on003710
NEMAR copy of ds003710

APPLESEED Example Dataset

Longitudinal EEG recordings from 13 infants collected at 4, 8, and 12 months of age, with test-retest reliability assessed through two sessions within one week at 4 months. The complete dataset comprises 48 recording sessions, including 11 infants with all four sessions and 2 infants with reliability sessions only. It was used to develop and validate the APPLESEED automated preprocessing pipeline for estimating scale-wise entropy from pediatric EEG, and is provided as an example dataset.

AI-generated description, may include mistakes
Issues GitHub OpenNeuro ds003710

Download this dataset

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

  1. Download archive (.zip) — 9.0 GB

    A single zip of the published version. Best for small/medium datasets.

    Download zip

  2. NEMAR CLI recommended

    Pulls the pinned version + annexed data and resumes cleanly. Install nemar-cli →

    nemar dataset download on003710
  3. DataLad

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

    datalad clone https://github.com/nemarDatasets/on003710 on003710
    cd on003710 && datalad get .
  4. git-annex

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

    git clone https://github.com/nemarDatasets/on003710 on003710
    cd on003710 && git annex get .
  5. 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/on003710/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.

  1. 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/on003710), and submit.
    nsgportal.org →

  2. 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 on003710
    cd on003710 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/on003710/ — 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.

Citations

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    Files

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    Signal viewer

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

    What it is

    Modalities
    EEG
    Participants
    13
    Size
    10.2 GB
    Tasks
    appleseedexample

    License and terms

    License
    CC0
    Recommended citation
    Williams, C. L., & Puglia, M. H. (2026). APPLESEED Example Dataset (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.on003710

    Where the bytes are

    Latest version (always current)
    https://data.nemar.org/on003710/latest/

    How to download

    The dataset
    nemar dataset download on003710 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 on003710 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
    A subset, step 1
    nemar dataset clone on003710 Clones git-annex pointers only; fetches no file content. Creates ./on003710.
    A subset, step 2
    cd on003710 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/on003710/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.