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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for a first lumbar vertebra of Sifrhippus grangeri (University of Michigan Museum of Paleontology catalog number UMMP VP 115547) as a series of TIFF images. Raw projections are not included in this dataset. The reconstructed slice data from the scan are offered here as a series of unsigned 16-bit integer TIFF images. The upper left corner of the first image (*_0000.tif) is the XYZ origin. In some publications this species is referred to as Hyracotherium grangeri.
- Keyword:
- Paleontology, Fossil, CT, Equidae, UMMP, University of Michigan Museum of Paleontology, Eocene, CTEES, and ef48281d-2984-86f2-2bee-052b26cf8da9
- Discipline:
- Science
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- Creator:
- Brian C. Weeks
- Description:
- Description: Each folder contains all of the data for a specific specimen; the folder names correspond to the University of Michigan Museum of Zoology catalog number for the specimen. Folders with a “-“ in the name are individual specimens that were photographed multiple independent times; the number following the “-“ indicates the repetition number (i.e. the folder named “UMMZ_242382-10” contains the tenth set of photographs for specimen UMMZ 242382). The photographs are necessary to train and test the Skelevision model, which is a computer vision approach to identifying and measuring elements of the skeleton (length of the tibiotarsus, tarsometatarsus, femur, humerus, ulna, radius, carpometacarpus, 2nd digit 1st phalanx, skull, and keel; the outer diameter of the sclerotic ring at its widest point; and the distance from the back of the skull to the tip of the bill). The data span 115 species of passerines across 79 genera from 59 families.
- Keyword:
- Bird skeleton, neural network, and functional traits
- Citation to related publication:
- Weeks, B.C., Zhou, Z., O’Brien, B., Darling, R., Dean, M., Dias, T., Hassena, G., Zhang, M., and Fouhey, D.F. 2022. A deep neural network for high throughput measurement of functional traits on museum skeletal specimens. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.13864
- Discipline:
- Science
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- Creator:
- Kort, Eric A., Plant, Genevieve, Smith, Mackenzie L., Brandt, Adam R., Chen, Yuanlei, Gorchov Negron, Alan M., Schwietzke, Stefan, and Zavala-Araiza, Daniel
- Description:
- As part of the Flaring & Fossil Fuels: Uncovering Emissions & Losses (F3UEL) project, in 2020 the aircraft measurement platform sampled offshore oil & gas facilities in the Gulf of Mexico to quantify facility-level emissions using the approach detailed in Conley et al. (2017). Onshore, the aircraft sampled downwind of flare combustion plumes in the Permian and Eagle Ford regions of Texas. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day. and Reference: Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss’s theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345 – 3358, 2017.
- Keyword:
- Offshore Oil & Gas, Flaring, Methane, and Nitrogen Oxides
- Discipline:
- Science
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- Creator:
- Kort, Eric A., Plant, Genevieve, Brandt, Adam R., Chen, Yuanlei, Gorchov Negron, Alan M., Schwietzke, Stefan, Smith, Mackenzie L., and Zavala-Araiza, Daniel
- Description:
- As part of the Flaring & Fossil Fuels: Uncovering Emissions & Losses (F3UEL) project, in 2021 the aircraft measurement platform sampled offshore oil & gas facilities in Alaska and California to quantify facility-level emissions using the approach detailed in Conley et al. (2017). Onshore, the aircraft sampled downwind of flare combustion plumes in the Bakken region of North Dakota. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day. and Reference: Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss’s theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345 – 3358, 2017.
- Keyword:
- Offshore Oil & Gas, Flaring, Methane, and Nitrogen Dioxides
- Discipline:
- Science
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- Creator:
- Murray, Kendra E, Niemi, Nathan A, and Clark, Marin C
- Description:
- These data were produced in the scope of research into understanding the application of zircon (U-Th)/He thermochronometric data derived from rocks with complex radiation damage distributions to the extraction of long-term (>1 Gyr) thermal histories of the Earth's upper crust. The samples used in this study were collected from the Front Range in Colorado, USA. The low-temperature (apatite and zircon (U-Th)/He) thermochronometric ages presented in this data set are sensitive to near-surface temperatures (~80C and 180C, respectively) and record the progressive exhumation of the rock mass from which the samples were collected towards the Earth's surface. These thermochronometric ages, and the differences between them, provide insight into the deep-time (~1000 Ma - 100 Ma) thermal history of the Colorado Front Range.
- Keyword:
- apatite, zircon, helium, (U-Th)/He, (U-Th-Sm)/He, thermochronometry, thermochronology, low-temperature, Colorado, Boulder, geology, Colorado Mineral Belt, and Front Range
- 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
-
- 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