Canonical Workflows to Make Data FAIR
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...
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Published in | Data intelligence Vol. 4; no. 2; pp. 286 - 305 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
One Rogers Street, Cambridge, MA 02142-1209, USA
MIT Press
01.04.2022
MIT Press Journals, The |
Subjects | |
Online Access | Get full text |
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Summary: | 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 or so, 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. |
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Bibliography: | 2022 |
ISSN: | 2641-435X 2641-435X |
DOI: | 10.1162/dint_a_00132 |