Applying the FAIR Principles to computational workflows

Journal Article · 23 February 2025 · Scientific Data (Online)

DOI: https://doi.org/10.1038/s41597-025-04451-9 · OSTI ID:2530828

Wilkinson, Sean R.  ^[1]; Aloqalaa, Meznah  ^[2]; Belhajjame, Khalid  ^[3]; Crusoe, Michael R.  ^[4]; de Paula Kinoshita, Bruno  ^[5]; Gadelha, Luiz  ^[6]; Garijo, Daniel  ^[7]; Gustafsson, Ove Ragnar  ^[8]; Juty, Nick  ^[2]; Kanwal, Sehrish  ^[9]; Khan, Farah Zaib  ^[8]; Köster, Johannes  ^[10]; Peters-von Gehlen, Karsten  ^[11]; Pouchard, Line Catherine  ^[12]; Rannow, Randy K.  ^[13]; Soiland-Reyes, Stian  ^[14]; Soranzo, Nicola  ^[15]; Sufi, Shoaib  ^[2]; Sun, Ziheng  ^[16]; Vilne, Baiba  ^[17] ; Wouters, Merridee A.  ^[18]; Yuen, Denis  ^[19]; Goble, Carole  ^[2]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
2. Univ. of Manchester (United Kingdom)
3. Paris Dauphine University (France)
4. Zuse Institute Berlin (ZIB) (Germany)
5. Barcelona Supercomputing Center (Spain)
6. German Cancer Research Center (DKFZ), Heidelberg (Germany)
7. Universidad Politécnica de Madrid (Spain)
8. Univ. of Melbourne, VIC (Australia)
9. University of Melbourne Centre for Cancer Research (UMCCR), Parkville, VIC (Australia)
10. University of Duisburg-Essen (Germany)
11. Deutsches Klimarechenzentrum GmbH, Hamburg (Germany)
12. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
13. Silverdraft Supercomputing, Boise, ID (United States)
14. Univ. of Manchester (United Kingdom); Univ. of Amsterdam (Netherlands)
15. Earlham Institute, Norwich (United Kingdom)
16. George Mason Univ., Fairfax, VA (United States)
17. Riga Stradins University (Latvia)
18. Univ. of New South Wales, Kensington, NSW (Australia)
19. Ontario Institute for Cancer Research, Toronto, ON (Canada)

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC05-00OR22725; NA0003525
• OSTI ID: 2530828
• Report Number(s): SAND--2025-02692J
• Journal Information: Scientific Data (Online), Journal Name: Scientific Data (Online) Journal Issue: 1 Vol. 12; ISSN 2052-4463
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English