An Ecosystem of Services for FAIR Computational Workflows
Sean R. Wilkinson, Johan Gustafsson, Finn Bacall, Khalid Belhajjame, Salvador Capella, Jose Maria Fernandez Gonzalez, Jacob Fosso Tande, Luiz Gadelha, Daniel Garijo, Patricia Grubel, Bjorn Gr\"uning, Farah Zaib Khan, Sehrish Kanwal, Simone Leo, Stuart Owen, Luca Pireddu

TL;DR
This paper discusses the importance of applying FAIR principles to computational workflows to enhance their findability, accessibility, interoperability, and reusability, thereby supporting reproducible and collaborative science.
Contribution
It proposes a comprehensive ecosystem framework for FAIR workflows, integrating persistent identifiers, metadata, tools, and community practices to facilitate adoption.
Findings
FAIR workflows improve reproducibility and reuse of scientific methods.
A proposed ecosystem supports workflow findability and interoperability.
Adoption of FAIR principles in workflows enhances interdisciplinary collaboration.
Abstract
Computational workflows, regardless of their portability or maturity, represent major investments of both effort and expertise. They are first class, publishable research objects in their own right. They are key to sharing methodological know-how for reuse, reproducibility, and transparency. Consequently, the application of the FAIR principles to workflows is inevitable to enable them to be Findable, Accessible, Interoperable, and Reusable. Making workflows FAIR would reduce duplication of effort, assist in the reuse of best practice approaches and community-supported standards, and ensure that workflows as digital objects can support reproducible and robust science. FAIR workflows also encourage interdisciplinary collaboration, enabling workflows developed in one field to be repurposed and adapted for use in other research domains. FAIR workflows draw from both FAIR data and software…
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Taxonomy
TopicsResearch Data Management Practices · Scientific Computing and Data Management · Cell Image Analysis Techniques
