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Division of Fishes
User Collection
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Division of Fishes
- Discipline:
- Science
48Works -
Division of Reptiles and Amphibians
User Collection
- Creator:
- University of Michigan Museum of Zoology
- Description:
- The Division of Reptiles and Amphibians maintains a collection that is worldwide in scope. The research collections contain over 200,000 catalogued lots representing approximately 435,000 individual specimens.
- Discipline:
- Science
13Works -
Division of Mammals
User Collection
- Creator:
- University of Michigan Museum of Zoology
- Description:
- The Division of Mammals at the Museum of Zoology was established in 1837, and has grown steadily to its current size, with over 150,000 specimens. An important feature of the mammal collection at the Museum of Zoology is our emphasis on non-traditional specimens.
- Discipline:
- Science
109Works -
University of Michigan Museum of Zoology
User Collection
- Creator:
- University of Michigan Museum of Zoology
- Description:
- The University of Michigan Museum of Zoology (UMMZ) is the center for the study of animal diversity on campus, focusing on the evolutionary origins of the planet’s animal species, the genetic information they contain and the communities and ecosystems they help form. Now an integral part of the Department of Ecology and Evolutionary Biology (EEB), the UMMZ houses world-class collections, containing more than 15 million specimens, span almost 200 years of regional and global biodiversity studies and that support a multi-faceted Departmental research and teaching program.
- Discipline:
- Science
4Sub-collections0Works -
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
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- Creator:
- Bautista-Arredondo, Luis F., Muñoz-Rocha, T. Verenice, Figueroa, José L., Téllez-Rojo, Martha M., Torres-Olascoaga, Libni A., Cantoral, Alejandra, Arboleda-Merino, Laura C., Leung, Cindy, Peterson, Karen E., and Lamadrid-Figueroa, Héctor
- Description:
- Data was collected from participants of the Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) study, which consists of three sequentially-enrolled birth cohorts of pregnant women. Research protocols of this study were approved by the Institutional Review Board at University of Michigan and the Mexico National Institute of Public Health. We obtained informed consent from study participants prior to enrollment.
- Keyword:
- Food Insecurity, COVID-19 Pandemic, Mexico, Cohort
- Citation to related publication:
- Bautista-Arredondo LF, Verenice Muñoz-Rocha T, Figueroa JL, Téllez-Rojo MM, Torres-Olascoaga LA, Cantoral A, Arboleda-Merino L, Leung L, Peterson KE, and Lamadrid-Figueroa H. A surge in food insecurity during the COVID-19 pandemic in a cohort in Mexico City. 2022. Article in process of publication.
- Discipline:
- Health Sciences
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- Creator:
- Dariya, Malyarenko, Tariq, Humera, Kushwaha, Aman, Mourad, Rami, Heist, Kevin, Chenevert, Thomas L, Ross, Brian D, Chen, Heang-Ping, and Hadjiiski, Lubomir
- Description:
- The 3D GRE MRI data for murine model of myelofbifrosis with expert segmentations of mouse tibia was used to train Attention UNET model to automate bone marrow segmentation for measurements of imaging biomarkers. This dataset consists of three archives: (1) containing the source MRI images in Meta-image-header (MHD) format with resulting segmentation labels by two experts and four UNET models with different training scenarios; (2) corresponding training models; and (3) deep-learning (DL)-based segmentation tools for application to future murine tibia MRI data. and The MHD images are an ITK compatible format that can be viewed in standard image viewer, like 3D Slicer. The image archive is structured with a directory tree that contains \"mouseID"\"scan-date"\"segmentaion-scenario"\. The "training model" archive containes DL-model labeled by the data subset, and "deployment" archive containes the DL-segmentation software.
