imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill imaging-data-commons --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Queries and downloads public cancer imaging data from NCI Imaging Data Commons using idc-index. Supports querying by metadata, downloading DICOM files, and visualizing results in a browser, with license checks. No authentication is required for data access.
How it works
- Uses the idc-index Python package to connect to IDC and run SQL-like queries against the primary index (and additional index tables when needed).
- Core workflow steps listed: 1) Query metadata via client.sql_query(), 2) Download DICOM files via client.download_from_selection(), 3) Visualize in browser via client.get_viewer_URL(seriesInstanceUID=...).
- Verifies IDC data version (should be "v23") and suggests upgrading idc-index to meet the REQUIRED_VERSION (0.11.9) when needed.
- Demonstrates loading additional index data with client.fetch_index("collections_index") and client.fetch_index("analysis_results_index") for richer metadata.
- Includes example SQL snippets to filter by Modality, BodyPartExamined, and to join with collections_index for cancer-type filtering.
When to use it
- When you need publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images
- When you want to filter datasets by cancer type, modality, anatomical site, or other metadata
- When you need to download DICOM data from IDC and verify licenses before use
- When you want to visualize images in a browser without local DICOM viewers
What it can touch
- The IDC data via idc-index (no authentication required for data access)
- Local Python environment to install and upgrade idc-index and optional analysis packages (pandas, numpy, pydicom)
Caveats
- IDC data licensing varies by dataset (mostly CC-BY, some CC-NC); license considerations are noted in the workflow.
- IDC data version is v23 and must be verified; upgrading idc-index may be required to match the required version 0.11.9 as shown in the snippet.
# Imaging Data Commons ## Overview Use the `idc-index` Python package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access. ## Routing Boundary Use this skill for NCI Imaging Data Commons, IDC, TCIA cancer imaging cohorts, DICOMWeb, public cancer imaging data, radiology datasets, and DICOM imaging
What does the imaging-data-commons skill do?
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.
How do I install it?
Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill imaging-data-commons --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.