Index Catalog // Deep Blue Data

Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages

User Collection

Creator:: Limon, Garrett
Description:: The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
Keyword:: Machine Learning, Climate Modeling, and Physics Emulators
Discipline:: Science and Engineering

2Works

Simple Physics CAM6 Dataset for Training Machine Learning Algorithms

Creator:: Limon, Garrett C.
Description:: The data represents weekly output from three 60-year CAM6 model runs. The output includes state (.h0. files) and tendency (.h1. files) fields for three difference model configurations of increasing complexity. State fields include temperature, surface pressure, specific humidity, among others; while tendencies include temperature tendencies, specific humidity tendencies, as well as precipitation rates. Using the state variables at a given time step, machine learning techniques can be trained to predict the following tendency field, which can then be applied to the state variables to provide the state at the next physics time step of the model.
Keyword:: Machine Learning, Climate Modeling, and Physics Emulation
Citation to related publication:: Limon, G. C., Jablonowski, C. (2022) Probing the Skill of Random Forest Emulators for Physical Parameterizations via a Hierarchy of Simple CAM6 Configurations [Preprint]. ESSOAr. https://10.1002/essoar.10512353.1
Discipline:: Engineering and Science

UWHandles Dataset

Creator:: Billings, Gideon H and Johnson-Roberson, Matthew
Description:: UWHandles is a dataset for 6D object pose estimation in underwater fisheye images. It provides 6D pose and 2D bounding box annotations for 3 different graspable handle objects used for ROV manipulation. The dataset consists of 28 image sequences collected in natural seafloor environments with a total of 20,427 annotated frames. and Meta repository for the dataset https://github.com/gidobot/UWHandles
Keyword:: Deep Learning, Pose Estimation, and Underwater Vision
Citation to related publication:: Billings, G., & Johnson-Roberson, M. (2020). SilhoNet-fisheye: Adaptation of a ROI based object pose estimation network to monocular fisheye images. IEEE Robotics and Automation Letters, 5(3), 4241-4248.
Discipline:: Engineering

UWslam Dataset

Creator:: Billings, Gideon H and Johnson-Roberson, Matthew
Description:: UWslam is a dataset for underwater stereo and hybrid monocular fisheye + stereo SLAM in natural seafloor environments. The dataset includes a spiral survey of a shallow reef captured with a diver operated stereo rig and 4 hybrid image sequences captured with a deep ocean ROV in different deep ocean environments. Ground truth pose estimates for the spiral stereo trajectory were obtained by processing the image sequence through COLMAP. Ground truth pose estimates for the hybrid sequences were obtained by distributing fiducials on the seafloor before capturing an image sequence and processing the image sequences with the ROS based TagSLAM package.
Keyword:: SLAM, Simultaneous Localization and Mapping, Visual Reconstruction, and Underwater
Citation to related publication:: G. Billings, R. Camilli and M. Johnson-Roberson, "Hybrid Visual SLAM for Underwater Vehicle Manipulator Systems," in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6798-6805, July 2022, doi: 10.1109/LRA.2022.3176448.
Discipline:: Engineering

Complete Global Total Electron Content Database based on the VISTA Algorithm

User Collection

Creator:: Sun, Hu
Description:: Complete Global Total Electron Content Database based on the VISTA Algorithm
Keyword:: Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
Discipline:: Engineering

2Works

Optimization of Ictal Aborting Stimulation Using the Dynamotype Taxonomy - Code & Data

Creator:: Szuromi, Matthew P. and Stacey, William C.
Description:: The data and scripts are meant to show how burster dynamics determine response to a single biphasic stimulus. The files include data which show trends in the propensity of termination for different burster types and the MATLAB scripts used to generate this data. The MATLAB scripts also allow the user to generate their own data sets for alternative bursting paths and stimulus parameter combinations. Furthermore, they allow the user to visually examine the effects of single stimuli in the voltage timeseries and in state space. How the user can access these features of the script is described in the file "ReadMe.pdf."
Keyword:: Epilepsy, Stimulation, Modelling, Dynamics, Seizure, and Dynamotype
Citation to related publication:: (PROVISIONAL) Optimization of Ictal Aborting Stimulation Using the Dynamotype Taxonomy
Discipline:: Health Sciences, Engineering, and Science

