload["update"] is False + assert "update_metadata_only" in payload + + +def test_validate_missing_fields(tmp_path: Path): + agent = MDFAgent.init( + path=str(tmp_path), + title="Test Dataset", + authors=["Doe, Jane"], + ) + agent.manifest.data_sources = [] + agent.save_manifest() + + validation = agent.validate() + assert any("data_sources" in err for err in validation["errors"]) + + +class _DummyUploadResponse: + status_code = 201 + text = "" + + +class _RecordingUploadClient: + created_verify_values = [] + + def __init__(self, *args, **kwargs): + self.verify = kwargs.get("verify") + self.__class__.created_verify_values.append(self.verify) + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc, tb): + return False + + def put(self, url, content=None, headers=None): + if content is not None: + b"".join(content) + return _DummyUploadResponse() + + +def test_https_put_verifies_tls_by_default(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + source = tmp_path / "data.csv" + source.write_text("a,b\n1,2\n", encoding="utf-8") + + _RecordingUploadClient.created_verify_values = [] + monkeypatch.delenv("MDF_SSL_VERIFY", raising=False) + monkeypatch.setattr("httpx.Client", _RecordingUploadClient) + monkeypatch.setattr(agent_module, "_INSECURE_SSL_WARNING_EMITTED", False) + + uri = agent_module._https_put_file(source, "/mdf_open/test/data.csv", "data-token") + + assert uri == "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/mdf_open/test/data.csv" + assert _RecordingUploadClient.created_verify_values == [True] + + +def test_https_put_warns_when_tls_verification_disabled( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, + capsys: pytest.CaptureFixture[str], +): + source = tmp_path / "data.csv" + source.write_text("a,b\n1,2\n", encoding="utf-8") + + _RecordingUploadClient.created_verify_values = [] + monkeypatch.setenv("MDF_SSL_VERIFY", "false") + monkeypatch.setattr("httpx.Client", _RecordingUploadClient) + monkeypatch.setattr(agent_module, "_INSECURE_SSL_WARNING_EMITTED", False) + + agent_module._https_put_file(source, "/mdf_open/test/data.csv", "data-token") + + captured = capsys.readouterr() + assert "MDF_SSL_VERIFY=false disables TLS certificate verification" in captured.err + assert _RecordingUploadClient.created_verify_values == [False] + + +# --------------------------------------------------------------------------- +# Zip archive tests +# --------------------------------------------------------------------------- + + +def test_zip_archive_created_for_directory(tmp_path: Path): + """Zip is created from a temp dir with subdirs; verify arcnames and compression.""" + data_dir = tmp_path / "data" + data_dir.mkdir() + (data_dir / "a.csv").write_text("a,b\n1,2\n") + sub = data_dir / "subdir" + sub.mkdir() + (sub / "b.csv").write_text("x,y\n3,4\n") + + zip_path = _create_zip_archive([(data_dir, "")]) + assert zip_path is not None + + try: + with zipfile.ZipFile(zip_path) as zf: + names = sorted(zf.namelist()) + assert names == ["a.csv", "subdir/b.csv"] + # Verify compression method + for info in zf.infolist(): + assert info.compress_type == zipfile.ZIP_DEFLATED + finally: + zip_path.unlink(missing_ok=True) + + +def test_zip_archive_preserves_structure(tmp_path: Path): + """Multiple directories get correct prefixed paths in zip.""" + dir_a = tmp_path / "alpha" + dir_a.mkdir() + (dir_a / "a.csv").write_text("1") + + dir_b = tmp_path / "beta" + dir_b.mkdir() + sub = dir_b / "nested" + sub.mkdir() + (sub / "b.csv").write_text("2") + + zip_path = _create_zip_archive([(dir_a, "alpha"), (dir_b, "beta")]) + assert zip_path is not None + + try: + with zipfile.ZipFile(zip_path) as zf: + names = sorted(zf.namelist()) + assert names == ["alpha/a.csv", "beta/nested/b.csv"] + finally: + zip_path.unlink(missing_ok=True) + + +def test_zip_archive_skipped_over_size_limit( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch, capsys: pytest.CaptureFixture[str], +): + """When total size exceeds limit, returns None and prints warning.""" + data_dir = tmp_path / "data" + data_dir.mkdir() + (data_dir / "big.bin").write_bytes(b"x" * 100) + + monkeypatch.setattr(agent_module, "_ZIP_MAX_TOTAL_BYTES", 50) + + result = _create_zip_archive([(data_dir, "")]) + assert result is None + + captured = capsys.readouterr() + assert "Skipping zip archive" in captured.err + + +def test_zip_archive_skipped_for_empty_dir(tmp_path: Path): + """Empty directory produces no zip.""" + empty = tmp_path / "empty" + empty.mkdir() + + result = _create_zip_archive([(empty, "")]) + assert result is None + + +def test_zip_skips_external_symlinks(tmp_path: Path): + """Symlinks pointing outside the source dir are excluded from zip.""" + data_dir = tmp_path / "data" + data_dir.mkdir() + (data_dir / "real.txt").write_text("hello") + + outside = tmp_path / "secret.txt" + outside.write_text("password") + (data_dir / "link.txt").symlink_to(outside) + + zip_path = _create_zip_archive([(data_dir, "")]) + assert zip_path is not None + + try: + with zipfile.ZipFile(zip_path) as zf: + names = zf.namelist() + assert "real.txt" in names + assert "link.txt" not in names + finally: + zip_path.unlink(missing_ok=True) + + +def test_upload_result_includes_archive( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch, +): + """_upload_local_files returns UploadResult with archive_url for directories.""" + data_dir = tmp_path / "data" + data_dir.mkdir() + (data_dir / "file.csv").write_text("a,b\n1,2\n") + + _RecordingUploadClient.created_verify_values = [] + monkeypatch.delenv("MDF_SSL_VERIFY", raising=False) + monkeypatch.setattr("httpx.Client", _RecordingUploadClient) + monkeypatch.setattr(agent_module, "_INSECURE_SSL_WARNING_EMITTED", False) + + result = agent_module._upload_local_files( + data_sources=[str(data_dir)], + data_token="fake-token", + source_id="test_dataset", + ) + + assert isinstance(result, UploadResult) + assert result.archive_url is not None + assert result.archive_url.endswith("/.mdf/data.zip") + assert result.archive_size is not None + assert result.archive_size > 0 + assert len(result.data_sources) == 1 # one file uploaded + + +# --------------------------------------------------------------------------- +# Clone / download tests +# --------------------------------------------------------------------------- + +NCSA_UUID = "82f1b5c6-6e9b-11e5-ba47-22000b92c6ec" + + +def test_globus_uri_to_https(): + """Converts NCSA globus:// URI to HTTPS URL.""" + uri = f"globus://{NCSA_UUID}/mdf_open/test_ds/data.csv" + result = _globus_uri_to_https(uri) + assert result == "https://data.materialsdatafacility.org/mdf_open/test_ds/data.csv" + + +def test_globus_uri_to_https_non_ncsa(): + """Non-NCSA endpoint URI returns None.""" + uri = "globus://aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee/some/path.csv" + result = _globus_uri_to_https(uri) + assert result is None + + +def test_globus_uri_to_https_non_globus(): + """Non-globus URI returns None.""" + assert _globus_uri_to_https("https://example.com/file.csv") is None + assert _globus_uri_to_https("stream://my-stream") is None + + +def test_resolve_globus_files_per_file_uris(): + """Per-file NCSA URIs produce correct (url, relative_path) pairs.""" + sources = [ + f"globus://{NCSA_UUID}/mdf_open/my_dataset/subdir/file1.csv", + f"globus://{NCSA_UUID}/mdf_open/my_dataset/file2.csv", + "https://example.com/other.csv", # should be skipped + "stream://my-stream", # should be skipped + ] + result = _resolve_globus_files(sources, "fake-transfer-token") + assert len(result) == 2 + assert result[0] == ( + "https://data.materialsdatafacility.org/mdf_open/my_dataset/subdir/file1.csv", + "subdir/file1.csv", + ) + assert result[1] == ( + "https://data.materialsdatafacility.org/mdf_open/my_dataset/file2.csv", + "file2.csv", + ) + + +def test_resolve_globus_files_directory_uri(monkeypatch: pytest.MonkeyPatch): + """Directory URI triggers operation_ls mock, expands to file list.""" + sources = [f"globus://{NCSA_UUID}/mdf_open/my_dataset/"] + + class FakeEntry(dict): + pass + + class FakeListing(list): + pass + + def fake_operation_ls(endpoint_id, path=None): + if path == "/mdf_open/my_dataset/": + return FakeListing([ + FakeEntry({"type": "file", "name": "a.csv"}), + FakeEntry({"type": "dir", "name": "sub"}), + ]) + elif path == "/mdf_open/my_dataset/sub/": + return FakeListing([ + FakeEntry({"type": "file", "name": "b.csv"}), + ]) + return FakeListing([]) + + class FakeTC: + def __init__(self, authorizer=None): + pass + operation_ls = staticmethod(fake_operation_ls) + + import globus_sdk + monkeypatch.setattr(globus_sdk, "TransferClient", FakeTC) + + result = _resolve_globus_files(sources, "fake-transfer-token") + assert len(result) == 2 + urls = [r[0] for r in result] + rels = [r[1] for r in result] + assert "https://data.materialsdatafacility.org/mdf_open/my_dataset/a.csv" in urls + assert "https://data.materialsdatafacility.org/mdf_open/my_dataset/sub/b.csv" in urls + assert "a.csv" in rels + assert "sub/b.csv" in rels + + +class _FakeStreamResponse: + """Mock httpx streaming response for download tests.""" + def __init__(self, content: bytes): + self._content = content + self.status_code = 200 + self.headers = {"content-length": str(len(content))} + + def raise_for_status(self): + pass + + def iter_bytes(self, chunk_size=None): + yield self._content + + def __enter__(self): + return self + + def __exit__(self, *args): + pass + + def close(self): + pass + + +class _FakeDownloadClient: + """Mock httpx.Client that returns a streaming download response.""" + def __init__(self, *args, **kwargs): + pass + + def __enter__(self): + return self + + def __exit__(self, *args): + pass + + def stream(self, method, url, **kwargs): + return _FakeStreamResponse(b"hello,world\n1,2\n") + + +def test_download_https_file(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + """Mock httpx GET → file written with correct content and parent dirs created.""" + monkeypatch.delenv("MDF_SSL_VERIFY", raising=False) + monkeypatch.setattr("httpx.Client", _FakeDownloadClient) + + dest = tmp_path / "sub" / "dir" / "file.csv" + result = _download_https_file( + "https://data.materialsdatafacility.org/mdf_open/test/file.csv", + dest, + "fake-data-token", + ) + assert result == dest + assert dest.exists() + assert dest.read_text() == "hello,world\n1,2\n" + + +def test_clone_with_zip(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + """Mock card with download_url → downloads zip, extracts, verifies output files.""" + # Create a test zip + zip_path = tmp_path / "source.zip" + with zipfile.ZipFile(zip_path, "w") as zf: + zf.writestr("data.csv", "a,b\n1,2\n") + zf.writestr("sub/nested.txt", "hello") + zip_bytes = zip_path.read_bytes() + + class FakeZipStreamResponse: + def __init__(self, *a, **kw): + self.status_code = 200 + self.headers = {"content-length": str(len(zip_bytes))} + + def raise_for_status(self): + pass + + def iter_bytes(self, chunk_size=None): + yield zip_bytes + + def __enter__(self): + return self + + def __exit__(self, *args): + pass + + def close(self): + pass + + class FakeZipClient: + def __init__(self, *a, **kw): + pass + + def __enter__(self): + return self + + def __exit__(self, *args): + pass + + def stream(self, method, url, **kw): + return FakeZipStreamResponse() + + fake_card = { + "source_id": "test_ds_v1.1", + "title": "Test Dataset", + "download_url": f"globus://{NCSA_UUID}/mdf_open/test_ds/.mdf/data.zip", + "archive_size": len(zip_bytes), + "data_sources": [f"globus://{NCSA_UUID}/mdf_open/test_ds/data.csv"], + } + + class FakeBackendClient: + _globus_data_token = "fake-data" + _globus_transfer_token = "fake-transfer" + + @classmethod + def authenticated(cls, **kw): + return cls() + + def get_card(self, source_id, version=None): + return fake_card + + def close(self): + pass + + monkeypatch.delenv("MDF_SSL_VERIFY", raising=False) + monkeypatch.setattr("httpx.Client", FakeZipClient) + monkeypatch.setattr(agent_module, "BackendClient", FakeBackendClient) + + out = tmp_path / "output" + agent = MDFAgent() + result = agent.clone(source_id="test_ds_v1.1", output_dir=str(out)) + + assert result["success"] is True + assert result["method"] == "zip" + assert result["files_count"] == 2 + assert (out / "data.csv").exists() + assert (out / "sub" / "nested.txt").exists() + + +def test_clone_without_zip_https(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + """Mock card with per-file data_sources → downloads each file individually.""" + fake_card = { + "source_id": "test_ds_v1.1", + "title": "Test Dataset", + "data_sources": [ + f"globus://{NCSA_UUID}/mdf_open/test_ds/file1.csv", + f"globus://{NCSA_UUID}/mdf_open/test_ds/sub/file2.csv", + ], + } + + class FakeBackendClient: + _globus_data_token = "fake-data" + _globus_transfer_token = "fake-transfer" + + @classmethod + def authenticated(cls, **kw): + return cls() + + def get_card(self, source_id, version=None): + return fake_card + + def close(self): + pass + + downloaded_urls = [] + + def fake_download(url, dest, token, progress_callback=None): + downloaded_urls.append(url) + dest.parent.mkdir(parents=True, exist_ok=True) + dest.write_text(f"content of {dest.name}") + return dest + + monkeypatch.setattr(agent_module, "BackendClient", FakeBackendClient) + monkeypatch.setattr(agent_module, "_download_https_file", fake_download) + + out = tmp_path / "output" + agent = MDFAgent() + result = agent.clone(source_id="test_ds_v1.1", output_dir=str(out)) + + assert result["success"] is True + assert result["method"] == "https" + assert result["files_count"] == 2 + assert len(downloaded_urls) == 2 + assert (out / "file1.csv").exists() + assert (out / "sub" / "file2.csv").exists() + + +def test_clone_transfer_no_gcp(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + """No GCP detected → raises error with helpful message.""" + fake_card = { + "source_id": "test_ds_v1.1", + "title": "Test Dataset", + "data_sources": [f"globus://{NCSA_UUID}/mdf_open/test_ds/data.csv"], + } + + class FakeBackendClient: + _globus_data_token = "fake-data" + _globus_transfer_token = "fake-transfer" + + @classmethod + def authenticated(cls, **kw): + return cls() + + def get_card(self, source_id, version=None): + return fake_card + + def close(self): + pass + + monkeypatch.setattr(agent_module, "BackendClient", FakeBackendClient) + monkeypatch.setattr(agent_module, "_detect_gcp_endpoint", lambda: None) + + agent = MDFAgent() + with pytest.raises(RuntimeError, match="Globus Transfer requires a local endpoint"): + agent.clone( + source_id="test_ds_v1.1", + output_dir=str(tmp_path / "out"), + method="transfer", + ) + + +def test_detect_gcp_from_config(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + """Config globus.local_endpoint_id used as fallback when SDK detection fails.""" + from mdf_agent.core import config as config_module + from mdf_agent.core.config import GlobalConfig + + config_path = tmp_path / "config.json" + cfg = GlobalConfig(path=config_path) + cfg.set("globus.local_endpoint_id", "11111111-2222-3333-4444-555555555555") + + # Mock SDK detection to fail + class FakeGCP: + endpoint_id = None + + import globus_sdk + monkeypatch.setattr(globus_sdk, "LocalGlobusConnectPersonal", FakeGCP) + + # Patch GlobalConfig constructor in the config module so _detect_gcp_endpoint + # picks up our test config + original_init = GlobalConfig.__init__ + + def patched_init(self, path=None): + original_init(self, path=config_path) + + monkeypatch.setattr(config_module, "GlobalConfig", type( + "GlobalConfig", (GlobalConfig,), {"__init__": patched_init} + )) + + result = _detect_gcp_endpoint() + assert result == "11111111-2222-3333-4444-555555555555" diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..2a67ae7 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,620 @@ +"""Tests for MDF Agent Pydantic models.""" + +import pytest +from datetime import datetime + +from mdf_agent.models.config import Author, DataSource, DerivedFrom, ManifestConfig +from mdf_agent.models.datacite import DataCite, Creator, Title, Description +from mdf_agent.models.submission import Submission + + +class TestAuthor: + """Tests for the Author model.""" + + def test_author_minimal(self): + """Author with just name.""" + author = Author(name="Jane Doe") + assert author.name == "Jane Doe" + assert author.affiliations == [] + assert author.orcid is None + + def test_author_full(self): + """Author with all fields.""" + author = Author( + name="Jane Doe", + affiliations=["MIT", "Argonne"], + orcid="0000-0001-2345-6789", + ) + assert author.name == "Jane Doe" + assert author.affiliations == ["MIT", "Argonne"] + assert author.orcid == "0000-0001-2345-6789" + + def test_author_extra_fields_allowed(self): + """Author allows extra fields.""" + author = Author(name="Jane Doe", email="jane@example.com") + assert author.name == "Jane Doe" + assert author.email == "jane@example.com" + + +class TestDataSource: + """Tests for the DataSource model.""" + + def test_data_source_minimal(self): + """DataSource with just path.""" + ds = DataSource(path="./data") + assert ds.path == "./data" + assert ds.include == [] + assert ds.exclude == [] + + def test_data_source_with_patterns(self): + """DataSource with include/exclude patterns.""" + ds = DataSource( + path="./data", + include=["**/*.csv", "**/*.json"], + exclude=["**/.git/**"], + ) + assert ds.include == ["**/*.csv", "**/*.json"] + assert ds.exclude == ["**/.git/**"] + + +class TestDerivedFrom: + """Tests for the DerivedFrom model.""" + + def test_derived_from_minimal(self): + """DerivedFrom with just source_id.""" + derived = DerivedFrom(source_id="mdf-dataset-v1") + assert derived.source_id == "mdf-dataset-v1" + assert derived.relationship is None + assert derived.description is None + + def test_derived_from_full(self): + """DerivedFrom with all fields.""" + derived = DerivedFrom( + source_id="mdf-dataset-v1", + relationship="filtered_subset", + description="Filtered to Fe-containing alloys only", + ) + assert derived.relationship == "filtered_subset" + assert derived.description == "Filtered to Fe-containing alloys only" + + +class TestManifestConfig: + """Tests for the ManifestConfig model.""" + + def test_manifest_empty(self): + """Empty manifest is valid.""" + manifest = ManifestConfig() + assert manifest.title is None + assert manifest.authors is None + assert manifest.data_sources == [] + + def test_manifest_minimal(self): + """Minimal manifest with required fields for submission.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + ) + assert manifest.title == "My Dataset" + assert manifest.authors == ["Jane Doe"] + + def test_manifest_with_author_objects(self): + """Manifest with Author objects.""" + manifest = ManifestConfig( + title="My Dataset", + authors=[ + Author(name="Jane Doe", affiliations=["MIT"]), + Author(name="John Smith", orcid="0000-0001-2345-6789"), + ], + ) + assert len(manifest.authors) == 2 + assert manifest.authors[0].name == "Jane Doe" + + def test_manifest_list_title(self): + """Manifest with list of titles.""" + manifest = ManifestConfig( + title=["Main Title", "Alternative Title"], + authors=["Jane Doe"], + ) + assert manifest.title == ["Main Title", "Alternative Title"] + + def test_manifest_data_sources_mixed(self): + """Manifest with mixed string and DataSource objects.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + data_sources=[ + "globus://endpoint/path", + DataSource(path="./local", include=["*.csv"]), + ], + ) + assert len(manifest.data_sources) == 2 + assert manifest.data_sources[0] == "globus://endpoint/path" + assert manifest.data_sources[1].path == "./local" + + def test_manifest_all_fields(self): + """Manifest with all common fields.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + description="A test dataset", + publisher="Materials Data Facility", + publication_year=2025, + resource_type="Dataset", + dataset_doi="10.1234/test", + related_dois=["10.1234/paper1"], + subjects=["materials science"], + organization="CHiMaD", + acl=["public"], + tags=["DFT", "alloys"], + auto_discover=True, + ) + assert manifest.publisher == "Materials Data Facility" + assert manifest.publication_year == 2025 + assert manifest.organization == "CHiMaD" + assert manifest.auto_discover is True + + def test_manifest_with_domains(self): + """Manifest with domains field.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + domains=["materials", "chemistry"], + ) + assert manifest.domains == ["materials", "chemistry"] + + def test_manifest_domains_none_by_default(self): + """Domains is None by default.""" + manifest = ManifestConfig() + assert manifest.domains is None + + def test_manifest_domains_single(self): + """Single domain in list.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + domains=["biology"], + ) + assert manifest.domains == ["biology"] + + def test_manifest_with_external_import(self): + """Manifest with external import fields.""" + manifest = ManifestConfig( + title="Imported Dataset", + authors=["Jane Doe"], + external_doi="10.5281/zenodo.1234567", + external_url="https://zenodo.org/record/1234567", + external_source="Zenodo", + ) + assert manifest.external_doi == "10.5281/zenodo.1234567" + assert manifest.external_url == "https://zenodo.org/record/1234567" + assert manifest.external_source == "Zenodo" + + def test_manifest_external_fields_none_by_default(self): + """External import fields are None by default.""" + manifest = ManifestConfig() + assert manifest.external_doi is None + assert manifest.external_url is None + assert manifest.external_source is None + + def test_to_metadata_payload_includes_author_name_parts(self): + manifest = ManifestConfig( + title="My Dataset", + authors=[ + Author( + name="Doe, Jane", + given_name="Jane", + family_name="Doe", + ) + ], + ) + payload = manifest.to_metadata_payload() + assert payload["authors"] == [ + { + "name": "Doe, Jane", + "given_name": "Jane", + "family_name": "Doe", + } + ] + + +class TestDataCiteCreatorParsing: + """Tests for DataCite author name parsing.""" + + def test_simple_name(self): + """Parse simple first last name.""" + author = Author(name="Jane Doe") + creator = DataCite._build_creator(author, []) + assert creator.creatorName == "Doe, Jane" + assert creator.familyName == "Doe" + assert creator.givenName == "Jane" + + def test_name_with_middle(self): + """Parse name with middle name.""" + author = Author(name="Jane Marie Doe") + creator = DataCite._build_creator(author, []) + assert creator.creatorName == "Doe, Jane Marie" + assert creator.givenName == "Jane Marie" + assert creator.familyName == "Doe" + + def test_last_first_format(self): + """Parse Last, First format.""" + author = Author(name="Doe, Jane") + creator = DataCite._build_creator(author, []) + assert creator.creatorName == "Doe, Jane" + assert creator.familyName == "Doe" + assert creator.givenName == "Jane" + + def test_name_with_suffix(self): + """Parse name with suffix (Jr, III, etc).""" + author = Author(name="John Smith Jr") + creator = DataCite._build_creator(author, []) + assert "Smith" in creator.creatorName + assert "Jr" in creator.familyName or "Jr" in creator.creatorName + + def test_string_author(self): + """Parse string author (not Author object).""" + creator = DataCite._build_creator("Jane Doe", ["MIT"]) + assert creator.creatorName == "Doe, Jane" + assert creator.affiliations == ["MIT"] + + def test_author_with_orcid(self): + """Author with ORCID gets name identifier.""" + author = Author(name="Jane Doe", orcid="0000-0001-2345-6789") + creator = DataCite._build_creator(author, []) + assert creator.nameIdentifiers is not None + assert len(creator.nameIdentifiers) == 1 + assert creator.nameIdentifiers[0]["nameIdentifier"] == "0000-0001-2345-6789" + assert creator.nameIdentifiers[0]["nameIdentifierScheme"] == "ORCID" + + def test_author_with_affiliations(self): + """Author with affiliations.""" + author = Author(name="Jane Doe", affiliations=["MIT", "Argonne"]) + creator = DataCite._build_creator(author, []) + assert creator.affiliations == ["MIT", "Argonne"] + + def test_single_name(self): + """Handle single name (no space).""" + author = Author(name="Madonna") + creator = DataCite._build_creator(author, []) + # Should handle gracefully, not crash + assert creator.creatorName is not None + + +class TestDataCiteFromManifest: + """Tests for DataCite.from_manifest().""" + + def test_minimal_manifest(self): + """Build DataCite from minimal manifest.""" + manifest = ManifestConfig(title="My Dataset", authors=["Jane Doe"]) + dc = DataCite.from_manifest(manifest) + assert len(dc.titles) == 1 + assert dc.titles[0].title == "My Dataset" + assert len(dc.creators) == 1 + assert dc.creators[0].creatorName == "Doe, Jane" + assert dc.publisher == "Materials Data Facility" + assert dc.publicationYear == str(datetime.now().year) + assert dc.resourceType.resourceTypeGeneral == "Dataset" + + def test_missing_title_raises(self): + """Missing title raises ValueError.""" + manifest = ManifestConfig(authors=["Jane Doe"]) + with pytest.raises(ValueError, match="title"): + DataCite.from_manifest(manifest) + + def test_missing_authors_raises(self): + """Missing authors raises ValueError.""" + manifest = ManifestConfig(title="My Dataset") + with pytest.raises(ValueError, match="authors"): + DataCite.from_manifest(manifest) + + def test_multiple_titles(self): + """Multiple titles are all included.""" + manifest = ManifestConfig( + title=["Main Title", "Alternative Title"], + authors=["Jane Doe"], + ) + dc = DataCite.from_manifest(manifest) + assert len(dc.titles) == 2 + assert dc.titles[0].title == "Main Title" + assert dc.titles[1].title == "Alternative Title" + + def test_multiple_authors(self): + """Multiple authors are all included.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe", "John Smith"], + ) + dc = DataCite.from_manifest(manifest) + assert len(dc.creators) == 2 + + def test_with_description(self): + """Description is included.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + description="A test dataset for testing.", + ) + dc = DataCite.from_manifest(manifest) + assert dc.descriptions is not None + assert len(dc.descriptions) == 1 + assert dc.descriptions[0].description == "A test dataset for testing." + assert dc.descriptions[0].descriptionType == "Other" + + def test_with_doi(self): + """DOI is included as identifier.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + dataset_doi="10.1234/test", + ) + dc = DataCite.from_manifest(manifest) + assert dc.identifier is not None + assert dc.identifier.identifier == "10.1234/test" + assert dc.identifier.identifierType == "DOI" + + def test_with_related_dois(self): + """Related DOIs are included.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + related_dois=["10.1234/paper1", "10.1234/paper2"], + ) + dc = DataCite.from_manifest(manifest) + assert dc.relatedIdentifiers is not None + assert len(dc.relatedIdentifiers) == 2 + assert dc.relatedIdentifiers[0].relatedIdentifier == "10.1234/paper1" + + def test_with_subjects(self): + """Subjects are included.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + subjects=["materials science", "DFT"], + ) + dc = DataCite.from_manifest(manifest) + assert dc.subjects is not None + assert len(dc.subjects) == 2 + assert dc.subjects[0].subject == "materials science" + + def test_custom_publisher(self): + """Custom publisher is used.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + publisher="My Organization", + ) + dc = DataCite.from_manifest(manifest) + assert dc.publisher == "My Organization" + + def test_custom_publication_year(self): + """Custom publication year is used.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + publication_year=2020, + ) + dc = DataCite.from_manifest(manifest) + assert dc.publicationYear == "2020" + + def test_publication_year_string(self): + """Publication year as string works.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + publication_year="2020", + ) + dc = DataCite.from_manifest(manifest) + assert dc.publicationYear == "2020" + + def test_invalid_publication_year_uses_current(self): + """Invalid publication year falls back to current year.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + publication_year="invalid", + ) + dc = DataCite.from_manifest(manifest) + assert dc.publicationYear == str(datetime.now().year) + + def test_dc_override(self): + """Manual dc block overrides generated values.""" + manifest = ManifestConfig( + title="My Dataset", + authors=["Jane Doe"], + dc={"publisher": "Override Publisher"}, + ) + dc = DataCite.from_manifest(manifest) + assert dc.publisher == "Override Publisher" + + def test_to_dict(self): + """DataCite.to_dict() returns proper dict.""" + manifest = ManifestConfig(title="My Dataset", authors=["Jane Doe"]) + dc = DataCite.from_manifest(manifest) + d = dc.to_dict() + assert "titles" in d + assert "creators" in d + assert "publisher" in d + assert "publicationYear" in d + assert "resourceType" in d + + +class TestSubmission: + """Tests for the Submission model.""" + + def test_submission_minimal(self): + """Minimal submission.""" + sub = Submission(dc={"titles": [{"title": "Test"}]}, data_sources=["https://example.com/data"]) + assert sub.test is False + assert sub.update is False + assert sub.update_metadata_only is False + + def test_submission_flags(self): + """Submission with flags.""" + sub = Submission( + dc={}, + data_sources=[], + test=True, + update=True, + update_metadata_only=True, + ) + assert sub.test is True + assert sub.update is True + assert sub.update_metadata_only is True + + def test_to_payload_minimal(self): + """to_payload() with minimal data.""" + sub = Submission(title="Test", data_sources=["https://example.com/data"]) + payload = sub.to_payload() + assert payload["title"] == "Test" + assert payload["data_sources"] == ["https://example.com/data"] + assert payload["test"] is False + assert payload["update"] is False + assert payload["update_metadata_only"] is False + + def test_to_payload_excludes_none(self): + """to_payload() excludes None optional fields.""" + sub = Submission(dc={}, data_sources=[]) + payload = sub.to_payload() + assert "mrr" not in payload + assert "custom" not in payload + assert "services" not in payload + assert "tags" not in payload + + def test_to_payload_includes_populated(self): + """to_payload() includes populated optional fields.""" + sub = Submission( + dc={}, + data_sources=[], + tags=["tag1", "tag2"], + services={"mdf_publish": True}, + custom={"field1": "value1"}, + ) + payload = sub.to_payload() + assert payload["tags"] == ["tag1", "tag2"] + assert payload["services"] == {"mdf_publish": True} + assert payload["custom"] == {"field1": "value1"} + + def test_to_payload_mdf_block(self): + """to_payload() includes mdf block.""" + sub = Submission( + dc={}, + data_sources=[], + mdf={"organization": "CHiMaD", "acl": ["public"]}, + ) + payload = sub.to_payload() + assert payload["mdf"] == {"organization": "CHiMaD", "acl": ["public"]} + + +class TestSubmissionPayloadShape: + """Tests to verify submission payload matches MDF Connect API expectations.""" + + def test_payload_has_required_keys(self): + """Payload has all required top-level keys.""" + sub = Submission(title="Test", data_sources=["https://example.com"]) + payload = sub.to_payload() + assert "title" in payload + assert "data_sources" in payload + assert "test" in payload + assert "update" in payload + assert "update_metadata_only" in payload + + def test_payload_data_types(self): + """Payload values have correct types.""" + sub = Submission( + title="Test", + data_sources=["https://example.com"], + test=True, + tags=["tag1"], + ) + payload = sub.to_payload() + assert isinstance(payload["title"], str) + assert isinstance(payload["data_sources"], list) + assert isinstance(payload["test"], bool) + assert isinstance(payload["update"], bool) + assert isinstance(payload["tags"], list) + + def test_payload_includes_domains(self): + """to_payload() includes domains when set.""" + sub = Submission(domains=["materials", "chemistry"]) + payload = sub.to_payload() + assert payload["domains"] == ["materials", "chemistry"] + + def test_payload_excludes_domains_when_none(self): + """to_payload() excludes domains when not set.""" + sub = Submission() + payload = sub.to_payload() + assert "domains" not in payload + + def test_payload_includes_external_import(self): + """to_payload() includes external import fields when set.""" + sub = Submission( + external_doi="10.5281/zenodo.1234567", + external_url="https://zenodo.org/record/1234567", + external_source="Zenodo", + ) + payload = sub.to_payload() + assert payload["external_doi"] == "10.5281/zenodo.1234567" + assert payload["external_url"] == "https://zenodo.org/record/1234567" + assert payload["external_source"] == "Zenodo" + + def test_payload_excludes_external_import_when_none(self): + """to_payload() excludes external import fields when not set.""" + sub = Submission() + payload = sub.to_payload() + assert "external_doi" not in payload + assert "external_url" not in payload + assert "external_source" not in payload + + +class TestToMetadataPayload: + """Tests for ManifestConfig.to_metadata_payload().""" + + def test_domains_in_payload(self): + """Domains flow through to metadata payload.""" + manifest = ManifestConfig( + title="Test", + authors=["Jane Doe"], + domains=["materials", "chemistry"], + ) + payload = manifest.to_metadata_payload() + assert payload["domains"] == ["materials", "chemistry"] + + def test_domains_omitted_when_none(self): + """Domains not in payload when not set.""" + manifest = ManifestConfig(title="Test", authors=["Jane Doe"]) + payload = manifest.to_metadata_payload() + assert "domains" not in payload + + def test_external_import_in_payload(self): + """External import fields flow through to metadata payload.""" + manifest = ManifestConfig( + title="Test", + authors=["Jane Doe"], + external_doi="10.5281/zenodo.1234567", + external_url="https://zenodo.org/record/1234567", + external_source="Zenodo", + ) + payload = manifest.to_metadata_payload() + assert payload["external_doi"] == "10.5281/zenodo.1234567" + assert payload["external_url"] == "https://zenodo.org/record/1234567" + assert payload["external_source"] == "Zenodo" + + def test_external_import_omitted_when_none(self): + """External import fields not in payload when not set.""" + manifest = ManifestConfig(title="Test", authors=["Jane Doe"]) + payload = manifest.to_metadata_payload() + assert "external_doi" not in payload + assert "external_url" not in payload + assert "external_source" not in payload + + def test_partial_external_import(self): + """Only set external import fields appear in payload.""" + manifest = ManifestConfig( + title="Test", + authors=["Jane Doe"], + external_doi="10.5281/zenodo.1234567", + ) + payload = manifest.to_metadata_payload() + assert payload["external_doi"] == "10.5281/zenodo.1234567" + assert "external_url" not in payload + assert "external_source" not in payload diff --git a/tests/test_repository.py b/tests/test_repository.py new file mode 100644 index 0000000..8a4cee1 --- /dev/null +++ b/tests/test_repository.py @@ -0,0 +1,403 @@ +"""Tests for MDF Agent git-backed Repository operations.""" + +import subprocess +import pytest +from pathlib import Path +from tempfile import TemporaryDirectory + +from mdf_agent.core.exceptions import NotARepositoryError +from mdf_agent.core.repository import Repository + + +def _git(args, cwd): + """Helper to run git commands in tests.""" + return subprocess.run( + ["git"] + args, cwd=str(cwd), capture_output=True, text=True, check=True, + ) + + +class TestRepositoryInit: + """Tests for repository initialization.""" + + def test_init_creates_structure(self): + """init_repo creates .git/, .gitignore, and mdf.yaml.