eegdash.dataset.DS003876#

participants.tsv (OpenNeuro ds003876). Access recordings and metadata through EEGDash.

Modality: [‘ieeg’] Tasks: 0 License: CC0 Subjects: 0 Recordings: 0 Source: openneuro

Dataset Information#

Dataset ID

DS003876

Title

participants.tsv

Year

2021

Authors

Gunnarsdottir, Kristin, Li, Adam, Smith, Rachel, Kang, Joon, Korzeniewska, Anna, Crone, Nathan, Rouse, Adam, Cheng, Jennifer, Kinsman, Michael, Landazuri, Patrick, Uysal, Utku, Ulloa, Carol, Cameron, Nathaniel, Cajigas, Iahn, Jagid, Jonathan, Kanner, Andres, Elarjani, Turki, Bicchi, Manuel, Inati, Sara, Zaghloul, Kareem, Boerwinkle, Varina, Wyckoff, Sarah, Barot, Niravkumar, Gonzalez-Martinez, Jorge, Sarma, Sridevi

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003876.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003876,
  title = {participants.tsv},
  author = {Gunnarsdottir, Kristin and Li, Adam and Smith, Rachel and Kang, Joon and Korzeniewska, Anna and Crone, Nathan and Rouse, Adam and Cheng, Jennifer and Kinsman, Michael and Landazuri, Patrick and Uysal, Utku and Ulloa, Carol and Cameron, Nathaniel and Cajigas, Iahn and Jagid, Jonathan and Kanner, Andres and Elarjani, Turki and Bicchi, Manuel and Inati, Sara and Zaghloul, Kareem and Boerwinkle, Varina and Wyckoff, Sarah and Barot, Niravkumar and Gonzalez-Martinez, Jorge and Sarma, Sridevi},
  doi = {10.18112/openneuro.ds003876.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003876.v1.0.2},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: 46, 134, 129 (8), 121, 170, 111 (2), 168, 47 (2), 101 (2), 95, 186, 114, 190, 86 (4), 118, 107, 146, 98 (4), 125, 110 (2), 128 (10), 135 (4), 193, 182, 147

  • Sampling rate (Hz): 999.4121105232217 (6), 499.7071044492829 (2), 1000.0 (25), 2000.0 (7), 500.0 (2), 1024.0 (5), 999.9999999999999 (4), 512.0, 1024.5997950800408 (2)

  • Duration (hours): 0

Tasks & conditions

  • Tasks: 0

  • Experiment type: Unknown

  • Subject type: Unknown

Files & format

  • Size on disk: Unknown

  • File count: Unknown

  • Format: Unknown

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003876.v1.0.2

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS003876

dataset = DS003876(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS003876(cache_dir="./data", subject="01")

dataset = DS003876(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

  • No dataset-specific caveats are listed in the available metadata.

API#

class eegdash.dataset.DS003876(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003876. Modality: ieeg; Experiment type: Unknown; Subject type: Unknown. Subjects: 39; recordings: 54; tasks: 3.

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/ds003876 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003876 DOI: https://doi.org/10.18112/openneuro.ds003876.v1.0.2

Examples

>>> from eegdash.dataset import DS003876
>>> dataset = DS003876(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#