FAIR Data Reuse - the Path through Data Citation
FAIR Data Reuse - the Path through Data Citation
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DOI：
10.1162/dint_a_00030
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发表时间：
2020-12-01
影响因子：
3.9
通讯作者：
Goble, Carole
中科院分区：
文献类型：
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作者：
Groth, Paul;Cousijn, Helena;Goble, Carole
FAIR指导原则的关键目标之一由其最终原则定义-优化数据集以供人类和机器重用。为此，数据提供者需要实现和支持一致的机器可读元数据来描述其数据集。对于数据提供者来说，这似乎是一项艰巨的任务，无论是确定应该在出处元数据中提供什么级别的细节，还是确定应该使用什么公共共享词汇表。此外，对于现有的数据集，通常不清楚应该采取什么步骤来实现最大限度的适当重用。数据引用已经在使数据可查找和可访问方面发挥了重要作用，为1 600多万个数据集提供了持久和唯一的标识符以及元数据。在本文中，我们讨论了数据引用及其底层基础设施，特别是相关的元数据，提供了一个重要的途径，使FAIR数据重用。
One of the key goals of the FAIR guiding principles is defined by its final principle - to optimize data sets for reuse by both humans and machines. To do so, data providers need to implement and support consistent machine readable metadata to describe their data sets. This can seem like a daunting task for data providers, whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used. Additionally, for existing data sets it is often unclear what steps should be taken to enable maximal, appropriate reuse. Data citation already plays an important role in making data findable and accessible, providing persistent and unique identifiers plus metadata on over 16 million data sets. In this paper, we discuss how data citation and its underlying infrastructures, in particular associated metadata, provide an important pathway for enabling FAIR data reuse.