Canonical Workflows to Make Data FAIR

IF 1.3 3区计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Data Intelligence Pub Date : 2022-03-07 DOI:10.1162/dint_a_00132

P. Wittenburg, A. Hardisty, Yann Le Franc, A. Mozaffari, Limor Peer, N. Skvortsov, Zhiming Zhao, A. Spinuso

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引用次数: 3

Abstract

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 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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规范工作流使数据公平

FAIR原则已被全球接受为改进数据驱动的科学和数据管理实践的指导方针，但目前激励研究人员改变其实践的动力很弱。此外，数据驱动的科学在接受工作流技术方面进展缓慢，尽管有明确的证据表明这种做法反复出现。为了克服这些挑战，研究规范工作流框架(CWFR)倡议建议大规模引入自文档工作流脚本，以自动化重复过程或其中的片段。这种以FAIR数字对象为基础的标准化方法将成为向FAIR数据过渡的一个重要里程碑，同时不会给研究人员增加额外的负担，而研究人员将从中受益最多。本文描述了CWFR方法和CWFR倡议在过去一年左右的过程中的活动，重点介绍了CWFR方法有希望的几个项目，包括Galaxy、Jupyter Notebook和RO Crate，最后对该领域的状态和未来的挑战进行了评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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