{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32b1","dataset_id":"ds002791","associated_paper_doi":null,"authors":[" Ahmad Mheich","Olivier Dufor","Sahar Yassine","Aya Kabbara","Fabrice Wendling","Mahmoud Hassan"],"bids_version":"2.1","contact_info":["Ahmad Mheich"],"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds002791.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":23,"ages":[23,31,19,23,24,30,19,22,34,19,26,21,20,33,20,30,40,27,24,39,33,25,23],"age_min":19,"age_max":40,"age_mean":26.304347826086957,"species":null,"sex_distribution":{"f":12,"m":11},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds002791","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"bf7c299dff432543cd6191da458e264bb9aa9dc18fb7e09ba2ee4c998536d4b8","license":"CC0","n_contributing_labs":null,"name":"DataSet1","readme":null,"recording_modality":["eeg"],"senior_author":"Mahmoud Hassan","sessions":["naming","spelling"],"size_bytes":50562457196,"source":"openneuro","study_design":null,"study_domain":null,"tasks":[],"timestamps":{"digested_at":"2026-04-22T12:25:29.622154+00:00","dataset_created_at":"2020-05-13T12:50:22.167Z","dataset_modified_at":"2020-05-14T12:57:15.000Z"},"total_files":92,"storage":{"backend":"s3","base":"s3://openneuro.org/ds002791","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"f97d69f4b1f80f60","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.7,"modality":0.4,"type":0.4},"reasoning":{"few_shot_analysis":"No few-shot example is directly similar in task paradigm because the provided metadata for DataSet1 contains no task/stimulus description. However, several few-shot examples illustrate the convention that when a dataset lists only basic demographics and does not mention any recruited clinical group/diagnosis, it is labeled as a normative cohort (e.g., the 'EEG: Three armed bandit gambling task' explicitly says 'Healthy control college students', and multiple other healthy datasets list only non-clinical participant descriptors). This convention guides selecting Pathology=Healthy here, while Modality/Type remain Unknown due to missing task/stimulus facts.","metadata_analysis":"Key available metadata is extremely sparse and contains no task or stimulus details.\nQuoted snippets:\n1) dataset_description: \"Name: DataSet1\" and \"DOI: 10.18112/openneuro.ds002791.v1.0.0\".\n2) participants_overview: \"Subjects: 23; Sex: {'f': 12, 'm': 11}; Age range: 19-40\".\nThere are no quoted mentions of patient groups, diagnoses, resting-state, sleep, motor, auditory/visual/tactile stimuli, or cognitive paradigm.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS only general demographics: \"Subjects: 23\" and \"Age range: 19-40\" with no diagnosis mentioned. Few-shot pattern SUGGESTS labeling such non-clinical, non-diagnosis-described cohorts as \"Healthy\" (normative cohort convention). ALIGN (no conflict).\n\nModality: Metadata SAYS nothing about stimuli/task (no auditory/visual/motor/rest/sleep mentions). Few-shot pattern cannot infer modality without stimulus/task facts. ALIGN in the sense that both provide insufficient evidence; choose \"Unknown\".\n\nType: Metadata SAYS nothing about research purpose/cognitive construct (no task, no resting state, no sleep staging, etc.). Few-shot pattern cannot infer type without paradigm details. ALIGN (insufficient evidence); choose \"Unknown\".","decision_summary":"Top-2 candidate selection per category:\n\nPathology:\n- Candidate 1: Healthy — Evidence: no clinical recruitment/diagnosis is mentioned; only demographics: \"Subjects: 23; ... Age range: 19-40\". Few-shot convention: when no disorder focus is stated, label as Healthy.\n- Candidate 2: Unknown — Evidence: metadata does not explicitly say \"healthy\".\nWinner: Healthy (metadata lacks any clinical facts; convention favors Healthy for normative cohorts).\nConfidence basis: 1 explicit snippet supporting non-clinical demographic-only description, but no explicit 'healthy' statement.\n\nModality:\n- Candidate 1: Unknown — Evidence: no task/stimulus modality described anywhere.\n- Candidate 2: Resting State — Evidence: none in provided metadata (only a weak possibility for many EEG datasets, but not stated).\nWinner: Unknown.\nConfidence basis: absence of any stimulus/task quote.\n\nType:\n- Candidate 1: Unknown — Evidence: no task/paradigm or study aim described.\n- Candidate 2: Resting-state — Evidence: none stated.\nWinner: Unknown.\nConfidence basis: absence of any task/aim quote."}},"nemar_citation_count":0,"computed_title":"DataSet1","nchans_counts":[{"val":256,"count":80},{"val":257,"count":12}],"sfreq_counts":[{"val":1000.0,"count":92}],"stats_computed_at":"2026-04-22T23:16:00.221761+00:00","source_url":"https://openneuro.org/datasets/ds002791","total_duration_s":48729.49,"canonical_name":null,"name_confidence":0.64,"name_meta":{"suggested_at":"2026-04-14T10:18:35.342Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Mheich2020_DataSet1"}}