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 ↩