idc-index-data 24.1.0

ImagingDataCommons index to query and download data.

idc-index-data

About

idc-index-data is a Python package providing index files to query and download data hosted by the NCI Imaging Data Commons (IDC).

The PyPI package bundles a core set of index files (Parquet, JSON schemas, SQL queries). Supplementary indices that are too large for PyPI distribution are published as release artifacts on GitHub and uploaded to a public Google Cloud Storage bucket on each release.

Index files

The package provides metadata for the following indices via the INDEX_METADATA dictionary:

Index	In PyPI package	Description
idc_index	yes	Core IDC DICOM study-level index
prior_versions_index	yes	Historical version tracking
collections_index	yes	Collection-level metadata
analysis_results_index	yes	Analysis results metadata
clinical_index	-	Clinical data (large)
sm_index	-	Slide microscopy index (large)
sm_instance_index	-	Slide microscopy instance-level index (large)
seg_index	-	Segmentation index
ann_index	-	Annotation index
ann_group_index	-	Annotation group index
contrast_index	-	Contrast agent index

Additionally, the following supplementary parquet files are generated and published alongside the index files (not included in the PyPI package):

File	Description
gdc_idc_mapping.parquet	Mapping of IDC patients to GDC cases
tcia_idc_subset.parquet	Subset of IDC index columns for TCIA workflows

All index files (including supplementary ones) are available from:

• GitHub Releases: attached as release assets
• Google Cloud Storage: publicly readable via HTTPS

Google Cloud Storage artifacts

Artifacts are uploaded to the idc-index-data-artifacts bucket on each release. Two paths are maintained:

Path	Description
gs://idc-index-data-artifacts/<version>/release_artifacts/	Artifacts for a specific release (e.g. 23.5.0)
gs://idc-index-data-artifacts/current/release_artifacts/	Always points to the latest release

Individual files can be accessed via HTTPS at:

https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/<filename>

For example:

File	URL
idc_index.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/idc_index.parquet
idc_index_schema.json	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/idc_index_schema.json
idc_index.sql	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/idc_index.sql
clinical_index.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/clinical_index.parquet
sm_index.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/sm_index.parquet
seg_index.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/seg_index.parquet
gdc_idc_mapping.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/gdc_idc_mapping.parquet
tcia_idc_subset.parquet	https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifacts/tcia_idc_subset.parquet

Replace current with a specific version tag (e.g. 23.5.0) to pin to a particular release.

Usage

This package is intended to be used by the idc-index Python package.

import idc_index_data

# Access core index file paths
idc_index_data.IDC_INDEX_PARQUET_FILEPATH
idc_index_data.PRIOR_VERSIONS_INDEX_PARQUET_FILEPATH

# Access unified metadata for all indices
idc_index_data.INDEX_METADATA["idc_index"]["parquet_filepath"]
idc_index_data.INDEX_METADATA["idc_index"]["schema"]  # pre-loaded dict
idc_index_data.INDEX_METADATA["idc_index"]["sql"]  # pre-loaded string

Acknowledgment

This software is maintained by the IDC team, which has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.

If this package helped your research, we would appreciate if you could cite IDC paper below.

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180