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- Creator:
- Stoev, Stilian and Hu, Weifeng
- Description:
- Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.
- Discipline:
- Engineering and Science
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- Creator:
- Brandt, Daniel, A. and Ridley, Aaron, J.
- Description:
- 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.
- Keyword:
- Thermosphere, GITM, GOCE, Neutral winds, and Thermospheric modeling
- Discipline:
- Science and Engineering
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- Creator:
- University of Michigan Transportation Research Institute, Rupp, Jonathan D., Klein, Katelyn F., and Reed, Matthew P.
- Description:
- The files include an Excel file with the x-, y-, and z- coordinates that make up the nodal coordinates for a surface model of small (5th percentle) female pelvis geometry, the finite element model (.k file) that represents the nodal coordinates, and two surface files that represent the geometry (.obj and .ply).
- Discipline:
- Engineering
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- Creator:
- Larson, Ronald G., Wen, Fei, Huang, Wenjun, and Huang, Ming
- Description:
- We provide the parameters used in Umbrella Sampling simulations reported in our study "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method", namely the set positions and spring constants for each window in simulations. Two tables are provided. Table 1 lists the names of the peptides and their corresponding sequences. Table 2 lists the parameters. The abstract of our work is the following: We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with Weighted Histogram Analysis Method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by biased-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remain-ing peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and relia-bly estimate the binding free energy between an arbitrary peptide and an MHC class II molecule.
- Keyword:
- Molecular Dynamics, Binding Free Energy, Protein, MHC, and Coarse-Grained
- Discipline:
- Science and Engineering
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- Creator:
- Ramasubramani, Vyas
- Description:
- The goal of the work is to elucidate the stability of a complex experimentally observed structure of proteins. We found that supercharged GFP molecules spontaneously assemble into a complex 16-mer structure that we term a protomer, and that under the right conditions an even larger assembly is observed. The protomer structure is very well defined, and we performed simulations to try and understand the mechanics underlying its behavior. In particular, we focused on understanding the role of electrostatics in this system and how varying salt concentrations would alter the stability of the structure, with the ultimate goal of predicting the effects of various mutations on the stability of the structure. There are two separate projects included in this repository, but the two are closely linked. One, the candidate_structures folder, contains the atomistic outputs used to generate coarse-grained configurations. The actual coarse-grained simulations are in the rigid_protein folder, which pulls the atomistic coordinates from the other folder. All data is managed by signac and lives in the workspace directories, which contain various folders corresponding to different parameter combinations. The parameters associated with a given folder are stored in the signac_statepoint.json files within each subdirectory. The atomistic data uses experimentally determined protein structures as a starting point; all of these are stored in the ConfigFiles folder. The primary output is the topology files generated from the PDBs by GROMACS; these topologies are then used to parametrize the Monte Carlo simulations. In some cases, atomistic simulations were actually run as well, and the outputs are stored alongside the topology files. In the rigid_protein folder, the ConfigFiles folder contains MSMS, the software used to generate polyhedral representations of proteins from the PDBs in the candidate_structures folder. All of the actual polyhedral structures are also stored in the ConfigFiles folder. The actual simulation trajectories are stored as general simulation data (GSD) files within each subdirectory of the workspace, along with a single .pos file that contains the shape definition of the (nonconvex) polyhedron used to represent a protein. The logged quantities, such as energies and MC move sizes, are stored in .log files. The logic for the simulations in the candidate_structures project is in the Python scripts project.py, operations.py, and scripts/init.py. The rigid_protein folder also includes the notebooks directory, which contains Jupyter notebooks used to perform analyses, as well as the Python scripts used to actually perform the simulations and manage the data space. In particular, the project.py, operations.py and scripts/init.py scripts contain most of the logic associated with the simulations.
- Keyword:
- Protein assembly, Cryo TEM, Hierarchical Assembly, Monte Carlo simulation, and Coarse-grained simulation
- Discipline:
- Science and Engineering
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- Creator:
- Payam Mirshams Shahshahani
- Description:
- The two R codes are related to the feasible balance region calculations for Figures 2, 3, and 4 in the paper. The MATLAB codes are related to the simulations of the recoverable initial quasi-static states, the results of which are shown in Figure 5 of the paper.
- Keyword:
- One-legged balance, Biomechanics, Hip Abductor, and Unipedal Stance
- Citation to related publication:
- Shahshahani, P. M., & Ashton-Miller, J. A. (2020). On the importance of the hip abductors during a clinical one legged balance test: A theoretical study. PLOS ONE, 15(11), e0242454. https://doi.org/10.1371/journal.pone.0242454
- Discipline:
- Health Sciences and Engineering
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- 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
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- Creator:
- Limon, Garrett C.
- Description:
- The work guides the processing of CAM6 data for use in machine learning applications. We also provide workflow scripts for training both random forests and neural networks to emulate physic s schemes from the data, as well as analysis scripts written in both Python and NCL in order to process our results.
- 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 [Pre Print]. ESSOAr. https://10.1002/essoar.10512353.1
- Discipline:
- Engineering and Science
-
SHIFDR: Sub-metered HVAC Implemented For Demand Response
User Collection- Creator:
- Lin, Austin and Mathieu, Johanna
- Description:
- The Sub-metered HVAC Implemented for Demand Response (SHIFDR) dataset is a massive dataset that captures the response of individual commercial building HVAC system components to demand response events. The dataset includes device-level power consumption during demand response events as well as during normal operation. We have organized the data into subsets, with each subset containing data from buildings in different parts of the world. Kindly refer to the README file within each subsection for specific information about how data is organized. Please reach out if you have data that you would like to share, find any mistakes in the data, or have any questions. We are always trying to improve SHIFDR.
- Discipline:
- Engineering
1Works -
- Creator:
- Lin, Austin J, Lei, Shunbo, Keskar, Aditya, Hiskens, Ian A, Johnson, Jeremiah X , Mathieu, Johanna L, Kennedy, Tim, DeMink, Scott, Morgan, Kevin, Flynn, Connor, Giessner, Paul, Anderson, David, Dongmo, Jordan, Afshari, Sina, Li, Han, and Ceilsinki, Andrew
- Description:
- This is a subset of the SHIFDR dataset collection containing data from 14 buildings in Southeast Michigan. The full dataset collection can be found at https://deepblue.lib.umich.edu/data/collections/vh53ww273?locale=en and Organization: We include a subfolder for each building, identified by name. All buildings have been renamed after lakes to protect the identity of the building. Within each building subfolder, there is fan power (i.e. current measurements from which fan power can be computed), building automation system (BAS), whole building electrical load (WBEL), and voltage data collected over the course of our experimentation from 2017 to 2021. All experiments were conducted in the summer months and a full schedule of Demand Response (DR) events is included along with each building in the ‘Event_Schedule.csv’ file. The building information file contains general information about the buildings, pertinent to the experiments we conducted. There is also a folder labeled ‘2021 Preprocessed data’ which contains combined BAS and fan power data from the summer of 2021. This data has been lightly processed to calculate fan power from current measurements and interpolate BAS data to 1 minute intervals. These act as an easy-to-use starting point for data analysis.
- Citation to related publication:
- A.J. Lin, S. Lei, A. Keskar, I.A. Hiskens, J.X. Johnson, and J.L. Mathieu. “The Sub-metered HVAC Implemented For Demand Response (SHIFDR) Dataset,” Submitted, 2023.
- Discipline:
- Engineering