The National Sleep Research Resource: towards a sleep data commons. (31st May 2018)

• Record Type: Journal Article
• Authors: Zhang, Guo-Qiang; Cui, Licong; Mueller, Remo; Tao, Shiqiang; Kim, Matthew; Rueschman, Michael; Mariani, Sara; Mobley, Daniel; Redline, Susan
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 10(2018)
• Page Start: 1351
• Page End: 1358
• Publication Date: 2018-05-31
• Subjects: data commons -- data integration -- data sharing -- common data element -- data provenance -- sleep research -- polysomnography -- data quality -- European Data Format; 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/ocy064
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes. Approach: We used a metadata-guided approach, with a set of common sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results: The authors curated and deposited retrospective data from 10 large, NIH-funded sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate sleep research.