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- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- 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.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 1 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- 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.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 6 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- 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.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - 12 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Bougher, S. W. (CLaSP Department, U. of Michigan), Roeten, K. J. (CLaSP Department, U. of Michigan), and Sharrar, R. (Astronomy Department, U. of Michigan)
- Description:
- The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking daily (systematic) measurements of the densities and temperatures in the upper atmosphere of Mars between about 140 to 240 km above the surface. Wind measurement campaigns are also conducted once per month for 5-10 orbits. These densities, temperatures and winds change with time (e.g. 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. Such global dust storm period measurements can be compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. This is the first detailed comparison between direct global dust storm period measurements in the upper atmosphere of Mars and simulated MGITM fields and is important because it can help to inform us what physical processes are acting on the upper atmosphere during such large dust events. 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 key context for understanding how the atmosphere behaves as a whole system. A basic version of the M-GITM code can be found on Github as follows: https:/github.com/dpawlows/MGITM and About 4 months of Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements of densities and winds have been made by the MAVEN team during the summer of 2018 (Elrod et al., 2019). Nine reference measurement intervals during this global dust storm (1-June through 30-August 2018) are selected for detailed study (Elrod et al. 2019). The Mars conditions for these nine intervals have been used to launch corresponding M-GITM code simulations, yielding 3-D neutral density, temperature and wind fields for comparison to these NGIMS measurements. The M-GITM datacubes used to extract the density, temperature and neutral winds, along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. README files are provided for each datacube, detailing the contents of each file. A general README file is also provided that summarizes the inputs and outputs of the M-GITM code simulations for this study.
- Keyword:
- Mars, MAVEN Spacecraft, Mars Thermosphere, and Mars Global Dust Storm of 2018
- Citation to related publication:
- Elrod, M. K., S. W. Bougher, K. Roeten, R. Sharrar, J. Murphy, Structural and Compositional Changes in the Upper Atmosphere related to the PEDE-2018 Dust Event on Mars as Observed by MAVEN NGIMS, Geophys. Res. Lett., (2019). doi: 10.1029/2019GL084378. and Jain, S. K., S. W. Bougher, J. Deighan, N. M. Schneider, F. Gonzalez-Galindo, A. I. F. Stewart, R. Sharrar, D. Kass, J. Murphy, and D. Pawlowski, Martian Thermospheric Warming Associated with the Planet Encircling Dust Storm Event of 2018, Geophys. Res. Lett., submitted (2019).
- Discipline:
- Engineering and Science
- Title:
- Mars Thermospheric Responses to a Global Dust Storm (PEDE-2018): Mars Global Ionosphere-Thermosphere Model (M-GITM) Simulated Datasets for Comparison to MAVEN Spacecraft Measurements
-
- Creator:
- Hall, Ryan J. and Larson, Ronald G.
- Description:
- This is data is a large assortment of over 50 1,4-polybutadiene star-linear blends that can be used for assessing and developing predictive models. The data are presented in CSV files.
- Keyword:
- polymers, rheology, star-linear polymer blends, and shear rheology
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Raw Rheology data in supplement to the 2019 Macromolecules publication: "Assessing the Range of Validity of Current Tube Models Through Analysis of a Comprehensive Set of Star-Linear 1,4-Polybutadiene Polymer Blends"
-
- Creator:
- Burgin, Tucker and Mayes, Heather B.
