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- Creator:
- Marchman, Timothy, Dominique, Daniel, Zhang, Huayile, Daneshvar, Fariborz, and Murphy Kevin
- Description:
- We created various files, including GIS files and data files for both the UM Hydrologic Modeling Team and for our own Escherichia coli sampling project. The UM Hydrologic Team used the files we created to make their models more accurate. For example, we edited Clinton River subwatershed files to better reflect below and above-ground infrastructure, and provided them to the modeling team. For our own E. coli subproject we created time series, GIS files, and R code to better understand the influence of precipitation and streamflow on E. coli dynamics. Our time-series data is based on baseline and storm sampling we conducted in the summer of 2021. We used GIS files to explore the subwatersheds of our E. coli sampling locations. Finally, we created R code to help us visualize and analyze the data.
- Discipline:
- Science
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- Creator:
- Towne, Aaron S. and Brès, Guillaume
- Description:
- This dataset contains data from a large eddy simulation of a turbulent jet at Mach number 0.9. The dataset contains 10000 time-resolved snapshots of three-dimensional velocity, density, and pressure fields spanning 2000 acoustic time units and also includes pre-processed azimuthal Fourier modes for each snapshot and the mean flow. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘jet_README.pdf’ file for more information. We recommend using the ‘jet_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides two examples of how these data can be used for reduced-complexity modeling. Users of these data should cite the two papers listed below.
- Keyword:
- fluid mechanics, jets, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892. and Brès, G. A., Jordan, P., Jaunet, V., Le Rallic, M., Cavalieri, A. V. G., Towne, A., Lele, S. K., Colonius, T., Schmidt, O. T. (2018) Importance of the nozzle-exit boundary-layer state in subsonic turbulent jets. J. Fluid Mech., 851:83–124.
- Discipline:
- Engineering and Science
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- Creator:
- Liemohn, Michael W, Adam, Joshua G, and Ganushkina, Natalia Y
- Description:
- Many statistical tools have been developed to aid in the assessment of a numerical model’s quality at reproducing observations. Some of these techniques focus on the identification of events within the data set, times when the observed value is beyond some threshold value that defines it as a value of keen interest. An example of this is whether it will rain, in which events are defined as any precipitation above some defined amount. A method called the sliding threshold of observation for numeric evaluation (STONE) curve sweeps the event definition threshold of both the model output and the observations, resulting in the identification of threshold intervals for which the model does well at sorting the observations into events and nonevents. An excellent data-model comparison will have a smooth STONE curve, but the STONE curve can have wiggles and ripples in it. These features reveal clusters when the model systematically overestimates or underestimates the observations. This study establishes the connection between features in the STONE curve and attributes of the data-model relationship. The method is applied to a space weather example.
- Keyword:
- space physics, statistical methods, and STONE curve
- Citation to related publication:
- Liemohn, M. W., Adam, J. G., & Ganushkina, N. Y. (2022). Analysis of features in a sliding threshold of observation for numeric evaluation (STONE) curve. Space Weather, 20, e2022SW003102. https://doi.org/10.1029/2022SW003102
- Discipline:
- Science
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- Creator:
- Xiantong Wang
- Description:
- Magnetospheric sawtooth oscillations are observed during strong and steady solar wind driving conditions. The simulation results of our global MHD model with embedded kinetic physics show that when the total magnetic flux carried by constant solar wind exceeds a threshold, sawtooth-like magnetospheric oscillations are generated. Different from previous works, this result is obtained without involving time-varying ionospheric outflow in the model. The oscillation period and amplitude agree well with observations. The simulated oscillations cover a wide range of local times, although the distribution of magnitude as a function of longitude is different from observations. Our comparative simulations using ideal or Hall MHD models do not produce global time-varying features, which suggests that kinetic reconnection physics in the magnetotail is a major contributing factor to sawtooth oscillations.
- Discipline:
- Science
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- Creator:
- Towne, Aaron
- Description:
- This database contains six datasets intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The six datasets are: large-eddy-simulation data for a turbulent jet, direct-numerical-simulation data for a zero-pressure-gradient turbulent boundary layer, particle-image-velocimetry data for the same boundary layer, direct-numerical-simulation data for laminar stationary and pitching flat-plate airfoils, particle-image-velocimetry and force data for an airfoil encountering a gust, and large-eddy-simulation data for the separated, turbulent flow over an airfoil. All data are stored within hdf5 files, and each dataset additionally contains a README file and a Matlab script showing how the data can be read and manipulated. Since all datafiles use the hdf5 format, they can alternatively be read within virtually any other programing environment. An example.zip file included for each dataset provides an entry point for users. The database is an initiative of the AIAA Discussion Group on Reduced-Complexity Modeling and is detailed in the paper listed below. For each dataset, the paper introduces the flow setup and computational or experimental methods, describes the available data, and provide an example of how these data can be used for reduced-complexity modeling. All users should cite this paper as well as appropriate primary sources contained therein. Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Keyword:
- fluid dynamics, reduced-complexity models, and data-driven models
- Discipline:
- Engineering and Science
6Works -
- Creator:
- Bacon, Elizabeth, Hanson, Erika N., Austin, Sarah, Delacroix, Emerson, Uhlmann, Wendy, Roberts, Scott, and Resnicow, Ken
- Description:
- Survey respondents were cancer-affected patients seen at an academic medical center, and self-reported experiences with genetic testing and counseling. This is raw dataset is saved in comma separated value (.csv) format.
