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National
Sleep Research
Resource

Advancing science globally
through data sharing

Latest announcements

We have created a public Zotero publication list for the NSRR. Please feel free to reach out to us if you have a relevant publication using NSRR data that should be added to this list.

This list is also an opportunity to identify collaborations and areas of overlap in analysis of NSRR data.

Download and Share Datasets

Share your research data on the NSRR.

Demographics, anthropometry, questionnaires, outcomes, and more.

Blog

A prospective study of the association of weekend catch‑up sleep and sleep duration with mortality in middle‑aged adults

Overview It remains to be determined whether and in which individuals weekend catch-up sleep (CUS) promotes health. The health effects of weekend CUS could differ depending on both the ability to obtain sufficient sleep during weekdays and amount of weekend CUS required to compensate for sleep lost during the week. We examined the longitudinal association of these two aspects of sleep with all-cause mortality. Keep reading

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By szhivotovsky on December 4, 2023 Dec 4, 2023 in Guest Blogger

U-Sleep’s resilience to AASM guidelines

Overview AASM guidelines are the result of decades of efforts aimed at standardizing sleep scoring procedures, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules according to age. Automated sleep scoring systems - e.g. Keep reading

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By szhivotovsky on October 25, 2023 Oct 25, 2023 in Guest Blogger

An accessible and versatile deep learning-based sleep stage classifier

Overview The Greifswald Sleep Stage Classifier (GSSC) is a highly accurate, deep learning-based automatic sleep stage classifier that priortises ease of use as well as accessibility for a broad range of potential applications and recording environments. Sleep staging can be performed within a simple GUI, the command-line, or integrated directly into the user's own Python code. Keep reading

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By szhivotovsky on October 17, 2023 Oct 17, 2023 in Guest Blogger
An NHLBI resource for the sleep research community.

47,869

Individuals Represented

47,869

Individuals Represented

8.2 TB

Stored on the Resource

8.2 TB

Stored on the Resource

1 PB

Shared with Researchers

1 PB

Shared with Researchers