DS007006#

VR-Compassion Cultivation Training

Access recordings and metadata through EEGDash.

Citation: Ying Wu, Enrique Carrillosulub, Leon Lange, Chloe Tanega, Nicole Wells, Erik Virre, Cassandra Vieten (2025). VR-Compassion Cultivation Training. 10.18112/openneuro.ds007006.v1.0.0

Modality: eeg Subjects: 10 Recordings: 280 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007006

dataset = DS007006(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007006(cache_dir="./data", subject="01")

Advanced query

dataset = DS007006(
    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{ds007006,
  title = {VR-Compassion Cultivation Training},
  author = {Ying Wu and Enrique Carrillosulub and Leon Lange and Chloe Tanega and Nicole Wells and Erik Virre and Cassandra Vieten},
  doi = {10.18112/openneuro.ds007006.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007006.v1.0.0},
}

About This Dataset#

VR-CCT Dataset

Compassion Island was a social world implemented in AltspaceVR by tecchnology collaborators Origami Air. It was specifically created for the study of VR-based augmentation of compassion cultivation training (CCT). It featured three main settings – a meditation hall, a garden courtyard with a large willow tree, and a clinic. During experimental sessions, participants interacted with two characters in these spaces,

View full README

VR-CCT Dataset

Compassion Island was a social world implemented in AltspaceVR by tecchnology collaborators Origami Air. It was specifically created for the study of VR-based augmentation of compassion cultivation training (CCT). It featured three main settings – a meditation hall, a garden courtyard with a large willow tree, and a clinic. During experimental sessions, participants interacted with two characters in these spaces, represented as avatars – namely, a guide, who helped the volunteer navigate from setting to setting and offered other assistance as needed, and Ivan, who was an agitated patient in the clinic. Both characters were animated by live actors in separate locations. Participants were able to converse freely with these characters whenever they were co-present with either character in the same space. All sessions began in the meditation hall, which featured a pulsating orb designed to help participants regulate their breathing during an audio-recorded guided meditation. Next, participants were ushered outside to the garden, where they were invited to contemplate a tree with a glowing core while listening to an audio-recorded compassion meditation and performing visualization exercises that centered on universal compassion for all beings. Lastly, participants were directed into a virtual clinic to converse with Ivan, an agitated patient waiting inside the clinic, where participants would have the opportunity to practice exercising the feeling of universal compassion from the garden meditation.

Dataset Information#

Dataset ID

DS007006

Title

VR-Compassion Cultivation Training

Year

2025

Authors

Ying Wu, Enrique Carrillosulub, Leon Lange, Chloe Tanega, Nicole Wells, Erik Virre, Cassandra Vieten

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007006.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007006,
  title = {VR-Compassion Cultivation Training},
  author = {Ying Wu and Enrique Carrillosulub and Leon Lange and Chloe Tanega and Nicole Wells and Erik Virre and Cassandra Vieten},
  doi = {10.18112/openneuro.ds007006.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007006.v1.0.0},
}

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: 10

  • Recordings: 280

  • Tasks: 5

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Affect

Files & format

  • Size on disk: 918.7 MB

  • File count: 280

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007006.v1.0.0

Provenance

API Reference#

Use the DS007006 class to access this dataset programmatically.

class eegdash.dataset.DS007006(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds007006. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 10; recordings: 50; tasks: 5.

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/ds007006 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007006

Examples

>>> from eegdash.dataset import DS007006
>>> dataset = DS007006(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#