- Keyword:
- Genetic Testing, Clinician Recommandation , NCCN Guidelines, Hereditary Genetic Testing, and Disparities in Genetic Counseling
- Citation to related publication:
- American Association of Kidney Patients: A List of Support Groups in Michigan. https://aakp.org/wp-content/uploads/2020/06/Support-Groups-Michigan.pdf
- Discipline:
- Science and Health Sciences
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- Creator:
- Iong, Daniel, Chen, Yang, Toth, Gabor, Zou, Shasha, Pulkkinen, Tuija I., Ren, Jiaen, Camporeale, Enrico, and Gombosi, Tamas I. I.
- Description:
- In this work, we trained gradient boosted trees using XGBoost to predict the SYM-H forecasting using different combinations of solar wind and interplanetary magnetic field (IMF) parameters. Data are in csv and Python pickle formats.
- Keyword:
- SYM-H forecasting
- Citation to related publication:
- Iong, D., Y. Chen, G. Toth, S. Zou, T. I. Pulkkinen, J. Ren, E. Camporeale, and T. I. Gombosi, New Findings from Explainable SYM-H Forecasting using Gradient Boosting Machines, Space Weather,11, accepted, 2022. https://doi.org/10.1002/essoar.10508063.3
- Discipline:
- Science
-
- Creator:
- Towne, Aaron, Saxton-Fox, Theresa, and Parthasarthy, Aadhy
- Description:
- This dataset contains experimental measurements of a zero-pressure-gradient flat-plate boundary layer at five different Reynolds numbers collected using particle image velocimetry. For each Reynolds number, the dataset contains approximately 6000 snapshots of planar velocity fields as well as raw particle image pairs. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLexp_README.pdf’ file for more information. We recommend using the ‘BLexp_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the papers listed below.
- Keyword:
- fluid mechanics
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science
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Prescribed Burn Single-Particle Mass Spectra, University of Michigan Biological Station October 2017
- Creator:
- Pratt, Kerri
- Description:
- This dataset corresponds to 5,500 single-particle, dual-polarity mass spectra of biomass burning particles measured from a mobile laboratory during the 2017 prescribed burn at the University of Michigan Biological Station near Pellston, Michigan. Size-resolved chemical composition of individual particles, primarily ranging from 0.15 - 0.60 μm in vacuum aerodynamic diameter, was measured using an aerosol time-of-flight mass spectrometer (ATOFMS).
- Keyword:
- aerosol chemistry
- Discipline:
- Science
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- Creator:
- Towne, Aaron S. and Lozano-Durán, Adrián
- Description:
- This dataset contains data from two direct numerical simulations of a turbulent zero-pressure-gradient flat-plate boundary layer spanning friction Reynolds numbers from 292 to 728 (BL1) and 488 to 1024 (BL2). The dataset contains time-resolved snapshots of the three-dimensional velocity field for both cases: roughly 10,000 snapshots spanning 20 eddy-turnover times for BL1 and 7,500 snapshots spanning 7 eddy-turnover times for BL2 . Also included for both cases are pre-processed correlations at several wall-normal distances, mean and root-mean-squared velocity and vorticity profiles, several boundary-layer metrics, and time-resolved velocity data in the streamwise-wall-normal plane. All data are stored within hdf5 files, and a Matlab script showing how the data can be read and manipulated is provided. Please see the ‘BLdns_README.pdf’ file for more information. We recommend using the ‘BLdns_example.zip’ file as an entry point to the dataset. and The dataset is part of “A database for reduced-complexity modeling of fluid flows” (see references below) and is intended to aid in the conception, training, demonstration, evaluation, and comparison of reduced-complexity models for fluid mechanics. The paper introduces the flow setup and computational methods, describes the available data, and provides an example of how these data can be used for reduced-complexity modeling. Users of these data should cite the paper listed below.
- Keyword:
- fluid mechanics, boundary layer, and turbulence
- Citation to related publication:
- Towne, A., Dawson, S., Brès, G. A., Lozano-Durán, A., Saxton-Fox, T., Parthasarthy, A., Biler, H., Jones, A. R., Yeh, C.-A., Patel, H., Taira, K. (2022). A database for reduced-complexity modeling of fluid flows. AIAA Journal 61(7): 2867-2892.
- Discipline:
- Engineering and Science