Results for "Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model"

Creator:: Jivani, Aniket, Sachdeva, Nishtha, Huang, Zhenguang, Chen, Yang, van der Holst, Bart, Manchester, Ward, Iong, Daniel, Chen, Hongfan, Zou, Shasha, Huan, Xun, and Toth, Gabor
Description:: In this work, we perform Global Sensitivity Analysis (GSA) for the background solar wind in order to quantify contributions from uncertainty of different model parameters to the variability of in-situ solar wind speed and density at 1au, both of which have a major impact on CME propagation and strength. Scripts written in the Julia language are used to build the PCE and calculate the sensitivity results. Data is available in csv, NetCDF and JLD files. A `Project.toml` file is included to activate and install all required dependencies (See README for details).
Keyword:: Uncertainty Quantification, Space Weather, and Global Sensitivity Analysis
Discipline:: Engineering

Electrostatic simulation dataset for electrospray emitter geometry design

Creator:: Eckels, Joshua D.
Description:: 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.
Keyword:: Electrospray design, Martinez-Sanchez hyperboloid solution, and Electrospray engineering toolkit (ESPET)
Citation to related publication:: 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
Discipline:: Engineering

Electromyography from bipolar electrodes implanted into Regenerative Peripheral Nerve Interfaces and residual muscles in persons with amputations

Creator:: Vaskov, Alex K, Vu, Philip P, North, Naia, Davis, Alicia J, Kung, Theodore A, Gates, Deanna H, Cederna, Paul S, and Chestek, Cynthia A
Description:: The data was used to calibrate and simulate pattern recognition algorithms for the following publication: Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands (medRxiv 2020, IEEE TRO in review). Each data file is named as follows Px_PostureSet.csv. Where Px is the patient number. The 1 of 10 posture set contains individual finger and intrinsic thumb movements, the grasps posture set contains a fewer number of combined finger movements. P1’s calibration data for individual fingers is labelled 1 of 12 because it also includes two grasps, which were removed for analysis in the publication. The first column of each .csv file is the experiment time in seconds. The second column is the posture of the cue hand at that timestamp. The rest of the columns are the raw EMG data in microvolts sampled at 30KSps. A legend of the movement postures, each patients EMG channels, and suggested signal processing and filtering is included in DataLabellingAndProcessing.pdf
Keyword:: pattern recognition, electromyography, regenerative peripheral nerve interface, intramuscular electrodes, and myoelectric prostheses
Citation to related publication:: Surgically Implanted Electrodes Enable Real-Time Finger and Grasp Pattern Recognition for Prosthetic Hands A. K. Vaskov, P. P. Vu, N. North, A. J. Davis, T. A. Kung, D. H. Gates, P. S. Cederna, C. A. Chestek medRxiv 2020.10.28.20217273; doi: https://doi.org/10.1101/2020.10.28.20217273
Discipline:: Science and Engineering

BigAnt v6 robot motion tracking data - RAW dataset

Creator:: BIRDS Lab, U. Michigan
Description:: 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.
Keyword:: robot, multilegged, and steering
Citation to related publication:: Dan Zhao and Shai Revzen 2020 Bioinspir. Biomim. 15 045001 https://doi.org/10.1088/1748-3190/ab84c0
Discipline:: Engineering

Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages

Simple Physics CAM6 Dataset for Training Machine Learning Algorithms

UWHandles Dataset

UWslam Dataset

Complete Global Total Electron Content Database based on the VISTA Algorithm

Optimization of Ictal Aborting Stimulation Using the Dynamotype Taxonomy - Code & Data

Results for "Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model"

Electrostatic simulation dataset for electrospray emitter geometry design

Electromyography from bipolar electrodes implanted into Regenerative Peripheral Nerve Interfaces and residual muscles in persons with amputations

BigAnt v6 robot motion tracking data - RAW dataset

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