nm000279 NEMAR-native dataset

ZuCo 2.0: EEG and Eye-Tracking during Natural Reading and Annotation

ZuCo 2.0 provides simultaneous EEG and eye-tracking recordings from adult native English speakers during natural reading and annotation tasks. Participants performed normal reading and task-specific reading of Wikipedia sentences while 128-channel EEG and eye-tracking data were collected. The dataset supports research on the neural and oculomotor correlates of natural language processing and reading comprehension.

AI-generated description, may include mistakes
Issues GitHub

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) — 17.8 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 nm000279
  3. DataLad

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

    datalad clone https://github.com/nemarDatasets/nm000279 nm000279
    cd nm000279 && datalad get .
  4. git-annex

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

    git clone https://github.com/nemarDatasets/nm000279 nm000279
    cd nm000279 && 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/nm000279/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/nm000279), 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 nm000279
    cd nm000279 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000279/ — 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
    18
    Size
    18.3 GB
    Tasks
    nr, tsr

    License and terms

    License
    CC-BY-4.0
    Recommended citation
    Hollenstein, N., Tröndle, M., Zhang, C., & Langer, N. (2026). ZuCo 2.0: EEG and Eye-Tracking during Natural Reading and Annotation (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000279

    Where the bytes are

    Latest version (always current)
    https://data.nemar.org/nm000279/latest/

    How to download

    The dataset
    nemar dataset download nm000279 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 nm000279 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
    A subset, step 1
    nemar dataset clone nm000279 Clones git-annex pointers only; fetches no file content. Creates ./nm000279.
    A subset, step 2
    cd nm000279 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/nm000279/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.