摘要: 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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期刊:
DATA INTELLIGENCE
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分类:
计算机科学
>>
计算机科学的集成理论
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引用:
ChinaXiv:202211.00439
(或此版本
ChinaXiv:202211.00439V1)
DOI:10.1162/ dint_a_00132
CSTR:32003.36.ChinaXiv.202211.00439.V1
- 推荐引用方式:
Peter, Wittenburg,Alex, Hardisty,Yann, Le Franc,Amirpasha, Mozaffari,Limor, Peer,Nikolay, A. Skvortsov,Zhiming, Zhao,Alessandro, Spinuso.(2022).Canonical Workflows to Make Data FAIR.DATA INTELLIGENCE.doi:10.1162/ dint_a_00132
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