- Keyword:
- deep-learning segmentation, preclinical MRI, murine tibia, and mouse model of myelofibrosis
- Citation to related publication:
- Kushwaha A, Mourad RF, Heist K, Tariq H, Chan HP, Ross BD, Chenevert TL, Malyarenko D, Hadjiiski LM. Improved Repeatability of Mouse Tibia Volume Segmentation in Murine Myelofibrosis Model Using Deep Learning. Tomography. 2023 Mar 7;9(2):589-602. doi: 10.3390/tomography9020048. PMID: 36961007; PMCID: PMC10037585. and https://github.com/dumichgh/MFJK1_Segmentation_MHDs
- Discipline:
- Health Sciences
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- Creator:
- Katz, Sarah A., Levin, Naomi E., Abbott, Mark B., Rodbell, Donald T., Passey, Benjamin H., DeLuca, Nicole N., Larsen, Darren J., and Woods, Arielle
- Description:
- This dataset presents stable isotope data (d13C, d18O, D47, D17O) from Holocene lake cores from three lakes in the Peruvian Andes (Lakes Junin, Pumacocha, and Mehcocha). We also present new radiocarbon (14C) data and core age models for Lakes Junin and Mehcocha. We use these data to explore trends in lake water temperatures and evaporative state (i.e., water balance) over the Holocene. Our clumped isotope (D47) results suggest lake water temperatures at all three lakes were stable over the Holocene and similar to present day lake temperatures. Our triple oxygen isotope (D’17O) results illustrate that lake water balance at all three lakes was variable over the Holocene and tracks changes in austral summertime insolation, suggesting a connection between central Andean water balance and the South American summer monsoon (SASM).
- Keyword:
- Holocene, Andes, temperature, water balance, lacustrine carbonate, lake hydrology, triple oxygen isotopes, and clumped isotopes
- Citation to related publication:
- Katz, S.A., Levin, N.E., Abbott, M.B., Rodbell, D.T., Passey, B.H., DeLuca, N.M., Larsen, D.J., Woods, A. "Holocene temperature and water stress in the Peruvian Andes: insights from lake carbonate clumped and triple oxygen isotopes," in review. and Katz, S.A., (2024) Andean interglacial climate and hydrology over the last 650,000 years. [PhD Thesis, University of Michigan]
- Discipline:
- Science
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- Creator:
- Heath, Jeffrey
- Description:
- images of plants, in nature or specimens, of family Malvaceae, genera A to G. Malvaceae (sensu lato) now includes former Bombacaceae (Adansonia, Ceiba, Bombax), Tiliaceae (Grewia, Corchorus, Triumfetta), and Sterculiaceae (Cola, Sterculia, Waltheria).
- Keyword:
- Malvaceae
- Discipline:
- Humanities
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- Creator:
- Agnit Mukhopadhyay, Sanja Panovska, Raven Garvey, Michael Liemohn, Natalia Ganjushkina, Austin Brenner, Ilya Usoskin, Michael Balikhin, and Daniel Welling
- Description:
- In the recent geological past, Earth’s magnetic field reduced to 4% of the modern values and the magnetic poles moved severely apart from the geographic poles causing the Laschamps geomagnetic excursion, which happened about 41 millennia ago. The excursion lasted for about two millennia, with the peak strength reduction and dipole tilting lasting for a shorter period of 300 years. During this period, the geomagnetic field exhibited significant differences from the modern nearly-aligned dipolar field, causing non-dipole variables to mimic a magnetic field akin to the outer planets while displaying a significantly reduced magnetic strength. However, the precise magnetospheric configuration and their electrodynamic coupling with the atmosphere have remained critically understudied. This dataset contains the first space plasma investigation of the exact geomagnetic conditions in the near-Earth space environment during the excursion. The study contains a full 3D reconstruction and analysis of the geospace system including the intrinsic geomagnetic field, magnetospheric system and the upper atmosphere, linked in sequence using feedback channels for distinct temporal epochs. The reconstruction was conducted using the LSMOD.2 model, Block Adaptive Tree Solar wind-Roe-Upwind Scheme (BATS-R-US) Model and the MAGnetosphere-Ionosphere-Thermosphere (MAGNIT) Auroral Precipitation Model, all of which are publicly-available models. The dataset contains the raw data from each of these models, in addition to the images/post-processing results generated using these models. Paleomagnetic data produced by LSMOD.2 can be visualized using a combination of linear plotting and contour plotting tools available commonly in visualization software like Python (e.g. Python/Matplotlib) or MATLAB. Standard tools to read and visualize BATS-R-US and MAGNIT output are already publicly available using IDL and Python (see SpacePy/PyBats - https://spacepy.github.io/pybats.html). For information and details about the post-processed data, visualization and analysis, please contact the authors for details. The anthropological dataset can be visualized using a shape file reader (e.g. Python/GeoPandas) and a linear plotting tool (e.g. Python/Matplotlib).
- Discipline:
- Engineering and Science