社区驱动的公平性评估治理:一个开放的问题,一个开放的讨论
Mark D Wilkinson1,2, Susanna-Assunta Sansone1,3, Eva Méndez4
1EOSC Task Force on FAIR Metrics and Data Quality, EOSC, Brussels, Belgium.
Open research Europe
|February 1, 2024
概括
定义公平性治理至关重要,因为当前的评估工具产生不一致的结果. 本文提出了利益相关者类别和治理模型,以确保可信,可持续的FAIR数据原则的实施.
科学领域:
- 信息科学 信息科学 信息科学
- 数据管理数据管理
- 研究政策研究政策
背景情况:
- 公平原则 (可查找,可访问,可互操作,可重复使用) 缺乏标准化的解释,导致不同的评估结果.
- 当前的FAIR评估指标和工具由于独立的解释而表现出不一致,导致研究人员,资助者和出版商之间的混乱.
- 需要对FAIR行为进行明确,透明的定义,以防止现有做法被错误地标记为符合FAIR.
研究的目的:
- 为了解决缺乏确的方法来确定研究文物的公平程度.
- 提出利益相关者类别和潜在的治理模式,以实施公平原则.
- 促进公开讨论关于公平治理的公开讨论,以获得可信,可持续的实施.
主要方法:
- 确定三个高级利益相关者类别:公平决策和政策制定者,公平监管者和公平实践者.
- 检查假设的利益相关者的需求,并提供具体的例子.
- 探索基于现有的同行努力和标准化机构的潜在治理模型.
主要成果:
- 定义了参与FAIR研究数据原则的三个关键利益相关者群体.
- 概述了每个利益相关者类别的具体需求和潜在好处.
- 建议建立强大的公平性治理结构的途径.
结论:
- 对公平性治理的标准化方法对于一致和可信的FAIR原则实施至关重要.
- 参与各种利益相关者和探索治理模型可以导致可持续的 FAIR 数据实践.
- 本白皮书作为FAIR数据治理持续对话和发展的基础.
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