{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33c3","dataset_id":"ds005241","associated_paper_doi":null,"authors":["Amilleah Rodriguez","Dan Zhao","Kyra Wilson","Ritika Saboo","Sergey V Samsonau","Alec Marantz"],"bids_version":"1.7.0","contact_info":["Amilleah Rodriguez"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005241.v1.1.0","datatypes":["meg"],"demographics":{"subjects_count":24,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"m":17,"f":14},"handedness_distribution":{"r":30,"l":1}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005241","osf_url":null,"github_url":null,"paper_url":null},"funding":["New York University Abu Dhabi Institute Grant G1001"],"ingestion_fingerprint":"616123104ac05dbd816e5bb599bf9c3dd929e400c81fff0bd4fb3d8591383e7c","license":"CC0","n_contributing_labs":null,"name":"NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis","readme":"KIT/Yokogawa MEG system with 208 magnetometer channels\n24 subjects amounting to over 17 hours of data\nSupplementary code can be found [here](github.com/amilleah/neuromorph)\nReferences\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\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":"Alec Marantz","sessions":["01","02","tark"],"size_bytes":150839266536,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["lexicaldecision","localizer"],"timestamps":{"digested_at":"2026-04-22T12:27:26.487433+00:00","dataset_created_at":"2024-06-12T15:20:44.636Z","dataset_modified_at":"2024-06-14T14:53:48.000Z"},"total_files":117,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005241","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"71423bb7fe7c40ff","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Other"],"confidence":{"pathology":0.7,"modality":0.55,"type":0.75},"reasoning":{"few_shot_analysis":"No few-shot example is directly language/lexical-decision focused. The closest convention guidance comes from Healthy non-clinical cognitive-task datasets (e.g., the gambling task and motor imagery examples), where absence of a named diagnosis implies Pathology=Healthy. For Modality and Type, the few-shot set demonstrates that labels should follow (a) the stimulus channel when explicitly described (e.g., auditory digits -> Auditory; braille -> Tactile) and (b) the primary construct when explicitly stated (e.g., reinforcement learning -> Decision-making/Learning). Here, stimulus channel is not explicitly described, so the convention suggests using Unknown rather than guessing.","metadata_analysis":"Key metadata facts: (1) Title indicates language focus: \"NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis\". (2) Readme describes the recording system and participants count but no clinical recruitment: \"24 subjects amounting to over 17 hours of data\" and \"KIT/Yokogawa MEG system with 208 magnetometer channels\". (3) Tasks listed are \"lexicaldecision\" and \"localizer\", but there is no explicit description of whether stimuli were visual or auditory: tasks: [\"lexicaldecision\", \"localizer\"].","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: (1) Metadata says participants are simply \"24 subjects\" with no diagnosis mentioned (e.g., \"24 subjects amounting to over 17 hours of data\"). (2) Few-shot pattern suggests Healthy when no clinical recruitment is stated. (3) ALIGN.\n\nModality: (1) Metadata says only the tasks are \"lexicaldecision\" and \"localizer\" and does not state stimulus channel. (2) Few-shot pattern suggests using explicit stimulus descriptions to choose Visual/Auditory/etc; when not stated, avoid inferring. (3) ALIGN toward Unknown (insufficient explicit modality evidence).\n\nType: (1) Metadata says the dataset is for \"Morpheme-Based Linguistic Analysis\" and includes a \"lexicaldecision\" task. (2) Few-shot pattern suggests mapping to a specific cognitive construct label only when it clearly matches an allowed Type (e.g., Memory, Motor, Perception). Language/morphology is not an allowed Type label here; thus choose Other. (3) ALIGN.","decision_summary":"Pathology top-2: (a) Healthy — supported by lack of any diagnostic recruitment language: \"24 subjects\"; no patient groups mentioned. (b) Unknown — possible if recruitment details are missing, but less consistent because dataset presents as a standard cognitive MEG dataset without clinical framing. Final: Healthy. Confidence=0.7 (one clear non-clinical participation quote, but no explicit 'healthy').\n\nModality top-2: (a) Unknown — supported by absence of any explicit stimulus-channel description; only task names provided: \"lexicaldecision\", \"localizer\". (b) Visual — plausible because lexical decision is often visual, but this is not stated in metadata. Final: Unknown. Confidence=0.55 (multiple plausible modalities; no direct quote specifying channel).\n\nType top-2: (a) Other — supported by explicit language goal: \"Morpheme-Based Linguistic Analysis\" which does not map cleanly onto allowed constructs like Memory/Attention/Motor. (b) Perception — plausible if framed as word recognition/discrimination, but not explicitly described as a sensory-perception study in metadata. Final: Other. Confidence=0.75 (explicit linguistic-analysis framing + lexical decision task, but construct-to-label mapping is coarse)."}},"nemar_citation_count":0,"computed_title":"NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis","nchans_counts":[{"val":256,"count":117}],"sfreq_counts":[{"val":1000.0,"count":27}],"stats_computed_at":"2026-04-22T23:16:00.309116+00:00","total_duration_s":13434.973,"canonical_name":null,"name_confidence":0.82,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Rodriguez2024"}}