DS003602#

Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms

Access recordings and metadata through EEGDash.

Citation: Ozlem Korucuoglu, Andrey P. Anokhin (2021). Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms. 10.18112/openneuro.ds003602.v1.0.1

Modality: eeg Subjects: 118 Recordings: 4247 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003602

dataset = DS003602(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003602(cache_dir="./data", subject="01")

Advanced query

dataset = DS003602(
    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{ds003602,
  title = {Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms},
  author = {Ozlem Korucuoglu and Andrey P. Anokhin},
  doi = {10.18112/openneuro.ds003602.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003602.v1.0.1},
}

About This Dataset#

Data collection took place at the Washington University School of Medicine, St. Louis, under the supervision of Dr. Andrey Anokhin (andrey@wustl.edu). The project was approved by the Washington University Institutional Review Board (IRB project # 201707051). Detailed task description and subject instructions can be found in a seperate PDF file under the folder stimuli. The task sequence file (stim program code) together with the visual stimuli used in the task are also provided in the stimulus folder. Participants were Monozygotic twin pairs, twin pairs have the same FamilyID (provided in participants.tsv)

Dataset Information#

Dataset ID

DS003602

Title

Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms

Year

2021

Authors

Ozlem Korucuoglu, Andrey P. Anokhin

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003602.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003602,
  title = {Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms},
  author = {Ozlem Korucuoglu and Andrey P. Anokhin},
  doi = {10.18112/openneuro.ds003602.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003602.v1.0.1},
}

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: 118

  • Recordings: 4247

  • Tasks: 1

Channels & sampling rate

  • Channels: 35

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 73.2 GB

  • File count: 4247

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003602.v1.0.1

Provenance

API Reference#

Use the DS003602 class to access this dataset programmatically.

class eegdash.dataset.DS003602(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003602. Modality: eeg; Experiment type: Decision-making; Subject type: Other. Subjects: 118; recordings: 699; tasks: 6.

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/ds003602 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003602

Examples

>>> from eegdash.dataset import DS003602
>>> dataset = DS003602(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#