eegdash.dataset.DS005752#

The NIMH Healthy Research Volunteer Dataset (OpenNeuro ds005752). Access recordings and metadata through EEGDash.

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

Dataset Information#

Dataset ID

DS005752

Title

The NIMH Healthy Research Volunteer Dataset

Year

Unknown

Authors

Allison C. Nugent, Adam G Thomas, Margaret Mahoney, Alison Gibbons, Jarrod Smith, Antoinette Charles, Jacob S Shaw, Jeffrey D Stout, Anna M Namyst, Arshitha Basavaraj, Eric Earl, Dustin Moraczewski, Emily Guinee, Michael Liu, Travis Riddle, Joseph Snow, Shruti Japee, Morgan Andrews, Adriana Pavletic, Stephen Sinclair, Vinai Roopchansingh, Peter A Bandettini, Joyce Chung

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005752.v2.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005752,
  title = {The NIMH Healthy Research Volunteer Dataset},
  author = {Allison C. Nugent and Adam G Thomas and Margaret Mahoney and Alison Gibbons and Jarrod Smith and Antoinette Charles and Jacob S Shaw and Jeffrey D Stout and Anna M Namyst and Arshitha Basavaraj and Eric Earl and Dustin Moraczewski and Emily Guinee and Michael Liu and Travis Riddle and Joseph Snow and Shruti Japee and Morgan Andrews and Adriana Pavletic and Stephen Sinclair and Vinai Roopchansingh and Peter A Bandettini and Joyce Chung},
  doi = {10.18112/openneuro.ds005752.v2.1.0},
  url = {https://doi.org/10.18112/openneuro.ds005752.v2.1.0},
}

Highlights#

Subjects & recordings

  • Subjects: 0

  • Recordings: 0

  • Tasks: 0

Channels & sampling rate

  • Channels: Unknown

  • Sampling rate (Hz): Unknown

  • 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.ds005752.v2.1.0

Provenance

Quickstart#

Install

pip install eegdash

Load a recording

from eegdash.dataset import DS005752

dataset = DS005752(cache_dir="./data")
recording = dataset[0]
raw = recording.load()

Filter/query

dataset = DS005752(cache_dir="./data", subject="01")

dataset = DS005752(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Quality & caveats#

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

API#

class eegdash.dataset.DS005752(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005752. Modality: meg; Experiment type: Unknown; Subject type: Unknown. Subjects: 253; recordings: 22385; 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/ds005752 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005752 DOI: https://doi.org/10.18112/openneuro.ds005752.v2.1.0

Examples

>>> from eegdash.dataset import DS005752
>>> dataset = DS005752(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#