Comment: A design framework and exemplar metrics for FAIRness
Comment: A design framework and exemplar metrics for FAIRness
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DOI：
10.1038/sdata.2018.118
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发表时间：
2018-06-26
期刊：
影响因子：
9.8
通讯作者：
Dumontier, Michel
中科院分区：
文献类型：
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作者：
Wilkinson, Mark D.;Sansone, Susanna-Assunta;Dumontier, Michel
公平原则1（https：//doi. org/10.25504/FAIRsharing. WWI 10 U）为数据集、代码、工作流和研究对象等数字资源的发布提供了指导方针，使其可查找、可解释、可互操作和可重用（FAIR）。这些原则迅速被出版商、资助者和泛学科基础设施方案和协会采用。这些原则是有抱负的，因为它们没有严格定义如何实现“公平”状态，而是描述了一系列特征、属性和行为，这些特征、属性和行为将使数字资源更接近这一目标。这种模糊性导致了对公平的广泛解释，有些资源甚至声称已经“公平”！越来越多的这样的声明，出现的主观和自我评估的公平性2，3，以及需要数据和服务提供商，期刊，资助机构，和监管机构定性或定量评估这些索赔，导致我们自我组装，并建立了一个公平研究小组（http：//fairmetrics。作为公平原则及其相关手稿的共同作者，建立这个小型焦点小组对我们来说是一个自然和及时的步骤，我们预见小组成员会根据各种利益相关者社区的需求和热情而扩大。尽管如此，在小组活动的第一阶段，我们并没有孤立地工作，而是从社区、组织和项目中收集用例和需求，我们是这些社区、组织和项目的核心成员，并且在这些社区、组织和项目中也开始了关于如何衡量公平性的讨论。我们的社区网络和正式参与包括通用和学科特定的举措，包括：全球和开放公平（http：//go-fair. org）、欧洲开放科学云（EOSC; https：//eoscpilot. eu）、研究数据联盟（RDA; https：//www.researchdataAlliance）工作组。联盟org）和Force11（https：//www.force11.org）。力量11. org）、批准4的数据印章、欧洲ELIXIR基础设施的节点（https：//www. elixir欧洲org），美国国立卫生研究院（NIH）的大数据知识计划（BD 2K）及其新的数据共享试点（https：//commonfund. nih。gov/bd2k/commons）。此外，通过FAIRsharing网络和咨询委员会（https：//fairsharing. org），我们还与开放标准开发社区和数据政策领导者以及编辑和出版商建立了联系，特别是那些在数据问题上非常活跃的人，例如：Springer Nature's Scientific Data，Nature Genetics and BioMedCentral，PloS Biology，
The FAIR Principles 1 (https://doi. org/10.25504/FAIRsharing. WWI10U) provide guidelines for the publication of digital resources such as datasets, code, workflows, and research objects, in a manner that makes them Findable, Accessible, Interoperable, and Reusable (FAIR). The Principles have rapidly been adopted by publishers, funders, and pan-disciplinary infrastructure programmes and societies. The Principles are aspirational, in that they do not strictly define how to achieve a state of" FAIRness", but rather they describe a continuum of features, attributes, and behaviors that will move a digital resource closer to that goal. This ambiguity has led to a wide range of interpretations of FAIRness, with some resources even claiming to already" be FAIR"! The increasing number of such statements, the emergence of subjective and self-assessments of FAIRness 2, 3, and the need of data and service providers, journals, funding agencies, and regulatory bodies to qualitatively or quantitatively evaluate such claims, led us to self-assemble and establish a FAIR Metrics group (http://fairmetrics. org) to pursue the goal of defining ways to measure FAIRness.As co-authors of the FAIR Principles and its associated manuscript, founding this small focus group was a natural and timely step for us, and we foresee group membership expanding and broadening according to the needs and enthusiasm of the various stakeholder communities. Nevertheless, in this first phase of group activities we did not work in isolation, but we gathered use cases and requirements from the communities, organizations and projects we are core members of, and where discussions on how to measure FAIRness have also started. Our community network and formal participation encompasses generic and discipline-specific initiatives, including: the Global and Open FAIR (http://go-fair. org), the European Open Science Cloud (EOSC; https://eoscpilot. eu), working groups of the Research Data Alliance (RDA; https://www. rd-alliance. org) and Force11 (https://www. force11. org), the Data Seal of Approval 4, Nodes of the European ELIXIR infrastructure (https://www. elixir-europe. org), projects under the USA National Institutes of Health (NIH)’s Big Data to Knowledge Initiative (BD2K) and its new Data Commons Pilots (https://commonfund. nih. gov/bd2k/commons). In addition, via the FAIRsharing network and advisory board (https://fairsharing. org), we are also connected to open standards-developing communities and data policy leaders, and also editors and publishers, especially those very active around data matters, such as: Springer Nature’s Scientific Data, Nature Genetics and BioMedCentral, PloS Biology,