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 LeonChaoX/qinyan-academic-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
Describes a skill that lets an AI agent query and download public cancer imaging data from the IDC via the idc-index package. It guides the agent to verify the idc-index version, query metadata, download DICOM data, and visualize data in a browser. It emphasizes checking IDC data version (v23) and upgrading idc-index to a required version (0.11.10) when needed.
How it works
- Use the idc-index Python package to interact with IDC without authentication.
- Verify the IDC data version via IDCClient().get_idc_version() and ensure it matches "v23".
- Upgrade idc-index if installed version is older than REQUIRED_VERSION (0.11.10) using a subprocess upgrade command.
- Initialize an IDCClient and perform core workflow steps:
- Query metadata with client.sql_query().
- Download DICOM files with client.download_from_selection().
- Visualize in browser with client.get_viewer_URL(seriesInstanceUID=...).
- Fetch and explore index tables (collections_index, analysis_results_index) for richer metadata.
- Access licensing information via license_short_name and ensure IDC data licenses are respected when using data.
- Use data access options like idc-index for queries/downloads, Cloud storage, or DICOMweb endpoints as needed.
When to use it
- When locating publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images.
- When filtering by metadata to select datasets by cancer type, modality, anatomy, or other fields.
- When downloading DICOM data from IDC for AI training or research.
- When verifying data licenses before use and visualizing images in-browser without local viewers.
What it can touch
- The skill relies on the idc-index package and IDCClient API to query and download data. It references:
- idc-index
- IDCClient
- client.sql_query
- client.download_from_selection
- client.get_viewer_URL
- collections_index, analysis_results_index
- license_short_name
Caveats
- IDC data licensing varies by dataset (mostly CC-BY, some CC-NC) and must be respected when using data.
- IDC data version must be verified (v23) and may require upgrading idc-index to meet the REQUIRED_VERSION (0.11.10).
- The workflow assumes public, unauthenticated access; some features may require different access paths (Cloud storage, BigQuery, DICOMweb) per the Data Access Options.
# 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 and upgrade if needed (run this FIRST):** ```python import idc_index REQUIRED_VERSION = "0.11.10" # Must match metadata.idc-index in this file installed = idc_index.__version__ if installed < REQUIRED_VERSION: print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...") import subprocess subprocess.run(["pip3", "install", "--upgrade", "--break-system-packages", "idc-index"], check=True) print("Upgrade complete. Restart Python to use new version.") else: print(f"idc-index {installed} meets requirement ({REQUIRED_VERSION})") ``` **Verify IDC data version and check current data scale:** ```python from idc_index import IDCClient client = IDCClient() # Verify IDC data version (should be "v23") print(f"IDC data version: {client.get_idc_version()}"
- 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
- 1. Data Discovery and Exploration
- 2. Querying Metadata with SQL
- 3. Downloading DICOM Files
pip install --upgrade idc-index pip install pandas numpy pydicom Download entire collection idc download rider_pilot --download-dir ./data Download specific series by UID idc download "1.3.6.1.4.1.9328.50.1.69736" --download-dir ./data Download multiple items (comma-separated) idc download "tcga_luad,tcga_lusc" --download-dir ./data Download from manifest file (auto-detected) idc download manifest.txt --download-dir ./data
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 LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.