on004635
NEMAR copy of ds004635

Gaffrey Lab Infant Microstates Reliability

This dataset comprises continuous EEG recordings from 48 infants (5-10 months old) during passive viewing of relaxing videos, collected to establish reliability metrics for microstate analysis in infant populations. The study demonstrates microstate analysis methodology applied to infant EEG data and provides a tutorial resource for researchers interested in this analytical approach. Data were acquired using a 128-channel geodesic sensor net at 1000 Hz sampling rate.

AI-generated description, may include mistakes
Issues GitHub OpenNeuro ds004635

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) — 32.2 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 on004635
  3. DataLad

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

    datalad clone https://github.com/nemarDatasets/on004635 on004635
    cd on004635 && datalad get .
  4. git-annex

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

    git clone https://github.com/nemarDatasets/on004635 on004635
    cd on004635 && 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/on004635/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/on004635), 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 on004635
    cd on004635 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/on004635/ — 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
    48
    Size
    30.6 GB
    Tasks
    resting

    License and terms

    License
    CC0
    Recommended citation
    Bagdasarov, A., & Gaffrey, M. S. (2026). Gaffrey Lab Infant Microstates Reliability (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.on004635

    Where the bytes are

    Latest version (always current)
    https://data.nemar.org/on004635/latest/

    How to download

    The dataset
    nemar dataset download on004635 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 on004635 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
    A subset, step 1
    nemar dataset clone on004635 Clones git-annex pointers only; fetches no file content. Creates ./on004635.
    A subset, step 2
    cd on004635 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/on004635/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.