Applying the FAIR Principles to computational workflows
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.
Authors
- Sehrish Kanwal (ORCID: https://orcid.org/0000-0002-5044-4692)
- Johannes Köster (ORCID: https://orcid.org/0000-0001-9818-9320)
- Ziheng Sun (ORCID: https://orcid.org/0000-0001-9810-0023)
- Michael R. Crusoe (ORCID: https://orcid.org/0000-0002-2961-9670)
- Shoaib Sufi (ORCID: https://orcid.org/0000-0001-6390-2616)
- Carole Goble (ORCID: https://orcid.org/0000-0003-1219-2137)
- Stian Soiland‐Reyes
- Daniel Garijo (ORCID: https://orcid.org/0000-0003-0454-7145)
- Sean R. Wilkinson (ORCID: https://orcid.org/0000-0002-1443-7479)
- Nicola Soranzo (ORCID: https://orcid.org/0000-0003-3627-5340)
- Khalid Belhajjame (ORCID: https://orcid.org/0000-0001-6938-0820)
- Farah Zaib Khan (ORCID: https://orcid.org/0000-0002-6337-3037)
- Line Pouchard (ORCID: https://orcid.org/0000-0002-2120-6521)
- Merridee A. Wouters (ORCID: https://orcid.org/0000-0002-2121-912X)
- Nick Juty (ORCID: https://orcid.org/0000-0002-2036-8350)
- Karsten Peters (ORCID: https://orcid.org/0000-0003-0158-2957)
- Bruno P. Kinoshita (ORCID: https://orcid.org/0000-0001-8250-4074)
- Denis Yuen (ORCID: https://orcid.org/0000-0002-6130-1021)
- Baiba Vilne (ORCID: https://orcid.org/0000-0002-1084-7067)
- Luiz Gadelha (ORCID: https://orcid.org/0000-0002-8122-9522)
- Ove Gustafsson (ORCID: https://orcid.org/0000-0002-2977-5032)
- Randy K. Rannow
- Meznah Aloqalaa (ORCID: https://orcid.org/0000-0002-1382-3896)
Institutions
- Ontario Institute for Cancer Research (CA)
- Oak Ridge National Laboratory (US)
- George Mason University (US)
- The University of Melbourne (AU)
- German Cancer Research Center (DE)
- Zuse Institute Berlin (DE)
- Heidelberg University (DE)
- Barcelona Supercomputing Center (ES)
- German Climate Computing Centre (DE)
- University of Manchester (GB)
- UNSW Sydney (AU)
- Earlham Institute (GB)
- Victorian Comprehensive Cancer Centre (AU)
- Sandia National Laboratories (US)
- Riga Stradiņš University (LV)
- Université Paris Dauphine-PSL (FR)
- University of Duisburg-Essen (DE)
- Universidad Politécnica de Madrid (ES)
- University of Amsterdam (NL)
- Universitat Politècnica de Catalunya (ES)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-02-24
- DOI
- https://doi.org/10.1038/s41597-025-04451-9
- Citations
- 35
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 145.91
Funders
- Biotechnology and Biological Sciences Research Council