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.
@article{arxiv.2410.03490,
title = {Applying the FAIR Principles to computational workflows},
author = {Sean R. Wilkinson and Meznah Aloqalaa and Khalid Belhajjame and Michael R. Crusoe and Bruno de Paula Kinoshita and Luiz Gadelha and Daniel Garijo and Ove Johan Ragnar Gustafsson and Nick Juty and Sehrish Kanwal and Farah Zaib Khan and Johannes Köster and Karsten Peters-von Gehlen and Line Pouchard and Randy K. Rannow and Stian Soiland-Reyes and Nicola Soranzo and Shoaib Sufi and Ziheng Sun and Baiba Vilne and Merridee A. Wouters and Denis Yuen and Carole Goble},
journal= {arXiv preprint arXiv:2410.03490},
year = {2025}
}