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Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary datasets were exposed on responsive web pages to support mining of gene lists, discovery of related datasets, and single-click citation integration with popular reference managers. Automated processes were established to embed digital object identifier-driven links to the secondary datasets in associated journal articles, small molecule and gene-centric databases, and a dataset search engine. Our model creates multiple points of access to reprocessed and reannotated derivative datasets across the digital biomedical research ecosystem, promoting their visibility and usability across disparate research communities.<\/jats:p>","DOI":"10.1093\/jamia\/ocw096","type":"journal-article","created":{"date-parts":[[2016,7,14]],"date-time":"2016-07-14T04:37:07Z","timestamp":1468471027000},"page":"388-393","source":"Crossref","is-referenced-by-count":4,"title":["Improving the discoverability, accessibility, and citability of omics datasets: a case report"],"prefix":"10.1093","volume":"24","author":[{"given":"Yolanda F","family":"Darlington","sequence":"first","affiliation":[{"name":"Dan L. Duncan Comprehensive Cancer Center Biomedical Informatics Group, Baylor College of Medicine, Houston, Texas"}]},{"given":"Alexey","family":"Naumov","sequence":"additional","affiliation":[{"name":"Dan L. 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