# Welcome!
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**Try out the natural language interface to IDC using this LLM skill:** [**https://github.com/ImagingDataCommons/idc-claude-skill**](https://github.com/ImagingDataCommons/idc-claude-skill)
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[**NCI Imaging Data Commons** **(IDC)**](https://imaging.datacommons.cancer.gov) is a cloud-based environment containing publicly available cancer imaging data co-located with analysis and exploration tools. IDC is a node within the broader NCI [Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov/) infrastructure that provides secure access to a large, comprehensive, and expanding collection of cancer research data.
## Highlights
* **>95 TB of data**: IDC contains radiology, brightfield (H\&E) and fluorescence slide microscopy images, along with image-derived data (annotations, segmentations, quantitative measurements) and accompanying clinical data
* **free**: all of the data in IDC is publicly available: no registration, no access requests
* **commercial-friendly**: >95% of the data in IDC is covered by the permissive CC-BY license, which allows commercial reuse (small subset of data is covered by the CC-NC license); each file in IDC is tagged with the license to make it easier for you to understand and follow the rules
* **cloud-based**: all of the data in IDC is available from both Google and AWS public buckets: fast and free to download, no out-of-cloud egress fees
* **harmonized**: all of the images and image-derived data in IDC is harmonized into standard DICOM representation
## Functionality
IDC is as much about data as it is about what you can do with the data! We maintain and actively develop a variety of tools that are designed to help you efficiently navigate, access and analyze IDC data:
* **exploration**: start with the [IDC Portal](https://portal.imaging.datacommons.cancer.gov/explore/) to get an idea of the data available
* **visualization**: examine images and image-derived annotations and analysis results from the convenience of your browser using integrated OHIF, VolView and Slim open source viewers
* **programmatic access**: use [`idc-index` python package](https://github.com/ImagingDataCommons/idc-index) to perform search, download and other operations programmatically
* **cohort building**: use rich and extensive metadata to build subsets of data programmatically using `idc-index` or BigQuery SQL
* **download**: use your favorite S3 API client or `idc-index` to efficiently fetch any of the IDC files from our public buckets
* **analysis**: conveniently access IDC files and metadata from the tools that are cloud-native, such as Google Colab or Looker; fetch IDC data directly into 3D Slicer using [SlicerIDCBrowser extension](https://github.com/ImagingDataCommons/SlicerIDCBrowser/)
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The overview of IDC is available in this open access publication. If you use IDC, please acknowledge us by citing it!
> 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).
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# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://learn.canceridc.dev/readme.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
