DS007463: fnirs dataset, 8 subjects#

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Access recordings and metadata through EEGDash.

Citation: Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver (2026). Very-High-Density Diffuse Optical Tomography System Validation Dataset. 10.18112/openneuro.ds007463.v1.1.1

Modality: fnirs Subjects: 8 Recordings: 88 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007463

dataset = DS007463(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS007463(cache_dir="./data", subject="01")

Advanced query

dataset = DS007463(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds007463,
  title = {Very-High-Density Diffuse Optical Tomography System Validation Dataset},
  author = {Morgan Fogarty and Sean M. Rafferty and Zachary E. Markow and Anthony C. O’Sullivan and Calamity F. Svoboda and Tessa George and Kelsey King and Dana Wilhelm and Kalyan Tripathy and Emily M. Mugler and Stephanie Naufel and Allen Yin and Jason W. Trobaugh and Adam T. Eggebrecht and Edward J. Richter and Joseph P. Culver},
  doi = {10.18112/openneuro.ds007463.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds007463.v1.1.1},
}

About This Dataset#

This dataset consists of 8 participants completing functional localizer and movie-viewing tasks in both Very High Density Diffuse Optical Tomography (VHD-DOT) and fMRI. Sessions 1 and 2 for each subject include the VHD-DOT data in SNIRF format while sessions 3 or more include the fMRI data in NIFTI format. Preprocessed fMRI data used for comparisons to VHD-DOT are in the /derivatives folder and are in NIFTI format. More information on this data can be found here: Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver; Functional brain mapping using whole-head very high-density diffuse optical tomography. Imaging Neuroscience 2025; 3 IMAG.a.54. doi: https://doi.org/10.1162/IMAG.a.54

Dataset Information#

Dataset ID	`DS007463`
Title	Very-High-Density Diffuse Optical Tomography System Validation Dataset
Author (year)	`Fogarty2026_Very`
Canonical	—
Importable as	`DS007463`, `Fogarty2026_Very`
Year	2026
Authors	Morgan Fogarty, Sean M. Rafferty, Zachary E. Markow, Anthony C. O’Sullivan, Calamity F. Svoboda, Tessa George, Kelsey King, Dana Wilhelm, Kalyan Tripathy, Emily M. Mugler, Stephanie Naufel, Allen Yin, Jason W. Trobaugh, Adam T. Eggebrecht, Edward J. Richter, Joseph P. Culver
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007463.v1.1.1
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 8
Recordings: 88
Tasks: 14

Channels & sampling rate

Channels: 19086 (14), 19620 (11), 21518 (11), 19426 (11), 19528 (11), 19908 (10), 20218 (10), 20874 (10)
Sampling rate (Hz): 7.8125
Duration (hours): 18.898382222222224

Electrode Layout#

Electrode layout — fNIRS · 507 sensors — 507 channels

Dataset Statistics#

Channel counts (ch)

1908619426195281962019908202182087421518

Sampling frequencies: 7.8125 Hz (n=88 recordings)

Total recording duration: 18 h 53 min

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

HED event descriptors word cloud

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS007463 class to access this dataset programmatically.

class eegdash.dataset.DS007463(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Very-High-Density Diffuse Optical Tomography System Validation Dataset

Study:: ds007463 (OpenNeuro)
Author (year):: Fogarty2026_Very
Canonical:: —

Also importable as: DS007463, Fogarty2026_Very.

Modality: fnirs; Experiment type: Perception; Subject type: Healthy. Subjects: 8; recordings: 88; tasks: 14.

Parameters:

cache_dir (str | Path) – Directory where data are cached locally.
query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.
s3_bucket (str | None) – Base S3 bucket used to locate the data.
**kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:: Path

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007463 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007463 DOI: https://doi.org/10.18112/openneuro.ds007463.v1.1.1

Examples

>>> from eegdash.dataset import DS007463
>>> dataset = DS007463(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()

__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)