RT Journal Article SR Electronic T1 DATS: the data tag suite to enable discoverability of datasets JF bioRxiv FD Cold Spring Harbor Laboratory SP 103143 DO 10.1101/103143 A1 Susanna-Assunta Sansone A1 Alejandra Gonzalez-Beltran A1 Philippe Rocca-Serra A1 George Alter A1 Jeffrey S. Grethe A1 Hua Xu A1 Ian Fore A1 Jared Lyle A1 Anupama E. Gururaj A1 Xiaoling Chen A1 Hyeon-eui Kim A1 Nansu Zong A1 Yueling Li A1 Ruiling Liu A1 Burak Ozyurt A1 Lucila Ohno-Machado A1 bioCADDIE Working Groups Members YR 2017 UL http://biorxiv.org/content/early/2017/01/25/103143.abstract AB Today’s 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)’s 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’s 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 datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.