{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3459","dataset_id":"ds006554","associated_paper_doi":null,"authors":["Yaner Su"],"bids_version":"1.8.0","contact_info":["Yaner Su"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006554.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":47,"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/ds006554","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"65ca32291490e0a9ebebc8badce59658c71f1bb5635c9cbf412bf5b0c7abad2f","license":"CC0","n_contributing_labs":null,"name":"Social Observation EEG raw data","readme":"# README\n# WARNING\nBelow is a template to write a README file for this BIDS dataset. If this message is still present, it means that the person exporting the file has decided not to update the template.If you are the researcher editing this README file, please remove this warning section.\nThe README is usually the starting point for researchers using your dataand serves as a guidepost for users of your data. A clear and informativeREADME makes your data much more usable.\nIn general you can include information in the README that is not captured by some otherfiles in the BIDS dataset (dataset_description.json, events.tsv, ...).\nIt can also be useful to also include information that might already bepresent in another file of the dataset but might be important for users to be aware ofbefore preprocessing or analysing the data.\nIf the README gets too long you have the possibility to create a `/doc` folderand add it to the `.bidsignore` file to make sure it is ignored by the BIDS validator.\nMore info here: https://neurostars.org/t/where-in-a-bids-dataset-should-i-put-notes-about-individual-mri-acqusitions/17315/3\n## Details related to access to the data\n- [ ] Data user agreement\nIf the dataset requires a data user agreement, link to the relevant information.\n- [ ] Contact person\nIndicate the name and contact details (email and ORCID) of the person responsible for additional information.\n- [ ] Practical information to access the data\nIf there is any special information related to access rights orhow to download the data make sure to include it.For example, if the dataset was curated using datalad,make sure to include the relevant section from the datalad handbook:http://handbook.datalad.org/en/latest/basics/101-180-FAQ.html#how-can-i-help-others-get-started-with-a-shared-dataset\n## Overview\n- [ ] Project name (if relevant)\n- [ ] Year(s) that the project ran\nIf no `scans.tsv` is included, this could at least cover when the data acquisitionstarter and ended. Local time of day is particularly relevant to subject state.\n- [ ] Brief overview of the tasks in the experiment\nA paragraph giving an overview of the experiment. This should include thegoals or purpose and a discussion about how the experiment tries to achievethese goals.\n- [ ] Description of the contents of the dataset\nAn easy thing to add is the output of the bids-validator that describes what type ofdata and the number of subject one can expect to find in the dataset.\n- [ ] Independent variables\nA brief discussion of condition variables (sometimes called contrastsor independent variables) that were varied across the experiment.\n- [ ] Dependent variables\nA brief discussion of the response variables (sometimes called thedependent variables) that were measured and or calculated to assessthe effects of varying the condition variables. This might also includequestionnaires administered to assess behavioral aspects of the experiment.\n- [ ] Control variables\nA brief discussion of the control variables --- that is what aspectswere explicitly controlled in this experiment. The control variables mightinclude subject pool, environmental conditions, set up, or other thingsthat were explicitly controlled.\n- [ ] Quality assessment of the data\nProvide a short summary of the quality of the data ideally with descriptive statistics if relevantand with a link to more comprehensive description (like with MRIQC) if possible.\n## Methods\n### Subjects\nA brief sentence about the subject pool in this experiment.\nRemember that `Control` or `Patient` status should be defined in the `participants.tsv`using a group column.\n- [ ] Information about the recruitment procedure- [ ] Subject inclusion criteria (if relevant)- [ ] Subject exclusion criteria (if relevant)\n### Apparatus\nA summary of the equipment and environment setup for theexperiment. For example, was the experiment performed in a shielded roomwith the subject seated in a fixed position.\n### Initial setup\nA summary of what setup was performed when a subject arrived.\n### Task organization\nHow the tasks were organized for a session.This is particularly important because BIDS datasets usually have task dataseparated into different files.)\n- [ ] Was task order counter-balanced?- [ ] What other activities were interspersed between tasks?\n- [ ] In what order were the tasks and other activities performed?\n### Task details\nAs much detail as possible about the task and the events that were recorded.\n### Additional data acquired\nA brief indication of data other than theimaging data that was acquired as part of this experiment. In additionto data from other modalities and behavioral data, this might includequestionnaires and surveys, swabs, and clinical information. Indicatethe availability of this data.