The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
The National Sleep Research Resource: towards a sleep data commons
Open accessAbstract
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.
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Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes
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Explainable AI for clinical and remote health applications: A survey on tabular and time series data
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Levy, J., Álvarez, D., Rosenberg, A. A., Alexandrovich, A., del Campo, F., & Behar, J. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00373-5 · Cited by 125
Metadata
- Authors
- Zhang, G.-Q., Cui, L., Mueller, R., Tao, S., Kim, M., Rueschman, M., Mariani, S., Mobley, D., & Redline, S.
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-31
- Volume
- 25
- Issue
- 10
- Pages
- 1351-1358
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- OpenAlex
- W2805075551
- Cited by
- 917
- References
- 31
- FWCI
- 61.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhang, G.-Q., Cui, L., Mueller, R., Tao, S., Kim, M., Rueschman, M., Mariani, S., Mobley, D., & Redline, S. (2018). The National Sleep Research Resource: towards a sleep data commons. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocy064
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