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- Creator:
- Bradshaw, Lisa, Vernon, Julianne, Schmidt, Thomas, James, Timothy, Zhang, Jianzhi, Archbold, Hilary, Cadigan, Ken, Wolfe, John P., and Goldberg, Deborah E.
- Description:
- This is the experimental data referenced in our manuscript entitled "Influence of CUREs on STEM retention depends on demographic identities." The dataset comprises csv files with results from student surveys given to students enrolled in Biology 173 from Fall 2015 through Fall 2019 as well as institutional data of their course grades and cumulative GPA at the time they enrolled in Biology 173, and graduation and major data for student who had graduated by 2021. The survey questions used in the analysis and the IRB consent form are also included as pdfs.
- Keyword:
- undergraduate research, STEM retention, CURE, introductory biology laboratory, and education research
- Citation to related publication:
- Bradshaw, L., Vernon J., Schmidt T., James T., Zhang J., Archbold H., Cadigan K., Wolfe J.P. & Goldberg D. 2023. Research article: Influence of CUREs on STEM retention depends on demographic identities. J Microbiol Biol Educ (accepted)
- Discipline:
- Science
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- Creator:
- Salaree, Amir, Spica, Zack, and Huang, Yihe
- Description:
- The items in this bundle are supporting videos to a study of subsea seismo-acoustics carried out regarding an earthquake in the Persian Gulf. The main data used in the study is a diver's recording of the acoustic waves from the earthquake. The epicenter and topography data used in this study are publicly available as cited in the README.txt file.
- Keyword:
- Seismo-acoustics, Persian Gulf, Divers’ Microphones, Seismic Hazard, Early Warning
- Discipline:
- 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
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Resources for Training Machine Learning Algorithms Using CAM6 Simple Physics Packages
User Collection- Creator:
- Limon, Garrett
- Description:
- The collection contains the code and the data used to train machine learning algorithms to emulate simplified physical parameterizations within the Community Atmosphere Model (CAM6). CAM6 is the atmospheric general circulation model (GCM) within the Community Earth System Model (CESM) framework, developed by the National Center for Atmospheric Research (NCAR). GCMs are made up of a dynamical core, responsible for the geophysical fluid flow calculations, and physical parameterization schemes, which estimate various unresolved processes. Simple physics schemes were used to train both random forests and neural networks in the interest of exploring the feasibility of machine learning techniques being used in conjunction with the dynamical core for improved efficiency of future climate and weather models. The results of the research show that various physical forcing tendencies and precipitation rates can be effectively emulated by the machine learning models.
- Keyword:
- Machine Learning, Climate Modeling, and Physics Emulators
- Discipline:
- Science and Engineering
2Works -
- 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:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for Right innominate (acetabulum region) of Remingtonocetus domandaensis (University of Michigan Museum of Paleontology catalog number GSP-UM 3408) 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.
- Keyword:
- Paleontology, Fossil, CT, Remingtonocetidae, UMMP, University of Michigan Museum of Paleontology, Eocene, and Geological Survey of Pakistan (GSP)
- Discipline:
- Science
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- Creator:
- University of Michigan Museum of Paleontology and CTEES
- Description:
- Reconstructed CT slices for phalanx (pathological) of phytosaur (University of Michigan Museum of Paleontology catalog number UMMP VP 13838) 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.
- Keyword:
- Paleontology, Fossil, CT, Phytosauria, UMMP, University of Michigan Museum of Paleontology, Triassic, and e06c6866-4cba-4532-2a68-d8e3357a674e
- Discipline:
- Science
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- Creator:
- Bueno-Junior, Lezio S., Ruckstuhl, Maxwell S., Lim, Miranda M., and Watson, Brendon O.
- Description:
- Rapid eye movement sleep (REM) is believed to have a binary temporal structure with “phasic” and “tonic" microstates, characterized by motoric activity versus quiescence, respectively. However, we observed in mice that the frequency of theta activity (a marker of rodent REM) fluctuates in a non-binary fashion, with the extremes of that fluctuation correlating with phasic-type and tonic-type facial motricity. Thus, phasic and tonic REM may instead represent ends of a continuum. These cycles of brain physiology and facial movement occurred at 0.01-0.06 Hz, or infraslow frequencies, and affected cross-frequency coupling and neuronal activity in the neocortex, suggesting network functional impact. We then analyzed human data and observed that humans also demonstrate non-binary phasic/tonic microstates, with continuous 0.01-0.04 Hz respiratory rate cycles matching the incidence of eye movements. These fundamental properties of REM can yield new insights into our understanding of sleep health.
- Keyword:
- REM sleep, Infraslow fluctuations, Facial movements, Theta oscillations, and Respiration rate
- Citation to related publication:
- L. S. Bueno-Junior, M. S. Ruckstuhl, M. M. Lim, B. O. Watson, The temporal structure of REM sleep shows minute-scale fluctuations across brain and body in mice and humans. Proc. Natl. Acad. Sci. U. S. A. In press (2023).
- Discipline:
- Science
-
Survey Data
User Collection- Creator:
- Galaty, Michael
- Description:
- All databases, field notebooks, paper maps, GIS files, photographs, and photo descriptions related to the intensive survey, of tracts and tumuli, and the collection of sites have been made available in PASH Deep Blue Data Realm 1. The data are broadly organized by team (A-K). The surveyed land was divided up into “tracts”. Tracts are labeled with team letter and a consecutive number: e.g., A-001, A-002, B-003, C-122, D-035.
- Keyword:
- Archaeology
- Discipline:
- Science
6Works -
F3UEL: Flaring & Fossil Fuels: Uncovering Emissions & Losses
User Collection- Creator:
- Kort, Eric and Plant, Genevieve
- Description:
- Fossil energy production, processing, flaring, and transmission all can harm climate and air quality by emitting greenhouse gases and air pollutants. Studies now show that onshore oil and gas production emit much more methane than what is inventoried, and that local air quality impacts can be significant, however, natural gas flaring and offshore systems have been largely overlooked. The F3UEL (Flaring & Fossil Fuels: Uncovering Emissions & Losses) project aims to address these gaps by improving our understanding of offshore emissions, characterizing how flares behave in the real world, identifying what portion of the offshore system is responsible for emissions, and determining how such systems can be monitored. Spanning three years (2020-2022), the project employed an aircraft platform to measure including both greenhouse gas and air quality measurements. To sample the largest regions of current and potential future offshore production and flaring, airborne measurements targeted the Gulf of Mexico, offshore California and Alaska, the Bakken Formation (North Dakota) and the Permian and Eagle Ford Basins (Texas). Data provided here includes the airborne measurements collected using Scientific Aviation’s Mooney aircraft platform, equipped with spectroscopic instrumentation to measure methane, carbon dioxide, water vapor, nitrous oxide, and nitrogen oxide, in addition to meteorological variables such as wind speed and direction. Data products from our analysis of these airborne measurements are also provided, including estimated flare destruction removal efficiency for the Bakken, Eagle Ford, and Permian basins. Each data file is in .csv format and is accompanied by a readme file with further information and descriptors of the variables included. All users should cite the papers and datasets provided in the readme files for each individual dataset. Website: https://graham.umich.edu/f3uel This project is funded by the Alfred P. Sloan Foundation with additional support from the Environmental Defense Fund, Scientific Aviation, and University of Michigan (College of Engineering, Climate and Space Sciences and Engineering; Graham Sustainability Institute).
- Keyword:
- offshore oil & gas, flaring, methane, Nitrogen oxides, natural gas flaring, and oil & gas
- Discipline:
- Science
4Works