This collection represents various raw data and analysis of cores extracted during the November 2008 mission of R/V Melville in the Santa Barbara Basin., The core included is the jumbo piston core MV0811-14JC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/903459
The research is funded by NSF OCE-1304327.
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.
This collection contains estimates of the water balance of the Laurentian Great Lakes that were produced by the Large Lakes Statistical Water Balance Model (L2SWBM). Each data set has a different configuration and was used as the supplementary for a published peer-reviewed article (see "Citations to related material" section in the metadata of individual data sets). The key variables that were estimated by the L2SWBM are (1) over-lake precipitation, (2) over-lake evaporation, (3) lateral runoff, (4) connecting-channel outflows, (5) diversions, and (6) predictive changes in lake storage. and Contact: Andrew Gronewold
Office: 4040 Dana
Phone: (734) 764-6286
Smith, J. P., & Gronewold, A. D. (2017). Development and analysis of a Bayesian water balance model for large lake systems. arXiv preprint arXiv:1710.10161., Gronewold, A. D., Smith, J. P., Read, L., & Crooks, J. L. (2020). Reconciling the water balance of large lake systems. Advances in Water Resources, 103505., and Do, H.X., Smith, J., Fry, L.M., and Gronewold, A.D., Seventy-year long record of monthly water balance estimates for Earth’s largest lake system (under revision)
This collection represents various raw data and analysis of cores extracted during the January 2009 mission of the research vessel Sproul in the Santa Barbara Basin., Cores included: box core SPR0901-04BC, box core SPR0901-unnamed, and Kasten core SPR0901-03KC. Core photos, physical properties and magnetic susceptibility from the multisensor track (MST), and the scanning X-ray fluorescence (XRF) data are included in the collection., and Cruise DOI: 10.7284/901089
This research is funded by NSF-OCE 0752093.
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 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 Evans Old Field Plant Database contains FileMaker and Excel files of data collected by Dr. Francis C. Evans during a 50-year study on successional change on Evans Old Field on the Edwin S. George Reserve. Data include plant phenology, location, and abundances observed from 1948 to 1997.
Raw data and analysis files for the figures corresponding to the manuscript submission entitled "CCL2 enhances macrophage inflammatory responses via miR-9 mediated downregulation of the ERK1/2 phosphatase Dusp6"
The rapid increases in solar wind dynamic pressure, termed sudden impulses (SIs), compress Earth’s dayside magnetosphere and strongly perturb the coupled Magnetosphere-Ionosphere (M-I) system. The compression of the dayside magnetosphere launches magnetohydrodynamic (MHD) waves, which propagate down to the ionosphere, changing the Auroral Field Aligned Currents (FACs), and into nightside magnetosphere. The global response to the compression front sweeping through the coupled system is not yet fully understood due to the sparseness of the measurements, especially those with the necessary time resolution to resolve the propagating disturbances. That’s why a study including modeling is necessary. On 15 August 2015 at 7.44 UT, Advanced Composition Explorer measured a sudden increase in the solar wind dynamic pressure from 1.11 nPa to 2.55 nPa as shown in Figure-1.
We use the magnetospheric spacecraft in the equatorial magnetosphere to identify the signatures of magnetosphere response to this SI event and examine the interaction of the propagating disturbances with the M-I system. With the increased time resolution of Active Magnetosphere and Polar Electrodynamics Response Experiment (AMPERE), the FAC pattern and intensity change due to SI can also be studied in more depth. We further use measurements from ground based magnetometer stations to increase our tracking capability for the disturbances in the ionosphere and to improve our understanding of their propagation characteristics. This is the first step in a comprehensive multi-point observation and a global magnetohydrodynamic simulation based investigation of the response of the coupled M-I system to sudden impulses.
Many data sets come as point patterns of the form (longitude, latitude, time, magnitude). The examples of data sets in this format includes tornado events, origins/destination of internet flows, earthquakes, terrorist attacks and etc. It is difficult to visualize the data with simple plotting. This research project studies and implements non-parametric kernel smoothing in Python as a way of visualizing the intensity of point patterns in space and time. A two-dimensional grid M with size mx, my is used to store the calculation result for the kernel smoothing of each grid points. The heat-map in Python then uses the grid to plot the resulting images on a map where the resolution is determined by mx and my. The resulting images also depend on a spatial and a temporal smoothing parameters, which control the resolution (smoothness) of the figure. The Python code is applied to visualize over 56,000 tornado landings in the continental U.S. from the period 1950 - 2014. The magnitudes of the tornado are based on Fujita scale.