{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a340b","dataset_id":"ds005648","associated_paper_doi":null,"authors":["Alexis Kidder(*)","Genevieve Quek","Tijl Grootswagers"],"bids_version":"1.10.0","contact_info":["Alexis Kidder"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds005648.v1.0.3","datatypes":["eeg"],"demographics":{"subjects_count":21,"ages":[28,30,35,26,19,20,24,62,20,19,18,22,22,18,23,25,28,30,28,21,24],"age_min":18,"age_max":62,"age_mean":25.80952380952381,"species":null,"sex_distribution":{"f":12,"m":9},"handedness_distribution":{"r":18,"l":1,"a":1}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005648","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"73672b84794fa033eac9776dcdf0ba3c495b462b3de7d539e2bc1fedc10c0794","license":"CC0","n_contributing_labs":null,"name":"Mapping object space dimensions: new insights from temporal dynamics","readme":"# README\nExperiment details for Mapping object space dimensions: new insights from temporal dynamics. The main folder contains the raw MEG data for all participants in standard bids format. See references.\nThe “sourcedata” folder contains the trial behavioral data collected during the EEG Session. The data in this folder follows the following trial structure:\n\t•\tsourcedata\n\t⁃\tsub-[participant number]_task-targets_events.csv: contains all the events for each trial in the EEG session, detailing what was shown on the screen\n\t•\tsub-[participant number]:contains BIDS formatted raw EEG data\n\t⁃\tsub-[participant name]_task-targets_events_short.tsv: information about the channels used and sampling rate for all trials\n\t⁃\tsub-[participant name]_task-targets_eeg.bdf: EEG raw data","recording_modality":["eeg"],"senior_author":"Tijl Grootswagers","sessions":[],"size_bytes":16672927073,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["targets"],"timestamps":{"digested_at":"2026-04-22T12:28:40.059641+00:00","dataset_created_at":"2024-11-20T22:13:29.697Z","dataset_modified_at":"2026-03-30T15:14:55.000Z"},"total_files":21,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005648","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.txt","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"724ef23db4a8b603","model":"openai/gpt-5.2","tagged_at":"2026-01-20T18:33:45.534434+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.7},"reasoning":{"few_shot_analysis":"Closest few-shot convention match is the visual discrimination dataset (\"Meta-rdk: Preprocessed EEG data\"), which maps a screen-based visual task to Modality=Visual and Type=Perception. Although that example involves schizophrenia, it demonstrates the labeling convention that visually presented stimuli (\"shown on the screen\") are labeled Visual modality and perception/object discrimination paradigms are labeled Perception type. No few-shot example suggests a different mapping for \"object space\"/visual object representations.","metadata_analysis":"Key available metadata is only the README. It indicates a stimulus-on-screen EEG task without mentioning any clinical recruitment.\nQuoted evidence:\n1) \"Mapping object space dimensions: new insights from temporal dynamics.\" (implies object representation/object space mapping, typically perceptual/object processing)\n2) \"...contains all the events for each trial in the EEG session, detailing what was shown on the screen\" (explicitly indicates visual presentation)\n3) \"...contains BIDS formatted raw EEG data\" and \"EEG raw data\" (confirms EEG acquisition but does not specify population/pathology)","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis/clinical recruitment is stated; only general description of participants and files (e.g., \"raw MEG data for all participants\"), with no pathology terms.\n- Few-shot pattern suggests: when no disorder is mentioned and it is a general cognitive experiment, label as Healthy.\n- Alignment: ALIGN (no metadata fact contradicts Healthy).\n\nModality:\n- Metadata says: \"what was shown on the screen\".\n- Few-shot pattern suggests: screen-based stimuli -> Visual (as in the visual discrimination example).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: \"Mapping object space dimensions\" and trial events detail stimuli \"shown on the screen\"; no mention of learning, memory, motor execution/imagery, rest, or sleep.\n- Few-shot pattern suggests: visual stimulus/object processing tasks map to Perception rather than Decision-making/Motor/Resting-state.\n- Alignment: ALIGN.","decision_summary":"Pathology top-2: (1) Healthy vs (2) Unknown.\n- Healthy evidence: absence of any clinical/disease terms in the only provided metadata (README) and generic phrasing \"all participants\".\n- Unknown evidence: participants are not explicitly described as healthy controls.\n- Decision: Healthy (conventionally used when no pathology is indicated).\n\nModality top-2: (1) Visual vs (2) Unknown.\n- Visual evidence: explicit quote \"what was shown on the screen\".\n- Unknown evidence: no explicit stimulus category beyond screen mention.\n- Decision: Visual.\n\nType top-2: (1) Perception vs (2) Attention.\n- Perception evidence: \"Mapping object space dimensions\" implies studying visual object representations/perceptual space; plus screen-presented stimuli.\n- Attention evidence: could involve attentional selection to targets (task name \"targets\"), but no explicit attention construct described.\n- Decision: Perception."}},"computed_title":"Mapping object space dimensions: new insights from temporal dynamics","nchans_counts":[{"val":64,"count":21}],"sfreq_counts":[{"val":2048.0,"count":21}],"stats_computed_at":"2026-04-22T23:16:00.310741+00:00","source_url":"https://openneuro.org/datasets/ds005648","total_duration_s":null,"author_year":"Kidder2024","canonical_name":null}}