The center for expanded data annotation and retrieval. (25th June 2015)

• Record Type: Journal Article
• Authors: Musen, Mark A; Bean, Carol A; Cheung, Kei-Hoi; Dumontier, Michel; Durante, Kim A; Gevaert, Olivier; Gonzalez-Beltran, Alejandra; Khatri, Purvesh; Kleinstein, Steven H; O'Connor, Martin J; Pouliot, Yannick; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Wiser, Jeffrey A
• Is Part Of: Journal of the American Medical Informatics Association. Volume 22:Number 6(2015:Nov.)
• Page Start: 1148
• Page End: 1152
• Publication Date: 2015-06-25
• Subjects: datasets as topic -- data curation -- data collection -- standards -- biological ontologies; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv048
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: 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.