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FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics.

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Summary
This summary is machine-generated.

FAIRSCAPE provides a reusable framework for transparently documenting computational results. It ensures reproducibility by creating FAIR Evidence Graphs, detailing data, software, and computation provenance for enhanced data sharing and validation.

Keywords:
AgumentationDigital CommonsEvidence graphFAIR dataFAIR softwareProvenanceReproducibility

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Area of Science:

  • Computational science
  • Data management
  • Scientific reproducibility

Background:

  • Computational analyses require transparent disclosure of supporting resources.
  • Large-scale analyses involve complex, time-separated processing steps.
  • Ensuring the correctness of results necessitates formal records of computations, data, and software.

Purpose of the Study:

  • To introduce FAIRSCAPE, a reusable computational framework.
  • To enable simplified access to scalable cloud-based components.
  • To extend FAIR data principles to computational evidence.

Main Methods:

  • FAIRSCAPE implements FAIR data principles and extends them to FAIR Evidence.
  • It utilizes a microservices framework to create Evidence Graphs for computational results.
  • The framework assigns persistent identifiers and FAIR metadata to all objects, including software and datasets.

Main Results:

  • FAIRSCAPE generates machine-interpretable provenance for datasets, software, and computations.
  • It creates a complete Evidence Graph for each result, linking to all components.
  • The Evidence Graph ontology (EVI) supports inferential reasoning over computational evidence.

Conclusions:

  • FAIRSCAPE enhances the accessibility, validation, reproducibility, and re-use of computational results.
  • It preserves provenance across nested or disjoint workflows, supporting various job types.
  • The framework ensures all objects are assigned persistent IDs and FAIR metadata.