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Licensing and Citation Guide for IDC

When to Use This Guide

Load this guide when: - A user asks whether IDC data can be used commercially, redistributed, or included in a product - You are assembling a cohort that mixes collections and need to know which terms govern it - A user is publishing results and needs formatted citations (APA, BibTeX, CSL JSON, RDF Turtle) - You need the parameters or output formats for citation generation

The obligation summary — check the license, generate citations — lives in SKILL.md. This guide holds the detail behind it.

These are the two IDC tasks least tied to any one access path. Licenses and citations are available identically from idc-index, the REST API, and the hosted MCP server. Use whichever route the session is already on rather than installing Python to answer a licensing question, or dropping out of an MCP session to run a script.

Task idc-index (Python) REST API MCP server
License breakdown for a selection sql_query on license_short_name POST /v3/licenses get_licenses
Citations for a selection citations_from_selection() POST /v3/citations get_citations

Route-specific detail lives in references/rest_api_guide.md (endpoint reference, filter syntax, and the body-shape pitfall that makes a mis-shaped filter return all of IDC) and references/mcp_guide.md (tool inventory). The license semantics below apply to all three.

Licenses in IDC

Every DICOM file in IDC is tagged with its license in the file metadata, and every row in the index table carries a license_short_name column. There is no single IDC-wide license.

License Share of data Commercial use Attribution required
CC BY 4.0 74.7% Yes Yes
CC BY 3.0 22.1% Yes Yes
CC BY-NC 4.0 2.1% No Yes
CC BY-NC 3.0 0.8% No Yes
NLM Terms and Conditions 0.3% Read the terms Yes

About 97% of IDC data by size permits commercial reuse; just under 3% is non-commercial. Treat any license_short_name that is not a recognizable Creative Commons string as custom, and report the exact value to the user rather than assuming it permits reuse.

Licenses attach to individual series, not to whole collections. 39 of IDC's 176 collections carry more than one license — analysis results and original images within one collection can differ, as can series from different sources. Never conclude that a collection is commercially usable from one series, or from the collection's headline license: group by license_short_name over the exact selection you intend to use.

When a cohort mixes licenses, the most restrictive term governs the combined dataset. If a selection contains any CC BY-NC series, either drop those series or tell the user the whole derived dataset is non-commercial.

Commercially restricted data is also physically separated in cloud storage: the idc-open-data-cr (AWS) / idc-open-cr (GCS) buckets hold the CC BY-NC collections. See references/cloud_storage_guide.md for bucket details.

Checking licenses

Via idc-index

from idc_index import IDCClient
client = IDCClient()

# Licenses across all collections
licenses = client.sql_query("""
    SELECT DISTINCT
      collection_id,
      license_short_name,
      COUNT(DISTINCT SeriesInstanceUID) as series_count
    FROM index
    GROUP BY collection_id, license_short_name
    ORDER BY collection_id
""")
print(licenses)

# Licenses present in one specific cohort — run this before handing a dataset to a user
cohort_licenses = client.sql_query("""
    SELECT license_short_name, COUNT(DISTINCT SeriesInstanceUID) as series_count
    FROM index
    WHERE Modality = 'MR' AND BodyPartExamined = 'BREAST'
    GROUP BY license_short_name
""")
print(cohort_licenses)

# Commercial-safe subset: exclude non-commercial collections outright
commercial_ok = client.sql_query("""
    SELECT collection_id, SeriesInstanceUID
    FROM index
    WHERE Modality = 'CT'
      AND license_short_name NOT LIKE '%NC%'
    LIMIT 20
""")

Via the REST API

POST /v3/licenses takes the filter object directly (not wrapped in a filters key) and returns the per-license breakdown with series counts and sizes:

B=https://api.imaging.datacommons.cancer.gov/v3
curl -s $B/licenses \
  -H 'content-type: application/json' \
  -d '{"terms": {"Modality": ["MR"], "BodyPartExamined": ["BREAST"]}}'

Response shape: licenses[{license_short_name, series, size_TB}]. A collection's licenses are also included in GET /v3/collections/{id}.

Via the MCP server

Call get_licenses with the same selection you built with build_cohort. The result carries the same per-license breakdown; the CC BY vs CC BY-NC distinction above applies unchanged.

Citations and attribution

The source_DOI column links to the publications describing how each dataset was generated. All three routes turn a selection into formatted citations that satisfy the attribution requirement common to every IDC license.

Generate citations from the same selection you downloaded, not from the collection as a whole — a five-series subset of a collection that spans several source publications should cite only the publications it actually draws on.

Via idc-index

# Citations for a collection (APA is the default format)
citations = client.citations_from_selection(collection_id="rider_pilot")
for citation in citations:
    print(citation)

# Citations for a specific set of series — matches what you actually downloaded
results = client.sql_query("""
    SELECT SeriesInstanceUID FROM index
    WHERE collection_id = 'tcga_luad' LIMIT 5
""")
citations = client.citations_from_selection(
    seriesInstanceUID=list(results['SeriesInstanceUID'].values)
)

# BibTeX, for LaTeX manuscripts
bibtex_citations = client.citations_from_selection(
    collection_id="tcga_luad",
    citation_format=IDCClient.CITATION_FORMAT_BIBTEX
)

citations_from_selection() takes the same selection filters as the download methods — collection_id, patientId, studyInstanceUID, seriesInstanceUID — plus citation_format.

Via the REST API

POST /v3/citations wraps the filter in a filters key (unlike /v3/licenses — this asymmetry is the single most common REST mistake; see references/rest_api_guide.md):

curl -s $B/citations \
  -H 'content-type: application/json' \
  -d '{"filters": {"terms": {"collection_id": ["rider_pilot"]}}, "citation_format": "bibtex"}'

The response separates the per-dataset citations[] from idc_acknowledgment (the IDC paper) and recommendation. Include both parts — see What to include when publishing below.

Via the MCP server

Call get_citations for a selection. It returns the per-dataset citations plus the IDC paper to acknowledge IDC itself, matching the REST response.

Citation formats

idc-index constant REST / MCP citation_format Output
IDCClient.CITATION_FORMAT_APA apa (default) APA string
IDCClient.CITATION_FORMAT_BIBTEX bibtex BibTeX entry, for LaTeX
IDCClient.CITATION_FORMAT_JSON csl-json CSL JSON
IDCClient.CITATION_FORMAT_TURTLE turtle RDF Turtle

What to include when publishing

  1. The dataset citations for every collection or series set used.
  2. The IDC data version — client.get_idc_version(), GET /v3/version, or the MCP get_idc_version tool. IDC releases are versioned and series are added and revised between them, so the version is what makes the selection reproducible.
  3. The IDC platform citation, to acknowledge IDC itself. The REST and MCP routes return this as idc_acknowledgment; when using idc-index, add it yourself:

Fedorov, A., et al. "National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence." RadioGraphics 43.12 (2023). https://doi.org/10.1148/rg.230180

  1. The series manifest — save the SeriesInstanceUID list alongside the analysis so the exact cohort can be rebuilt.

Troubleshooting

Issue: Fewer citations returned than collections selected

Issue: POST /v3/citations returns citations for all of IDC

Resources