{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a335d","dataset_id":"ds004521","associated_paper_doi":"10.1101/2022.08.12.503778","authors":["Edward Ester","Paige Pytel"],"bids_version":"1.8.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004521.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":34,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://www.biorxiv.org/content/biorxiv/early/2022/12/03/2022.08.12.503778.full.pdf"},"funding":[],"ingestion_fingerprint":"c09ffa22c9b6fbf363cb6ce02a2d09339ed672a2c581ed600786c46ca955241f","license":"CC0","n_contributing_labs":null,"name":"Changes in behavioral priority influence the accessibility of working memory content - Experiment 1","readme":"Preprocessed data from Experiment 1 of Ester & Pytel \"Changes in behavioral priority influence the accessibility of working memory content\". Analytic scripts for this project can be found on OSF: https://osf.io/gtd5f/. Note that to analyze the BIDS data, you'll need to modify the analysis scripts to read in the BIDS .set files rather than the expected .mat files. See the OSF wiki for more information","recording_modality":["eeg"],"senior_author":null,"sessions":[],"size_bytes":11470544191,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["Postcues"],"timestamps":{"digested_at":"2026-05-31T16:15:20.461372+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":34,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004521","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","task-Postcues_events.json"]},"nemar_citation_count":3,"computed_title":"Changes in behavioral priority influence the accessibility of working memory content - Experiment 1","nchans_counts":[{"val":62,"count":34}],"sfreq_counts":[{"val":250.0,"count":34}],"stats_computed_at":"2026-05-31T19:34:32.599295+00:00","tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Memory"],"confidence":{"pathology":0.7,"modality":0.5,"type":0.8},"reasoning":{"few_shot_analysis":"In the few-shot examples, there is a dataset related to a working memory task involving auditory stimuli, EEG recordings, and memory evaluation. This provides a fitting reference for type classification as it relates to a working memory paradigm. A similar example is the dataset with 'EEG: Privacy and the brain', with a focus on working memory tasks that may guide the type as 'Memory'.","metadata_analysis":"The dataset title states 'Changes in behavioral priority influence the accessibility of working memory content', indicating a focus on working memory. No specific pathology is listed, suggesting a likely 'Healthy' cohort. The metadata lacks specific details about the sensory modality, so there is some uncertainty there.","paper_abstract_analysis":"The abstract link is provided but no specific details are mentioned in the dataset metadata provided, thus no further direct insights from the abstract can be procured for analysis.","evidence_alignment_check":"1. Pathology: Metadata does not specify any clinical condition, suggesting the default assumption of 'Healthy'. This aligns with the absence of explicit pathology indicators. 2. Modality: Metadata does not specify the sensory channel of stimuli explicitly. Without specific information, it is uncertain. 3. Type: The title directly references 'working memory content', aligning strongly with 'Memory' as type.","decision_summary":"For pathology, 'Healthy' is selected with 0.7 confidence owing to the absence of pathology information. For modality, 'Unknown' is selected with 0.5 confidence due to uncertainty without explicit modality details. For type, 'Memory' is selected with strong alignment from the dataset title and metadata indicating a working memory focus, granting 0.8 confidence."}},"total_duration_s":181398.0,"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"canonical_name":null,"name_confidence":0.88,"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":"Ester2023_Changes_behavioral","bad_channels_info":null,"references_and_links":["https://www.biorxiv.org/content/10.1101/2022.08.12.503778v3.abstract"],"associated_paper_meta":{"channel":"text/normalized-doi","confidence":"high","author_overlap":0,"is_oa":true,"oa_status":"green","source":"paper_resolver","method":"normalization"}}}