Applying the FAIR Principles to computational workflows

Abstract

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative's FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

18 pages, 1 figure, 1 table

References in corpus (6)

• Parsl: Pervasive Parallel Programming in Python
• Packaging research artefacts with RO-Crate
• Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language
• Middleware Building Blocks for Workflow Systems
• Recording provenance of workflow runs with RO-Crate
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Cited by in corpus (3)

• WorkflowHub: a registry for computational workflows
• A Terminology for Scientific Workflow Systems
• FAIR Ecosystems for Science at Scale