Welcome to FAIRSCAPE

FAIRSCAPE is a computational framework in Python that implements the Bridge2AI AI-readiness principles on biomedical datasets and provides ethical FAIRness 1 with deep semantic provenance graphs on components such as datasets, software, computations, runtime parameters, environment and personnel involved in a computational analysis. FAIRSCAPE generates detailed human- and machine-readable biomedical Datasheets including Croissant and Croissant RAI metadata for direct interface to Kaggle and other AI/ML Ops packages, including all AI-readiness criteria, and provides a rich data commons environment for biomedical AI researchers.

FAIRSCAPE provides a command line client tool and a graphical user interface (GUI) to package and validate the components with metadata, a schema generation and validation component for the datasets, a REST API to perform various operations on the server-side, and a front-end web server to visualize the posted metadata. Together, these tools enable users to interact with FAIRSCAPE in the way that best suits their workflow.

Ready to Get Started?

Follow our Getting Started Guide to quickly set up a local FAIRSCAPE environment and create your first Research Object Crate (RO-Crate). You'll learn how to:

  • Deploy FAIRSCAPE locally using Docker Compose
  • Create and package your first RO-Crate
  • Upload and view your research objects through our web interface

Key Components

FAIRSCAPE Server

The FAIRSCAPE server is responsible for metadata management, implemented in Python using FastAPI. Handles package storage, metadata extraction, provenance tracking, and provides a REST API interface.

Command Line Client (CLI)

A pip-installable validation and packaging utility for creating and managing RO-Crates with descriptive metadata using schema.org vocabulary. Supports both direct packaging and URI referencing of data.

Graphical User Interface (GUI)

A user-friendly electron and React-based interface that provides visual forms for RO-Crate creation, step-by-step workflow guidance, package validation, and direct upload capabilities.

Web Client

A React-based web interface for browsing, managing, and sharing RO-Crates uploaded to FAIRSCAPE. Features package visualization, metadata management, and provenance graph display.

Use Cases

FAIRSCAPE was initially created to support fully-provenanced complex computations in predictive analytics for clinical research 2. The current version supports the NIH Bridge2AI program’s Functional Genomics Grand Challenge 3, as well as ongoing clinical work in predictive analytics. We believe it is a useful tool for developing pre-model explainability in AI applications 4, and plan to extend it further in this direction. It creates and provides rich, human-readable Datasheets in html, extending the basic conceptions of Gebru et al. 2021 5 to cover additional metadata required for biomedical AI-readiness.

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.

  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: doi:10.1101/2024.10.23.619844. Published online May 6, 2025. ↩