{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3339","dataset_id":"ds004346","associated_paper_doi":null,"authors":["Oscar Ferrante","Ling Liu","Tamas Minarik","Urszula Gorska","Tara Ghafari","Huan Luo","Ole Jensen"],"bids_version":"1.7.0","contact_info":["Tara Ghafari","Oscar Ferrante","Oscar Ferrante"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds004346.v1.0.8","datatypes":["meg"],"demographics":{"subjects_count":1,"ages":[44],"age_min":44,"age_max":44,"age_mean":44.0,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004346","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"5c83d1ffa183df6069ab7bfa903252ce52198d1ae9031250be35ca70c8694f9f","license":"CC0","n_contributing_labs":null,"name":"FLUX: A pipeline for MEG analysis","readme":"References\n----------\nFerrante, O., Liu, L., Minarik, T., Gorska, U., Ghafari, T., Luo, H., & Jensen, O. (2022). FLUX: A pipeline for MEG analysis. NeuroImage, 253, 119047. https://doi.org/10.1016/j.neuroimage.2022.119047\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":"Ole Jensen","sessions":["01"],"size_bytes":3857854581,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["SpAtt"],"timestamps":{"digested_at":"2026-04-22T12:26:28.916983+00:00","dataset_created_at":"2022-11-28T16:33:17.402Z","dataset_modified_at":"2024-06-07T09:19:58.000Z"},"total_files":3,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004346","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"39eb8527b630434d","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Attention"],"confidence":{"pathology":0.6,"modality":0.4,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot convention is the DPX cognitive control/attention dataset (\"EEG: DPX Cog Ctl Task in Acute Mild TBI\"), which maps an explicitly attention/cognitive-control paradigm to Type=\"Attention\". For Modality conventions, few-shot examples only assign Visual/Auditory/etc. when the stimulus channel is explicitly described (e.g., \"three-stim auditory oddball\" -> Auditory; \"visual discrimination\" -> Visual). Here, the task name suggests attention, but stimulus modality is not described, so following the few-shot style the modality should remain Unknown rather than guessed.","metadata_analysis":"Key metadata facts available are sparse and largely pipeline/paper references rather than task details. Relevant snippets: (1) dataset title: \"FLUX: A pipeline for MEG analysis\"; (2) tasks field: \"tasks\": [\"SpAtt\"]; (3) participants: \"Subjects: 1; Age range: 44-44\". There is no explicit description of stimuli (visual/auditory/etc.) or clinical recruitment criteria/diagnosis in the provided metadata.","paper_abstract_analysis":"No useful paper information. (Only citations are provided; no abstract content is included in the metadata.)","evidence_alignment_check":"Pathology: Metadata says nothing about a disorder (e.g., only \"Subjects: 1; Age range: 44-44\"). Few-shot pattern suggests that when no clinical population is stated, label as Healthy. ALIGN (metadata absence + convention).\nModality: Metadata only gives task label \"SpAtt\" and provides no stimulus description. Few-shot pattern suggests Modality should be inferred from explicit stimulus descriptions; otherwise use Unknown. ALIGN.\nType: Metadata provides task name \"SpAtt\", plausibly shorthand for Spatial Attention. Few-shot pattern maps attention paradigms to Type=\"Attention\" (e.g., DPX dataset). ALIGN, though evidence is only the task label without a description.","decision_summary":"Top-2 candidates per category:\n- Pathology: (1) Healthy — supported by lack of any stated diagnosis (\"Subjects: 1\") and few-shot convention that non-clinical/unspecified cohorts are Healthy; (2) Unknown — plausible because recruitment criteria are not stated. Winner: Healthy.\n- Modality: (1) Unknown — supported by no stimulus channel description (only \"tasks\": [\"SpAtt\"]); (2) Visual — plausible guess because many spatial-attention MEG tasks are visual, but not supported by metadata. Winner: Unknown.\n- Type: (1) Attention — supported by task name \"SpAtt\" (interpretable as spatial attention) and few-shot convention mapping attention tasks to Type=\"Attention\"; (2) Other — plausible if \"SpAtt\" referred to something else, but less likely. Winner: Attention.\nConfidence justification: Pathology confidence is limited because it is an inference from missing clinical info (no explicit 'healthy' statement). Modality confidence is low because stimuli are not described. Type confidence is moderate because \"SpAtt\" strongly suggests attention but lacks an explicit task description."}},"nemar_citation_count":0,"computed_title":"FLUX: A pipeline for MEG analysis","nchans_counts":[{"val":343,"count":2}],"sfreq_counts":[{"val":1000.0,"count":2}],"stats_computed_at":"2026-04-22T23:16:00.307415+00:00","total_duration_s":2890.998,"canonical_name":null,"name_confidence":0.78,"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":"Ferrante2022"}}