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DATS, the data tag suite to enable discoverability of datasets

Publication Abstract

Sansone, Susanna-Assunta, George C. Alter, Jared A. Lyle, Alejandra Gonzalez-Beltran, Philippe Rocca-Serra, Jeffrey S. Grethe, Hua Xu, Ian M. Fore, Anupama E. Gururaj, Xiaoling Chen, Hyeon-eui Kim, Nansu Zong, Yueling Li, Ruiling Liu, I. Burak Ozyurt, and Lucila Ohno-Machado. 2017. "DATS, the data tag suite to enable discoverability of datasets." Scientific Data, 4.

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.

DOI:10.1038/sdata.2017.59 (Full Text)

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