{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3318","dataset_id":"ds004107","associated_paper_doi":null,"authors":["M.P. Weisend","F.M. Hanlon","R. Montano","S.P. Ahlfors","A.C. Leuthold","D. Pantazis","J.C. Mosher","A.P. Georgopoulos","M.S. Hamalainen","C.J. Aine"],"bids_version":"1.6.0","contact_info":["Alexandre Gramfort"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004107.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":9,"ages":[34,40],"age_min":34,"age_max":40,"age_mean":37.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004107","osf_url":null,"github_url":null,"paper_url":null},"funding":["This work was supported by U.S. DOE Award Number DE-FG02-99ER62764 to The MIND Institute","NIBIB grant R01-EB002010","by The Center for Functional Neuromaging Technologies (NIH grant P41-RR14075)","and by Los Alamos National Security","LLC","for the NNSA of the U.S. DOE under contract DE-AC52-06NA25396."],"ingestion_fingerprint":"40f41d994580928a717c6eec545be87e7e230598582f73ae2132453724050c42","license":"CC0","n_contributing_labs":null,"name":"MIND DATA","readme":"This data was part of the study of:\nM.P. Weisend, F.M. Hanlon, R. Montaño, S.P. Ahlfors, A.C. Leuthold, D. Pantazis, J.C. Mosher, A.P. Georgopoulos, M.S. Hämäläinen, C.J. Aine,, V. (2007). Paving the way for cross-site pooling of magnetoencephalography (MEG) data. International Congress Series, Volume 1300, Pages 615-618,.\nIt was converted to BIDS with MNE-BIDS:\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\nFollowing the MEG-BIDS format:\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110","recording_modality":["meg"],"senior_author":"C.J. Aine","sessions":["01","02","19121026","19121027","19121101","19121105","19130110","19130111","19130116","19130117","19130118","19130119","19130120","19130627","19130628"],"size_bytes":82901417999,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["auditory","index","median","noise","rest","visual"],"timestamps":{"digested_at":"2026-04-22T12:26:08.098218+00:00","dataset_created_at":"2022-04-22T16:06:47.154Z","dataset_modified_at":"2022-04-22T19:50:04.000Z"},"total_files":89,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004107","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"10c07b30c0cb8cb4","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Multisensory"],"type":["Other"],"confidence":{"pathology":0.7,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot conventions: (1) The music/speech ABR dataset (Healthy, Auditory, Perception) shows that when stimuli are clearly auditory, Modality is labeled by stimulus channel and Type can be Perception. (2) The sleep deprivation resting-state dataset (Healthy, Resting State, Resting-state) shows that when the paradigm is purely resting, Modality/Type go to Resting State/Resting-state. For the current dataset, the task list explicitly includes multiple sensory paradigms (auditory, visual, and likely somatosensory via median/index) plus rest; this aligns best with choosing a broader Modality label (Multisensory) rather than a single channel, and Type is less clearly a single construct (more of a multi-paradigm/benchmark dataset), which by convention maps to Type=Other when the primary goal is not a specific cognitive construct.","metadata_analysis":"Key metadata facts: (1) Study purpose is methodological: \"Paving the way for cross-site pooling of magnetoencephalography (MEG) data.\" (2) Participants are described only by count and age, with no diagnosis mentioned: \"Subjects: 9; Age range: 34-40\". (3) Multiple tasks/modalities are explicitly listed: tasks = [\"auditory\", \"index\", \"median\", \"noise\", \"rest\", \"visual\"]. These indicate a mixed set of sensory evoked (auditory/visual/somatosensory) plus resting recordings rather than a single-modality experiment.","paper_abstract_analysis":"No useful paper information. (Only a citation is provided; no abstract text in metadata.)","evidence_alignment_check":"Pathology: Metadata says nothing about a clinical recruitment group (quote: \"Subjects: 9; Age range: 34-40\"), and no disorder terms appear. Few-shot convention: in the absence of any clinical diagnosis terms, label as Healthy. ALIGN.\n\nModality: Metadata explicitly lists multiple stimulus/task channels (quote: tasks include \"auditory\" and \"visual\" and also \"median\"/\"index\" plus \"rest\"). Few-shot convention: modality follows stimulus channel; when more than one sensory channel is central, use Multisensory. ALIGN.\n\nType: Metadata emphasizes cross-site pooling/benchmarking (quote: \"cross-site pooling of magnetoencephalography (MEG) data\") rather than a single cognitive construct; tasks span sensory evoked and rest (quote: tasks list includes \"rest\" plus multiple sensory tasks). Few-shot convention would suggest Perception when the dataset is primarily sensory discrimination/evoked responses, or Resting-state if only rest. Here, neither single construct dominates and the explicit goal is methodological/benchmarking, so Type=Other is more consistent. PARTIAL CONFLICT (few-shot Perception mapping) resolved in favor of metadata emphasis on multi-paradigm pooling/benchmarking.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n- Healthy (winner): no clinical population stated; only \"Subjects: 9; Age range: 34-40\".\n- Unknown (runner-up): because health status is not explicitly stated as healthy controls.\nDecision: Healthy. Evidence alignment: aligns with few-shot convention (no diagnosis => Healthy).\n\nModality:\n- Multisensory (winner): tasks list includes multiple modalities: \"auditory\", \"visual\", and likely somatosensory (\"median\", \"index\"), plus \"rest\".\n- Other (runner-up): could be considered a methodological MEG benchmarking set spanning paradigms.\nDecision: Multisensory. Confidence supported by explicit task list showing multiple sensory channels.\n\nType:\n- Other (winner): primary stated aim is methodological: \"cross-site pooling of ... MEG data\" and the dataset spans multiple paradigms.\n- Perception (runner-up): because it includes sensory tasks (auditory/visual/somatosensory) that could be used for sensory-evoked/perceptual processing.\nDecision: Other. Confidence moderate because no explicit cognitive construct is described beyond pooling/standardization."}},"nemar_citation_count":1,"computed_title":"MIND DATA","nchans_counts":[{"val":318,"count":84},{"val":317,"count":5}],"sfreq_counts":[{"val":1792.8858642578125,"count":57},{"val":1250.0,"count":32}],"stats_computed_at":"2026-04-22T23:16:00.307010+00:00","total_duration_s":83223.41852135921,"canonical_name":null,"name_confidence":0.72,"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":"Weisend2022"}}