The goal of the research was to train a surrogate model for the prediction of electric field distribution for a given electrospray emitter geometry design. The surrogate is to be used in reduced-fidelity modeling of electrospray thruster arrays. The code repository is included in the README.txt file.
J.D. Eckels, C.B. Whittaker, B.A. Jorns, A.A. Gorodetsky, B. St. Peter, R.A. Dressler, “Simulation-based surrogate methodology of electric field for electrospray emitter geometry design and uncertainty quantification”, presented at the 37th International Electric Propulsion Conference, Boston, MA USA, June19-23, 2022 Available: https://www.electricrocket.org/IEPC_2022_Papers.html
We collected hours of functional magnetic resonance imaging data from human subjects listening to natural stories. We developed a predictive model of the voxel-wise response and further applied it to thousands of new words to understand how the brain stores and connects different concepts. and This is a dataset for the paper:
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w. This project is also documented at https://osf.io/eq2ba/.
Zhang, Y., Han, K., Worth, R., & Liu, Z. (2020). Connecting concepts in the brain by mapping cortical representations of semantic relations. Nature communications, 11(1), 1-13. https://doi.org/10.1038/s41467-020-15804-w
This data is a subset of the data used to generate figures similar to figures 1, 2, 3, and 4 in Nason et al., 2020, Nature Biomedical Engineering. The purpose of the study was to demonstrate the benefits of using spiking band power, a low-power but single unit specific recording signal, for brain-machine interfaces with nonhuman primates with the potential to impact low-power brain-machine interfaces with humans. All of the data is contained in .mat files, which can be commonly opened by Matlab and the Python scipy library.
Nason, S.R., Vaskov, A.K., Willsey, M.S., Welle, E.J., An, H., Vu, P.P., Bullard, A.J., Nu, C.S., Kao, J.C., Shenoy, K.V., Jang, T., Kim, H.-S., Blaauw, D., Patil, P.G., and Chestek, C.A. (2020). A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat. Biomed. Eng. 4, 973–983. https://doi.org/10.1038/s41551-020-0591-0
This data is a subset of the data used to generate components of all figures in the manuscript and supplement in Nason et al., 2021, Neuron. The purpose of the study was to demonstrate the first-ever simultaneous brain-control of two independent groups of fingers in one hand with some analysis of cortical tuning to finger movements in nonhuman primates. This advises future brain-machine interfaces for the control of finger movements with humans. All of the data is contained in .mat files, which can be commonly opened by Matlab and the Python scipy library. The Matlab packages (and versions) used for the manuscript are: MATLAB (9.4), Signal Processing Toolbox (8.0), Statistics and Machine Learning Toolbox (11.3), and Curve Fitting Toolbox (3.5.7).
Nason, S.R., Mender, M.J., Vaskov, A.K., Willsey, M.S., Ganesh Kumar, N., Kung, T.A., Patil, P.G., and Chestek, C.A. (2021). Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface. Neuron (accepted).
Penner, J. E., Zhou, C., Garnier, A., & Mitchell, D. L. (2018). Anthropogenic aerosol indirect effects in cirrus clouds. Journal of Geophysical Research: Atmospheres,123, 11,652–11,677. https://doi.org/10.1029/2018JD029204
