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- Creator:
- Wittkopp, Patricia J and Massey, Jonathan H
- Description:
- Data provided in this record were collected in the course of studying the genetic basis of differences in wing pigmentation and wing display between Drosophila elegans and Drosophila gunungcola.
- Citation to related publication:
- Discipline:
- Science
- Title:
- Supporting Data from "Co-evolving wing spots and mating displays are genetically separable traits in Drosophila" by Massey et al.
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- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 1 month time window - 1980 through 2015 monthly summary data and model output
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- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/ , and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - Laurentian Great Lakes - 6 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Smith, Joeseph P., Gronewold, Andrew D., Read, Laura, Crooks, James L., School for Environment and Sustainability, University of Michigan, Department of Civil and Environmental Engineering, University of Michigan, and Cooperative Institute for Great Lakes Research, University of Michigan
- Description:
- Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
- Keyword:
- Water, Balance, Great Lakes, Laurentian, Machine Learning, Machine, Learning, Lakes, Bayesian, and Network
- Citation to related publication:
- Discipline:
- Engineering and Science
- Title:
- Large Lake Statistical Water Balance Model - 12 month time window - 1980 through 2015 monthly summary data and model output
-
- Creator:
- Steiner, Allison and Bryan, Alex
- Description:
- Included are RegCM simulations driven by three different types of boundary conditions 1. ERA - present day only (1979-2005) 2. GFDL - present day (1978-2005) and future (2041-2065) 3. HadGEM - present day (1978-2005) and future (2041-2065) Each directory has three files with monthly averaged values: ATM: includes 4D (t,z,y,x) atmospheric fields (pressure, winds, temperature, specific humidity, cloud water) and some 3D fields (t,y,x) precipitation, soil temperature, soil water SRF: includes 3D (t,y,x) surface variables (surface pressure, 10m winds, drag coefficient, surface temperature, 2m air temperature, soil moisture, precipitation, runoff, snow, sensible heat flux, latent heat flux, surface radiation components (SW, LW), PBL height, albedo, sunshine duration) RAD: includes 4D radiative transfer variables (SW and LW heating, TOA fluxes, cloud fraction, ice water content) clm_h0 files: CLM land surface files, includes canopy variables, surface fluxes, soil moisture by layers, etc. "
- Keyword:
- climate
- Citation to related publication:
- Bryan, A. M., A. L. Steiner, and D. J. Posselt (2015), Regional modeling of surface-atmosphere interactions and their impact on Great Lakes hydroclimate, J. Geophys. Res. Atmos., 120, 1044–1064. https://doi.org/10.1002/2014JD022316
- Discipline:
- Science
- Title:
- Regional Climate Model simulations
-
- Creator:
- Bustamante, Angela C., Opron, Kristopher, Ehlenbach, William J., Crane, Paul K., Keene, Dirk, Standiford, Theodore J., and Singer, Benjamin H.
- Description:
- This study was conducted to detect and analyze modules, or clusters of genes, associated with sepsis, using RNAseq data obtained from 12 participants who died of sepsis and 12 participants who died of non-infectious critical illness while hospitalized. This deposit contains the input data and parameters needed to reproduce the weighted gene co-expression network analysis (WGCNA) and gene enrichment analysis performed on this data. This analysis requires the R packages "WGCNA" version 1.68 and "DESeq2" version 1.22.2 available for download from bioconductor ( http://bioconductor.org). The external bioinformatics tool DAVID version 6.8 ( https://david.ncifcrf.gov/) was used as an additional gene enrichment analysis. Please see the supplemental methods document within this deposit and published research letter for more detailed information.
- Keyword:
- Sepsis, RNAseq, Transcriptomics, Human, and Brain
- Citation to related publication:
- Discipline:
- Science
- Title:
- Transcriptomic Profiles of Sepsis in the Human Brain
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- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:mammals:124092 (Phyllops falcatus) - WholeBody. Raw - Dataset includes 1601 TIF images (each 1085 x 1386 x 1 voxel at 0.0328326086085239 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:mammals:124092 (Phyllops falcatus) - WholeBody. Reconstructed - Dataset includes 2000 TIF images (each 1085 x 1386 x 1 voxel at 0.032833 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Mammalia, Chiroptera, Phyllostomidae, Phyllops falcatus, 1987339099, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987339099
- Discipline:
- Science
- Title:
- Computed tomography voxel dataset for ummz:mammals:124092-Phyllops falcatus-WholeBody
-
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - Skull. Raw - Dataset includes 1601 TIF images (each 1416 x 914 x 1 voxel at 0.0150099083890765 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - Skull. Reconstructed - Dataset includes 654 TIF images (each 1416 x 914 x 1 voxel at 0.015010 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction.
- Keyword:
- Animalia, Chordata, Reptilia, OPHIDIA, COLUBRIDAE, THAMNODYNASTES PALLIDUS, 1987213257, computed tomography, X-ray, and 3D
- Citation to related publication:
- For more information on the original UMMZ specimen, see: https://www.gbif.org/occurrence/1987213257
- Discipline:
- Science
- Title:
- Computed tomography voxel dataset for ummz:herps:246849-THAMNODYNASTES PALLIDUS-Skull
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- Creator:
- Liu, Meichen
- Description:
- We intend to figure out the difference of stress drops, which is a characteristic source parameter, between shallow and deep-focus earthquakes. Significant stress drop difference may shed light on the difference of physical mechanisms of shallow and deep-focus earthquakes, which has been a elusive question. We select from deep-focus earthquakes (> 400 km) in 2000-2018 and obtain their stress drops using P and S waves. We find that stress drops of deep-focus earthquakes are about one order of magnitude higher than that of shallow earthquakes, indicating about one order of magnitude higher shear strength of shallow faults than faults in the mantle. The wide range of stress drops further suggests coexistence of phase transformation and shear-induced melting mechanisms of deep-focus earthquakes.
- Citation to related publication:
- Discipline:
- Science
- Title:
- Stress drop variation of deep-focus earthquakes tables
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- Creator:
- Ozturk, Dogacan S
- Description:
- The modeling research conducted to produce this dataset focuses on the solar wind dynamic pressure drop events and how they affect the Earth's intrinsically coupled Magnetosphere, Ionosphere and Thermosphere systems. This study specifically focuses on the 11 June 2017 event, where the solar wind dynamic pressure dropped significantly following a period of higher pressure. We model the response to this pressure drop using University of Michigan Space Weather Modeling Framework ( http://csem.engin.umich.edu/tools/swmf/). The simulation results were created using BATS-R-US and GITM models. The observational data required for model comparisons were taken from OMNI ( https://omniweb.gsfc.nasa.gov) and CDAWeb ( https://cdaweb.gsfc.nasa.gov/sp_phys/) Databases.
- Keyword:
- GITM, BATS-R-US, Solar wind dynamic pressure, Magnetosphere-Ionosphere-Thermosphere, and MHD
- Citation to related publication:
- Discipline:
- Science
- Title:
- Dataset for the "Response of the Geospace System to the Solar Wind Dynamic Pressure Decrease on 11 June 2017: Numerical Models and Observations"