{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a341d","dataset_id":"ds005857","associated_paper_doi":"10.21428/8e6ba8ef.4cb04050","authors":["[Adam Broitman]","[Michael Kahana]"],"bids_version":"1.7.0","contact_info":null,"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":{"paper_url":"https://jocnf.pubpub.org/pub/fqj2734s/download/pdf"},"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":null,"sessions":["0","1","2","3","4","5"],"size_bytes":305359281869,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ltpDelayRepFRReadOnly"],"timestamps":{"digested_at":"2026-05-31T16:21:49.372976+00:00","dataset_created_at":null,"dataset_modified_at":null},"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":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Auditory"],"type":["Memory"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"The few-shot examples offer insights into categorizing datasets focused on memory tasks or encoding-recall procedures. A relevant few-shot example is the dataset titled \"EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest\", which involves a memory task described as working memory measurement indicated as \"Memory\" type and auditory stimuli indicating the participant should \"listen to sequences.\" This tells us that tasks that focus prominently on memorizing or recalling information typically fall under the Type label 'Memory' and if no explicit pathology is indicated, default to 'Healthy.'","metadata_analysis":"The dataset is titled \"ltpDelayRepFRReadOnly,\" and the readme does not provide specifics about clinical populations, but the task appears to include lexical items labeled as \"REC_WORD.\" These task event labels such as \"WORD\" and \"REC_WORD\" suggest a focus on word recall or memory tasks, aligning with memory labeling in few-shot examples where word recall is a key task target.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"The title and events in the dataset metadata indicate word recording and presumably recall activities (e.g., \"REC_WORD\"), which aligns well with memory-focused datasets classified under the Type label 'Memory' in the few-shot examples. There are no indications of a clinical pathology, aligning with 'Healthy.' There is no mention of specific sensory modality in terms of visual or auditory prompts, complicating the Modality assignment. However, typical memory tasks in the few-shot include a verbal element, leaning towards 'Auditory,' which matches recall and verbal stimuli convention.","decision_summary":"In summary, the Pathology is 'Healthy' since no clinical population is indicated, and this is consistent with the default categorization where no pathology is specified. Modality is supported by the assumption of word/recall (hence verbal) tasks commonly engaging 'Auditory' channels in memory tasks. The Type is clearly geared towards 'Memory' as the dataset revolves around encoding/decoding word tasks similar to examples of memory tasks. With 2 metadata quotations, the confidence level for Healthy and Memory is 0.8 each, reflecting the specific alignment between metadata details and previous examples, but absence of direct sensory stimuli requires a 0.7 confidence in Auditory based on inference from task labels like REC_WORD."}},"computed_title":"ltpDelayRepFRReadOnly","nchans_counts":[{"val":137,"count":110}],"sfreq_counts":[{"val":2048.0,"count":110}],"stats_computed_at":"2026-05-31T19:34:32.601910+00:00","total_duration_s":363772.0,"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","bad_channels_info":null,"associated_paper_meta":{"channel":"crossref-biblio","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}