{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a341d","dataset_id":"ds005857","associated_paper_doi":null,"authors":["[Adam Broitman]","[Michael Kahana]"],"bids_version":"1.7.0","contact_info":["Adam Broitman"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005857.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/ds005857","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"9aa3178382596561ac49688e69585dc5b6f3afed2cb9759c7a0482b0437c2f3e","license":"CC0","n_contributing_labs":null,"name":"ltpDelayRepFRReadOnly","readme":"References\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8","recording_modality":["eeg"],"senior_author":"[Michael Kahana]","sessions":["0","1","2","3","4","5"],"size_bytes":305355180585,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ltpDelayRepFRReadOnly"],"timestamps":{"digested_at":"2026-04-22T12:28:56.355511+00:00","dataset_created_at":"2025-01-16T16:22:01.499Z","dataset_modified_at":"2025-04-02T18:37:15.000Z"},"total_files":110,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005857","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"e35a1b8f6fd3c885","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.4,"modality":0.6,"type":0.6},"reasoning":{"few_shot_analysis":"Closest few-shot by research construct is the digit span dataset (Healthy / Auditory / Memory): it shows the convention that explicit memory paradigms (e.g., serial recall) are labeled Type=Memory and Modality is based on the stimulus channel (auditory digits there). Here, the task name contains \"FR\" (commonly used abbreviation for free recall in Kahana-lab task naming), suggesting a memory/free-recall paradigm; however, this dataset provides far less explicit task/stimulus detail than the few-shot example, so labels beyond Type must rely on weaker inference.","metadata_analysis":"Key available metadata is sparse. Relevant snippets: (1) Title/task name: \"ltpDelayRepFRReadOnly\" (also listed under tasks: \"tasks\": [\"ltpDelayRepFRReadOnly\"]). (2) Participants: \"Subjects: 29\". (3) Authors: \"Authors: [Adam Broitman], [Michael Kahana]\". There is no explicit description of participant diagnosis/clinical recruitment, and no explicit description of stimulus modality (visual/auditory) or task instructions (e.g., recall/recognition).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: (1) Metadata says: no diagnosis stated; only \"Subjects: 29\" and author names. (2) Few-shot pattern suggests: nothing directly; only that when a clinical group is explicitly stated (e.g., Parkinson's, schizophrenia, epilepsy) we label it. (3) Alignment: cannot align—metadata lacks pathology facts. Decision: use Unknown because no explicit recruitment pathology is provided.\n\nModality: (1) Metadata says: only the task name \"ltpDelayRepFRReadOnly\"; no stimulus description. (2) Few-shot pattern suggests: modality follows stimulus channel (e.g., digit span = auditory). (3) Alignment: weak—must infer modality from task name. Decision: tentatively Visual because \"ReadOnly\" implies reading (typically visually presented text), but this is not explicitly stated.\n\nType: (1) Metadata says: task name includes \"FR\" in \"ltpDelayRepFRReadOnly\" and nothing else. (2) Few-shot pattern suggests: paradigms involving recall/span/list learning are labeled Memory (digit span example). (3) Alignment: partial—task name hints at free recall (memory), but not explicitly described. Decision: label Memory with moderate uncertainty.","decision_summary":"Pathology top-2: (A) Unknown — supported by lack of any explicit diagnosis/clinical recruitment info (quotes: \"Subjects: 29\"; no other participant descriptors). (B) Epilepsy — weak contextual possibility given the Kahana lab often works with clinical iEEG cohorts, but not stated here. Head-to-head: Unknown wins because metadata contains no pathology facts. Confidence=0.4 (insufficient direct evidence).\n\nModality top-2: (A) Visual — inferred from task suffix \"ReadOnly\" suggesting reading/visual presentation (quote: \"ltpDelayRepFRReadOnly\"). (B) Unknown/Other — because no stimulus channel is explicitly described. Head-to-head: Visual slightly stronger as a semantic inference from the task name; still no explicit stimulus description. Confidence=0.6 (contextual inference only).\n\nType top-2: (A) Memory — inferred from \"FR\" (commonly free recall) in the task name (quote: \"ltpDelayRepFRReadOnly\"), consistent with the few-shot Memory labeling convention (digit span example). (B) Unknown/Other — because task instructions are not described. Head-to-head: Memory wins given the strong conventional meaning of \"FR\" in cognitive task naming, but remains unconfirmed. Confidence=0.6 (contextual inference, limited explicit support)."}},"computed_title":"ltpDelayRepFRReadOnly","nchans_counts":[{"val":137,"count":110}],"sfreq_counts":[{"val":2048.0,"count":110}],"stats_computed_at":"2026-04-22T23:16:00.310981+00:00","total_duration_s":363771.9462890625,"canonical_name":null,"name_confidence":0.55,"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":"Broitman2025"}}