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- Creator:
- Schiffmann, Philipp, Sick, Volker, and Reuss, David L
- Description:
- This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields from various measurement planes with maximized field of view, in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Fired operation was with stoichiometric propane air, 40kPa MAP, at 1300 RPM.
- Keyword:
- TCC III engine, internal combustion engine, particle image velocimetry, in-cylinder flow, turbulence in engines, CFD validation data, cyclic variability, optical engine, combustion variability, and PIV
- Citation to related publication:
- dx.doi.org/10.1177/1468087417720558
- Discipline:
- Engineering
-
- Creator:
- Klinich, Kathleen D, Lin, Brian, and Moore, Jamie L.
- Description:
- This dataset allows comparison of the different strategies implemented by vehicle manufacturers being used to communicate with drivers. Spreadsheets were created in MS Excel to summarize data for each vehicle, and include page numbers in each vehicle owner's manual for reference. The photos taken of each vehicle control panel allow detailed inspection of the displays and controls.
- Keyword:
- vehicle, controls, displays, and FMVSS 101
- Discipline:
- Engineering
-
- Creator:
- Gliske, Stephen V and Stacey, William C
- Description:
- This data is part of a large program to translate detection and interpretation of HFOs into clinical use. A zip file is included which contains hfo detections, metadata, and Matlab scripts. The matlab scripts analyze this input data and produce figures as in the referenced paper (note: the blind source separation method is stochastic, and so the figures may not be exactly the same). A file "README.txt" provides more detail about each individual file within the zip file.
- Keyword:
- hfo, high frequency oscillation, ripple, fast ripple, blind source separation, non-negative matrix factorization, and temporal variability
- Discipline:
- Science, Engineering, and Health Sciences
-
- Creator:
- Gliske, Stephen V and Stacey, William C
- Description:
- 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.
- Keyword:
- high frequency oscillation, HFO, high frequency activity, and epilepsy
- Citation to related publication:
- (under review)
- Discipline:
- Science, Engineering, and Health Sciences
-
- Creator:
- Sugrue, Dennis P.
- Description:
- Our work seeks to better understand the financial risks to corporate operations as a basis for exploring alternative public-private investment strategies. We applied network analysis to model financial relationships within this sector and its connectedness to primary commodities transported on the Great Lakes. The financial network maps were used to quantitatively analyze the industry risk exposure using corporate financial metrics and to query the financial interdependencies of companies relative to the Great Lakes waterway. Results demonstrate that inventory turnover ratio is a robust proxy to quantify weighted financial risks of water dependency across the entire supply chain network. All data was manually collected from the Bloomberg Terminal and FactSet which are licensed by the University of Michigan. The SPLC module in the Terminal restricts data download and information must be captured manually. All data was collected from September-November 2018.
- Keyword:
- Iron Ore, Supply Chain, Bloomberg Terminal, and Great Lakes
- Citation to related publication:
- Sugrue, Dennis, Abigail Martin, and Peter Adriaens. (under review). “Financial Network Analysis to Inform Infrastructure Investment: Great Lakes Waterway and the Steel Supply Chain.” Journal of Infrastructure Systems, American Society of Civil Engineers.
- Discipline:
- Engineering
-
- Creator:
- Huffaker, Jordan S., Kummerfeld, Jonathan K., Lasecki, Walter S., and Ackerman, Mark S.
- Description:
- The following files include supplementary materials for our CHI 2020 paper "Crowdsourced Detection of Emotionally Manipulative Language". Namely, these materials include the dataset that was used in the evaluation. See the paper for more details.
- Keyword:
- Crowdsourcing, Media Manipulation, Rhetoric, and Emotion
- Citation to related publication:
- J.S. Huffaker, J.K. Kummerfeld, W.S. Lasecki, M.S. Ackerman. Crowdsourced Detection of Emotionally Manipulative Language. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2020). Honolulu, HI. 2020.
- Discipline:
- Engineering
-
- 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
-
- 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
-
- 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
-
- 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