EEG: Probabilistic Selection and Depression

EEG recordings from 122 college-age participants performing a probabilistic selection task, with participants scoring reliably high or low on the Beck Depression Inventory. Data were collected around 2008-2010 in the John J.B. Allen lab at the University of Arizona to study reward and punishment processing in depression. Analysis code to reproduce the associated publication is included.

EEG

Issues GitHub OpenNeuro ds003474

Download this dataset

Pick a method. Large datasets skip the zip and use the streaming methods below — all resumable. Full download guide →

Download archive (.zip) — 16.0 GB
A single zip of the published version. Best for small/medium datasets.

Download zip
NEMAR CLI recommended
Pulls the pinned version + annexed data and resumes cleanly. Install nemar-cli →
```
nemar dataset download on003474
```

DataLad

Clone the dataset repo and fetch file content on demand. Docs →

datalad clone https://github.com/nemarDatasets/on003474 on003474
cd on003474 && datalad get .

git-annex

Plain git + git-annex against the dataset repo. Docs →

git clone https://github.com/nemarDatasets/on003474 on003474
cd on003474 && git annex get .

Direct files (wget / curl / rclone)
Every file with a stable, range-resumable URL from the manifest. Needs curl, jq, wget (or rclone/aria2c). Docs →
```
curl -s https://data.nemar.org/on003474/v1.0.0/manifest.json | jq -r '.[].bytes_url' > urls.txt
wget -xc -i urls.txt
```

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

NeuroScience Gateway (NSG) portal.
NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/on003474), and submit.
nsgportal.org →
Local processing with nemar-cli.
Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning.
```
npm install -g nemar-cli
nemar dataset clone on003474
cd on003474 && nemar dataset get
```
Just the files.
rclone, aria2c, or any HTTPS client works against data.nemar.org/on003474/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.

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Files

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How to use the data (for agentic research) license, citation, download commands

What it is

Modalities: EEG
Participants: 122
Size: 16.6 GB
Tasks: ProbabilisticSelection

License and terms

License: CC0
Recommended citation: jcavanagh@unm.edu, J. F. C. (2026). EEG: Probabilistic Selection and Depression (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.on003474

Where the bytes are

Latest version (always current): https://data.nemar.org/on003474/latest/
This version (v1.0.0): https://data.nemar.org/on003474/v1.0.0/

How to download

The dataset: nemar dataset download on003474 Clones and fetches in one step. Content under stimuli/ and derivatives/ is skipped by default because those trees can be large; add --stimuli --derivatives for the whole thing.
A subset, one step: nemar dataset download on003474 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
A subset, step 1: nemar dataset clone on003474 Clones git-annex pointers only; fetches no file content. Creates ./on003474.
A subset, step 2: cd on003474 The get command below reads the clone's annex, so it only works from inside the clone.
A subset, step 3: nemar dataset get <files> Pulls the files you actually need. Skips stimuli/ and derivatives/ unless the path you ask for is under one of them.
One small file: https://data.nemar.org/on003474/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.

Assess fit without downloading

Participants table: https://data.nemar.org/on003474/v1.0.0/participants.tsv
Dataset description: https://data.nemar.org/on003474/v1.0.0/dataset_description.json
Directory listing: https://data.nemar.org/on003474/v1.0.0/?format=json
Catalog record: https://api.nemar.org/datasets/on003474