{"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":null,"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":null,"sessions":["01","02","tark"],"size_bytes":150840251328,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["lexicaldecision","localizer"],"timestamps":{"digested_at":"2026-05-31T16:17:12.945035+00:00","dataset_created_at":null,"dataset_modified_at":null},"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":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"The few-shot examples that align with the task paradigm involve linguistic and auditory tasks. For instance, datasets focusing on auditory discrimination and auditory oddball tasks provide guidance on modality classification. However, the current dataset appears to focus on linguistic processing, as indicated by the title and task details.","metadata_analysis":"The dataset titled 'NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis' involves MEG recordings focused on linguistic analysis, specifically morpheme-based tasks. The readme states 'KIT/Yokogawa MEG system with 208 magnetometer channels’. One crucial note is there is no mention of a specific pathology, implying a likely normative cohort.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"1. Pathology: The metadata does not indicate any specific clinical population. This aligns with few-shot patterns for normative linguistic studies, suggesting 'Healthy'. They ALIGN. \n2. Modality: The paradigm suggests linguistic stimuli, often processed visually or auditorily. The task and the mentions of morphemes support ‘Visual’ when considering visual word processing. ALIGN. \n3. Type: The detailed focus on linguistic analysis suggests 'Perception', which covers sensory processing of language structure. ALIGN.","decision_summary":"For this dataset, 'Healthy' is the top choice for Pathology with no conflicting clinical condition. 'Visual' is chosen for Modality given the linguistic analysis focus, common in visual word processing studies despite the absence of explicit visual stimuli mention. 'Perception' is the chosen Type due to the emphasis on linguistic feature analysis. Confidence scores reflect clear alignment with the few-shot examples and no conflicting metadata."}},"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":"2026-05-31T19:34:32.600666+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","bad_channels_info":null}}