{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3444","dataset_id":"ds006366","associated_paper_doi":null,"authors":["Laura Rose","Alexander Neergaard Zahid","Javier García Ciudad","Christine Egebjerg","Louise Piilgaard","Frederikke Lynge Sørensen","Mie Andersen","Tessa Radovanovic","Anastasia Tsopanidou","Maiken Nedergaard","Sébastien Arthaud","Renato Maciel","Christelle Peyron","Chiara Berteotti","Viviana Lo Martire","Alessandro Silvani","Giovanna Zoccoli","Micaela Borsa","Antoine Adamantidis","Morten Mørup","Birgitte Rahbek Kornum"],"bids_version":"1.10.0","contact_info":["Javier García Ciudad"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006366.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":92,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006366","osf_url":null,"github_url":null,"paper_url":null},"funding":["Lundbeck Foundation (R344-2020-749)"],"ingestion_fingerprint":"d3b12e8a08af51d912ff613fcce012cddb202c3f574b093953d0f4244b91796a","license":"CC0","n_contributing_labs":null,"name":"Mouse Sleep Staging Validation dataset (MSSV)","readme":"# Mouse Sleep Staging Validation dataset (MSSV)\nThis dataset contains EEG recordings with sleep scores from 92 healthy mice. The recordings and sleep scores were collected from five different labs:\n- Department of Biomedical and Neuromotor Sciences, Università di Bologna, Italy.\n- Center for Translational Neuroscience, University of Copenhagen, Denmark.\n- Department of Neuroscience, University of Copenhagen, Denmark.\n- Zentrum für Experimentelle Neurologie, Department of Neurology, Inselspital University Hospital Bern, Bern, Switzerland.\n- Lyon Neuroscience Research Center, Lyon, France.\n## Overview\n-   It contains EEG data from healthy mice in both dark and light phases.\n-   All recordings contain at least one EEG and one EMG channel. The number of EEG channels available varies between labs (see labs.tsv).\n-   The dataset is formatted according to the Brain Imaging Data Structure. See the 'dataset_description.json' file for the specific BIDS version used. The recordings are stored in .EDF format.\n-   This dataset has been used and is fully described in https://doi.org/10.1093/sleepadvances/zpaf025.\n## Methods\n-   There are 92 healthy mice from 5 different labs. Each mouse is scored by a single sleep expert. See participants.tsv to see what lab each mouse belongs to.\n-   All recordings have been downsampled to 128Hz and have SI units (volts). The epoch length is 4 seconds.\n## Contact\nFor questions regarding this dataset, contact Birgitte Rahbek Kornum, kornum@sund.ku.dk","recording_modality":["eeg"],"senior_author":"Birgitte Rahbek Kornum","sessions":[],"size_bytes":6526725895,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["sleep"],"timestamps":{"digested_at":"2026-04-22T12:29:18.189465+00:00","dataset_created_at":"2025-06-17T12:46:03.516Z","dataset_modified_at":"2025-09-05T13:06:20.000Z"},"total_files":148,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006366","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","labs.tsv","participants.json","participants.tsv","task-sleep_eeg.json","task-sleep_events.json"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"fc692382349e8a2e","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:55:29.019906+00:00"},"tags":{"pathology":["Healthy"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"Closest few-shot match is the \"Surrey cEEGrid sleep data set\" example, labeled (Pathology=Healthy, Modality=Sleep, Type=Sleep). That example sets the convention that datasets focused on sleep recordings/staging (even without an explicit cognitive task) should be labeled with Modality=Sleep and Type=Sleep, and that non-clinical cohorts are labeled Healthy. The present dataset is explicitly a sleep scoring/staging dataset, aligning strongly with that convention.","metadata_analysis":"Key facts from provided README:\n- Population: \"EEG recordings with sleep scores from 92 healthy mice.\" and \"There are 92 healthy mice from 5 different labs.\"\n- Sleep focus: Title/description \"Mouse Sleep Staging Validation dataset (MSSV)\" and \"It contains EEG data ... in both dark and light phases.\" plus \"The epoch length is 4 seconds.\" (typical sleep staging epochs) and \"All recordings contain at least one EEG and one EMG channel.\"","paper_abstract_analysis":"No useful paper information. (Only a DOI link is provided; no abstract text included in the metadata snippet.)","evidence_alignment_check":"Pathology:\n1) Metadata says: \"92 healthy mice\" / \"92 healthy mice from 5 different labs.\" \n2) Few-shot suggests: sleep datasets with non-clinical subjects are labeled Healthy (e.g., Surrey sleep dataset).\n3) Alignment: ALIGN.\n\nModality:\n1) Metadata says: \"Sleep Staging Validation\" and \"EEG recordings with sleep scores\" (sleep context).\n2) Few-shot suggests: sleep staging/overnight recordings -> Modality=Sleep.\n3) Alignment: ALIGN.\n\nType:\n1) Metadata says: \"Sleep Staging Validation dataset\" and mentions \"sleep scores\" and fixed \"epoch length\" typical for staging.\n2) Few-shot suggests: when the purpose is sleep monitoring/staging -> Type=Sleep.\n3) Alignment: ALIGN.","decision_summary":"Pathology top-2: (1) Healthy vs (2) Unknown.\n- Healthy evidence: \"92 healthy mice\"; \"There are 92 healthy mice\".\n- Unknown would apply only if health status were not stated.\nWinner: Healthy. Alignment status: aligned with few-shot sleep example.\n\nModality top-2: (1) Sleep vs (2) Resting State.\n- Sleep evidence: \"Sleep Staging Validation\"; \"sleep scores\"; dark/light phase recordings; EEG+EMG channels typical for sleep scoring.\n- Resting State is weaker because this is not an awake resting paradigm but explicitly sleep staged.\nWinner: Sleep. Alignment status: aligned with few-shot sleep example.\n\nType top-2: (1) Sleep vs (2) Other.\n- Sleep evidence: \"sleep scores\"; \"Sleep Staging Validation\"; \"epoch length is 4 seconds\" (staging epochs).\n- Other would apply if the goal were unrelated methodology without sleep scoring focus.\nWinner: Sleep. Alignment status: aligned with few-shot sleep example.\n\nConfidence justification: multiple explicit quotes directly state healthy population and sleep staging purpose, plus strong few-shot analog to a sleep dataset labeled Healthy/Sleep/Sleep."}},"computed_title":"Mouse Sleep Staging Validation dataset (MSSV)","nchans_counts":[{"val":3,"count":71},{"val":2,"count":43},{"val":5,"count":34}],"sfreq_counts":[{"val":128.0,"count":148}],"stats_computed_at":"2026-04-22T23:16:00.311495+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.96,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Rose2025"}}