The Michigan–Mellon Project on the Egalitarian Metropolis supported several impactful programs and initiatives. These collaborative projects developed through several mediums, primarily: faculty and student-led research, public-engaged scholarship, and community-led place-based projects. Explore each project below! Original website at https://sites.lsa.umich.edu/egalitarianmetropolis/.
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.
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.
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.
The Novel Sensors for Autonomous Vehicle Perception Collection of datasets are sequences collected with an autonomous vehicle platform including data from novel sensors. The dataset collection platform is a Ford Fusion vehicle with a roof-mounted novel sensing suite, which specifically consists of forward-facing stereo uncooled thermal cameras (FLIR 40640U050-6PAAX), event cameras (iniVation DVXplorer), monochrome cameras (FLIR BFS-PGE-16S2M), and RGB cameras (FLIR BFS-PGE-50S5C) time synchronized with ground truth poses from a high precision navigation system. Sequences include ~8 km routes, driven repeatedly under varying lighting conditions and/or opposing viewpoints. Further information and resources are available on the project website: https://umautobots.github.io/nsavp
GRAND is a five-year, cluster randomized controlled trial. The study is registered on ClinicalTrials.gov, [ID#: NCT04033003] and is a collaboration between University of Michigan in the United States and the Dodowa Health Research Center in Ghana. and The study setting for GRAND is four districts (Akwapim North, Yilo Krobo, Nsawam-Adoagyiri, and Lower Manya Krobo) within the Eastern Region of Ghana. Health facilities were selected based the number ANC registrants per month and average gestational age of women at registration in each facility. Facilities were then matched based on facility type, district, and number of monthly ANC registrants.
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.
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