The ToMCAT Dataset
Welcome to the ToMCAT dataset!
The dataset currently is served via a single SQLite database,
but we may add additional ones in the future.
For users who are not familiar with SQL, we will provide some sample SQL
queries to demonstrate how to use this database and the associated Datasette
interface.
For more advanced users, feel free to try different queries, use the
programmatic APIs provided by Datasette, or simply download the whole SQLite
database. Note that queries that take
too long to execute (i.e., > 10 seconds) will be aborted, in order to keep the
site running smoothly. If you would like to perform such complex queries, we
recommend downloading the database locally to perform them.
The entity relationship diagram describing the database schema diagram is available here: ERD
diagram.
Sign up for our mailing list to get updates on the dataset!
Citation
If you use this dataset, please cite our NeurIPS 2023
paper that introduces
the dataset.
BibTeX Format
@inproceedings{
pyarelal2023the,
title={The To{MCAT} Dataset},
author={
Adarsh Pyarelal
and Eric Duong
and Caleb Jones Shibu
and Paulo Soares
and Savannah Boyd
and Payal Khosla
and Valeria Pfeifer
and Diheng Zhang
and Eric S Andrews
and Rick Champlin
and Vincent Paul Raymond
and Meghavarshini Krishnaswamy
and Clayton Morrison
and Emily Butler
and Kobus Barnard
},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=ZJWQfgXQb6}
}
APA Format
Adarsh Pyarelal, Eric Duong, Caleb Jones Shibu, Paulo Soares, Savannah Boyd, Payal Khosla, Valeria Pfeifer, Diheng Zhang, Eric S Andrews, Rick Champlin, Vincent Paul Raymond, Meghavarshini Krishnaswamy, Clayton Morrison, Emily Butler, & Kobus Barnard (2023). The ToMCAT Dataset. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
973,223,862 rows in 23 tables
eeg_raw, gaze_raw, fnirs_sync, gsr_sync, eeg_sync, ...
Funding Acknowledgment
- The creation of this dataset was funded by the Army Research Office and was
accomplished under Grant Number W911NF-20-1-0002. The grant was awarded through
the Defense Advanced Research Projects Agency (DARPA).
- We would also like to acknowledge intramural seed funding from the University
of Arizona's SensorLab.
- Continued support (documentation updates, replying to questions from dataset
users, etc.) is supported by Army Research Office (ARO) Award Number
W911NF-24-2-0034.