Applying the FAIR Principles to Computational Workflows

• URL: http://arxiv.org/abs/2410.03490v1
• Date: Fri, 4 Oct 2024 15:00:17 GMT
• Title: Applying the FAIR Principles to Computational Workflows
• Authors: Sean R. Wilkinson, Meznah Aloqalaa, Khalid Belhajjame, Michael R. Crusoe, Bruno de Paula Kinoshita, Luiz Gadelha, Daniel Garijo, Ove Johan Ragnar Gustafsson, Nick Juty, Sehrish Kanwal, Farah Zaib Khan, Johannes Köster, Karsten Peters-von Gehlen, Line Pouchard, Randy K. Rannow, Stian Soiland-Reyes, Nicola Soranzo, Shoaib Sufi, Ziheng Sun, Baiba Vilne, Merridee A. Wouters, Denis Yuen, Carole Goble,
• Abstract summary: computational tools are increasingly recognized as tools for productivity, trends, and democratized access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. We present our recommendations with commentary that reflects our discussions and justifies our choices and adaptations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity, reproducibility, and democratized 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 our recommendations with commentary that reflects our discussions and justifies our choices and adaptations. Like the software and data principles on which they are based, these are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guide rails for adoption and fodder for discussion. Workflows are becoming more prevalent as documented, automated instruments for data analysis, data collection, AI-based predictions, and simulations. 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.

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