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo( + root=root, + title="Test Dataset", + authors=["Jane Doe"], + ) + + assert (root / ".git").is_dir() + assert (root / "mdf.yaml").is_file() + assert (root / ".gitignore").is_file() + + def test_init_creates_manifest_with_metadata(self): + """init_repo creates mdf.yaml with correct content.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo( + root=root, + title="Test Dataset", + authors=["Jane Doe", "John Smith"], + description="A test dataset", + publisher="Test Publisher", + publication_year=2025, + ) + + manifest = repo.load_manifest() + assert manifest.title == "Test Dataset" + assert manifest.authors == ["Jane Doe", "John Smith"] + assert manifest.description == "A test dataset" + assert manifest.publisher == "Test Publisher" + assert manifest.publication_year == 2025 + + def test_init_creates_git_commit(self): + """init_repo creates an initial git commit.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + Repository.init_repo( + root=root, + title="Test Dataset", + authors=["Jane Doe"], + ) + + result = _git(["log", "--oneline"], cwd=root) + assert "Initialize MDF dataset" in result.stdout + + def test_init_nested_directory(self): + """init_repo creates nested directories if needed.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) / "nested" / "path" / "dataset" + Repository.init_repo( + root=root, + title="Nested Dataset", + authors=["Jane Doe"], + ) + + assert root.exists() + assert (root / ".git").is_dir() + assert (root / "mdf.yaml").is_file() + + def test_init_does_not_overwrite_manifest(self): + """init_repo preserves existing mdf.yaml.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + + # Create initial manifest + manifest_path = root / "mdf.yaml" + root.mkdir(parents=True, exist_ok=True) + manifest_path.write_text("title: Existing Dataset\nauthors:\n - Existing Author\n") + + # Init should not overwrite + repo = Repository.init_repo( + root=root, + title="New Dataset", + authors=["New Author"], + ) + + manifest = repo.load_manifest() + assert manifest.title == "Existing Dataset" + assert manifest.authors == ["Existing Author"] + + def test_init_existing_git_repo(self): + """init_repo works in an existing git repo without re-initializing.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + _git(["init"], cwd=root) + # Create an existing commit + (root / "README.md").write_text("hello") + _git(["add", "README.md"], cwd=root) + _git(["commit", "-m", "Initial commit"], cwd=root) + + repo = Repository.init_repo( + root=root, + title="Test Dataset", + authors=["Jane Doe"], + ) + + # Should have both commits + result = _git(["log", "--oneline"], cwd=root) + assert "Initial commit" in result.stdout + assert "Initialize MDF dataset" in result.stdout + + def test_init_gitignore_contains_mdf(self): + """init_repo creates .gitignore that excludes .mdf/.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + Repository.init_repo(root=root, title="Test", authors=["Jane"]) + + gitignore = (root / ".gitignore").read_text() + assert ".mdf/" in gitignore + + +class TestRepositoryLoad: + """Tests for loading existing repositories.""" + + def test_load_existing_repo(self): + """load() loads an existing repository.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + + # Create a repo + Repository.init_repo(root, title="Test", authors=["Jane"]) + + # Load it + repo = Repository.load(root) + assert repo.root == root + + def test_load_nonexistent_raises(self): + """load() raises NotARepositoryError if no mdf.yaml or not a git repo.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + with pytest.raises(NotARepositoryError): + Repository.load(root) + + def test_load_no_git_raises(self): + """load() raises NotARepositoryError if mdf.yaml exists but no git repo.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "mdf.yaml").write_text("title: Test\nauthors:\n - Jane\n") + with pytest.raises(NotARepositoryError): + Repository.load(root) + + +class TestRepositoryStage: + """Tests for staging files.""" + + def test_stage_single_file(self): + """stage() adds a single file to git staging area.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + staged = repo.stage(["data.csv"]) + + assert staged == ["data.csv"] + # Verify git sees it as staged + result = _git(["diff", "--cached", "--name-only"], cwd=root) + assert "data.csv" in result.stdout + + def test_stage_multiple_files(self): + """stage() adds multiple files.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data1.csv").write_text("a,b,c") + (root / "data2.csv").write_text("d,e,f") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + staged = repo.stage(["data1.csv", "data2.csv"]) + + assert len(staged) == 2 + + def test_stage_glob_pattern(self): + """stage() supports glob patterns.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data1.csv").write_text("a,b,c") + (root / "data2.csv").write_text("d,e,f") + (root / "readme.txt").write_text("test") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + staged = repo.stage(["*.csv"]) + + assert len(staged) == 2 + assert all(f.endswith(".csv") for f in staged) + + def test_stage_directory(self): + """stage() handles directories.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + data_dir = root / "data" + data_dir.mkdir() + (data_dir / "file1.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + staged = repo.stage(["data"]) + + assert "data" in staged + + def test_stage_no_match_raises(self): + """stage() raises if no files match.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + with pytest.raises(FileNotFoundError, match="No files matched"): + repo.stage(["nonexistent.csv"]) + + +class TestRepositoryCommit: + """Tests for committing staged files.""" + + def test_commit_creates_git_commit(self): + """commit() creates a real git commit.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + repo.stage(["data.csv"]) + commit_data = repo.commit("Add data file") + + assert commit_data["message"] == "Add data file" + assert "data.csv" in commit_data["staged_files"] + assert len(commit_data["hash"]) == 40 # full SHA + + # Verify in git log + result = _git(["log", "--oneline"], cwd=root) + assert "Add data file" in result.stdout + + def test_commit_no_staged_raises(self): + """commit() raises if nothing is staged.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + with pytest.raises(ValueError, match="No staged files"): + repo.commit("Empty commit") + + def test_multiple_commits(self): + """Multiple commits are recorded in git log.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data1.csv").write_text("a,b,c") + (root / "data2.csv").write_text("d,e,f") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + repo.stage(["data1.csv"]) + repo.commit("First commit") + + repo.stage(["data2.csv"]) + repo.commit("Second commit") + + result = _git(["log", "--oneline"], cwd=root) + assert "First commit" in result.stdout + assert "Second commit" in result.stdout + + +class TestRepositoryStatus: + """Tests for repository status.""" + + def test_get_status_clean(self): + """get_status() shows clean state after commit.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + status = repo.get_status() + assert status["staged_files"] == [] + assert status["modified_files"] == [] + assert status["untracked_files"] == [] + assert len(status["commits"]) >= 1 + + def test_get_status_with_staged(self): + """get_status() shows staged files.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + repo.stage(["data.csv"]) + + status = repo.get_status() + assert "data.csv" in status["staged_files"] + + def test_get_status_with_untracked(self): + """get_status() shows untracked files.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + (root / "new_file.csv").write_text("data") + + status = repo.get_status() + assert "new_file.csv" in status["untracked_files"] + + def test_get_status_with_modified(self): + """get_status() shows modified tracked files.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + repo.stage(["data.csv"]) + repo.commit("Add data") + + # Modify the file + (root / "data.csv").write_text("a,b,c,d") + + status = repo.get_status() + assert "data.csv" in status["modified_files"] + + +class TestRepositoryTrackedFiles: + """Tests for get_tracked_files.""" + + def test_tracked_excludes_infrastructure(self): + """get_tracked_files() excludes mdf.yaml and .gitignore.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + (root / "data.csv").write_text("a,b,c") + + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + repo.stage(["data.csv"]) + repo.commit("Add data") + + tracked = repo.get_tracked_files() + assert "data.csv" in tracked + assert "mdf.yaml" not in tracked + assert ".gitignore" not in tracked + + +class TestRepositoryTags: + """Tests for git tag operations.""" + + def test_tag_and_list(self): + """tag() creates tags, get_tags() lists them.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + repo.tag("mdf/v1.0") + tags = repo.get_tags() + assert "mdf/v1.0" in tags + + def test_has_commits(self): + """has_commits() returns True after init.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + assert repo.has_commits() + + +class TestRepositoryManifest: + """Tests for manifest operations.""" + + def test_load_manifest(self): + """load_manifest() returns ManifestConfig.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + manifest = repo.load_manifest() + assert manifest.title == "Test" + assert manifest.authors == ["Jane"] + + def test_save_manifest(self): + """save_manifest() persists changes.""" + with TemporaryDirectory() as tmpdir: + root = Path(tmpdir) + repo = Repository.init_repo(root, title="Test", authors=["Jane"]) + + manifest = repo.load_manifest() + manifest.title = "Updated Title" + manifest.description = "Added description" + repo.save_manifest(manifest) + + # Reload and verify + manifest2 = repo.load_manifest() + assert manifest2.title == "Updated Title" + assert manifest2.description == "Added description" diff --git a/tests/test_submission_normalize.py b/tests/test_submission_normalize.py new file mode 100644 index 0000000..945068f --- /dev/null +++ b/tests/test_submission_normalize.py @@ -0,0 +1,105 @@ +"""Tests for data source URL normalization.""" + +import pytest + +from mdf_agent.core.submission import normalize_data_source, resolve_data_sources +from mdf_agent.auth.globus import NCSA_MDF_COLLECTION_UUID + + +class TestNormalizeDataSource: + """Tests for normalize_data_source().""" + + def test_globus_file_manager_url(self): + """Globus File Manager URL is converted to globus:// URI.""" + url = ( + "https://app.globus.org/file-manager" + "?