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FAIRSCAPE

FAIRSCAPE

FAIRSCAPE packages research data, software and computations as RO-Crates with deep provenance: what was used, what produced it, and who ran it. It implements the Bridge2AI AI-readiness principles and ethical FAIRness1, and produces human- and machine-readable datasheets, including Croissant and Croissant RAI metadata.

FAIRSCAPE is a set of small Python packages. Each one does one step:

Create→View & assess→Publish
Quick start Install the packages, then go from a datasheet to a published crate in five commands.

1. Create an RO-Crate

fairscape-models The data model: Pydantic classes for every entity in a crate. Build and validate ro-crate-metadata.json.
fairscape-conversion Turn metadata you already have (D4D, Snakemake, Cromwell, Galaxy, MLflow, REDCap, Frictionless, …) into a crate.

2. View and assess it

fairscape-artifacts Build the pages people read: an HTML datasheet, an interactive evidence graph and an entity preview.
AI-Readiness grader Grade a crate against the AI-Ready rubric and get a review page with suggested fixes.

3. Publish it

fairscape-publish Push the crate and its files to Dataverse, Zenodo or Figshare, or mint a DataCite DOI.
fairscape-lite Run your own server: it indexes a folder of crates and gives you search, ARK resolution and evidence graphs.

Use Cases

FAIRSCAPE was first built to support fully provenanced, complex computations in predictive analytics for clinical research2. It now supports the NIH Bridge2AI program’s Functional Genomics Grand Challenge3, as well as ongoing clinical work in predictive analytics. We think it is useful for building pre-model explainability into AI applications4, and we plan to extend it in that direction. Its HTML datasheets extend the basic ideas of Gebru et al. 20215 to cover the extra metadata that biomedical AI-readiness requires.

Funding

FAIRSCAPE was developed with funding from the U.S. National Institutes of Health awards OT2OD032742, OT2OD032701, 5R01HD072071-10; and from the University of Virginia’s Frederick Thomas Fund.

Footnotes

  1. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3, 160018. doi:10.1038/sdata.2016.18. ↩

  2. Niestroy JC, Moorman JR, Levinson MA, et al. Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis. npj Digit Med. 2022;5(1):6. doi:10.1038/s41746-021-00551-z. ↩

  3. Clark T, Schaffer LV, Obernier K, et al. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. doi:10.1101/2024.05.21.589311. Published online May 6, 2024. ↩

  4. Clark T, Caufield H, Parker JA, et al. AI-readiness for Biomedical Data: Bridge2AI Recommendations. Published online October 25, 2024. doi:10.1101/2024.10.23.619844. ↩

  5. Al Manir S, Levinson MA, Niestroy J, Churas C, Parker JA, Clark T. FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research. doi:10.1101/2024.12.23.629818. Published online May 6, 2025. ↩