{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3390","dataset_id":"ds004842","associated_paper_doi":null,"authors":["Gabriella Larkin","James A. Davis","Victor Paul","Marcel Cannon","Chris Manteuffel","Ben Brewster","Tony Johnson","Mike Dunkel","Stephen Gordon","Kevin King"],"bids_version":"1.8.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004842.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":14,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004842","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"03532ce9129ccb21684fb2a0cdccccd74c17605f2a8d6ed55a3de8297ae8e518","license":"CC0","n_contributing_labs":null,"name":"TX15","readme":"TX15 dataset","recording_modality":["eeg"],"senior_author":null,"sessions":["C1C","C1D"],"size_bytes":5589704802,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["DriveOnMission"],"timestamps":{"digested_at":"2026-05-31T16:16:09.633561+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":102,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004842","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.tsv","task-DriveOnMission_events.json"]},"nemar_citation_count":0,"computed_title":"TX15","nchans_counts":[{"val":70,"count":94},{"val":72,"count":8}],"sfreq_counts":[{"val":256.0,"count":102}],"stats_computed_at":"2026-05-31T19:34:32.600003+00:00","tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Decision-making"],"confidence":{"pathology":0.7,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"The available dataset examples provide useful conventions for identifying the pathology, modality, and type classifications. No direct few-shot examples match the specific `DriveOnMission` task, but there are examples with unique tasks and how they're classified such as those with descriptors heavily focused on specific cognitive tasks or conditions. This helps infer possible cognitive constructs when those tasks are skewed more towards a specific paradigm like navigation or real-world mission scenarios.","metadata_analysis":"The metadata for the dataset `title` and `name` indicate the name 'TX15' which lacks specificity regarding the task or population. The `dataset_description` doesn't provide further clarity on pathological conditions or specific sensory modalities as it is primarily a list of investigators. The `tasks` field lists `DriveOnMission`, suggesting a task related to driving or navigation.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"1. Pathology: Metadata does not explicitly mention any clinical condition; few-shot examples suggest normative cohorts use 'Healthy'. 2. Modality: The 'DriveOnMission' task hints towards tasks involving movement or motor activity but lacks specific sensory stimuli indications; few-shot examples indicate 'Visual' is common in navigation tasks unless specifically informed by multiple senses. 3. Type: Given the task is 'DriveOnMission', it infers a possibility towards 'Motor' due to possible engagement with driving-related motor tasks; however, many few-shot examples suggest real-world tasks evaluated under experimental conditions tend towards 'Decision-making'.","decision_summary":"Based on the limited metadata: - Pathology: 'Healthy' is selected as there is no mention of any specific clinical cohort, aligning with few-shot conventions for datasets that do not indicate a disorder. - Modality: Likely 'Visual' since common navigation or driving tasks typically engage visual cues predominantly, unless specified otherwise. - Type: 'Decision-making', similar to participants pressing buttons under choice conditions during navigation-type missions, matching the cognitive complexity likely expected from a 'DriveOnMission' task. Confidence is moderate (0.7) due to the lack of specific descriptive metadata."}},"total_duration_s":72595.0,"canonical_name":null,"name_confidence":0.55,"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":"Larkin2023_TX15","bad_channels_info":null,"references_and_links":[""],"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"}}}