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec" + "&origin_path=%2Ftmp%2Fstaging%2F" + ) + result = normalize_data_source(url) + assert result == "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/tmp/staging/" + + def test_globus_file_manager_with_file(self): + """Globus File Manager URL with file path.""" + url = ( + "https://app.globus.org/file-manager" + "?origin_id=aaaa1111-2222-3333-4444-555566667777" + "&origin_path=%2Fdata%2Ftest.csv" + ) + result = normalize_data_source(url) + assert result == "globus://aaaa1111-2222-3333-4444-555566667777/data/test.csv" + + def test_mdf_data_domain(self): + """data.materialsdatafacility.org URL maps to NCSA collection.""" + url = "https://data.materialsdatafacility.org/tmp/staging/file.csv" + result = normalize_data_source(url) + assert result == f"globus://{NCSA_MDF_COLLECTION_UUID}/tmp/staging/file.csv" + + def test_mdf_data_domain_root_path(self): + """data.materialsdatafacility.org with just a path.""" + url = "https://data.materialsdatafacility.org/my-dataset/" + result = normalize_data_source(url) + assert result == f"globus://{NCSA_MDF_COLLECTION_UUID}/my-dataset/" + + def test_globus_uri_passthrough(self): + """globus:// URIs pass through unchanged.""" + url = "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/path/to/data" + assert normalize_data_source(url) == url + + def test_stream_uri_passthrough(self): + """stream:// URIs pass through unchanged.""" + url = "stream://my-stream-id" + assert normalize_data_source(url) == url + + def test_external_https_passthrough(self): + """External HTTPS URLs pass through unchanged.""" + url = "https://zenodo.org/record/12345/files/data.zip" + assert normalize_data_source(url) == url + + def test_http_passthrough(self): + """HTTP URLs pass through unchanged.""" + url = "http://example.com/data.csv" + assert normalize_data_source(url) == url + + def test_globus_file_manager_missing_origin_id(self): + """Globus File Manager URL without origin_id passes through.""" + url = "https://app.globus.org/file-manager?origin_path=%2Ftmp%2F" + assert normalize_data_source(url) == url + + def test_globus_file_manager_encoded_path(self): + """URL-encoded path components are decoded.""" + url = ( + "https://app.globus.org/file-manager" + "?origin_id=82f1b5c6-6e9b-11e5-ba47-22000b92c6ec" + "&origin_path=%2Fpath%20with%20spaces%2Ffile.csv" + ) + result = normalize_data_source(url) + assert result == "globus://82f1b5c6-6e9b-11e5-ba47-22000b92c6ec/path with spaces/file.csv" + + +class TestResolveDataSourcesNormalization: + """Tests that resolve_data_sources() applies normalization.""" + + def test_normalizes_https_urls(self, tmp_path): + """HTTPS URLs go through normalize_data_source.""" + sources = ["https://data.materialsdatafacility.org/test/data.csv"] + result = resolve_data_sources(sources, tmp_path) + assert result == [f"globus://{NCSA_MDF_COLLECTION_UUID}/test/data.csv"] + + def test_stream_urls_pass_through(self, tmp_path): + """stream:// URLs pass through normalization unchanged.""" + sources = ["stream://my-stream"] + result = resolve_data_sources(sources, tmp_path) + assert result == ["stream://my-stream"] + + def test_globus_urls_pass_through(self, tmp_path): + """globus:// URLs pass through normalization unchanged.""" + sources = ["globus://uuid-here/path/data"] + result = resolve_data_sources(sources, tmp_path) + assert result == ["globus://uuid-here/path/data"] + + def test_local_paths_resolved(self, tmp_path): + """Local paths are resolved relative to root.""" + (tmp_path / "data.csv").touch() + result = resolve_data_sources(["data.csv"], tmp_path) + assert result == [str(tmp_path / "data.csv")] diff --git a/v2-review.md b/v2-review.md new file mode 100644 index 0000000..0a1fc90 --- /dev/null +++ b/v2-review.md @@ -0,0 +1,777 @@ +# MDF Connect v2 Review and Remediation + +Date: February 6, 2026 +Scope: `cs/aws/v2` backend, related local tooling, and validation tests. + +## What we reviewed + +The v2 backend architecture and implementation were reviewed for: + +- Security and authorization boundaries +- Data integrity across SQLite/DynamoDB code paths +- Storage/path safety +- Version and pagination correctness +- Local developer workflow drift + +## High-impact issues found and corrected + +### 1) Server token leakage in upload URL responses + +**Issue:** Globus upload URL responses included an `Authorization` header built from server credentials. +**Risk:** Credential exposure to clients. + +**Fix implemented:** + +- Removed server bearer token from upload URL responses. +- Upload URL now returns content headers plus `auth_type: bearer` hint. + +**Files:** + +- `cs/aws/v2/storage/globus_https.py` + +--- + +### 2) Missing ownership checks on stream/submission mutations + +**Issue:** Authenticated users could mutate resources they did not own (append, close, upload, status update, etc.). +**Risk:** Unauthorized modification of other users' data. + +**Fix implemented:** + +- Added centralized owner-or-curator guard helpers. +- Enforced stream owner/curator checks on stream and file mutation routes. +- Enforced owner/curator checks on stream previews. +- Made submission status updates curator-only. +- Restricted org-wide submissions listing to curators. + +**Files:** + +- `cs/aws/v2/app/auth.py` +- `cs/aws/v2/app/routers/submissions.py` +- `cs/aws/v2/app/routers/streams.py` +- `cs/aws/v2/app/routers/files.py` +- `cs/aws/v2/app/routers/preview.py` + +--- + +### 3) Path traversal and unsafe path handling + +**Issue:** Storage path construction and reads accepted traversal-style inputs (for example `../`). +**Risk:** Out-of-scope file read/write attempts in local storage and unsafe remote path usage. + +**Fix implemented:** + +- Added strict `stream_id` and `filename` sanitization in storage base class. +- Local storage now resolves and validates paths under base directory. +- Globus storage now rejects URL-style and traversal paths. +- File confirm/download routes now verify that paths belong to the requested stream. + +**Files:** + +- `cs/aws/v2/storage/base.py` +- `cs/aws/v2/storage/local.py` +- `cs/aws/v2/storage/globus_https.py` +- `cs/aws/v2/app/routers/files.py` + +--- + +### 4) SQLite upsert dropped `dataset_profile` + +**Issue:** `INSERT OR REPLACE` path omitted `dataset_profile`, which could erase profiling data during curation updates. +**Risk:** Data loss and degraded preview/card behavior. + +**Fix implemented:** + +- Included `dataset_profile` in SQLite write/upsert SQL and serialization logic. + +**Files:** + +- `cs/aws/v2/store.py` + +--- + +### 5) Incorrect "latest version" selection + +**Issue:** Some paths used lexicographic version ordering, where `1.9` could beat `1.10`. +**Risk:** Wrong version returned by default. + +**Fix implemented:** + +- Unified latest-version selection on semantic comparison (`latest_version(...)`). +- Updated generic store `get(...)` and status endpoints to use semantic version choice. + +**Files:** + +- `cs/aws/v2/store.py` +- `cs/aws/v2/app/routers/submissions.py` + +--- + +### 6) Incomplete DynamoDB pagination + +**Issue:** Single-page scans could miss matching records for status lists/search when datasets exceeded one page. +**Risk:** Partial/incorrect query results. + +**Fix implemented:** + +- Added explicit pagination loops for: + - Submission `list_by_status(...)` + - Submission `list_all(...)` + - Stream `list_all(...)` + +**Files:** + +- `cs/aws/v2/store.py` +- `cs/aws/v2/stream_store.py` + +--- + +### 7) Local tooling drift (legacy local server assumptions) + +**Issue:** Local scripts referenced removed `local_server.py` and old response shapes. +**Risk:** Broken local workflows and onboarding friction. + +**Fix implemented:** + +- Updated `local_start.sh` to run the FastAPI app (`python -m v2.app.main`) from correct working directory. +- Added startup verification and log file output. +- Updated local test scripts to parse FastAPI JSON response format. +- Replaced broken `local_runner.py` with a working HTTP-based local command runner. +- Updated deployment docs to reflect router-based FastAPI endpoint model. + +**Files:** + +- `cs/aws/v2/local_start.sh` +- `cs/aws/v2/local_test.sh` +- `cs/aws/v2/local_test_stream.sh` +- `cs/aws/v2/local_runner.py` +- `cs/aws/DEPLOYMENT.md` + +## Validation completed + +- Static compile check: + - `python -m compileall cs/aws/v2 src/mdf_agent/core/backend_client.py src/mdf_agent/cli/backend.py src/mdf_agent/cli/stream.py` +- Added and ran hardening tests: + - `pytest -q cs/aws/v2/test_v2_hardening.py` + - Result: `6 passed` + +### New test coverage + +`cs/aws/v2/test_v2_hardening.py` verifies: + +- Semantic version latest selection (`1.10` over `1.9`) +- SQLite upsert preserves `dataset_profile` +- Local storage traversal rejection +- Globus upload URL does not expose server auth header +- Stream append requires ownership +- Status update requires curator privileges + +## Remaining recommended future directions + +1. Move profiling/DOI/indexing into async workers (queue-driven) to keep request latency predictable. +2. Add policy-driven ACL model for dataset visibility and org scope. +3. Expand API integration test suite for curation transitions, path validation edge cases, and Dynamo pagination behavior under larger datasets. +4. Replace in-process search with external indexed search synchronization. + +--- + +## Additional remediation pass (append) + +Date: February 6, 2026 (follow-up pass) + +### A1) `upload-confirm` accepted non-existent paths + +**Issue:** `POST /stream/{id}/upload-confirm` could update stream accounting even if file path did not exist. +**Risk:** Inflated file stats and inconsistent metadata. + +**Fix implemented:** + +- Added existence check (`storage.get_file(path)`) before updating stream counts. + +**Files:** + +- `cs/aws/v2/app/routers/files.py` + +--- + +### A2) Curation APIs defaulted to version `1.0` + +**Issue:** Curation endpoints defaulted to `1.0` when version was omitted. +**Risk:** Reviewing/approving/rejecting the wrong version for multi-version datasets. + +**Fix implemented:** + +- Added resolver that selects the latest semantic version when `version` is omitted. +- Updated `GET /curation/{source_id}`, approve, and reject flows to use resolver. +- Added fallback handling in pending-list title parsing for malformed metadata records. + +**Files:** + +- `cs/aws/v2/app/routers/curation.py` + +--- + +### A3) `clone_url` token exfiltration risk to arbitrary hosts + +**Issue:** Clone operations could send bearer token to any caller-supplied URL host. +**Risk:** Credential leakage if a malicious URL is provided. + +**Fix implemented:** + +- Added strict HTTPS host validation for clone downloads. +- Tokens are now only sent to trusted host(s): configured `GLOBUS_HTTPS_SERVER` plus optional `GLOBUS_ALLOWED_HOSTS`. + +**Files:** + +- `cs/aws/v2/clone.py` + +--- + +### A4) Remaining UTC deprecation warnings in touched paths + +**Issue:** Multiple code paths used deprecated `datetime.utcnow()` usage. +**Risk:** Runtime warnings and future Python compatibility risk. + +**Fix implemented:** + +- Switched touched paths to timezone-aware UTC timestamps (`datetime.now(timezone.utc)`). + +**Files:** + +- `cs/aws/v2/store.py` +- `cs/aws/v2/stream_store.py` +- `cs/aws/v2/app/routers/submissions.py` +- `cs/aws/v2/app/routers/streams.py` +- `cs/aws/v2/app/routers/files.py` +- `cs/aws/v2/storage/globus_https.py` + +--- + +### A5) Deployment monitoring snippet still had stale function naming + +**Issue:** Monitoring command assumed a static Lambda resource name pattern. +**Risk:** Broken monitoring commands after stack/function name changes. + +**Fix implemented:** + +- Updated docs to fetch physical Lambda function name from CloudFormation resources first. + +**Files:** + +- `cs/aws/DEPLOYMENT.md` + +## Follow-up validation + +- `pytest -q cs/aws/v2/test_v2_hardening.py` + Result: `9 passed` +- `python -m compileall cs/aws/v2` + Result: success + +### Added tests in this pass + +- `test_upload_confirm_requires_existing_file` +- `test_curation_without_version_uses_latest` +- `test_clone_rejects_untrusted_host_for_token_use` + +--- + +### A6) Remaining stale local-server reference in demo script + +**Issue:** Demo summary still instructed users to run removed `v2/local_server.py`. +**Risk:** Confusing onboarding path. + +**Fix implemented:** + +- Updated demo text to