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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
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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, 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
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- 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
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- 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
-
- Creator:
- Engel, Daniel D. , Evans, Mary Anne, Low, Bobbi S., and Schaeffer, Jeff
- Description:
- This dataset was compiled as an attempt to understand how natural resource managers and research ecologists in the Great Lakes region integrate the ecosystem services (ES) paradigm into their work. The following text is the adapted abstract from a thesis associated with this data. Ecosystem services, or the benefits people obtain from ecosystems, have gained much momentum in natural resource management in recent decades as a relatively comprehensive approach to provide quantitative tools for improving decision-making and policy design. However, to date we know little about whether and how natural resource practitioners, from natural resource managers to research ecologists (hereafter managers and ecologists respectively), have adopted the ES paradigm into their respective work. Here, we addressed this knowledge gap by asking managers and ecologists about whether and how they have adopted the ES paradigm into their respective work. First, we surveyed federal, state, provincial and tribal managers in the Great Lakes region about their perception and use of ES as well as the relevance of specific services to their work. Although results indicate that fewer than 31% of the managers said they currently consider economic values of ES, 79% of managers said they would use economic information on ES if they had access to it. Additionally, managers reported that ES-related information was generally inadequate for their resource management needs. We also assessed managers by dividing them into identifiable groups (e.g. managers working in different types of government agencies or administrative levels) to evaluate differential ES integration. Overall, results suggest a desire among managers to transition from considering ES concepts in their management practices to quantifying economic metrics, indicating a need for practical and accessible valuation techniques. Due to a sample of opportunity at the USGS Great Lakes Science Center (GLSC), we also evaluated GLSC research ecologists’ integration of the ES paradigm because they play an important role by contributing requisite ecological knowledge for ES models. Managers and ecologists almost unanimously agreed that it was appropriate to consider ES in resource management and also showed convergence on the high priority ES. However, ecologists appeared to overestimate the adequacy of ES-related information they provide as managers reported the information was inadequate for their needs. This divergence may reflect an underrepresentation of ecological economists in this system who can aid in translating ecological models into estimates of human well-being. As a note, the dataset for the research ecologists has had some data removed as it could be considered personally identifiable information due to the small sample size in that population. The surveys associated with both datasets have also been included in PDF format. Curation Notes: Three files were added to the data set on Dec 21, 2017. Two csv files: "Ecosystem services and Research Ecologists - Data Index.csv" and "Ecosystem services and Research Managers - Data Index.csv" and one text file: "Ecosystem Services Adoption Readme.txt". The file names of the original four files were altered to replace an ampersand with the word "and".
- Keyword:
- Research Ecologist, Decision-Making, Ecosystem Services, Natural Resource Management, Paradigm Adoption, and Ecological Economics
- Citation to related publication:
- Engel, D.D., Evans, M.A., Low, B.S., Schaeffer, J. (2017) “Understanding Ecosystem Services Adoption by Natural Resource Managers and Research Ecologists.” Journal of Great Lakes Research, 43(3), 169-179. https://doi.org/10.1016/j.jglr.2017.01.005
- Discipline:
- Social Sciences and Science
- Title:
- Understanding Ecosystem Services Adoption by Natural Resource Managers and Research Ecologists: Survey Data
-
- Creator:
- University of Michigan Museum of Zoology
- Description:
- Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - WholeBody. Raw - Dataset includes 1601 TIF images (each 2000 x 2000 x 1 voxel at 0.0327439804971583 mm resolution, derived from 1601 scan projections), xtek and vgi files for volume reconstruction. and Scan of specimen ummz:herps:246849 (THAMNODYNASTES PALLIDUS) - WholeBody. Reconstructed - Dataset includes 1240 TIF images (each 2000 x 2000 x 1 voxel at 0.032744 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-WholeBody