- Description:
- This project aimed to discover and analyze the molecular mechanism of synthesis of two particular fucosylated oligosaccharide products in a mutant enzyme, Thermatoga maratima Alpha-L-Fucosidase D224G, whose wild type performs the opposite reaction (cleavage of fucosyl glycosidic bonds). Discovery of the mechanism was performed using an unbiased simulations method known as aimless shooting, whereas analysis of the mechanism in terms of the energy profile was performed using a separate method known as equilibrium path sampling. The data here concerns the latter method. and The contents of the atesa_master.zip are the ATESA GitHub project. A Python program for automating transition path sampling with aimless shooting using Amber. https://github.com/team-mayes/atesa
- Keyword:
- Equilibrium Path Sampling, Transition Path Sampling, Enzymatic Mechanism, and GH29
- Citation to related publication:
- 10.1039/C8RE00240A
- Discipline:
- Engineering
- Title:
- Equilibrium Path Sampling Data for Two Glycosynthetic Reactions of Thermatoga maratima Alpha-L-Fucosidase D224G
-
- Creator:
- Yao, Mengqi, Mathieu, Johanna L., Hiskens, Ian A., Molzahn, Daniel K., Koorehdavoudi, Kasra , and Roy, Sandip
- Description:
- The files include all the published paper and presentation source codes. Please install Matpower before running the code. The Matpower version is 5.1, which can be found in https://matpower.org/download/ Talks, papers, and poster in Deep Blue: http://hdl.handle.net/2027.42/150104
- Keyword:
- Demand response, Optimal power flow, Power system voltage stability, and Power system small signal stability L
- Citation to related publication:
- https://ieeexplore.ieee.org/document/8686211, https://ieeexplore.ieee.org/abstract/document/7980798, https://pdfs.semanticscholar.org/a83a/cb9c9af878f8b5d77f4b4230dfb5bd8c5325.pdf, https://ieeexplore.ieee.org/abstract/document/8619091, and https://ieeexplore.ieee.org/abstract/document/8262731
- Discipline:
- Engineering
- Title:
- Demand response algorithms to improve electric power system stability margins
-
- Creator:
- Ruas, Terry, Ferreira, Charles H. P., Grosky, William, França, Fabrício O., and Medeiros, Débora M. R,
- Description:
- The relationship between words in a sentence often tell us more about the underlying semantic content of a document than its actual words, individually. Recent publications in the natural language processing arena, more specifically using word embeddings, try to incorporate semantic aspects into their word vector representation by considering the context of words and how they are distributed in a document collection. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II that combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings into a single decoupled system. In short, our approach has three main contributions: (i) unsupervised techniques that fully integrate word embeddings and lexical chains; (ii) a more solid semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. Knowledge-based systems that use natural language text can benefit from our approach to mitigate ambiguous semantic representations provided by traditional statistical approaches. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show that the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems. Github: https://github.com/truas/LexicalChain_Builder
- Keyword:
- document classification, lexical chains, word embeddings, synset embeddings, chain2vec, and natural language processing
- Citation to related publication:
- Discipline:
- Engineering, Science, and Other
- Title:
- Enhanced word embeddings using multi-semantic representation through lexical chains
-
- Creator:
- Crisp, Dakota N., Cheung, Warwick, Gliske, Stephen V., Lai, Alan, Freestone, Dean R., Grayden, David B., Cook, Mark J., and Stacey, William C.
- Description:
- The data and the scripts are to show that seizure onset dynamics and evoked responses change over the progression of epileptogenesis defined in this intrahippocampal tetanus toxin rat model. All tests explored in this study can be repeated with the data and scripts included in this repository. and Dataset citation: Crisp, D.N., Cheung, W., Gliske, S.V., Lai, A., Freestone, D.R., Grayden, D.B., Cook, MJ., Stacey, W.C. (2019). Epileptogenesis modulates spontaneous and responsive brain state dynamics [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/r6vg-9658
- Keyword:
- evoked response, stimulation, bifurcation, epilepsy, seizure, divergence, and dynamics
- Citation to related publication:
- Discipline:
- Engineering, Science, and Health Sciences
- Title:
- Epileptogenesis modulates spontaneous and responsive brain state dynamics - Code & Data
-
- Creator:
- Crisp, Dakota N., Saggio, Maria L., Scott, Jared, Stacey, William C., Nakatani, Mitsuyoshi, Gliske, Stephen V., and Lin, Jack
- Description:
- This data and scripts are meant to test and show seizure differentiation based on bifurcation theory. A zip file is included which contains real and simulated seizure waveforms, Matlab scripts, and metadata. The matlab scripts allow for visual review validation and objective feature analysis. The file “README.txt” provides more detail about each individual file within the zip file. and Data citation: Crisp, D.N., Saggio, M.L., Scott, J., Stacey, W.C., Nakatani, M., Gliske, S.F., Lin, J. (2019). Epidynamics: Navigating the map of seizure dynamics - Code & Data [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/ejhy-5h41
- Keyword:
- Bifurcation, Epilepsy, Seizure, and Divergence
- Citation to related publication:
- Discipline:
- Engineering, Science, and Health Sciences
- Title:
- Epidynamics: Navigating the map of seizure dynamics - Code & Data
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