Welcome to FAIRSCAPE

FAIRSCAPE is a computational framework written in Python that implements the FAIR1 data principles on components such as datasets, software, computations, runtime parameters, environment and personnel involved in a computational analysis. It generates fully FAIR evidence of correctness of the analysis by recording formal representations of the components and their interactions in the form of a graph called Evidence Graph. For every computational result, FAIRSCAPE creates a machine interpretable Evidence Graph whose nodes and edges may contain persistent identifiers with metadata resolvable to the underling components.

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 research2. The current version supports the NIH Bridge2AI program’s Functional Genomics Grand Challenge3, as well as ongoing clinical work in predictive analytics. We believe it is a useful tool for developing pre-model explainability in AI applications4, and plan to extend it further in this direction.

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. https://doi.org/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. 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 ↩