University of Minnesota Twin Cities
Persistent link for this communityhttps://hdl.handle.net/11299/1
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Browsing University of Minnesota Twin Cities by Type "Dataset"
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Item 16S RNA data for biofilm in contact with antimicrobial peptide coatings(2020-04-23) Aparicio, Conrado; Moussa, Dina G.; apari003@umn.edu; Aparicio, Conrado; Minnesota Dental Research Center for Biomaterials and BiomechanicsDual-indexed 16S rRNA gene amplicon sequencing using the V3-V4 region on the Illumina MiSeq platform 300PE for triplicate samples of biofilm from stocks of oral plaque sample grown on hydroxyapaptite discs without (CTRL) and with antimicrobial peptide (1018, DJK2, D-GL13K) coatings, treated and non-treated with PMA. This data was collected as part of the NIDCR funded project R01-DE to determine the effects of antimicrobial peptides in the microbiome of biofilms from oral plaque stocks to prevent degradation of dental restorations. The data was generated at the University of Minnesota Genomics Center and analyzed at the University of Minnesota Informatics Institute. The data is released to be of public access following submission of a manuscript presenting and analyzing this data.Item 1920-1995 Twin Cities State Highway Network(2014-03-21) Chen, Wei; Levinson, David M; dlevinson@umn.edu; Levinson, David M.Illustrates the development of the highway network in the Twin Cities metropolitan region. GIS maps of the state highway network were created for 1920-1995 (these were not previously digitized). These were used to build Markov Chain Cellular Automata models of land use change and network growth.Item 1958 Twin Cities Land Use Map from the Twin Cities Metropolitan Planning Commission, GIS Data Files(2013-11-22) Chen, Wei; Levinson, David M; dlevinson@umn.edu; Levinson, David M.High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1958 that were developed from paper maps (no GIS version existed previously).Item 1968 Twin Cities Land Use Map from the Twin Cities Metropolitan Planning Commission, GIS Data Files(2014-03-03) Levinson, David M; Chen, Wei; dlevinson@umn.edu; Levinson, David M.High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1968 that were developed from paper maps (no GIS version existed previously).Item 1978 Twin Cities Land Use Map from the Twin Cities Metropolitan Planning Commission, GIS Data Files(2014-03-03) Levinson, David M; Chen, Wei; dlevinson@umn.edu; Levinson, David M.High-quality GIS land use maps for the Twin Cities Metropolitan Area for 1978 that were developed from paper maps (no GIS version existed previously).Item 2003 Rock Properties Database: Density, Magnetic Susceptibility, and Natural Remanent Magnetization of Rocks in Minnesota(Minnesota Geological Survey, 2010-08-06) Chandler, V.W; Lively, R.S; mgs@umn.edu; Minnesota Geological SurveyGeologic interpretation of gravity and magnetic anomaly data in a given area is greatly enhanced if density, magnetic susceptibility and natural remanent magnetization (NRM) data are available for representative rock-types. Along with outcrop and drill-hole information, rock property data help relate geophysical anomaly signatures to probable rock types, and provide constraints on the use of anomaly data as a tool for mapping and for modeling geology at depth. Most of the density and magnetization data contained in this database were acquired over the last two decades by the Minnesota Geological Survey (MGS) as part of an on-going program to collect rock properties. A group of Paleozoic samples were collected from Iowa and included in the database because they provide a representative suite of data for rocks present, but not widely exposed in Minnesota. Additional data were derived from studies by the U. S. Geological Survey (Bath, 1962; Beck, 1970; Beck and Lindsley, 1969; Books, 1972; Jahren, 1965), The University of Minnesota (Bleifuss, 1952, Mooney and Bleifuss, 1952), The University of Western Ontario (Palmer, 1970), and the Geological Survey of Canada (Dubois, 1962).Item ableC: Extensible Specification of C Using the Silver Attribute Grammar System(2017-08-24) Kaminski, Ted; Kramer, Lucas; Carlson, Travis; Van Wyk, Eric; evw@umn.edu; Van Wyk, Eric; University of Minnesota, Department of Computer Science and Engineering, Minnesota Extensible Language Tools GroupThis is the Silver specification of ableC: a specification of C at the ISO C11 standard. There may be newer, unarchived versions of this software at http://melt.cs.umn.edu.Item Abnormal Recovery from Acute Stress in Huntingtons Disease Mice(2018-03-12) Zacharoff, Lori; Hamid, Arif; Engeland, William C; Dubinsky, Janet M; dubin001@umn.edu; Dubinsky, Janet MComparisons of hypothalamic dysfunction between Huntington’s Disease (HD) patients and rodent models of HD have not always yielded similar results. Cortisol levels in HD patients have been contradictory, with reports ranging from hypo- to hypercorticoidism of morning measurements. Initial reports of major elevations in circulating corticosterone levels in the R6/2 mouse model of HD have only been followed up in one other closely related model, the R6/1 mouse, and the results were not perfectly congruent. To determine if abnormal stress hormones were a characteristic of disease, we examined diurnal and stress-induced corticosterone levels in multiple HD mouse models.Item Acceptability of Neuroscientific Interventions in Education(2021-03-22) Schmied, Astrid; Varma, Sashank; Dubinsky, Janet M; sashank@umn.edu; Varma, Sashank; University of Minnesota Departments of Neuroscience and Educational PsychologyResearchers are increasingly applying neuroscience technologies that probe or manipulate the brain to improve educational outcomes. However, their use remains fraught with ethical controversies. Here, we investigate the acceptability of neuroscience applications to educational practice in two groups of young adults: those studying bioscience who will be driving future basic neuroscience research and technology transfer, and those studying education who will be choosing among neuroscience-derived applications for their students. Respondents rated the acceptability of six scenarios describing neuroscience applications to education spanning multiple methodologies, from neuroimaging to neuroactive drugs to brain stimulation. They did so from two perspectives (student, teacher) and for three recipient populations (low-achieving, high-achieving students, students with learning disabilities). Overall, the bioscience students were more favorable to all neuroscience applications than the education students. Scenarios that measured brain activity (i.e., EEG or fMRI) to assess or predict intellectual abilities were deemed more acceptable than manipulations of mental activity by drug use or stimulation techniques, which may violate body integrity. Enhancement up to the norm for low-achieving students and especially students with learning disabilities was more favorably viewed than enhancement beyond the norm for high-achieving students. Finally, respondents rated neuroscientific applications to be less acceptable when adopting the perspective of a teacher than that of a student. Future studies should go beyond the coarse acceptability ratings collected here to delineate the role that concepts of access, equity, authenticity, agency and personal choice play in guiding respondents’ reasoning.Item Access Across America: Auto 2018 Data(2020-01-31) Murphy, Brendan; Owen, Andrew; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by auto in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by auto, and it allows for a direct comparison of the auto accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. The data available describe access to jobs by auto in the states of Arkansas, California, District of Columbia, Florida, Illinois, Iowa, Maryland, Massachusetts, Minnesota, North Carolina, Tennessee, Washington, and Virginia, and the metropolitan areas within these states.Item Access Across America: Auto 2021 Data(2023-09-21) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by auto in the 50 largest (by population) metropolitan areas in the United States. The data include access at realistic observed driving speeds by time of day and road segment. The underlying speed data inputs restrict data sharing to participating sponsor states. The data available describe access to jobs by auto in the states/districts of California, Connecticut, District of Columbia, Florida, Illinois, Maryland, Massachusetts, Michigan, Minnesota, North Carolina, Texas; and the metropolitan areas within these states. These data are part of a longitudinal study. Auto data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Access Across America: Auto 2022 Data(2024-10-21) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by auto in the 50 largest (by population) metropolitan areas in the United States. The data include access at realistic observed driving speeds by time of day and road segment. The underlying speed data inputs restrict data sharing to participating sponsor states. The data available describe access to jobs by auto in the states/districts of California, Connecticut, District of Columbia, Florida, Illinois, Maryland, Massachusetts, Michigan, Minnesota, North Carolina, Texas, and Virginia; and the metropolitan areas within these states. These data are part of a longitudinal study. Auto data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592Item Access Across America: Bike 2019 Data(2021-01-29) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by bicycling in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by biking, and incorporates a Level of Traffic Stress analysis to allow calculation of access to jobs on bike networks of different traffic stress tolerances. This dataset allows for a direct comparison of the biking accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Access Across America: Bike 2017 data are available at https://conservancy.umn.edu/handle/11299/211418, however the 2017 version of this dataset was produced without implementation of Level of Traffic Stress analysis, and the methodologies differ substantially.Item Access Across America: Bike 2021 Data(2023-08-28) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by bicycling across the United States. It is the most detailed evaluation to date of access to jobs by biking, and incorporates a Level of Traffic Stress analysis to allow calculation of access to jobs on bike networks of different traffic stress tolerances. This dataset allows for a direct comparison of the biking accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Previous datasets (Access Across America: Bike 2019) are available at https://conservancy.umn.edu/handle/11299/218194.Item Access Across America: Bike 2022 Data(2024-10-21) Owen, Andrew; Liu, Shirley Shiqin; Jain, Saumya; Hockert, Matthew; Lind, Eric; owenx148@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examines the accessibility to jobs by biking across the United States. It is the most detailed evaluation to date of access to jobs by cycling, and it allows for a direct comparison of the bike accessibility performance of America's metropolitan areas. These data are part of a longitudinal study. Biking data for additional years can be found in the Accessibility Observatory Data collection: https://hdl.handle.net/11299/200592Item Access Across America: Transit 2014 Data(2014-12-05) Owen, Andrew; Levinson, David M; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThis data was created as part of a study that examined the accessibility to jobs by transit in 46 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas.Item Access Across America: Transit 2015 Data(2017-02-02) Owen, Andrew; Levinson, David M; Murphy, Brendan; aowen@umn.edu; Owen, AndrewThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study; Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2016 Data(2018-03-28) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study; Access Across America: Transit 2015 data are available at https://conservancy.umn.edu/handle/11299/183801. Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2017 Data(2018-10-08) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in 49 of the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Access Across America: Transit 2016 data are available at https://conservancy.umn.edu/handle/11299/195065. Access Across America: Transit 2015 data are available at https://conservancy.umn.edu/handle/11299/183801. Access Across America: Transit 2014 data are available at http://hdl.handle.net/11299/168064.Item Access Across America: Transit 2018 Data(2020-01-31) Owen, Andrew; Murphy, Brendan; aowen@umn.edu; Owen, Andrew; University of Minnesota Center for Transportation Studies, Accessibility ObservatoryThese data were created as part of a study that examined the accessibility to jobs by transit in the 50 largest (by population) metropolitan areas in the United States. It is the most detailed evaluation to date of access to jobs by transit, and it allows for a direct comparison of the transit accessibility performance of America's largest metropolitan areas. These data are part of a longitudinal study. Transit data for additional years can be found in the Accessibility Observatory Data collection: http://hdl.handle.net/11299/200592