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 K-Dense-AI/scientific-agent-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
Uses the idc-index package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). It supports querying by metadata, downloading DICOM files, and visualizing in a browser. It also includes steps to verify IDC data version and check licenses before use.
How it works
Core workflow:
- Query metadata via client.sql_query().
- Download DICOM files via client.download_from_selection().
- Visualize in browser via client.get_viewer_URL(seriesInstanceUID=...). The skill includes a critical check (reported, not installed) to compare the installed idc-index version against REQUIRED_VERSION (0.11.14) and suggests upgrading in a virtual environment if needed. It verifies IDC data version using IDCClient().get_idc_version() and prints the current version. It also demonstrates how to query and load index tables (e.g., index, clinical_index) and how to fetch clinical data tables. It guides installation steps pinned to idc-index==0.11.14 and warns against breaking system-packages. It describes how to access data via cloud storage and DICOMweb, and provides examples for verifying data version and data scale.
When to use it
- When finding publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images
- When selecting image subsets by cancer type, modality, anatomical site, or other metadata
- When downloading DICOM data from IDC
- When checking data licenses before use in research or commercial applications
- When visualizing medical images in a browser without local DICOM viewer software
What it can touch
The skill references the core API calls of the idc-index package:
IDCClient()client.sql_query()client.download_from_selection()client.get_viewer_URL(seriesInstanceUID=...)client.fetch_index("table_name")to load tables such asclinical_index(implied usage)
Caveats
- IDC data licensing varies by dataset (e.g., CC-BY, CC-NC); data-specific licenses must be respected.
- The skill emphasizes verification steps rather than automatic upgrades; upgrading idc-index should be approved by the user.
- It requires a virtual environment for package installation and pins to tested versions to preserve reproducibility.
- No authentication is required for IDC data access; licensing of data collections remains user-responsible.
# 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. **Current IDC Data Version: v23** (always verify with `IDCClient().get_idc_version()`) **Primary tool:** `idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index)) **CRITICAL - Check package version before anything else (run this FIRST):** This block only *reports*. It never installs. If the version is too old, show the user the suggested command and wait for them to approve it — do not run an install on their behalf. ```python import idc_index REQUIRED_VERSION = "0.11.14" # Must match metadata.idc-index in this file installed = idc_index.__version__ def _parts(version): # Compare numerically: "0.9.0" < "0.11.14" is False as a string comparison. return tuple(int(p) if p.isdigit() else 0 for p in version.split(".")[:3]) if _parts(installed) < _parts(REQUIRED_VERSION): print(f"idc-index {installed} is older than the tested {REQUIRED_VERSION}.") print("Ask the user before installing. Suggested command, in a virtual environment:") print(f" uv pip inst
- Overview
- When to Use This Skill
- Quick Navigation
- IDC Data Model
- Index Tables
- Available Tables
- Joining Tables
- Clinical Data Access
- Data Access Options
- Installation and Setup
- Core Capabilities
- Common Use Cases
- Best Practices
- Troubleshooting
uv pip install 'idc-index==0.11.14' uv pip install pandas numpy pydicom
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 K-Dense-AI/scientific-agent-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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.