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Sci Data. 2017 Jun 6;4:170059. doi: 10.1038/sdata.2017.59.

DATS, the data tag suite to enable discoverability of datasets.

Author information

1
University of Oxford, Oxford e-Research Centre, 7 Keble Road, Oxford OX1 3QG, UK.
2
Inter-university Consortium for Political and Social Research, University of Michigan, PO Box 1248, Ann Arbor, Michigan 48106-1248, USA.
3
University of California San Diego, 9500 Gilman Dr, La Jolla, California 92093, USA.
4
The University of Texas Health Science Center at Houston, 7000 Fannin St, Houston, Texas 77030, USA.
5
National Cancer Institute, National Institutes of Health, 9609 Medical Center Dr, Rockville, Maryland 20850, USA.

Abstract

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 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.

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