imaging-data-commons
NCI Imaging Data Commons
Overview
NCI Imaging Data Commons (IDC) is NCI's cloud-based repository for cancer imaging data, hosting 50+ TB of publicly accessible DICOM images spanning radiology (CT, MRI, PET) and pathology (whole slide images) across 100+ collections. All data is hosted on Google Cloud Storage and BigQuery, enabling SQL queries over DICOM metadata without downloading. IDC integrates with Google Colab and BigQuery, making large-scale imaging research accessible without local storage.
When to Use
- Searching for publicly available cancer imaging datasets by modality, cancer type, or anatomical site
- Downloading DICOM image series for model training (segmentation, classification, detection)
- Querying DICOM metadata at scale using SQL (BigQuery) without downloading the full dataset
- Exploring available imaging collections before committing to a full download
- Accessing pathology whole-slide images (WSI) and radiology scans from TCIA collections
- Building reproducible imaging ML pipelines with versioned public datasets
- For local DICOM file processing use
pydicom-medical-imaging; for WSI preprocessing usehistolab
Prerequisites
More from jaechang-hits/sciagent-skills
scientific-brainstorming
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
12snakemake-workflow-engine
Python-based workflow management system for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake automatically determines the execution order and parallelism. Supports local, SLURM, LSF, AWS, and Google Cloud execution via profiles; per-rule conda/Singularity environments. Use for bioinformatics NGS pipelines, ML training workflows, and any multi-step file-processing analysis. Use Nextflow instead for Groovy-based dataflow pipelines or when nf-core ecosystem integration is required.
11esm-protein-language-model
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and protein embeddings. Use when designing novel proteins, extracting sequence representations for downstream ML, or predicting structure from sequence. Local GPU or EvolutionaryScale Forge cloud API. For traditional structure prediction use AlphaFold; for small-molecule cheminformatics use RDKit.
11biopython-sequence-analysis
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
11shap-model-explainability
>-
11archs4-database
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
11