current local startup flow (`cd cs/aws && ./deploy.sh local`). + +**Files:** + +- `cs/aws/demo_mdf_v2.py` + +**Validation:** + +- `python -m py_compile cs/aws/demo_mdf_v2.py` + Result: success + +--- + +## Next-steps implementation pass (append) + +Date: February 6, 2026 (next-step execution) + +This section addresses the follow-up priorities: + +- Near-term items 1-4: implemented +- Medium-term items 5-7: not fully implemented in this pass (tracked) +- Low-priority item 9: partially implemented (request-correlated structured logs) + +### B1) Rate limiting / request throttling + +**Implemented:** + +- Added HTTP middleware with per-actor, per-route-family in-memory rate limits. +- Added endpoint-class defaults and env-tunable limits: + - `RATE_LIMIT_DEFAULT_PER_MIN` + - `RATE_LIMIT_SUBMIT_PER_MIN` + - `RATE_LIMIT_STREAM_CREATE_PER_MIN` + - `RATE_LIMIT_STREAM_UPLOAD_PER_MIN` + - `RATE_LIMIT_STREAM_MUTATION_PER_MIN` + - `RATE_LIMIT_WINDOW_SECONDS` +- Added SAM HTTP API default throttle settings (rate + burst) at gateway layer. + +**Files:** + +- `cs/aws/v2/app/middleware.py` +- `cs/aws/v2/app/__init__.py` +- `cs/aws/template.yaml` + +--- + +### B2) Input size limits + +**Implemented:** + +- Added global request body cap middleware (`MAX_REQUEST_BYTES`). +- Added submit metadata size cap (`MAX_SUBMIT_METADATA_BYTES`). +- Added submit object cardinality caps (`MAX_SUBMIT_DATA_SOURCES`, `MAX_SUBMIT_AUTHORS`). +- Added stream-append caps (`MAX_STREAM_APPEND_COUNT`, `MAX_STREAM_APPEND_BYTES`). +- Added model-level list/count constraints in request models. + +**Files:** + +- `cs/aws/v2/app/middleware.py` +- `cs/aws/v2/app/routers/submissions.py` +- `cs/aws/v2/app/routers/streams.py` +- `cs/aws/v2/app/models.py` + +--- + +### B3) Expanded integration test coverage + +**Implemented:** + +- Added end-to-end happy path: + - submit -> pending_curation -> approve (DOI) -> card +- Added throttling and request-size enforcement test. +- Added submissions pagination test with 55 records and multiple pages. +- Added stream path validation edge-case test for download-url ownership binding. +- Added curation transition test for reject rules (invalid and valid transitions). + +**Files:** + +- `cs/aws/v2/test_v2_integration.py` + +--- + +### B4) `_mint_doi_for_stream` timestamp consistency + +**Implemented:** + +- Updated DOI publication year derivation to timezone-aware UTC: + - `datetime.now(timezone.utc).year` + +**Files:** + +- `cs/aws/v2/app/routers/streams.py` + +--- + +### B5) Structured logging (low-priority item 9, partial) + +**Implemented:** + +- Added request-correlated logging for completion/error events. +- Added automatic `X-Request-Id` response header. +- Logs include method, path, status, and duration in structured JSON format. + +**Files:** + +- `cs/aws/v2/app/middleware.py` + +--- + +### B6) Additional UTC cleanup in touched runtime paths + +**Implemented:** + +- Replaced remaining deprecated `datetime.utcnow()` in test-exercised runtime code. + +**Files:** + +- `cs/aws/v2/storage/base.py` +- `cs/aws/v2/datacite.py` + +## Validation for this pass + +- `pytest -q cs/aws/v2/test_v2_hardening.py cs/aws/v2/test_v2_integration.py` + Result: `14 passed` +- `python -m compileall cs/aws/v2` + Result: success + +## Remaining items not completed in this pass + +1. Async workers for profiling/DOI/indexing (item 5): still inline in request path; needs queue + worker architecture. +2. Policy-driven ACL model (item 6): current model remains owner-or-curator. +3. External search backend (item 7): current implementation still in-process scan/filter. +4. `action_id` schema cleanup (item 8): intentionally deferred. + +## Cost optimization pass (AWS deployment) + +Date: February 6, 2026 (cost review + implementation) + +Objective: keep AWS spend low for expected throughput: + +- Publishing APIs: hundreds of queries/month +- Streams workload: ~10k requests/month + +### C1) Cost review findings + +1. Lambda sizing/timeouts were higher than needed for low-throughput serverless usage: + - API Lambda timeout was 120s (while API Gateway integration timeout is much lower). + - Both Lambdas were fixed at 512 MB. +2. CloudWatch log retention was unbounded (default indefinite retention can grow storage cost over time). +3. Search endpoint allowed large caller limits and scanned full table caps by default (`list_all()` defaults), increasing variable Dynamo read cost risk. +4. Async worker could invoke at very low message density (no batch window), causing extra Lambda invoke overhead. + +### C2) Implemented cost controls + +#### Infrastructure right-sizing (`cs/aws/template.yaml`) + +- Added tunable parameters with low-cost defaults: + - `ApiMemorySizeMb` (default `256`) + - `ApiTimeoutSeconds` (default `30`) + - `AsyncWorkerMemorySizeMb` (default `256`) + - `AsyncWorkerTimeoutSeconds` (default `60`) + - `ApiReservedConcurrency` (default `10`) + - `AsyncWorkerReservedConcurrency` (default `3`) + - `AsyncWorkerBatchSize` (default `10`) + - `AsyncWorkerBatchWindowSeconds` (default `5`) + - `AsyncQueueMessageRetentionSeconds` (default `345600` = 4 days) + - `LogRetentionDays` (default `14`) +- Updated function resources to use those parameters. +- Added explicit CloudWatch log groups with bounded retention: + - `/aws/lambda/${ApiFunction}` + - `/aws/lambda/${AsyncWorkerFunction}` +- Set API runtime search caps via env vars: + - `SEARCH_MAX_RESULTS=50` + - `SEARCH_MAX_DATASET_SCAN=1000` + - `SEARCH_MAX_STREAM_SCAN=2000` + +#### Search read-cost bounding (`cs/aws/v2/search.py`, `cs/aws/v2/app/routers/search.py`) + +- Added environment-driven scan caps for dataset and stream searches. +- Search now uses `store.list_all(limit=...)` with caps instead of unbounded defaults. +- Added max-results clamp at API layer (`SEARCH_MAX_RESULTS`, default 50), preventing expensive oversized result requests. + +#### Regression test coverage (`cs/aws/v2/test_v2_integration.py`) + +- Added `test_search_limit_is_capped`: + - Seeds 60 matching records. + - Confirms `/search` clamps output to 50 even when client requests `limit=500`. + +### C3) Validation + +- `pytest -q cs/aws/v2/test_v2_hardening.py cs/aws/v2/test_v2_integration.py cs/aws/v2/test_v2_async_jobs.py` + - Result: `17 passed` +- `python -m compileall cs/aws/v2` + - Result: success + +### C4) Cost posture for expected traffic + +Given current architecture (Lambda + HTTP API + DynamoDB on-demand + SQS), this remains a low fixed-cost, pay-per-use stack and is appropriate for your stated volume. The new concurrency caps, memory defaults, scan limits, and log retention reduce surprise spend risk without changing product behavior. + +### C5) Optional future cost lever (not enabled in this pass) + +- Evaluate DynamoDB table class `STANDARD_INFREQUENT_ACCESS` once dataset size grows and access patterns are confirmed cold-heavy. This can reduce storage cost but increases per-request read/write pricing. + +## CLI-v2 wiring plan intake (shared context) + +Date: February 6, 2026 +Source plan reviewed: `/Users/ben/.claude/plans/serene-enchanting-planet.md` + +### D1) Plan review status + +- Read and validated the full phased plan for wiring CLI auth/routing to v2 backend. +- Confirmed objective: unify all CLI paths through `BackendClient` with Globus/dev auth support, add login/logout/whoami, and deprecate legacy direct submission path. + +### D2) Completion snapshot against phases (current codebase) + +1. Phase 1 (`backend_client.py` auth + URL resolution): **not implemented** + - `BackendClient` still only takes `base_url` and sends no auth headers. + - No `authenticated()` factory, no `_api_url_for_service()`, no service URL map. +2. Phase 2 (`login`/`logout`/`whoami` CLI): **not implemented** + - Commands not present in `src/mdf_agent/cli/main.py`. +3. Phase 3 (`mdf publish` rewrite): **not implemented** + - `publish` still uses `--local` split path and legacy `agent.publish(authorizer=...)`. +4. Phase 4 (`backend` + `stream` shared auth options): **not implemented** + - No Typer callback-level `--service/--token/--dev-user` auth options in `src/mdf_agent/cli/backend.py` or `src/mdf_agent/cli/stream.py`. +5. Phase 5 (search + agent stream helpers + skill handlers): **not implemented** + - `search` still uses unauthenticated `BackendClient` creation path. + - `MDFAgent.stream_*` and skill handlers still use unauthenticated `BackendClient(base_url)/from_env()`. +6. Phase 6 (deprecate `submit_submission`): **not implemented** + - `submit_submission()` currently has no deprecation warning and is still used by `MDFAgent.publish()`. + +### D3) What has been completed for this plan + +- Plan fully reviewed and translated into a concrete code-level gap analysis against current implementation. +- No code changes from this plan have been applied yet in this pass. + +## CLI-v2 wiring implementation pass (completed) + +Date: February 6, 2026 +Plan source: `/Users/ben/.claude/plans/serene-enchanting-planet.md` + +### E1) Phase 1 complete: BackendClient auth + URL resolution + +Implemented in `src/mdf_agent/core/backend_client.py`: + +- Added `_V2_API_URLS` and `_api_url_for_service()` with `MDF_API_URL` override support. +- Extended `BackendClient.__init__` to accept `token` and `user_id`. +- Updated `_request()` to inject: + - `Authorization: Bearer ` when token present + - `X-User-Id` when dev user present and no token +- Updated `from_env()` to read: + - `MDF_API_URL` + - `MDF_CONNECT_TOKEN` + - `MDF_DEV_USER_ID` +- Added `BackendClient.authenticated(...)` factory with resolution order: + 1. explicit token + 2. `MDF_CONNECT_TOKEN` + 3. explicit/env dev user (`MDF_DEV_USER_ID`, plus `LOCAL_USER_ID` for local) + 4. interactive Globus login (lazy import) +- Local service (`service_instance=local`) now avoids forced OAuth when no credentials are present. + +### E2) Phase 2 complete: `login` / `logout` / `whoami` + +Implemented in `src/mdf_agent/cli/main.py`: + +- Added `mdf login` with `--service` and optional `--token`. +- Added `mdf logout` to clear cached tokens. +- Added `mdf whoami` to show auth status and token store path. +- Used lazy imports inside command functions to avoid requiring Globus SDK for unrelated CLI usage. + +### E3) Phase 3 complete: unified `mdf publish` + +Implemented in `src/mdf_agent/cli/main.py` and `src/mdf_agent/core/agent.py`: + +- Removed `--local` branch logic in main publish command. +- `--service` now supports `prod/dev/local`; added `--dev-user`. +- All submit paths now go through `BackendClient.authenticated(...).submit(...)`. +- Removed `action_id` output emphasis from CLI success output. +- `MDFAgent.publish()` now supports: + - `token`, `service_instance`, `api_url`, `dev_user_id` + - legacy `authorizer` extraction for backward compatibility + +### E4) Phase 4 complete: auth callback pattern in backend/stream subcommands + +Implemented in: + +- `src/mdf_agent/cli/backend.py` +- `src/mdf_agent/cli/stream.py` + +Changes: + +- Added Typer callbacks with shared options: + - `--service` + - `--token` + - `--dev-user` +- Updated shared `_client()` helper in both files to use `BackendClient.authenticated(...)`. +- Existing subcommands retained their command-level behavior while inheriting shared auth routing. + +### E5) Phase 5 complete: remaining consumers + +Implemented in: + +- `src/mdf_agent/cli/main.py` (`mdf search`) +- `src/mdf_agent/core/agent.py` (`stream_*` helpers) +- `src/mdf_agent/skill/handlers.py` + +Changes: + +- `mdf search` now accepts `--service`, `--token`, `--dev-user`. + - Uses authenticated client when credentials are provided. + - Falls back to unauthenticated client when no creds are provided (public search path). +- `MDFAgent.stream_create/append/status/close/snapshot` now accept auth params and route through `BackendClient.authenticated(...)