文章基本信息

标题：DATS, the data tag suite to enable discoverability of datasets
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作者：Susanna-Assunta Sansone ; Alejandra Gonzalez-Beltran ; Philippe Rocca-Serra 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2017
卷号：4
DOI：10.1038/sdata.2017.59
语种：English
出版社：Nature Publishing Group
摘要：Today鈥檚 science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)鈥檚 Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMed鈥檚 goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of dataset, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as an annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.