Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource

Abstract

Professional sleep societies have identified a need for strategic research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the sleep research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight sleep study; annotation files with scored events; the raw physiological signals from the sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for sleep research in the context of the national sleep research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.

Common and rare exonic MUC5B variants associated with type 2 diabetes in Han Chinese

“…Replication analysis was performed in 10,401 African ancestry samples obtained from the Atherosclerosis Risk in Communities (ARIC, n = 3,137) [10], the Cleveland Family Study (CFS, n = 653) [11], the Howard University Family Study (HUFS, n = 1,976) [12], Jackson Heart Study (JHS, n = 2,187) [13], Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,611) [14], and Africa America Diabetes Mellitus Study (AADM, n = 1802) [15]. Analysis was conducted using human genomic reference (hg19) coordinates.…”

Avaliação De Representações Transformadas Para Compressão De Sinais De Eletroencefalografia

“…Os sinais de EEG considerados são adquiridos a partir da base de dados da Physionet, banco de dados de sinais fisiológicos de livre acesso. Mais especificamente, foi adotado um estudo de polissonografia denominado Sleep Heart Health Study [9], [10], [11].…”

“…Outra aplicação diz respeito ao monitoramento de sinais de EEG de pacientes que sofrem de epilepsia, um dos usos mais comuns de EEG [8]. Pode-se citar ainda o uso do EEG no estudo do sono em polissonografia [9] [10] [11].…”

Evaluation of Relational and NoSQL Approaches for Cohort Identification from Heterogeneous Data Sources in the National Sleep Research Resource

“…Funded by the National Heart, Lung and Blood Institute, NSRR was designed to share de-identified sleep data obtained from NIH-funded cohort studies and clinical trials with the sleep research community [11]. NSRR provides a web-based data portal [12] that aggregates and organizes signal and clinical data from over 26, 000 patient subjects.…”