{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c4d","dataset_id":"ds007477","associated_paper_doi":"10.1371/journal.pone.0072425","authors":["Niu，Haijing","Zheng, Sha","Yuan, Haodong"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds007477.v1.0.1","datatypes":["fnirs"],"demographics":{"subjects_count":18,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0072425&type=printable"},"funding":[],"ingestion_fingerprint":"945859376c4274cf5fadfd937392c24afba6ea144338ceeda9de5bd6b3613ee7","license":"CC0","n_contributing_labs":null,"name":"TimeSeries BIDS converted","readme":"This dataset was converted from TimeSeriesHbORT_18sub_twoSessionICAdenoise(1).mat using `convert_mat_to_bids.py`.\nNotes:\n- Review and confirm `*_nirs.json` (SamplingFrequency, NIRSChannelCount, source/detector mapping) before public release.\n- This README is a placeholder to satisfy BIDS recommendations; replace with dataset-specific information as needed.","recording_modality":["fnirs"],"senior_author":null,"sessions":["01","02"],"size_bytes":1644361255,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007477","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["ort"],"timestamps":{"digested_at":"2026-05-31T16:29:25.023207+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":36,"computed_title":"TimeSeries BIDS converted","nchans_counts":[{"val":1,"count":36}],"sfreq_counts":[{"val":10.0,"count":36}],"stats_computed_at":"2026-05-10T19:09:03.501312+00:00","total_duration_s":null,"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.7,"modality":0.5,"type":0.5},"reasoning":{"few_shot_analysis":"The few-shot examples provide datasets where explicit information about pathology, modality, and type is mentioned. However, this dataset lacks such information. The focus in few-shot examples on specific tasks, experiments, and populations guides label decision in cases where task details or participant information is available.","metadata_analysis":"The metadata primarily contains technical details about data conversion ('converted from TimeSeriesHbORT_18sub_twoSessionICAdenoise(1).mat') and lacks specific information about participants, tasks, or sensory stimuli presented ('Notes:...confirm `*_nirs.json`...'). There are no explicit references to any cognitive, sensory, or pathological conditions being studied.","paper_abstract_analysis":"No useful paper information provided regarding experimental design, subject population, or study objectives.","evidence_alignment_check":"1. Pathology: Metadata suggests no specific clinical recruitment ('This README is a placeholder...' and missing participant details), resembling cases labeled 'Healthy' in few-shot examples. 2. Modality: No metadata indicating stimulus modality. Few-shot examples use 'Unknown' when modality details are absent. 3. Type: Lack of task or study type information means this aligns with examples where 'Unknown' is used when the study aims are unmentioned.","decision_summary":"Given the absence of any substantive task, stimuli, or population information in the metadata, all categories (Pathology, Modality, Type) lean towards 'Unknown' due to lack of detail. Pathology is labeled 'Healthy' by default for datasets not indicating clinical recruitment or specific pathology."}},"author_year":"Niu2026","canonical_name":null,"bad_channels_info":null,"associated_paper_meta":{"channel":"openneuro/associatedPaperDOI","confidence":"high","author_overlap":-1,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}