The center for expanded data annotation and retrieval

Mark A. Musen*, Carol A. Bean, Kei-Hoi Cheung, Michel Dumontier, Kim A. Durante, Olivier Gevaert, Alejandra Gonzalez-Beltran, Purvesh Khatri, Steven H. Kleinstein, Martin J. O'Connor, Yannick Pouliot, Philippe Rocca-Serra, Susanna-Assunta Sansone, Jeffrey A. Wiser

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Web of Science)


The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments. ? The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email:
Original languageEnglish
Pages (from-to)1148-1152
JournalJournal of the American Medical Informatics Association
Issue number6
Publication statusPublished - Nov 2015
Externally publishedYes


  • datasets as topic
  • data curation
  • data collection
  • standards
  • biological ontologies

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