The research that produced this data focused on conducting a statistical comparison between horizontal winds modeled with GITM and those derived from the accelerometer aboard the GOCE satellite. The winds from GITM and GOCE were compared by constructing their respective probability densities under different levels of geomagnetic activity, and by distributing them as a function of geomagnetic activity, magnetic latitude, magnetic local time, day-of-the-year, and solar radio flux.
These codes were produced as part of the Army Research Office Multi-University Research Initiative ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems"
The code can be run using the runAll.sh shell script (in Linux and OS-X); code should work similarly under windows.
This repository contains both the data and python3 code that reads this data and reproduces the relevant figures. The code depends on NumPy >1.17 and matplotlib >3.1 and was tested on python 3.8
These data were produced in an attempt to characterize the turning and steering behaviors of 1-DoF multi-legged (hexpedal in this case) robots. Such turning behaviors require sliding contact points.
All the data is provided in a single, large .csv.gz file (416256 rows); additional details and example code in the README
These data were produced in an attempt to characterize the turning and steering behaviors of 1-DoF multi-legged (hexpedal in this case) robots. Such turning behaviors require sliding contact points.
The .tar file contains multiple trials in .csv.gz format, with names following an informative naming convention documented in the README.
Additional metadata for the trials is given in the metadata.py file in both machine and human readable form.
The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking systematic measurements of the densities and deriving temperatures in the upper atmosphere of Mars between about 140 to 240 km above the surface since late 2014. Wind measurement campaigns are also conducted once per month for 5-10 orbits. These densities, temperatures and winds change with time (e.g. solar cycle, season, local time) and location, and sometimes fluctuate quickly. Global dust storm events are also known to significantly impact these density, temperature and wind fields in the Mars thermosphere.
For the current project, the inert light species helium is used to trace the circulation patterns and constrain wind magnitudes throughout the Mars thermosphere. Presently, more than 6 years of Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements of helium densities have been obtained by the MAVEN team (e.g. Elrod et al., 2017; 2021; Gupta et al., 2021). Measured helium distributions are compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. Since the global circulation plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere of Mars also provides the needed context for understanding helium distributions and how the atmosphere behaves as a whole system.
Three dimensional M-GITM simulations for the Mars four cardinal seasons (Ls = 0, 90, 180, 270, for Mars Year 33) were conducted for detailed comparisons with NGIMS helium and CO2 distributions (Gupta et al. 2021). The M-GITM datacubes used to extract these densities (plus winds) along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. README files are also provided for each datacube, detailing the contents of each file. In addition, a general README file is provided that summarizes the inputs and outputs of the M-GITM code simulations for this study. Finally, a basic version of the M-GITM code can be found on Github at https:/github.com/dpawlows/MGITM.
Gupta, N., N. V. Rao, S. W. Bougher, and M. K. Elrod, Latitudinal and Seasonal Asymmetries of the Helium Bulge in the Martian Upper Atmosphere J. Geophys. Res., 126, XXXX-XXXX. doi:10.1002/2021JEXXXXXX
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).
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
The goal of this research is to investigate the impact of fast formation protocol on battery lifetime.
The dataset has also been used to explore data-driven approaches in battery lifetime estimation (manuscript under review). Source code used to generate the results for this work has been included.
The file contents contain a detailed README.md file which describes the organization of the files.
This data repository includes the quantitative features of high frequency, intracranial EEG along with all necessary scripts to reproduce the figures of the accompanying manuscript.