{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33c4","dataset_id":"ds005261","associated_paper_doi":"10.1038/s41597-025-05127-0","authors":["Snezana Todorovic","Elin Runnqvist","Valerie Chanoine","Jean-Michel Badier"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds005261.v3.0.0","datatypes":["meg"],"demographics":{"subjects_count":17,"ages":[24,27,24,28,22,26,27,27,34,21,23,25,28,33,24,22,26],"age_min":21,"age_max":34,"age_mean":25.941176470588236,"species":null,"sex_distribution":{"f":11,"m":6},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://www.nature.com/articles/s41597-025-05127-0.pdf"},"funding":["This research has been supported by funding from the Institute of Convergence ILCB (France 2030, ANR-16-CONV-0002) and the Excellence Initiative of Aix-Marseille University A*MIDEX (ANR-11-IDEX-0001-02)"],"ingestion_fingerprint":"ce91cec85fbf673dd4d0f8c71e60c0d469c89dcd83069b0194ad3bca9f9894d3","license":"CC0","n_contributing_labs":null,"name":"Gloups_MEG","readme":"README\nSeventeen adult participants completed a learning task and a resting-state condition during MEG recording (4D NeuroImaging system with 248 magnetometer channels).\nCurrent dataset: OpenNeuro MEG Dataset ds005261 (Gloups_MEG, https://openneuro.org/datasets/ds005261/versions/2.0.0; see Todorović et al., in revision).\nThe same participants performed an identical learning task during fMRI scanning.\nRelated dataset: OpenNeuro fMRI Dataset ds004597 (Gloups, https://openneuro.org/datasets/ds004597/versions/2.0.0; see Todorović et al., 2023).\nNote: Participant identifiers differ between the fMRI and MEG datasets. For details, refer to Table 1 in Todorović et al., in revision.\nReferences MNE-BIDS\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\nTodorović, S., Anton, J.-L., Sein, J., Nazarian, B., Chanoine, V., Rauchbauer, B., Kotz, S. A., & Runnqvist, E. (2023). Cortico-Cerebellar Monitoring of Speech Sequence Production. Neurobiology of Language, 1–21.\nTodorović, S., Chanoine, V., Nazarian, B., Badier, J-M., Kanzari, K., Brovelli, A., Kotz, S. A., & Runnqvist, E. (in revision). Dataset for Evaluating the Production of Phonotactically Legal and Illegal Pseudowords. Scientific Data.","recording_modality":["meg"],"senior_author":null,"sessions":[],"size_bytes":241629506287,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["gloups","rest"],"timestamps":{"digested_at":"2026-05-31T16:17:14.170237+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":128,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005261","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":["Multisensory"],"type":["Learning"],"confidence":{"pathology":0.9,"modality":0.7,"type":0.9},"reasoning":{"few_shot_analysis":"The dataset pertains to a learning task and resting-state condition recorded via MEG, with similar elements found in the few-shot examples involving learning tasks and resting states but lacks a specific pathology. Most similar few-shot examples involving learning tasks for healthy participants use 'Healthy' as the pathology and 'Learning' as the type. No exact few-shot examples involving MEG; however, EEG datasets with learning tasks were indicative of using 'Learning' under type.","metadata_analysis":"1. \"Seventeen adult participants completed a learning task and a resting-state condition during MEG recording.\" 2. \"The same participants performed an identical learning task during fMRI scanning.\" 3. The task events described include 'Illegal_Correct' and 'Legal_Correct', indicating a study on pseudoword learning and categorization.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"1. Pathology: Metadata states, 'Seventeen adult participants' with no clinical condition stated, aligning with 'Healthy' typical pattern in the few-shot. \n2. Modality: Metadata describes 'MEG recording', aligning with no specific sensory channel but experiential task nature suggests 'Multisensory'. No conflict with few-shot. \n3. Type: Metadata states a 'learning task', aligning with 'Learning' type. Few-shot examples similarly categorize learning tasks under 'Learning'.","decision_summary":"Pathology: Healthy (alignment on general healthy participant description). Modality: Multisensory (inferential, no specific sensory channel, similar to other MEG tasks). Type: Learning (alignment on learning task and structured task events). Overall, the metadata and few-shot patterns align well in type, support inferential alignment for modality, and confirm the pathology label."}},"nemar_citation_count":0,"computed_title":"Gloups_MEG","nchans_counts":[{"val":248,"count":71},{"val":278,"count":31},{"val":245,"count":24}],"sfreq_counts":[{"val":2034.5100996195154,"count":31},{"val":2034.5101318359375,"count":7}],"stats_computed_at":"2026-05-31T19:34:32.600680+00:00","total_duration_s":10949.75387866373,"canonical_name":null,"name_confidence":0.56,"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":"Todorovic2024","acknowledgements":"MEG data acquisition was performed in the MEG Centre (Timone Hospital, Marseille, France)","generated_by":[{"Name":"MNE-BIDS","Version":"0.14","Description":"MNE-BIDS is a Python package that allows you to read and write BIDS-compatible datasets with the help of MNE-Python."}],"references_and_links":["a data paper","a resource to be cited when using the data"],"source_datasets":[{"DOI":"doi:10.18112/openneuro.ds005261.v2.0.0","URL":"https://openneuro.org/datasets/ds005261","Version":"2.0.0"}],"bad_channels_info":null,"associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":3,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}