Concepedia

Publication | Open Access

Applying the FAIR Principles to computational workflows

33

Citations

28

References

2025

Year

TLDR

Computational workflows are increasingly adopted for productivity and reproducibility, and the FAIR principles—Findable, Accessible, Interoperable, Reusable—provide a framework for sharing them, while the Workflows Community Initiative’s FAIR Workflows Working Group has systematically addressed applying these principles to workflows across disciplines. The paper offers recommendations and commentary to guide workflow users, authors, management system developers, and service providers in adopting FAIR principles. The authors compile recommendations and commentary derived from the WCI‑FW discussions, justifying adaptations to the FAIR framework for workflows. 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.

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.

References

YearCitations

Page 1