{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33ac","dataset_id":"ds005007","associated_paper_doi":null,"authors":["Yu Kitazawa","Eishi Asano"],"bids_version":"1.7.0","contact_info":["Kitazawa Yu"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005007.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":40,"ages":[26,33,36,32,19,54,25,36,20,16,22,22,25,10,6,17,26,29,15,37,38,24,16,51,23,11,26,19,19,27,20,30,17,21,11,33,9,37,22,40],"age_min":6,"age_max":54,"age_mean":25.0,"species":null,"sex_distribution":{"m":24,"f":16},"handedness_distribution":{"r":40}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005007","osf_url":null,"github_url":null,"paper_url":null},"funding":["N/A"],"ingestion_fingerprint":"76d81db276237453405ca56ff9a5a3141d062bb35f81ac5ba5d5026ad888e035","license":"CC0","n_contributing_labs":null,"name":"Auditory naming task with questions that begin or end with a wh-interrogative","readme":null,"recording_modality":["ieeg"],"senior_author":"Eishi Asano","sessions":["01","02"],"size_bytes":8896470075,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["namingtask"],"timestamps":{"digested_at":"2026-04-22T12:27:12.821742+00:00","dataset_created_at":"2024-03-05T23:44:50.042Z","dataset_modified_at":"2024-03-07T17:44:30.000Z"},"total_files":42,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005007","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"31b466fdef629090","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Other"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.6},"reasoning":{"few_shot_analysis":"Most similar few-shot examples by stimulus channel are the Healthy/Auditory digit-span dataset (auditory presentation of digits; labeled Modality=Auditory, Type=Memory) and the Healthy/Auditory music-vs-speech ABR dataset (listening to audio; labeled Modality=Auditory, Type=Perception). These guide the convention that the stimulus channel (auditory listening) determines Modality=Auditory. However, unlike digit span (explicit working memory) or ABR (auditory encoding/perception), the present dataset is an auditory language/naming paradigm, which does not map cleanly onto the provided Type labels; thus Type is best captured by the catch-all 'Other' rather than forcing Memory/Perception.","metadata_analysis":"Key metadata facts:\n- Title explicitly indicates auditory language stimuli and naming: \"Auditory naming task with questions that begin or end with a wh-interrogative\".\n- Task list reinforces a naming paradigm: tasks = [\"namingtask\"].\n- Participants span a wide age range with no diagnosis field: \"Age range: 6-54\" and \"Subjects: 40\"; no recruitment pathology/diagnosis is mentioned.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no disorder/diagnosis is stated (only \"Subjects: 40\" and \"Age range: 6-54\").\n- Few-shot pattern suggests: when no clinical population is described, label as Healthy (e.g., multiple few-shots labeled Healthy with generic participant descriptions).\n- Alignment: ALIGN (metadata absence of pathology matches the Healthy convention).\n\nModality:\n- Metadata says: \"Auditory naming task...\" (auditory questions).\n- Few-shot pattern suggests: listening-based paradigms are Modality=Auditory (e.g., digit span; music/speech ABR).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: a \"naming task\" involving \"wh-interrogative\" question structure.\n- Few-shot pattern suggests: cognitive-construct labels should reflect the primary construct; but there is no dedicated 'Language' type, and this is not clearly Memory, Attention, Decision-making, Motor, Resting-state, or Sleep.\n- Alignment: PARTIAL; few-shot conventions don’t provide a direct mapping for language/naming, so 'Other' is selected as the best fit within allowed labels.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Healthy — evidence: no diagnosis/clinical recruitment described; only demographics (\"Age range: 6-54\", \"Subjects: 40\"). Matches few-shot convention for non-clinical samples.\n2) Development — evidence: includes children (age starts at 6), but the sample also includes adults up to 54 and there is no developmental-disorder focus stated.\nDecision: Healthy. (Alignment: aligns with few-shot convention; limited explicit evidence)\n\nModality:\n1) Auditory — evidence: title explicitly \"Auditory naming task...\"; task implies auditory questions.\n2) Multisensory — would require explicit additional sensory channels (not present).\nDecision: Auditory. (Alignment: strong with both metadata and few-shot analogs)\n\nType:\n1) Other — evidence: naming/wh-question language paradigm doesn’t map cleanly to provided constructs.\n2) Perception — could be argued because it involves processing auditory questions, but the task goal is linguistic naming/production rather than sensory discrimination.\nDecision: Other. (Alignment: no conflict; chosen due to label-set limitation)\n\nConfidence justifications:\n- Pathology=0.6: inference from absence of pathology statements (no explicit 'healthy controls' phrase).\n- Modality=0.8: explicit \"Auditory\" in title + clear few-shot analogs.\n- Type=0.6: only contextual inference (language/naming) with multiple plausible mappings (Other vs Perception)."}},"nemar_citation_count":0,"computed_title":"Auditory naming task with questions that begin or end with a wh-interrogative","nchans_counts":[{"val":100,"count":3},{"val":58,"count":2},{"val":74,"count":2},{"val":82,"count":2},{"val":78,"count":2},{"val":86,"count":2},{"val":116,"count":2},{"val":66,"count":2},{"val":156,"count":2},{"val":137,"count":1},{"val":184,"count":1},{"val":122,"count":1},{"val":127,"count":1},{"val":88,"count":1},{"val":128,"count":1},{"val":51,"count":1},{"val":68,"count":1},{"val":114,"count":1},{"val":140,"count":1},{"val":48,"count":1},{"val":124,"count":1},{"val":94,"count":1},{"val":102,"count":1},{"val":120,"count":1},{"val":154,"count":1},{"val":155,"count":1},{"val":129,"count":1},{"val":72,"count":1},{"val":163,"count":1},{"val":142,"count":1},{"val":138,"count":1},{"val":91,"count":1}],"sfreq_counts":[{"val":1000.0,"count":42}],"stats_computed_at":"2026-04-22T23:16:00.308821+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.42,"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":"Kitazawa2024"}}