{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3448","dataset_id":"ds006377","associated_paper_doi":"10.1002/uog.22088","authors":["Meryem A. Yücel","Jessica E. Anderson","De'Ja Rogers","Parisa Hajirahimi","Parya Farzam","Yuanyuan Gao","Rini I. Kaplan","Emily J. Braun","Nishaat Mukadam","Sudan Duwadi","Laura Carlton","David Beeler","Lindsay K. Butler","Erin Carpenter","Jaimie Girnis","John Wilson","Vaibhav Tripathi","Yiwen Zhang","Bettina Sorger","Alexander von Lühmann","David C. Somers","Alice Cronin-Golomb","Swathi Kiran","Terry D. Ellis","David A. Boas"],"bids_version":"1.8.0","contact_info":["MERYEM YUCEL"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds006377.v1.0.2","datatypes":["fnirs"],"demographics":{"subjects_count":115,"ages":[21,21,24,19,20,40,20,20,25,23,23,23,19,20,22,24,21,20,20,24,21,20,24,27,21,23,23,21,21,52,23,19,27,18,24,50,27,27,32,29,26,33,24,22,24,34,22,27,33,25,24,27,21,25,28,20,20,20,22,34,20,22,24,24,24,23,22,19,30,23,45,20,20,36,20,33,29,28,26,29,30,24,25,25,24,22,30,28,30,23,24,21,29,20,23,25,27,18,19,23,18,19,19,19,20,27,24,22,26,20,50,74,71,72,60],"age_min":18,"age_max":74,"age_mean":26.408695652173915,"species":null,"sex_distribution":{"f":63,"m":52},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/uog.22088"},"funding":["Meta Reality Labs (formerly Facebook Technologies, LLC)","NIH U01EB0239856","NSF Research Traineeship Program (DGE-1633516)","NWiOd i-VGrant VI.Vidi.191.210","German Federal Ministry of Education and Research (BIFOLD24B)"],"ingestion_fingerprint":"789b50071b62765c6a320ad89a44738fa58d725034c1d276ad013e2947f10110","license":"CC0","n_contributing_labs":null,"name":"InclusionStudy","readme":null,"recording_modality":["fnirs"],"senior_author":"David A. Boas","sessions":[],"size_bytes":1480974514,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["001RestingLH","002RestingRH","003RestingLH","004RestingRH","009BallLH","010BallRH"],"timestamps":{"digested_at":"2026-05-10T17:15:20.954092+00:00","dataset_created_at":"2025-06-20T16:37:11.522Z","dataset_modified_at":"2025-09-19T15:39:14.000Z"},"total_files":690,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006377","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.json","participants.tsv"]},"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Other"],"confidence":{"pathology":0.8,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"The few-shot examples reveal patterns for labeling. For example, when metadata focuses on methodological feasibility or instrument validation (seen in datasets related to TBI and Parkinson's), they are often labeled as Clinical/Intervention despite task-level specifics. This example does not strongly match other datasets by pathology but aligns with technical feasibility focus helping to suggest a Healthy pathology similar to the 'Surrey cEEGrid sleep data set' as there is no specific disorder focus.","metadata_analysis":"The dataset, titled 'InclusionStudy', involves fNIRS and investigates how hair and skin characteristics impact fNIRS signal quality, aiming for enhanced inclusivity. Metadata mentions: 'Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity.' The 'participants_overview' lists demographic attributes without indicating any specific pathology.","paper_abstract_analysis":"No specific paper abstract provided in the dataset; only bibliographic references to inclusion and impact of skin and hair characteristics are cited but not detailed.","evidence_alignment_check":"1. Pathology: The metadata suggests a Healthy population inference (\"focused on impact of hair and skin characteristics\") and aligns with the dataset description focusing on technological assessments without mentioning any clinical issue; this matches few-shot Healthy labels in technology-focused studies. 2. Modality: Based on the method fNIRS discussed, it uses light for detection, which directly relates to Visual modality, unlike the few-shot focused examples involving EEG or other sensory tasks. 3. Type: The overview of methods and participant descriptions implies an investigatory focus around fNIRS signal quality without a specific perceptual or cognitive task, suggesting 'Other,' aligning with readers examining equipment efficacy rather than subject perception or task performance.","decision_summary":"Pathology: Healthy based on absence of condition-specific recruitment and alignment with feasibility study in few-shots. Modality: Visual due to light-based fNIRS technical focus. Type: Other, reflected in the non-task orientation and methodological focus, 0.8 confidence supported by explicit mentions."}},"computed_title":"InclusionStudy","nchans_counts":[{"val":52,"count":690}],"sfreq_counts":[{"val":10.172526041666666,"count":643},{"val":10.172526041666664,"count":24},{"val":10.172526041666668,"count":21},{"val":10.172526043256445,"count":2}],"stats_computed_at":"2026-05-10T19:09:03.500255+00:00","total_duration_s":null,"author_year":"Yucel2025_InclusionStudy","canonical_name":null,"bad_channels_info":null,"associated_paper_meta":{"channel":"search","confidence":"medium","author_overlap":1,"is_oa":true,"oa_status":"hybrid","source":"paper_resolver"}}}