nm000113 NEMAR-native dataset

2020 BCI competition, track 3

EEG recordings from 15 participants performing imagined speech of five commands ("Hello", "Help me", "Stop", "Thank you", "Yes"), released for the 2020 BCI Competition Track 3. The data are split into training, validation and test runs to support machine-learning decoding of imagined speech for brain-computer interfaces. The dataset was converted from the original MATLAB files to BIDS-compliant EDF with trial annotations.

EEG Zarr

Issues GitHub

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) — 0.5 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 nm000113
```

DataLad

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

datalad clone https://github.com/nemarDatasets/nm000113 nm000113
cd nm000113 && datalad get .

git-annex

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

git clone https://github.com/nemarDatasets/nm000113 nm000113
cd nm000113 && 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/nm000113/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/nm000113), 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 nm000113
cd nm000113 && nemar dataset get
```
Just the files.
rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000113/ — 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, Zarr access

What it is

Modalities: EEG
Participants: 15
Size: 585 MB
Tasks: imaginedSpeech

License and terms

License: CC-BY-4.0
Recommended citation: Lee, S., Müller, K., & Millán, J. D. R. (2026). 2020 BCI competition, track 3 (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000113
Reference: https://doi.org/10.21105/joss.01896

Where the bytes are

Latest version (always current): https://data.nemar.org/nm000113/latest/
This version (v1.0.0): https://data.nemar.org/nm000113/v1.0.0/

How to download

The dataset: nemar dataset download nm000113 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 nm000113 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
A subset, step 1: nemar dataset clone nm000113 Clones git-annex pointers only; fetches no file content. Creates ./nm000113.
A subset, step 2: cd nm000113 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/nm000113/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.

Assess fit without downloading

Participants table: https://data.nemar.org/nm000113/v1.0.0/participants.tsv
Dataset description: https://data.nemar.org/nm000113/v1.0.0/dataset_description.json
Directory listing: https://data.nemar.org/nm000113/v1.0.0/?format=json
Catalog record: https://api.nemar.org/datasets/nm000113

Working with the Zarr copy

1. Start at the index: https://zarr.nemar.org/nm000113/zarr/index.json The mandatory entry point. Never hardcode a bucket path.
2. Pick a store entry: stores[].zarr, stores[].groups[].name These two fields exist in every index format version, so a recipe that keys on them works against the whole catalog while the back conversion is still in flight.
3. Build the store URI: s3://nemar/nm000113/zarr/{store.zarr} Derivable from the store entry alone. An index at format_version 3 or later also publishes contract_base, data_base and s3_uri; use them when they are there, never require them.
4. Open the store anonymously: zarr.open_group(store=..., mode="r", zarr_format=3) Anonymous FsspecStore.from_url in region us-east-2, no credentials. zarr_format=3 is required: without it zarr-python probes for Zarr v2 sidecars, and because anonymous ListBucket is denied, S3 answers a missing key with 403 rather than 404 and the open raises.
5. Read the level-0 array: root[store.groups[0].name]["0"] Level 0 is the full-rate signal. Never read a view/ array for inference; those exist for display.
6. Dequantize the samples: physical = digital * scale + offset scale and offset are attributes of the level-0 array, one entry per channel; the unit is on the group's channels attribute.
7. Slice, don't download: signal[0:4, 0:500] Stream a window of channels and samples; download only when you will touch most of the array.
8. Know the HTTP contract: index.json Only index.json is always proxied and edge-cached. A plain GET for a store object, manifest.json or events.parquet 302s to the public S3 object for non-browser clients, so follow redirects, and HEAD is never redirected.
9. Read the attribution before reuse: root.attrs["nemar"] The store carries its own dataset id, DOI, license, citation and source commit.
10. Filter for pipelines: has_zarr=1 This is the converted filter. has_zarr_verified is the stricter one, and its result set can be empty until the daily fidelity sweep reaches a dataset; verification is reported, never a precondition for serving (nemar-cli ADR 0005).