ECoG/EMG mouse data collected from two labs, across four different strains and two recording modalities.

These are data used in the paper "Sleep Identification Enabled by Supervised Training Algorithms (SIESTA): An Open-Source Platform for Automatic Sleep Staging of Rodent Electrocorticographic and Electromyographic Data", excluding data gathered from other open source depositories (for data from Ellen & Dash, see doi:10.5281/zenodo.5227351; for data from Sippel et al., see doi:10.25493/A2KP-FKD). DLI comprises data gathered in the de la Iglesia lab at University of Washington. This includes data from each of the four genetic strains (wild-type [WT], SCN1a */-, APP-PS1, and NmsVgat), as well as data captured by a telemetric device (TSE) and data scored in 5-second epochs (5sec). Raw data are in .edf data format, with corresponding extracted features (for each available channel) and manual scores. This folder additionally contains unscored data used to generate sample trace and jetlag figures as well as code used to extract features and organize data. FK comprises data gathered in the Kalume Lab. In these data, the raw data are in the .edf format but are generally separated such that each file only contains a given sleep stage. These data also have corresponding extracted features (for each channel) and manual scores, as well as code used to extract features and organize data. Training .csv files comprise data used to train corresponding models contained in files in .file format. For more information, please read methods and supplemental methods in the original paper, doi:10.1177/07487304251336649.