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A metadata schema for data objects in clinical research

Overview of attention for article published in Trials, November 2016
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Title
A metadata schema for data objects in clinical research
Published in
Trials, November 2016
DOI 10.1186/s13063-016-1686-5
Pubmed ID
Authors

Steve Canham, Christian Ohmann

Abstract

A large number of stakeholders have accepted the need for greater transparency in clinical research and, in the context of various initiatives and systems, have developed a diverse and expanding number of repositories for storing the data and documents created by clinical studies (collectively known as data objects). To make the best use of such resources, we assert that it is also necessary for stakeholders to agree and deploy a simple, consistent metadata scheme. The relevant data objects and their likely storage are described, and the requirements for metadata to support data sharing in clinical research are identified. Issues concerning persistent identifiers, for both studies and data objects, are explored. A scheme is proposed that is based on the DataCite standard, with extensions to cover the needs of clinical researchers, specifically to provide (a) study identification data, including links to clinical trial registries; (b) data object characteristics and identifiers; and (c) data covering location, ownership and access to the data object. The components of the metadata scheme are described. The metadata schema is proposed as a natural extension of a widely agreed standard to fill a gap not tackled by other standards related to clinical research (e.g., Clinical Data Interchange Standards Consortium, Biomedical Research Integrated Domain Group). The proposal could be integrated with, but is not dependent on, other moves to better structure data in clinical research.

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Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Other 5 10%
Student > Ph. D. Student 5 10%
Student > Bachelor 4 8%
Librarian 3 6%
Other 8 16%
Unknown 13 27%
Readers by discipline Count As %
Computer Science 8 16%
Medicine and Dentistry 6 12%
Social Sciences 6 12%
Agricultural and Biological Sciences 4 8%
Nursing and Health Professions 2 4%
Other 8 16%
Unknown 15 31%