NEMAR copy of ds007955
You're viewing the NEMAR copy at v1.0.0.
Each OpenNeuro pull is a NEMAR major bump
(vN.0.0); intermediate versions are NEMAR-side fixes.
EEG and autonomic responses during emotional word association
This dataset comprises synchronized electroencephalographic (EEG), autonomic physiological, and behavioral recordings acquired during an emotional word association task. Participants performed a lexical task involving fear-related concepts, selecting or generating words in response to prompts while neural activity, autonomic responses (electrodermal activity, blood volume pulse, interbeat interval, and skin temperature), and behavioral events were recorded. The dataset includes 8-channel EEG data sampled at 250 Hz alongside physiological signals from an Empatica E4 wristband, with semantic similarity measures between consecutive word responses. This is the enriched version of OpenNeuro dataset ds007955 with additional analysis and documentation. Note: Physiological data for participant 7 are unavailable due to a data acquisition error with the Empatica E4 recording system; EEG and behavioral data remain available for this participant.
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Coming soon. Per-file data-quality summaries are precomputed by the NEMAR processing pipeline. The static aggregate is on the way — tracked at nemar-cli#511.
Files
How to use the data (for agentic research) license, citation, download commands, Zarr access
What it is
- Modalities
- EEG
- Participants
- 9
- Size
- 29.4 MB
- Tasks
- wordassociation
License and terms
- License
- CC0
- Recommended citation
- Cebral-Loureda, M. (2026). EEG and autonomic responses during emotional word association (Version v1.0.0) [Data set]. NEMAR. https://doi.org/10.82901/nemar.on007955
Where the bytes are
- Latest version (always current)
- https://data.nemar.org/on007955/latest/
- This version (v1.0.0)
- https://data.nemar.org/on007955/v1.0.0/
How to download
- The dataset
-
nemar dataset download on007955Clones 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 on007955 --subjects sub-01,02Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude. - A subset, step 1
-
nemar dataset clone on007955Clones git-annex pointers only; fetches no file content. Creates ./on007955. - A subset, step 2
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cd on007955The 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/on007955/v1.0.0/participants.tsv A direct HTTPS fetch works for any single file.
Assess fit without downloading
- Participants table
- https://data.nemar.org/on007955/v1.0.0/participants.tsv
- Dataset description
- https://data.nemar.org/on007955/v1.0.0/dataset_description.json
- Directory listing
- https://data.nemar.org/on007955/v1.0.0/?format=json
- Catalog record
- https://api.nemar.org/datasets/on007955
Working with the Zarr copy
- 1. Start at the index
- https://zarr.nemar.org/on007955/zarr/index.json The mandatory entry point. Never hardcode a bucket path.
- 2. Pick a store entry
-
stores[].zarr, stores[].groups[].nameThese 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
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s3://nemar/on007955/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
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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
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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
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physical = digital * scale + offsetscale 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
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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
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index.jsonOnly 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
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root.attrs["nemar"]The store carries its own dataset id, DOI, license, citation and source commit. - 10. Filter for pipelines
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has_zarr=1This 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).