DS004067#

Moral conviction and metacognitive ability shape multiple stages of information processing

Access recordings and metadata through EEGDash.

Citation: Yoder, Keith J, Decety, Jean (2022). Moral conviction and metacognitive ability shape multiple stages of information processing. 10.18112/openneuro.ds004067.v1.0.1

Modality: eeg Subjects: 80 Recordings: 257 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004067

dataset = DS004067(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004067(cache_dir="./data", subject="01")

Advanced query

dataset = DS004067(
    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{ds004067,
  title = {Moral conviction and metacognitive ability shape multiple stages of information processing},
  author = {Yoder, Keith J and Decety, Jean},
  doi = {10.18112/openneuro.ds004067.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004067.v1.0.1},
}

About This Dataset#

Experiment Details

Human electroencephalography recordings from 80 participants. Participants first provided their attitudes about a set of sociopolitical issues, then view photographs of protests that were ostensibly about those same issues. Prior to each photo, they saw a pie chart indicating social support for the issue (low, medium, or high). After each photo, they indicated their support for the protestors.

Other data and analysis scripts can be found on OSF (DOI 10.17605/OSF.IO/32DAS) or at the github repository for the project (Social-Cognitive-Neuroscience-Lab/EEGMoralization)

Dataset Information#

Dataset ID

DS004067

Title

Moral conviction and metacognitive ability shape multiple stages of information processing

Year

2022

Authors

Yoder, Keith J, Decety, Jean

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004067.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004067,
  title = {Moral conviction and metacognitive ability shape multiple stages of information processing},
  author = {Yoder, Keith J and Decety, Jean},
  doi = {10.18112/openneuro.ds004067.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004067.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: 80

  • Recordings: 257

  • Tasks: 1

Channels & sampling rate

  • Channels: 63

  • Sampling rate (Hz): 2000.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 100.8 GB

  • File count: 257

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004067.v1.0.1

Provenance

API Reference#

Use the DS004067 class to access this dataset programmatically.

class eegdash.dataset.DS004067(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004067. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 80; recordings: 84; 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/ds004067 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004067

Examples

>>> from eegdash.dataset import DS004067
>>> dataset = DS004067(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#