- A semi-physical global modeling approach is used to estimate diffuse & discrete sources of auroral precipitation during the Galaxy15 event.
- Diffuse sources contribute 74% of the total auroral power. Discrete sources are strongly driven by activity and can contribute up to 61%.
- Broadband precipitation contributes 31% of the auroral Pedersen conductance playing a significant role in ionospheric electrodynamics.
Please refer to the "README.txt" for more details., MATLAB R2018a (Mathworks, Natick, MA, USA) was used to process this data., and Excel (Microsoft Office) was used to store survey data on the comfort of both systems and also to provide absolute and relative intraobserver variablities for the DM device.
Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system Attari A, Chey WD, Baker JR, Ashton-Miller JA (2020) Comparison of anorectal function measured using wearable digital manometry and a high resolution manometry system. PLOS ONE 15(9): e0228761. https://doi.org/10.1371/journal.pone.0228761
This dataset contains the videos used for https://doi.org/10.7302/m05a-0d90 (the "raw" motion tracking dataset), and is intended to be unpacked into the same directory tree. The data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion. The data were also used extensively in examining multi-contact slipping, in the studying the influence of number of legs on otherwise identical locomotion patterns, and in the use of geometric mechanics models for multilegged locomotion. Folder and file names encode the meta-data, with names following an informative naming convention documented in the README.
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 repository includes the quantitative features of high frequency, intracranial EEG along with all necessary scripts to reproduce the figures of the accompanying manuscript.
Jerboas (Jaculus jaculus) are bipedal hopping rodents that frequently transition between gaits (running, hopping, and skipping) throughout their entire speed range. It has been hypothesized that these non-cursorial bipedal gait transitions are likely to enhance their maneuverability and predator evasion ability. However, it is difficult to use the underlying dynamics of these locomotion patterns to predict gait transitions due to the large number of degrees of freedom expressed by the animals. To this end, we used empirical jerboa kinematics and dynamics to develop a unified Spring Loaded Inverted Pendulum model with defined passive swing leg motions. The simulated trajectories from the model precisely matched the experimental data. Jerboas were observed to apply different neutral swing leg angles during locomotion. By investigating the gait structure of the model with coupled and uncoupled neutral swing leg, we found two set of mechanism may explain the frequent gait transitions of jerboas.
Ding, Moore, Gan (submitted) A template model explains jerboa gait transitions across a broad range of speeds. Frontiers in Bioengineering And Biotechnology
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.
Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/
, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time.
SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar Flare Intensity Prediction With Machine Learning Models. Space Weather, 18(7), e2020SW002440. https://doi.org/10.1029/2020SW002440 and Chen, Y., & Manchester, W. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue. https://doi.org/10.7302/qnsq-cs38
These data were produced for ARO W911NF-14-1-0573 "Morphologically Modulated Dynamics" and ARO MURI W911NF-17-1-0306 "From Data-Driven Operator Theoretic Schemes to Prediction, Inference, and Control of Systems" to explore the trade-offs between various oscillator coupling models in modeling multilegged locomotion. The data were also used extensively in examining multi-contact slipping, in the studying the influence of number of legs on otherwise identical locomotion patterns, and in the use of geometric mechanics models for multilegged locomotion. Folder and file names encode the meta-data, with names following an informative naming convention documented in the README.