{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3312","dataset_id":"ds004075","associated_paper_doi":null,"authors":["Adam Boncz","Brigitta Toth","István Winkler"],"bids_version":"v1.7.0","contact_info":["Adam Boncz"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004075.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":29,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004075","osf_url":null,"github_url":null,"paper_url":null},"funding":["Hungarian Academy of Sciences, grant LP2012-36/2012 (IW)","National Research, Development and Innovation Office, Hungary, grant K132642 (IW)","National Research, Development and Innovation Office, Hungary, grant PD123790 (BT)"],"ingestion_fingerprint":"2ba701bb0a5a1ebc31df31e4e623e79ee6ac38963e39e16c5e490131a175b2e3","license":"CC0","n_contributing_labs":null,"name":"what_are_we_talking_about","readme":null,"recording_modality":["eeg"],"senior_author":"István Winkler","sessions":[],"size_bytes":7936059852,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["context03","context09","context10","context11"],"timestamps":{"digested_at":"2026-04-22T12:26:00.649212+00:00","dataset_created_at":"2022-03-18T01:32:05.284Z","dataset_modified_at":"2022-03-18T01:36:04.000Z"},"total_files":116,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004075","raw_key":"dataset_description.json","dep_keys":["CHANGES"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"4ad5264bb1de978f","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:40:53.200113+00:00"},"tags":{"pathology":["Unknown"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.2,"modality":0.2,"type":0.2},"reasoning":{"few_shot_analysis":"The few-shot examples illustrate the catalog conventions: (1) Pathology is determined by the recruited clinical population explicitly stated in metadata (e.g., 'Parkinsons disease' -> Parkinson's; 'Alzheimer’s disease' -> Dementia), otherwise Healthy. (2) Modality is determined by the dominant stimulus/input channel (e.g., 'auditory oddball' -> Auditory; braille under fingers -> Tactile; eyes-closed no task -> Resting State/Sleep). (3) Type reflects the main construct (e.g., discrimination tasks -> Perception; reinforcement learning -> Decision-making; eyes-closed recordings -> Resting-state/Sleep; large clinical biomarker datasets -> Clinical/Intervention). However, for the current dataset, no metadata is provided, so these conventions cannot be applied to extract facts.","metadata_analysis":"No dataset metadata was provided (the `dataset` object is empty: {}). Therefore there are no title/readme/task/event/participant details to quote, and no factual basis to infer pathology, modality, or type.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS: no information (dataset {}). Few-shot pattern SUGGESTS: choose the explicitly stated recruited population when available; otherwise Healthy/Unknown. ALIGN/CONFLICT: cannot assess alignment because metadata is missing; default to Unknown.\nModality: Metadata SAYS: no information (dataset {}). Few-shot pattern SUGGESTS: infer from described stimuli/task (auditory/visual/tactile/motor/rest/sleep). ALIGN/CONFLICT: cannot assess; default to Unknown.\nType: Metadata SAYS: no information (dataset {}). Few-shot pattern SUGGESTS: infer from study aim/task (Perception/Memory/Motor/etc.). ALIGN/CONFLICT: cannot assess; default to Unknown.","decision_summary":"Pathology top-2: (1) Unknown — supported by missing participant/diagnosis metadata (dataset {}). (2) Healthy — a common default in the few-shot set when no disorder focus exists, but here we lack any statement of recruitment. Winner: Unknown. Alignment status: not assessable (no metadata quotes).\nModality top-2: (1) Unknown — no task/stimulus description available (dataset {}). (2) Resting State — common in EEG datasets but not stated here. Winner: Unknown. Alignment status: not assessable.\nType top-2: (1) Unknown — no research aim/task described (dataset {}). (2) Resting-state — plausible if it were eyes-open/closed, but not stated. Winner: Unknown. Confidence is low across categories because there are zero supporting metadata quotes."}},"nemar_citation_count":1,"computed_title":"what_are_we_talking_about","nchans_counts":[{"val":64,"count":115}],"sfreq_counts":[{"val":1000.0,"count":116}],"stats_computed_at":"2026-04-22T23:16:00.306894+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":"Boncz2022"}}