DS006545#

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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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
Year	2025
Authors	Kernel
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006545.v1.0.0
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: 49
Recordings: 98
Tasks: 1

Channels & sampling rate

Channels: 3678 (2), 6708 (2), 8340 (2), 6180 (2), 12180 (2), 6498 (2), 11682, 6768, 5130, 5400, 8646, 13014, 4866, 12300, 6060, 5640, 4098, 16464, 6744, 15354, 5076, 10458, 8730, 4734, 4530, 12468, 4278, 4308, 4752, 5766, 8874, 14520, 4062, 7800, 7890, 4194, 7794, 8082, 7464, 6282, 4614, 5082, 5256, 7932, 8592, 4170, 6522, 9012, 4986, 4764, 3900, 5190, 11094, 7524, 9426, 16086, 5568, 6432, 9180, 5676, 8832, 9276, 6930, 14592, 15288, 6990, 4116, 9966, 10224, 10254, 11142, 5280, 7332, 14142, 7596, 6696, 4122, 16122, 5862, 6126, 3552, 14820, 4626, 12738, 13494, 16266, 3630, 3792, 8250, 9558, 7044, 3570
Sampling rate (Hz): 3.7593844649490076, 3.7593764941046097, 3.759327715365909, 3.7593841548655034, 3.7593830794615157, 3.759340320968606, 3.759337444344509, 3.7593841728783493, 3.75938652671013, 3.759376802130892, 3.7593859613989697, 3.7593798802765264, 3.759336916674929, 3.7593804486200146, 3.759379675664703, 3.7593368622422507, 3.759380476653631, 3.7593320784412283, 3.759384668748248, 3.7593801964283244, 3.7593783750690566, 3.759332685108552, 3.7593792061899447, 3.7593815029752466, 3.7593757537230927, 3.759380549030849, 3.759331427389511, 3.759377394526281, 3.759335491657337, 3.7593816783733027, 3.75938158151899, 3.759382926882352, 3.75933672882927, 3.7593814407919455, 3.7593316689597076, 3.7593374155646906, 3.7593764966001504, 3.7593815330436198, 3.7593851070356754, 3.7593343198689566, 3.7593852258423093, 3.7593917495093443, 3.7593797563033773, 3.759332720066484, 3.759327770404511, 3.7593834552836913, 3.759389442742258, 3.759381014194889, 3.759380220764679, 3.759378131656764, 3.7593821349246923, 3.759384944435528, 3.759336320191231, 3.7593859458888867, 3.7593808053564546, 3.759382410127993, 3.759381561915346, 3.759385538565235, 3.7593800192877977, 3.7593278601126636, 3.759335334223433, 3.7593360211640108, 3.759383854917815, 3.7593827348988054, 3.759382867121934, 3.759381147438357, 3.7593266384012547, 3.7593794232712234, 3.759384908721897, 3.759380897280349, 3.7593770035234657, 3.7593787725752463, 3.7593790288058946, 3.759383655551909, 3.7593826417073126, 3.7593813477897906, 3.759380541825277, 3.7593764115478234, 3.7593400623176056, 3.7593809119130897, 3.759384688523407, 3.759326944731349, 3.7593335689320417, 3.759377231180893, 3.7593852959156377, 3.7593818001216643, 3.7593790725510097, 3.75933410440123, 3.759381637968117, 3.759382548055328, 3.759382593611545, 3.7593821400544667, 3.7593750038748928, 3.7593369412842033, 3.7593847313363185, 3.7593806230201263, 3.759384299582689, 3.759384261106816
Duration (hours): 0.0

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

OpenNeuro dataset ds006545. Modality: fnirs; Experiment type: Unknown; Subject type: Unknown. Subjects: 50; recordings: 838; 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

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