The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science

Ying Zhang; Matthew Kim; Michael Prerau; Daniel Mobley; Michael Rueschman; Kathryn Sparks; Meg Tully; Shaun Purcell; Susan Redline

doi:10.1093/sleep/zsae088

The National Sleep Research Resource: making data findable, accessible, interoperable, reusable and promoting sleep science

Sleep. 2024 Apr 30:zsae088. doi: 10.1093/sleep/zsae088. Online ahead of print.

Authors

Ying Zhang¹, Matthew Kim², Michael Prerau¹, Daniel Mobley¹, Michael Rueschman¹, Kathryn Sparks¹, Meg Tully¹, Shaun Purcell³, Susan Redline¹

Affiliations

¹ Division of Sleep Medicine and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
² Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
³ Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

PMID: 38688470
DOI: 10.1093/sleep/zsae088

Abstract

This paper presents a comprehensive overview of the National Sleep Research Resource (NSRR), a National Heart Lung and Blood Institute-supported repository developed to share data from clinical studies focused on the evaluation of sleep disorders. The NSRR addresses challenges presented by the heterogeneity of sleep-related data, leveraging innovative strategies to optimize the quality and accessibility of available datasets. It provides authorized users with secure centralized access to a large quantity of sleep-related data including polysomnography, actigraphy, demographics, patient-reported outcomes, and other data. In developing the NSRR, we have implemented data processing protocols that ensure de-identification and compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Heterogeneity stemming from intrinsic variation in the collection, annotation, definition, and interpretation of data has proven to be one of the primary obstacles to efficient sharing of datasets. Approaches employed by the NSRR to address this heterogeneity include (1) development of standardized sleep terminologies utilizing a compositional coding scheme, (2) specification of comprehensive metadata, (3) harmonization of commonly used variables, and (3) computational tools developed to standardize signal processing. We have also leveraged external resources to engineer a domain-specific approach to data harmonization. We describe the scope of data within the NSRR, its role in promoting sleep and circadian research through data sharing, and harmonization of large datasets and analytical tools. Finally, we identify opportunities for approaches for the field of sleep medicine to further support data standardization and sharing.

Keywords: FAIR; data repository; data reuse; data-sharing; harmonization; informatics; metadata.

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