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Canonical workflows to make data FAIR

Wittenburg, Peter, Hardisty, Alex ORCID: https://orcid.org/0000-0002-0767-4310, Le Franc, Yann, Mozaffari, Amirpasha, Peer, Limor, Skvortsov, Nikolay A. and Zhao, Zhiming 2022. Canonical workflows to make data FAIR. Data Intelligence 4 (2) , pp. 286-305. 10.1162/dint_a_00132

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Abstract

The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices, yet the incentives for researchers to change their practices are presently weak. In addition, data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices. To overcome these challenges, the Canonical Workflow Frameworks for Research (CWFR) initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof. This standardised approach, with FAIR Digital Objects as anchors, will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it. This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year, highlights several projects that hold promise for the CWFR approaches, including Galaxy, Jupyter Notebook, and RO Crate, and concludes with an assessment of the state of the field and the challenges ahead.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Uncontrolled Keywords: Workflow, Data management, FAIR Principles, Digital Objects
Additional Information: Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
Publisher: Massachusetts Institute of Technology Press
ISSN: 2641-435X
Funders: EC Horizon 2020
Date of First Compliant Deposit: 29 March 2022
Date of Acceptance: 5 February 2022
Last Modified: 10 Nov 2022 10:54
URI: https://orca.cardiff.ac.uk/id/eprint/148616

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