`. +- Skill publish/stream handlers now pass through `token`, `service_instance`, `dev_user_id`, and use authenticated backend path. + +### E6) Phase 6 complete: deprecate old submission path + +Implemented in `src/mdf_agent/core/submission.py`: + +- Added `DeprecationWarning` in `submit_submission()` directing callers to `BackendClient.authenticated(...).submit(...)`. +- Removed direct `submit_submission` usage from `MDFAgent.publish()` (now backend-client based). + +### E7) Additional test coverage for auth routing + +Added new tests in `tests/test_backend_client_auth.py`: + +- `from_env()` reads auth env vars. +- request header injection behavior (token precedence over dev user). +- authenticated client uses env token when present. +- local service avoids forced OAuth when no creds. +- service URL helper respects env override. + +### E8) Validation + +- `pytest -q tests/test_backend_client_auth.py tests/test_cli.py` + - Result: `26 passed` +- `python -m compileall src/mdf_agent` + - Result: success + +### E9) Compatibility notes + +- `submit_submission()` remains callable (deprecated, not removed). +- `MDFAgent.publish(authorizer=...)` remains supported via legacy token extraction. +- `src/mdf_agent/cli/publish.py` is now aligned with the authenticated backend flow. + +### E10) Phase 2 verification addendum + +Additional validation for `login/logout/whoami` was implemented in `tests/test_cli.py`: + +- `test_login_invokes_auth` +- `test_logout_reports_success` +- `test_whoami_uses_env_token_status` + +Validation run: + +- `pytest -q tests/test_cli.py tests/test_backend_client_auth.py` + - Result: `29 passed` + +### E11) Final remaining-phase cleanup + +Implemented final legacy cleanup in `src/mdf_agent/cli/publish.py`: + +- Removed direct `get_authorizer(...)` usage. +- Routed submit path through `agent.publish(..., token/service_instance/api_url/dev_user_id)` so it follows the same `BackendClient.authenticated(...)` flow as the main CLI. +- Added `--api-url` and `--dev-user`; updated `--service` help to include `local`. + +Additional validation: + +- `pytest -q tests/test_agent_backend_routing.py tests/test_cli.py tests/test_backend_client_auth.py` + - Result: `34 passed` +- `python -m compileall src/mdf_agent` + - Result: success + +All phases in the `serene-enchanting-planet` plan are now implemented in code. + +## Part II + III implementation pass (client sync + agentic optimization) + +Date: February 7, 2026 +Scope: `src/mdf_agent/*` and `tests/*` in the client repo. + +### E1) BackendClient sync with server endpoints + +Implemented in `src/mdf_agent/core/backend_client.py`: + +- Added health method: + - `health()` -> `GET /health` +- Added curation methods: + - `curation_pending(limit, offset, organization)` -> `GET /curation/pending` + - `curation_detail(source_id, version)` -> `GET /curation/{source_id}` + - `curation_approve(source_id, mint_doi, notes, metadata_updates, version)` -> `POST /curation/{source_id}/approve` + - `curation_reject(source_id, reason, suggestions, version)` -> `POST /curation/{source_id}/reject` +- Added dataset preview methods: + - `dataset_preview(source_id)` -> `GET /preview/{source_id}` + - `dataset_files(source_id)` -> `GET /preview/{source_id}/files` + - `dataset_file_detail(source_id, path)` -> `GET /preview/{source_id}/files/{path}` (with URL encoding) + - `dataset_sample(source_id)` -> `GET /preview/{source_id}/sample` +- Expanded stream close payload: + - `stream_close(stream_id, mint_doi, title, description, authors, keywords, license)` +- Replaced `stream_clone` server import hack with pure client clone flow: + - list files -> get download URL per file -> download via `httpx` -> write locally + +### E2) Headless confidential client auth path + +Implemented in `src/mdf_agent/core/backend_client.py` inside `authenticated()`: + +- Added auth resolution step between bearer token and dev-user fallback: + - uses `MDF_CLIENT_ID` + `MDF_CLIENT_SECRET` + - obtains OAuth client-credentials tokens via `globus_sdk.ConfidentialAppAuthClient` + - uses service token as Bearer and data token as `X-Globus-Token` when available + +Resolution order now: +1. Explicit token / `MDF_CONNECT_TOKEN` +2. Confidential credentials (`MDF_CLIENT_ID` + `MDF_CLIENT_SECRET`) +3. Dev user (`dev_user_id` / `MDF_DEV_USER_ID`) +4. Interactive login + +### E3) Manifest author model enhancements + +Implemented in `src/mdf_agent/models/config.py`: + +- Added optional `Author.given_name` and `Author.family_name` +- `ManifestConfig.to_metadata_payload()` now propagates both fields in author entries + +### E4) CLI additions for backend sync + +Implemented in `src/mdf_agent/cli/backend.py`: + +- Added commands: + - `health` + - `curation-pending` + - `curation-detail` + - `curation-approve` + - `curation-reject` + - `preview` (profile, files, file detail, sample) +- Updated `stream-close` to accept DOI/metadata close-time fields. + +Also propagated close-time fields in `src/mdf_agent/cli/stream.py`. + +### E5) Agentic handlers and normalization + +Implemented in `src/mdf_agent/skill/handlers.py`: + +- Added `@agent_safe` decorator and applied it across handlers +- Added curation handlers: + - `curation_list_pending` + - `curation_review` + - `curation_approve` + - `curation_reject` +- Added discovery/status handlers: + - `check_status` + - `list_submissions` + - `search_datasets` + - `get_citation` + - `get_card` + - `dataset_preview` + - `dataset_sample` + - `health_check` +- Kept existing handler coverage (`scan_folder`, `publish`, stream operations) and normalized failures to structured dicts. + +### E6) Tools registry and exports + +- Added `src/mdf_agent/skill/mdf_tools.py` with flat `TOOLS` mapping for agentic/MCP use. +- Updated package export in `src/mdf_agent/__init__.py` to expose `BackendClient`. + +### E7) Tests added/updated + +Added: + +- `tests/test_backend_client_sync.py` + - covers all 9 new sync methods + - covers expanded `stream_close` payload + - covers rewritten `stream_clone` flow + +Updated: + +- `tests/test_backend_client_auth.py` + - added confidential credentials auth-path test +- `tests/test_models.py` + - added author name-parts payload propagation test + +### E8) Validation run + +Commands executed: + +- `python -m pytest tests/test_backend_client_sync.py -v` + - Result: `11 passed` +- `python -m pytest tests/test_backend_client_auth.py -v` + - Result: `7 passed` +- `python -m pytest tests/test_models.py -k "author_name_parts or TestAuthor" -v` + - Result: `4 passed` + +No failures in this pass. + +### E9) Scope note + +This pass intentionally covered plan Parts II and III only. Part I (server search wiring/deploy) and Part IV (server cleanup/CORS/SSM production credential hardening) were not changed in this implementation. diff --git a/walkthrough.md b/walkthrough.md new file mode 100644 index 0000000..d92f38c --- /dev/null +++ b/walkthrough.md @@ -0,0 +1,473 @@ +# Publishing a Local Dataset to MDF: Complete Walkthrough + +This walkthrough covers the full end-to-end flow of publishing a dataset from a local directory to the Materials Data Facility, from initial setup through publication and discovery. + +--- + +## Prerequisites + +```bash +# Install mdf_agent (with optional extractors for auto-metadata) +pip install -e ".[extractors]" + +# Authenticate with Globus (opens browser for OAuth) +mdf login +``` + +If running against a local backend for testing: + +```bash +# Terminal 1: start the local backend +cd cs/aws +STORE_BACKEND=sqlite AUTH_MODE=dev python -m v2.app.main +# Server starts at http://127.0.0.1:8080 + +# Terminal 2: all commands below use --service local +``` + +--- + +## Step 1: Prepare your data + +Suppose you have a directory of experimental results: + +``` +my_experiment/ + measurements.csv # 500 rows of X-ray diffraction data + parameters.json # Instrument settings and sample metadata + supplementary/ + calibration.csv # Calibration reference data +``` + +--- + +## Step 2: Initialize the dataset + +You can provide metadata via flags or interactively. + +**With flags:** + +```bash +cd my_experiment + +mdf init . \ + --title "X-ray Diffraction Study of Iron Oxide Nanoparticles" \ + --author "Jane Doe" \ + --author "John Smith" \ + --description "XRD patterns for Fe2O3 and Fe3O4 nanoparticles" +``` + +**Interactively (just run `mdf init`):** + +``` +$ mdf init . + +Initialize MDF dataset + +Dataset title: X-ray Diffraction Study of Iron Oxide Nanoparticles +Enter author names one per line. Empty line to finish. +Author: Jane Doe +Author: John Smith +Author: +Description (optional): XRD patterns for Fe2O3 and Fe3O4 nanoparticles +``` + +**What happens:** This creates two things in your directory: +- `mdf.yaml` — the dataset manifest (title, authors, description, data sources) +- `.mdf/` — internal state directory (staged files, commits) + +Output: +``` +Initialized MDF repository at . + Title: X-ray Diffraction Study of Iron Oxide Nanoparticles + Authors: Jane Doe, John Smith +``` + +--- + +## Step 3: Stage and commit files + +Stage your data files. Use `--discover` to automatically extract metadata from CSVs, PDFs, and Excel files: + +```bash +mdf add measurements.csv parameters.json supplementary/ --discover +``` + +Output: +``` +Staged: + + measurements.csv + + parameters.json + + supplementary/calibration.csv +``` + +Now commit: + +```bash +mdf commit -m "Initial dataset with XRD measurements and calibration" +``` + +Output: +``` +Committed: Initial dataset with XRD measurements and calibration + 3 files recorded +``` + +--- + +## Step 4: Check status and validate + +```bash +mdf status +``` + +Output: +``` +No files staged + +Commits (1): +┏━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ # ┃ Message ┃ Files ┃ Time ┃ +┡━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ 1 │ Initial dataset with XRD measurements and cal... │ 3 │ 2026-02-27T14:30:00 │ +└────┴──────────────────────────────────────────────────┴───────┴─────────────────────┘ +``` + +Validate the manifest before publishing: + +```bash +mdf validate +``` + +Output: +``` +Validation passed +``` + +--- + +## Step 5: Preview the submission (dry run) + +```bash +mdf publish +``` + +By default, `publish` does a dry run. It shows you the exact JSON payload that would be submitted: + +``` +Dry run - would submit: +Target: https://hjccjf3eqg.execute-api.us-east-1.amazonaws.com/staging (staging) +{ + "title": "X-ray Diffraction Study of Iron Oxide Nanoparticles", + "authors": [ + {"name": "Jane Doe"}, + {"name": "John Smith"} + ], + "description": "XRD patterns for Fe2O3 and Fe3O4 nanoparticles", + "publisher": "Materials Data Facility", + "publication_year": 2026, + "resource_type": "Dataset", + "data_sources": [ + "/Users/jane/my_experiment/measurements.csv", + "/Users/jane/my_experiment/parameters.json", + "/Users/jane/my_experiment/supplementary/calibration.csv" + ], + "test": false, + "update": false +} +``` + +Review the payload. If something looks wrong, edit `mdf.yaml` and re-run. + +--- + +## Step 6: Publish + +When ready, use `--submit` to actually send: + +```bash +mdf publish --submit +``` + +**What happens behind the scenes:** + +1. **File upload**: Local files are uploaded to MDF's Globus HTTPS storage via streaming PUT (8 MB chunks). A progress bar shows upload status for each file. +2. **Payload submission**: The `data_sources` are replaced with `globus://` URIs pointing to the uploaded files, and the metadata payload is POSTed to `POST /submit`. +3. **Backend processing**: The backend validates the metadata (via the `DatasetMetadata` Pydantic model), generates a `source_id`, sets version to `1.0`, stores the record as `pending_curation`, and enqueues a profiling job to scan the data files. + +Output: +``` +measurements.csv ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.1 MB 1.2 MB/s +parameters.json ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.0 KB 2.0 MB/s +calibration.csv ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 512 KB 1.5 MB/s + +Published successfully! + Source ID: xrd-iron-oxide-nanoparticles + Version: 1.0 +``` + +The source ID and version are saved to your global config so you can check status easily. + +--- + +## Step 7: Check backend status + +```bash +mdf status +``` + +Now shows both local repo state and backend status: + +``` +No files staged + +Commits (1): +... + +Backend status: xrd-iron-oxide-nanoparticles v1.0 + Status: pending_curation + Title: X-ray Diffraction Study of Iron Oxide Nanoparticles + Next: Waiting for curation review +``` + +The `Next` hint tells you what happens next. Your dataset is now in the curation queue. + +--- + +## Step 8: Curation (curator perspective) + +A curator reviews and approves your dataset: + +```bash +# Curator lists pending datasets +mdf pending + +# Output: +# Pending curation (3): +# ┏━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +# ┃ # ┃ Source ID ┃ Title ┃ Version ┃ Submitted ┃ +# ┡━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +# │ 1 │ xrd-iron-oxide-nanoparticles │ X-ray Diffraction...