Applying the FAIR Principles to Computational Workflows

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

doi:10.48550/arXiv.2410.03490

Applying the FAIR Principles to Computational Workflows

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 VilneMerridee A. Wouters, Denis Yuen, Carole Goble

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	328
Journal	Scientific Data
Volume	12
DOIs	https://doi.org/10.48550/arXiv.2410.03490 https://doi.org/10.1038/s41597-025-04451-9
Publication status	Published - 24 Feb 2025

Keywords

FAIR
workflow

Access to Document

10.48550/arXiv.2410.03490Licence: CC BY
10.1038/s41597-025-04451-9Licence: CC BY-NC-ND

2410.03490v1Submitted manuscript, 482 KBLicence: CC BY
s41597-025-04451-9Final published version, 1.21 MBLicence: CC BY-NC-ND

https://s11.no/2024/fair-principles-applied-to-workflows/Licence: CC BY

Cite this

Wilkinson, S. R., Aloqalaa, M., Belhajjame, K., Crusoe, M. R., Kinoshita, B. D. P., Gadelha, L., Garijo, D., Gustafsson, O. J. R., Juty, N., Kanwal, S., Khan, F. Z., Köster, J., Gehlen, K. P., Pouchard, L., Rannow, R. K., Soiland-Reyes, S., Soranzo, N., Sufi, S., Sun, Z., ... Goble, C. (2025). Applying the FAIR Principles to Computational Workflows. Scientific Data, 12, Article 328. https://doi.org/10.48550/arXiv.2410.03490, https://doi.org/10.1038/s41597-025-04451-9

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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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Wilkinson, SR, Aloqalaa, M, Belhajjame, K, Crusoe, MR, Kinoshita, BDP, Gadelha, L, Garijo, D, Gustafsson, OJR, Juty, N, Kanwal, S, Khan, FZ, Köster, J, Gehlen, KP, Pouchard, L, Rannow, RK, Soiland-Reyes, S, Soranzo, N, Sufi, S, Sun, Z, Vilne, B, Wouters, MA, Yuen, D & Goble, C 2025, 'Applying the FAIR Principles to Computational Workflows', Scientific Data, vol. 12, 328. https://doi.org/10.48550/arXiv.2410.03490, https://doi.org/10.1038/s41597-025-04451-9

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AU - Aloqalaa, Meznah

AU - Belhajjame, Khalid

AU - Crusoe, Michael R.

AU - Kinoshita, Bruno de Paula

AU - Gadelha, Luiz

AU - Garijo, Daniel

AU - Gustafsson, Ove Johan Ragnar

AU - Juty, Nick

AU - Kanwal, Sehrish

AU - Khan, Farah Zaib

AU - Köster, Johannes

AU - Gehlen, Karsten Peters-von

AU - Pouchard, Line

AU - Rannow, Randy K.

AU - Soiland-Reyes, Stian

AU - Soranzo, Nicola

AU - Sufi, Shoaib

AU - Sun, Ziheng

AU - Vilne, Baiba

AU - Wouters, Merridee A.

AU - Yuen, Denis

AU - Goble, Carole

PY - 2025/2/24

Y1 - 2025/2/24

N2 - 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.

AB - 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.

KW - FAIR

KW - workflow

UR - https://workflows.community/groups/fair/

U2 - 10.48550/arXiv.2410.03490

DO - 10.48550/arXiv.2410.03490

M3 - Article

SN - 2052-4463

VL - 12

JO - Scientific Data

JF - Scientific Data

M1 - 328

ER -

Applying the FAIR Principles to Computational Workflows

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

ESG: EuroScienceGateway

FAIR-IMPACT

Recording provenance of workflow runs with RO-Crate

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

FAIR Computational Workflows

Workflows Community Initiative (External organisation)

Cite this

Applying the FAIR Principles to Computational Workflows

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Projects

ESG: EuroScienceGateway

FAIR-IMPACT

Research output

Recording provenance of workflow runs with RO-Crate

Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

FAIR Computational Workflows

Activities

Workflows Community Initiative (External organisation)

Cite this