Visualizing data is almost always useful, but this can be difficult when you have a lot of complex data. This is where dimension reduction techniques can play an important role. As described here, we applied one such technique to sleep EEG spectra from over 16,000 individuals in the NSRR, to get a feel for some of the sources of individual differences (both physiological and artefactual) in these data. Keep reading
Luna is a C/C++ library focused on the analysis of large numbers of sleep studies, such as those available from the NSRR. Luna is completely open-source and not dependent on proprietary software. If you're interested in analyzing sleep signals from the NSRR, you might want to start by looking at Luna. Currently, there is a command-line tool (lunaC) and an extension library for R (lunaR). Keep reading
Many analyses of sleep EEG data are effectively agnostic to the polarity of the EEG signal. That is, you could flip the signal (i.e. multiply every sample value by -1) and still obtain equivalent results, e.g. from most spectral analyses. For certain analyses that consider the phase of a signal, however, it will in fact matter that the polarity of the signal is correct. Keep reading
The NSRR gem v5.0.0 has been released. This new version of the gem support Ruby 2.4.6 or greater, and has added support for Ruby 2.6.3. To update to the latest version of the gem, type: gem install nsrr --no-document Please note that we are also dropping support for v0.3.0 and v0.4.0 of the gem that rely on an older data access API that is being removed. Leave a reply below or email us at support@sleepdata.org if you have any questions or issues using the new release. Keep reading
Spout is an open-source Ruby tool that helps the National Sleep Research Resource team curate, manage, and version-control data dictionaries that describe underlying datasets on the site. These data dictionaries provide metadata about the variables (columns) within a dataset. A well-defined data dictionary is an essential tool for researchers who want to understand and analyze a given dataset. The NSRR team started development on Spout in 2013. Version 1.0.0 was released on February 1, 2019. Keep reading
Do you do statistics programming using R? Do you wish you could download files from the NSRR without going through the process of installing Ruby? Well, John Muschelli and Ciprian Crainiceanu sure think so. John and Ciprian created an open-source "R" implementation of the NSRR downloader that is available for download on CRAN, https://cran.r-project.org/web/packages/nsrr/index.html. Keep reading
We are happy to announce the release of the One Year of Actigraphy (OYA) dataset on the National Sleep Research Resource. This is a unique personal project undertaken by a researcher dedicated to sleep science. The purpose of the project is to make a year's worth of one person's actigraphy data available to the research community in the spirit of promoting the creation of new methods for sleep and circadian data analysis. Keep reading
Earlier this week we deposited data from the Apnea, Bariatric surgery, and CPAP (ABC) study in the National Sleep Research Resource. ABC aimed to assess the role of bariatric (weight loss) surgery as compared to continuous positive airway pressure (CPAP) therapy plus weight loss counseling for the treatment of patients with class II obesity and those who have severe obstructive sleep apnea (OSA). Keep reading
We've recently pushed an update to the NSRR that drops support for versions v0.1.0, v0.1.1, and v0.2.0 of the NSRR gem. Version v0.2.0 was released on May 29, 2015 and many users have already updated to the newer v0.3.0 and v0.4.0 releases. If you are using one of these older releases you will need to update using following instructions. Keep reading
We're happy to announce the publication of "The National Sleep Research Resource: towards a sleep data commons" in the Journal of the American Medical Informatics Association (JAMIA). The paper is available online for open access here: https://doi.org/10.1093/jamia/ocy064 For researchers publishing on data on the NSRR, you can now add the following citation as well: Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S. Keep reading