Journal Article: “Data Discovery with DATS: Exemplar Adoptions and Lessons Learned”

The following article was recently published in the Journal of the American Medical Informatics Association.
Title
Data Discovery with DATS: Exemplar Adoptions and Lessons Learned
Authors
Alejandra N Gonzalez-Beltran
Oxford e-Research Centre
John Campbell
Northrup Grumman Information Systems
Patrick Dunn
Northrup Grumman Information Systems
Diana Guijarro
UCSD
Sanda Ionescu
ICPSR
Hyeoneui Kim
UCSD
Jared Lyle
ICPSR
Jeffrey Wiser
Northrup Grumman Information Systems
Susanna-Assunta Sansone
Oxford e-Research Centre
Philippe Rocca-Serra
Oxford e-Research Centre
Source
Journal of the American Medical Informatics Association
ocx119
doi.org/10.1093/jamia/ocx119
Abstract

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.

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