TY - JOUR
T1 - Ten quick tips for building FAIR workflows
AU - de Visser, Casper
AU - Johansson, Lennart F
AU - Kulkarni, Purva
AU - Mei, Hailiang
AU - Neerincx, Pieter
AU - Joeri van der Velde, K
AU - Horvatovich, Péter
AU - van Gool, Alain J
AU - Swertz, Morris A
AU - Hoen, Peter A C 't
AU - Niehues, Anna
N1 - Copyright: © 2023 de Visser et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/9
Y1 - 2023/9
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pcbi.1011369
DO - 10.1371/journal.pcbi.1011369
M3 - Article
C2 - 37768885
SN - 1553-7358
VL - 19
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 9
M1 - e1011369
ER -