Data discovery with DATS: exemplar adoptions and lessons learned.

Published

Journal Article

The DAta Tag Suite (DATS) is a model supporting dataset description, indexing, and discovery. It is available as an annotated serialization with schema.org, a vocabulary used by major search engines, thus making the datasets discoverable on the web. DATS underlies DataMed, the National Institutes of Health Big Data to Knowledge Data Discovery Index prototype, which aims to provide a "PubMed for datasets." The experience gained while indexing a heterogeneous range of >60 repositories in DataMed helped in evaluating DATS's entities, attributes, and scope. In this work, 3 additional exemplary and diverse data sources were mapped to DATS by their representatives or experts, offering a deep scan of DATS fitness against a new set of existing data. The procedure, including feedback from users and implementers, resulted in DATS implementation guidelines and best practices, and identification of a path for evolving and optimizing the model. Finally, the work exposed additional needs when defining datasets for indexing, especially in the context of clinical and observational information.

Full Text

Duke Authors

Cited Authors

  • Gonzalez-Beltran, AN; Campbell, J; Dunn, P; Guijarro, D; Ionescu, S; Kim, H; Lyle, J; Wiser, J; Sansone, S-A; Rocca-Serra, P

Published Date

  • January 2018

Published In

Volume / Issue

  • 25 / 1

Start / End Page

  • 13 - 16

PubMed ID

  • 29228196

Pubmed Central ID

  • 29228196

Electronic International Standard Serial Number (EISSN)

  • 1527-974X

International Standard Serial Number (ISSN)

  • 1067-5027

Digital Object Identifier (DOI)

  • 10.1093/jamia/ocx119

Language

  • eng