│ 1.0 │ 2026-02-27T14:35:00 │ +# └───┴──────────────────────────────────┴─────────────────────┴─────────┴─────────────────────┘ + +# Curator approves +mdf approve xrd-iron-oxide-nanoparticles --notes "Metadata complete, data verified" + +# Output: +# Approved: xrd-iron-oxide-nanoparticles +# DOI: https://doi.org/10.18126/xxxxx +``` + +After approval, the backend enqueues a publish job that mints a DOI via DataCite and indexes the dataset in Globus Search. + +--- + +## Step 9: Discover your published dataset + +Check status again: + +```bash +mdf status +``` + +``` +Backend status: xrd-iron-oxide-nanoparticles v1.0 + Status: published + Title: X-ray Diffraction Study of Iron Oxide Nanoparticles + DOI: https://doi.org/10.18126/xxxxx + Root version: xrd-iron-oxide-nanoparticles-1.0 + Next: Dataset is live! +``` + +### Browse your datasets + +```bash +mdf list +``` + +``` +Your datasets (1): + +┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Source ID ┃ Title ┃ Version ┃ Status ┃ Updated ┃ +┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ xrd-iron-oxide-nanoparticles │ X-ray Diffraction Study of Iro... │ 1.0 │ published │ 2026-02-27T14:40:00 │ +└──────────────────────────────────┴───────────────────────────────────┴─────────┴───────────┴─────────────────────┘ +``` + +### View the dataset card + +```bash +mdf show xrd-iron-oxide-nanoparticles +``` + +``` +╭─ xrd-iron-oxide-nanoparticles v1.0 ─────────────────────────────────────╮ +│ X-ray Diffraction Study of Iron Oxide Nanoparticles │ +│ XRD patterns for Fe2O3 and Fe3O4 nanoparticles │ +╰─────────────────────────────────────────────────────────────────────────╯ + + Authors Jane Doe, John Smith + Publisher Materials Data Facility + Year 2026 + DOI https://doi.org/10.18126/xxxxx + Status published + + Files: 3 sources | Types: csv, json +``` + +### Get a citation + +```bash +mdf show xrd-iron-oxide-nanoparticles --cite +``` + +Appends to the card output: +``` +Citation: + Doe, J. & Smith, J. (2026). X-ray Diffraction Study of Iron Oxide + Nanoparticles [Dataset]. Materials Data Facility. https://doi.org/10.18126/xxxxx +``` + +### Search for it + +```bash +mdf search "iron oxide XRD" +``` + +``` +Found 1 results for 'iron oxide XRD' + +┏━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓ +┃ # ┃ Type ┃ Title ┃ ID ┃ Status ┃ +┡━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩ +│ 1 │ dataset │ X-ray Diffraction Study of Iron Oxide ... │ xrd-iron-oxide-nanoparticles v1.0 │ published │ +└───┴─────────┴──────────────────────────────────────────┴───────────────────────────────────────────┴───────────┘ +``` + +--- + +## Step 10: Update the dataset (new version) + +You've collected more data and want to publish an update: + +```bash +mdf update --data ./new_measurements/ --title "Updated XRD Study" --submit +``` + +This automatically: +- Uses the last published source ID from config +- Sets `update=True` so the backend increments the version to `1.1` +- Uploads the new files and submits + +``` +new_scan_003.csv ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8 MB 1.1 MB/s + +Updated successfully! + Source ID: xrd-iron-oxide-nanoparticles + Version: 1.1 +``` + +### View version history + +```bash +mdf versions xrd-iron-oxide-nanoparticles +``` + +``` +Versions for xrd-iron-oxide-nanoparticles + +┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓ +┃ Version ┃ Title ┃ Status ┃ DOI ┃ Updated ┃ +┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩ +│ 1.0 │ X-ray Diffraction Study of Iron Oxide ...│ published │ https://doi.org/10.18126/xxxxx │ 2026-02-27T14:40:00 │ +│ 1.1 │ Updated XRD Study │ pending_curation │ │ 2026-02-27T15:10:00 │ +└─────────┴──────────────────────────────────────────┴───────────────────┴──────────────────────────────────────┴─────────────────────┘ + +Dataset DOI: https://doi.org/10.18126/xxxxx +``` + +--- + +## Alternative: Direct publish (no repository) + +If you don't need the git-style workflow, publish directly in a single command: + +```bash +mdf publish ./my_experiment/ \ + --title "X-ray Diffraction Study of Iron Oxide Nanoparticles" \ + --author "Jane Doe" \ + --author "John Smith" \ + --description "XRD patterns for Fe2O3 and Fe3O4 nanoparticles" \ + --submit +``` + +This skips `init`/`add`/`commit` entirely. The directory contents become data sources and are uploaded directly. + +--- + +## Alternative: Python SDK + +```python +from mdf_agent import MDFAgent + +# Repository mode +agent = MDFAgent.init( + path="./my_experiment", + title="X-ray Diffraction Study of Iron Oxide Nanoparticles", + authors=["Jane Doe", "John Smith"], + description="XRD patterns for Fe2O3 and Fe3O4 nanoparticles", +) +agent.add("*.csv", "*.json", discover=True) +agent.commit("Initial dataset") +result = agent.publish(service_instance="staging", dry_run=False) + +print(f"Published: {result['source_id']} v{result['version']}") + +# Later: check versions +versions = agent.versions(result["source_id"], service_instance="staging") +print(versions) + +# Get citation +citation = agent.cite(result["source_id"], format="bibtex", service_instance="staging") +print(citation.get("bibtex")) +``` + +--- + +## What happens at each stage + +| Stage | CLI Command | What happens on the backend | +|-------|-------------|----------------------------| +| **Init** | `mdf init` | Creates local `mdf.yaml` manifest and `.mdf/` state directory | +| **Add** | `mdf add` | Records files in local staging area; `--discover` extracts metadata from CSVs/PDFs | +| **Commit** | `mdf commit` | Snapshots staged files into a local commit record | +| **Validate** | `mdf validate` | Checks manifest for required fields (title, authors) and common issues | +| **Dry run** | `mdf publish` | Builds the JSON payload locally, displays it without sending | +| **Publish** | `mdf publish --submit` | Uploads files via HTTPS PUT, submits metadata to `POST /submit` | +| **Profile** | *(automatic)* | Backend scans uploaded files, extracts schema and statistics | +| **Curation** | `mdf approve` | Curator reviews and approves; backend enqueues publish job | +| **Publish** | *(automatic)* | Backend mints DOI via DataCite, indexes in Globus Search | +| **Discovery** | `mdf show` / `mdf search` | Dataset is visible, citable, and searchable | +| **Update** | `mdf update --submit` | Creates new version (1.1), previous version marked as non-latest | + +--- + +## Error handling + +The CLI provides actionable error messages: + +| Scenario | CLI output | +|----------|-----------| +| Not authenticated | `Error: Authentication required` / `Run: mdf login` | +| Dataset not found | `Error: Not found` | +| Rate limited | `Error: Rate limited — try again shortly` | +| Server error (502/503) | Automatic retry (3 attempts with backoff) | +| Connection failure | Automatic retry, then `Connection failed after 4 attempts` | +| Validation error | Field-by-field error details | + +Use `--json` on any `mdf backend` or `mdf stream` command to get raw JSON output for scripting: + +```bash +mdf backend status --source-id my_dataset --json | jq '.submission.status' +``` diff --git a/work-completed.md b/work-completed.md new file mode 100644 index 0000000..048f499 --- /dev/null +++ b/work-completed.md @@ -0,0 +1,52 @@ +# Work Completed: `domains` field + external import metadata (client-side) + +## What was done + +Added 4 new fields to the client-side schema and wired them through the full data flow. **Backend (`cs/`) changes are still needed** for these fields to be stored and returned by the API. + +### New fields + +| Field | Type | Purpose | +|-------|------|---------| +| `domains` | `Optional[List[str]]` | Scientific domain categorization (e.g., `["materials", "chemistry"]`) for UI sectioning and search filtering | +| `external_doi` | `Optional[str]` | Original DOI from an externally-published dataset being imported | +| `external_url` | `Optional[str]` | Original URL where the external dataset is published | +| `external_source` | `Optional[str]` | Name of the external repository (e.g., "Zenodo", "Dryad", "Figshare") | + +### Files modified + +1. **`src/mdf_agent/models/config.py`** — Added 4 fields to `ManifestConfig` + updated `to_metadata_payload()` to emit them +2. **`src/mdf_agent/models/submission.py`** — Added 4 fields to `Submission` (v2 section, not legacy) + updated `to_payload()` to emit them +3. **`src/mdf_agent/core/submission.py`** — Wired 4 fields through `build_submission()` Submission constructor call +4. **`tests/test_models.py`** — Added 12 unit tests: + - `TestManifestConfig`: domains (set, default, single), external import (all set, defaults) + - `TestSubmissionPayloadShape`: domains include/exclude, external import include/exclude + - `TestToMetadataPayload` (new class): domains in/out of payload, external import in/out/partial +5. **`examples/test_domains_external_e2e.py`** (new) — E2E test script for staging: submits datasets with domains, external import, and combined, then checks round-trip via status endpoint + +### Data flow + +``` +mdf.yaml (ManifestConfig) + → to_metadata_payload() → flat dict with domains, external_doi, external_url, external_source + → build_submission() → Submission model + → to_payload() → API payload dict sent to backend +``` + +## What still needs to be done (backend) + +The backend server (`cs/aws/v2/`) needs to accept, store, and return these fields. Key files to update: + +- **`cs/aws/v2/metadata.py`** — Add `domains`, `external_doi`, `external_url`, `external_source` to `DatasetMetadata` model +- **Submit endpoint** — Should already accept extra fields if using `model_config = ConfigDict(extra="allow")`, but explicit fields are better for validation +- **Status endpoint** — Needs to return these fields in the response so they round-trip +- **Search ingest** (`cs/aws/v2/search.py`) — Should index `domains` for search/filtering; external import fields should be stored but may not need indexing +- **Server tests** (`cs/aws/v2/test_v2_*.py`) — Add tests for submitting with these fields and verifying they appear in status/search responses + +### E2E test readiness + +The `examples/test_domains_external_e2e.py` script is written and ready. It uses PASS/FAIL characterization-style assertions — fields will show as FAIL until the backend supports them, then flip to PASS. Run it after backend changes: + +```bash +cd mdf_client && python examples/test_domains_external_e2e.py +```