{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33c7","dataset_id":"ds005274","associated_paper_doi":null,"authors":["Yukako Ito"],"bids_version":"1.6.0","contact_info":["Yukako Ito"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005274.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":22,"ages":[22,23,24,24,21,21,24,22,23,23,21,21,23,26,26,22,25,24,22,22,23,20],"age_min":20,"age_max":26,"age_mean":22.818181818181817,"species":null,"sex_distribution":{"m":16,"f":6},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005274","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"387ef44213197915a6fab999847846bddadc8cda45db05cbe3ab772d3b593c1c","license":"CC0","n_contributing_labs":null,"name":"UV_EEG","readme":null,"recording_modality":["eeg"],"senior_author":"Yukako Ito","sessions":[],"size_bytes":75399884,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["1"],"timestamps":{"digested_at":"2026-04-22T12:27:29.343301+00:00","dataset_created_at":"2024-06-21T19:59:14.305Z","dataset_modified_at":"2024-06-21T20:16:16.000Z"},"total_files":22,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005274","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.tsv"]},"nemar_citation_count":0,"computed_title":"UV_EEG","nchans_counts":[{"val":6,"count":22}],"sfreq_counts":[{"val":500.0,"count":22}],"stats_computed_at":"2026-04-22T23:16:00.309161+00:00","tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.6,"modality":0.4,"type":0.4},"reasoning":{"few_shot_analysis":"Few-shot convention for Pathology: multiple examples label datasets as Healthy when there is no explicit clinical recruitment/diagnosis mentioned in the metadata (e.g., 'Surrey cEEGrid sleep data set' and 'A Resting-state EEG Dataset for Sleep Deprivation' both explicitly describe healthy/non-clinical cohorts, and are labeled Healthy). In this dataset, no disorder terms appear, so by convention it should be treated as a non-clinical cohort → Healthy (while noting this is an inference, not an explicit fact).\nFew-shot convention for Modality/Type: when the task/stimuli are not described, few-shot examples do not support inferring modality/type from title alone; therefore Unknown is used rather than guessing (consistent with the catalog rule to base Modality on stimulus description and Type on the research purpose/task description).","metadata_analysis":"Available metadata is very sparse and does not describe stimuli, paradigm, or purpose.\nKey snippets:\n1) Title: \"UV_EEG\".\n2) Participants: \"Subjects: 22; Sex: {'m': 16, 'f': 6}; Age range: 20-26\".\n3) Tasks field: \"tasks\": [\"1\"].\nNo README/events/task description is provided that would indicate sensory modality (auditory/visual/etc.) or cognitive construct (memory/attention/etc.), and no clinical diagnoses are mentioned.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis/clinical group is stated; only \"Subjects: 22\" and \"Age range: 20-26\".\n- Few-shot pattern suggests: when no clinical recruitment is stated, label as Healthy.\n- Alignment: ALIGN (no conflicting explicit clinical fact).\n\nModality:\n- Metadata says: only \"tasks\": [\"1\"] and title \"UV_EEG\"; no stimulus description.\n- Few-shot pattern suggests: Modality should be derived from explicit stimulus channel (e.g., tones→Auditory, dots→Visual, braille→Tactile); otherwise use Unknown.\n- Alignment: ALIGN (insufficient info → Unknown).\n\nType:\n- Metadata says: no description of experimental aim or construct; only task label \"1\".\n- Few-shot pattern suggests: Type requires an explicit task/construct (resting-state, motor imagery, oddball, digit span, etc.); otherwise use Unknown.\n- Alignment: ALIGN (insufficient info → Unknown).","decision_summary":"Top-2 candidates per category with head-to-head selection:\n\nPathology:\n- Candidate 1: Healthy — Evidence: absence of any disorder terms; only demographics given (\"Subjects: 22... Age range: 20-26\"). Matches few-shot convention that non-clinical/unspecified recruitment defaults to Healthy.\n- Candidate 2: Unknown — Could apply because health status is not explicitly stated.\n→ Select Healthy because catalog convention treats datasets without any clinical recruitment language as normative cohorts.\nEvidence alignment: aligned (no explicit pathology contradicts).\n\nModality:\n- Candidate 1: Unknown — Evidence: no task/stimulus description beyond \"tasks\": [\"1\"].\n- Candidate 2: Visual — Weak inference only from title string \"UV_EEG\" (could suggest 'ultraviolet', but not explicit and could mean something else).\n→ Select Unknown because modality must be based on described stimuli, not title speculation.\nEvidence alignment: aligned (insufficient info).\n\nType:\n- Candidate 1: Unknown — Evidence: no paradigm/construct described; task listed only as \"1\".\n- Candidate 2: Other — Would be used if a task existed but didn’t map cleanly; here the task is not described at all.\n→ Select Unknown because the research purpose/construct cannot be determined.\nEvidence alignment: aligned (insufficient info).\n\nConfidence justification:\n- Pathology (0.6): inference based on lack of clinical terms (no explicit 'healthy' statement).\n- Modality (0.4): no stimulus/task description; cannot infer reliably.\n- Type (0.4): no description of paradigm/construct; cannot infer reliably."}},"total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a50a83e08df874ee","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"canonical_name":null,"name_confidence":0.28,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Ito2024"}}