\nThis is especially relevant if the data are not included in a `phenotype` folder.https://bids-specification.readthedocs.io/en/stable/03-modality-agnostic-files.html#phenotypic-and-assessment-data\n### Experimental location\nThis should include any additional information regarding thethe geographical location and facility that cannot be includedin the relevant json files.\n### Missing data\nMention something if some participants are missing some aspects of the data.This can take the form of a processing log and/or abnormalities about the dataset.\nSome examples:\n- A brain lesion or defect only present in one participant- Some experimental conditions missing on a given run for a participant because  of some technical issue.- Any noticeable feature of the data for certain participants- Differences (even slight) in protocol for certain participants.\n### Notes\nAny additional information or pointers to information thatmight be helpful to users of the dataset. Include qualitative informationrelated to how the data acquisition went.","recording_modality":["eeg"],"senior_author":"Yaner Su","sessions":[],"size_bytes":13014195680,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["SocialObservation"],"timestamps":{"digested_at":"2026-04-22T12:29:33.423530+00:00","dataset_created_at":"2025-08-12T20:38:53.778Z","dataset_modified_at":"2025-08-12T23:36:26.000Z"},"total_files":47,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006554","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-SocialObservation_events.json"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"8e736213f96914f4","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.4,"modality":0.5,"type":0.5},"reasoning":{"few_shot_analysis":"Closest few-shot matches are the Visual task examples where the task name/description clearly implies a stimulus channel (e.g., the schizophrenia dataset uses a stated “visual discrimination task” and is labeled Modality=Visual; the motor imagery dataset has on-screen “target appears” instructions and is labeled Modality=Visual). These examples guide the convention that modality is inferred from the presented stimulus channel (not responses). However, unlike the few-shot examples, the current dataset provides no explicit stimulus description beyond the task name “SocialObservation”, so any mapping to Visual would be an inference rather than a stated fact. For pathology, few-shot examples show that clinical labels are only used when explicitly described (e.g., “Parkinsons disease”, “mTBI”, “epilepsy”); otherwise cohorts are typically treated as Healthy only when the metadata states or clearly implies healthy controls.","metadata_analysis":"Key available metadata is extremely sparse and does not describe population health status or the stimulus types. Quotes: (1) Title: “Social Observation EEG raw data”. (2) Tasks field: “SocialObservation”. (3) Participants summary: “Subjects: 47”. The README is a generic BIDS template and contains no study-specific details about recruitment, diagnosis, or sensory stimuli.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS only “Subjects: 47” with no diagnosis/health statement; few-shot pattern SUGGESTS using clinical labels only when explicit, and otherwise Healthy only when clearly stated. ALIGNMENT: aligns with choosing Unknown (insufficient evidence).\nModality: Metadata SAYS only task name “SocialObservation” and title “Social Observation”; few-shot pattern SUGGESTS modality tracks stimulus channel (e.g., clearly visual tasks labeled Visual). ALIGNMENT: partial/weak—task name suggests (but does not state) observation, which is often visual; not enough explicit evidence.\nType: Metadata SAYS only “SocialObservation” with no cognitive construct description; few-shot pattern SUGGESTS Type should reflect the main construct (e.g., Memory for digit span; Motor for imagery; Resting-state for passive). ALIGNMENT: weak—‘social observation’ could map to social cognition (not an allowed Type label) so the closest allowed bucket is Other, but this is inference.","decision_summary":"Pathology (top-2): (A) Unknown—supported by lack of any diagnosis/health quote beyond “Subjects: 47”; (B) Healthy—possible default assumption for non-clinical studies but not stated. Head-to-head: Unknown wins because no explicit healthy/control wording is present. Confidence justified by: only one minimal participant-count snippet and no recruitment info.\nModality (top-2): (A) Visual—suggested by the word “Observation” in “Social Observation EEG raw data” and task “SocialObservation”; (B) Unknown—no explicit stimulus modality described in README/description. Head-to-head: Unknown wins because stimulus channel is not explicitly documented. Confidence justified by: only task/title wording without stimulus details.\nType (top-2): (A) Other—‘social observation’ most likely targets social cognition/processing, which is not a dedicated allowed Type label; (B) Unknown—no description of experimental goal/construct. Head-to-head: Unknown slightly stronger due to complete lack of task description (no stimuli, no instructions, no outcomes). Confidence justified by: only task name “SocialObservation” and template README."}},"computed_title":"Social Observation EEG raw data","nchans_counts":[{"val":64,"count":47}],"sfreq_counts":[{"val":500.0,"count":47}],"stats_computed_at":"2026-04-22T23:16:00.311788+00:00","total_duration_s":101622.702,"author_year":"Su2025","canonical_name":null}}