DS006545: fnirs dataset, 49 subjects#

Reliability-Dubois2024

Access recordings and metadata through EEGDash.

Citation: Kernel (2025). Reliability-Dubois2024. 10.18112/openneuro.ds006545.v1.0.0

Modality: fnirs Subjects: 49 Recordings: 98 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006545

dataset = DS006545(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006545(cache_dir="./data", subject="01")

Advanced query

dataset = DS006545(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

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

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

BibTeX

@dataset{ds006545,
  title = {Reliability-Dubois2024},
  author = {Kernel},
  doi = {10.18112/openneuro.ds006545.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006545.v1.0.0},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID

DS006545

Title

Reliability-Dubois2024

Author (year)

ReliabilityDubois2024

Canonical

Dubois2024

Importable as

DS006545, ReliabilityDubois2024, Dubois2024

Year

2025

Authors

Kernel

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006545.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006545,
  title = {Reliability-Dubois2024},
  author = {Kernel},
  doi = {10.18112/openneuro.ds006545.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006545.v1.0.0},
}

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: 49

  • Recordings: 98

  • Tasks: 1

Channels & sampling rate

  • Channels: 6180 (2), 6498 (2), 8340 (2), 3678 (2), 6708 (2), 12180 (2), 6990, 6696, 5400, 6282, 4614, 6432, 11682, 4170, 9558, 5640, 7890, 4752, 7794, 5076, 12300, 5676, 3552, 12738, 8730, 9012, 5280, 14520, 7524, 16266, 14592, 15288, 9966, 8874, 11094, 5568, 9276, 3630, 13014, 7932, 3570, 4278, 5256, 7464, 6060, 11142, 6126, 12468, 4194, 16086, 6768, 6744, 15354, 5190, 10224, 6930, 14820, 5862, 13494, 8250, 4866, 5130, 4986, 7332, 4626, 3792, 10458, 4530, 6522, 14142, 8646, 4062, 4122, 8082, 4734, 7596, 16122, 7044, 16464, 5766, 8832, 4116, 4098, 8592, 3900, 4764, 5082, 7800, 4308, 9180, 10254, 9426

  • Sampling rate (Hz): 3.7593757537230927, 3.759380549030849, 3.759383854917815, 3.7593844649490076, 3.7593369412842033, 3.759382548055328, 3.759381637968117, 3.7593335689320417, 3.7593809119130897, 3.7593790288058946, 3.7593368622422507, 3.759384668748248, 3.75938652671013, 3.759327715365909, 3.759378131656764, 3.7593917495093443, 3.759335491657337, 3.7593770035234657, 3.759381147438357, 3.759382410127993, 3.7593847313363185, 3.759326944731349, 3.7593764115478234, 3.7593815029752466, 3.759380476653631, 3.7593798802765264, 3.7593841548655034, 3.7593343198689566, 3.7593316689597076, 3.75938158151899, 3.7593827348988054, 3.759335334223433, 3.7593859458888867, 3.7593821349246923, 3.7593764941046097, 3.7593750038748928, 3.759382593611545, 3.7593818001216643, 3.759380541825277, 3.759340320968606, 3.759327770404511, 3.7593764966001504, 3.759382926882352, 3.759380897280349, 3.759385538565235, 3.759336320191231, 3.759384688523407, 3.7593320784412283, 3.7593804486200146, 3.759336916674929, 3.759376802130892, 3.7593834552836913, 3.7593794232712234, 3.7593266384012547, 3.7593813477897906, 3.759383655551909, 3.7593783750690566, 3.759379675664703, 3.7593859613989697, 3.7593797563033773, 3.759332720066484, 3.7593852258423093, 3.759381014194889, 3.7593815330436198, 3.7593816783733027, 3.759377394526281, 3.7593787725752463, 3.759384908721897, 3.7593360211640108, 3.7593806230201263, 3.7593790725510097, 3.7593852959156377, 3.75933410440123, 3.7593801964283244, 3.7593830794615157, 3.759380220764679, 3.7593374155646906, 3.75933672882927, 3.759382867121934, 3.7593800192877977, 3.759381561915346, 3.7593808053564546, 3.759384261106816, 3.759384299582689, 3.7593826417073126, 3.759332685108552, 3.7593841728783493, 3.7593851070356754, 3.759331427389511, 3.7593278601126636, 3.759384944435528, 3.7593821400544667, 3.759377231180893, 3.7593400623176056, 3.7593792061899447, 3.759337444344509, 3.759389442742258, 3.7593814407919455

  • Duration (hours): Not calculated

Tags

  • Pathology: Not specified

  • Modality: Auditory

  • Type: —

Files & format

  • Size on disk: 46.7 GB

  • File count: 98

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds006545.v1.0.0

Provenance

API Reference#

Use the DS006545 class to access this dataset programmatically.

class eegdash.dataset.DS006545(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Reliability-Dubois2024

Study:

ds006545 (OpenNeuro)

Author (year):

ReliabilityDubois2024

Canonical:

Dubois2024

Also importable as: DS006545, ReliabilityDubois2024, Dubois2024.

Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 49; recordings: 98; tasks: 1.

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/ds006545 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006545 DOI: https://doi.org/10.18112/openneuro.ds006545.v1.0.0

Examples

>>> from eegdash.dataset import DS006545
>>> dataset = DS006545(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#