Welcome to FAIRSCAPE¶

FAIRSCAPE¹ is a computational framework written in Python that implements the FAIR² 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 to package and validate the components with metadata, a schema generation and validation component for the datasets, and a REST API to perform various operations on the server-side.

FAIRSACPE CLI - A Validation and Packaging Command Line Tool: A data validation and packaging utility for the FAIRSCAPE ecosystem. provides a command line interface that allows the client side remote teams to create RO-Crate and BagIt.
Schema Validation: ...
FAIRSCAPE REST docs UI: It is provided by Swagger UI and allows a client to use Try it out button to run operations on the endpoints.
FAIRSCAPE REST ReDoc UI: It is provided by ReDoc offering a three panel responsive layout containing a search bar on the left, documentation in the middle, and request and response examples on the right.

Levinson, M. A., Niestroy, J., Al Manir, S., Fairchild, K., Lake, D. E., Moorman, J. R., & Clark, T. (2022). FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics. Neuroinformatics, 20(1), 187–202. https://doi.org/10.1007/s12021-021-09529-4 ↩
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J. W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3, 160018. https://doi.org/10.1038/sdata.2016.18 ↩