Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 28;19(9):e1011369.
doi: 10.1371/journal.pcbi.1011369. eCollection 2023 Sep.

Ten quick tips for building FAIR workflows

Casper de Visser  1 , Lennart F Johansson  2 , Purva Kulkarni  1   3   4 , Hailiang Mei  5 , Pieter Neerincx  2 , K Joeri van der Velde  2 , Péter Horvatovich  6 , Alain J van Gool  3   4 , Morris A Swertz  2 , Peter A C 't Hoen  1 , Anna Niehues  1   3
Affiliations

Ten quick tips for building FAIR workflows

Casper de Visser et al. PLoS Comput Biol. .

Abstract

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they do not have competing interests.

Figures

Fig 1
Fig 1. Ten quick tips for building FAIR workflows.
See this image and copyright information in PMC

References

    1. Horton N, Alexander R, Parker M, Piekut A, Rundel C. The Growing Importance of Reproducibility and Responsible Workflow in the Data Science and Statistics Curriculum. J Stat Data Sci Educ. 2022;30:207–208. doi: 10.1080/26939169.2022.2141001 - DOI
    1. Madduri R, Chard K, D’Arcy M, Jung SC, Rodriguez A, Sulakhe D, et al.. Reproducible big data science: A case study in continuous FAIRness. PLoS ONE. 2019;14(4):1–22. doi: 10.1371/journal.pone.0213013 - DOI - PMC - PubMed
    1. Atkinson M, Gesing S, Montagnat J, Taylor I. Scientific workflows: Past, present and future. 2017. doi: 10.1016/j.future.2017.05.041 - DOI
    1. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al.. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3(1):1–9. doi: 10.1038/sdata.2016.18 - DOI - PMC - PubMed
    1. Barker M, Chue Hong NP, Katz DS, Lamprecht AL, Martinez-Ortiz C, Psomopoulos F, et al.. Introducing the FAIR Principles for research software. Sci Data. 2022;9(1):622. doi: 10.1038/s41597-022-01710-x - DOI - PMC - PubMed