{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33c4","dataset_id":"ds005261","associated_paper_doi":null,"authors":["Snezana Todorovic","Elin Runnqvist","Valerie Chanoine","Jean-Michel Badier"],"bids_version":"1.7.0","contact_info":["Valérie Chanoine"],"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":{"source_url":"https://openneuro.org/datasets/ds005261","osf_url":null,"github_url":null,"paper_url":null},"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":"Jean-Michel Badier","sessions":[],"size_bytes":147368587261,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["gloups","rest"],"timestamps":{"digested_at":"2026-04-22T12:27:27.141717+00:00","dataset_created_at":"2024-06-17T13:06:26.783Z","dataset_modified_at":"2025-05-12T07:26:44.000Z"},"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":{"config_hash":"3557b68bca409f28","metadata_hash":"9f00ad40b9d6e13b","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Learning"],"confidence":{"pathology":0.7,"modality":0.4,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot conventions by construct:\n- The example “EEG: Probabilistic Learning with Affective Feedback: Exp #2” is labeled Type=Learning, showing that when metadata explicitly frames the paradigm as a learning task, the catalog Type should be Learning.\n- The example “A Resting-state EEG Dataset for Sleep Deprivation” is labeled Modality=Resting State and Type=Resting-state when the protocol is explicitly resting/eyes open/closed.\nThis dataset contains both a learning task and a resting condition; following the learning-task convention, I treat the named “learning task” as the primary Type for the dataset rather than labeling it solely as resting-state.","metadata_analysis":"Key quoted metadata facts:\n1) Population/task context: “Seventeen adult participants completed a learning task and a resting-state condition during MEG recording...”\n2) Tasks listed: \"tasks\": [\"gloups\", \"rest\"]\n3) Participants summary: “Subjects: 17; ... Age range: 21-34”\nNo diagnosis/clinical recruitment criteria are mentioned, and no explicit stimulus sensory channel (visual vs auditory vs tactile) is described for the learning task in the provided metadata.","paper_abstract_analysis":"No useful paper information. (Only citations are provided; no abstract text for the MEG dataset is included.)","evidence_alignment_check":"Pathology:\n- Metadata says: “Seventeen adult participants ...” with no disorder/diagnosis mentioned.\n- Few-shot pattern suggests: absence of clinical recruitment typically maps to Healthy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: “learning task” but does not specify whether stimuli were visual, auditory, etc.\n- Few-shot pattern suggests: choose Visual/Auditory/etc. only when stimulus type is explicitly described; otherwise use Unknown.\n- Alignment: ALIGN (insufficient stimulus-channel facts → Unknown).\n\nType:\n- Metadata says: “completed a learning task and a resting-state condition” and tasks include “gloups” and “rest”.\n- Few-shot pattern suggests: explicit learning paradigms → Learning; explicit rest-only protocols → Resting-state.\n- Alignment: PARTIAL (both constructs present). Chosen label prioritizes the explicitly named “learning task” as the main non-rest experimental paradigm in this dataset.","decision_summary":"Top-2 candidate labels with head-to-head comparisons:\n\nPathology:\n1) Healthy — Evidence: “Seventeen adult participants ...” with no clinical group stated; participants described only by age/sex (“Age range: 21-34”).\n2) Unknown — Would apply if recruitment status unclear, but metadata context strongly implies a standard adult research cohort.\nWinner: Healthy (metadata contains no pathology facts).\n\nModality:\n1) Unknown — Evidence: learning task is not described in terms of stimulus modality; only “learning task” is stated.\n2) Resting State — Evidence: “resting-state condition” and task list includes “rest”, but this does not characterize the stimulus modality of the learning task and the dataset is not resting-only.\nWinner: Unknown (insufficient information about stimulus/input channel for the learning task; mixed tasks present).\n\nType:\n1) Learning — Evidence: “completed a learning task ...”; dataset is named “Gloups_MEG” and explicitly frames a “learning task” plus rest.\n2) Resting-state — Evidence: “resting-state condition” and tasks include “rest”, but the dataset includes an additional explicit learning paradigm.\nWinner: Learning (explicitly stated and likely the primary experimental manipulation; rest appears as an additional condition).\n\nConfidence justification:\n- Pathology 0.7: supported by one clear participant description plus absence of any clinical recruitment language.\n- Modality 0.4: no direct metadata describing the sensory channel of stimuli for the learning task.\n- Type 0.8: two explicit metadata indicators (“learning task” statement + tasks include non-rest task “gloups”) and strong alignment with few-shot learning-task convention."}},"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":"2026-04-22T23:16:00.309127+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"}}