Search Constraints

You searched for: Discipline Science Remove constraint Discipline: Science

Search Results

  1. Large-eddy simulation (LES) model simulations

    Work
    Title: Large-eddy simulation (LES) model simulations
    Creator: Steiner, Allison and Li, Yang
    Description: Case 2 of Li et al. (2016) LES simulations for the DISCOVER-AQ 11 campaign, including three different grid resolutions (96, 197 and 320 grid cell resolutions), plus simulations at the 192 grid resolution with and without aqueous chemistry
  2. Large-eddy simulation of BVOC during the 2011 DISCOVER-AQ

    Work
    Title: Large-eddy simulation of BVOC during the 2011 DISCOVER-AQ
    Creator: Li, Yang and Steiner, Allison
    Description: Case 1: A fair weather condition; Case 2: A convective event; Case 3: A polluted event with high temperature and convection
  3. Understanding Ecosystem Services Adoption by Natural Resource Managers and Research Ecologists: Survey Data

    0p0966902?file=thumbnail
    Title: Understanding Ecosystem Services Adoption by Natural Resource Managers and Research Ecologists: Survey Data
    Creator: Schaeffer, Jeff, Engel, Daniel D, Low, Bobbi S, and Evans, Mary Anne
    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 ecologist 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, both CSV files in this dataset have two tabs: 1) the raw data, and 2) an index describing each column. 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.
  4. Regional Climate Model simulations

    Work
    Title: Regional Climate Model simulations
    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. "
  5. Data from: Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality

    Work
    Title: Data from: Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality
    Creator: Blesh, Jennifer
    Description: This dataset contains three data files used in: Blesh, J. 2017. Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality. Journal of Applied Ecology. There are also three corresponding metadata files. The file “Ecosystem_functions_soil_species.xls” contains data organized by farm, treatment, replicate block, and species combining the fall and spring sampling time points. These data include aboveground biomass, nitrogen and carbon content, and biological nitrogen fixation for the plant species. The dataset also includes measured soil characteristics for each farm site. The file “Ecosystem_functions_soil_treatment.xls” contains data organized by farm, treatment, and replicate block for the fall and spring sampling time points combined. These data include aboveground biomass, nitrogen and carbon content, and biological nitrogen fixation aggregated by treatment. The dataset also includes measured soil characteristics for each farm site. The file “Traits_unstandardized.xls” contains individual plant trait data, a subset of which were used to calculate an index of functional diversity after they were standardized to have zero mean and unit variance. These data are organized by farm, treatment, replicate block, and species. The corresponding metadata files: “Ecosystem_functions_soil_species_metadata.xls”, “Ecosystem_functions_soil_treatment_metadata.xls”, and “Traits_unstandardized_metadata.xls” provide a detailed description of all variables in each dataset and any abbreviations used.
  6. JGR-Space-2012-Data

    Work
    Title: JGR-Space-2012-Data
    Creator: Moldwin, Mark B
    Description: Tab delimited file containing the records of all papers published in JGR-Space Physics in 2012. The records were pulled from Thomsen-Reuters ISI-Web-of-Science on June 3, 2016 including citations. Gender was identified independently by the creator of the file.
  7. Gelada foraging ecology in the Simien Mountains, Ethiopia

    Work
    Title: Gelada foraging ecology in the Simien Mountains, Ethiopia
    Creator: Jarvey, Julie C
    Description: This includes data used for analysis for the publication: "Graminivory and fallback foods: Annual diet profile of geladas (Theropithecus gelada) living in the Simien Mountains National Park, Ethiopia".
  8. Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR

    Work
    Title: Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR
    Creator: Singh, Deepak
    Description: This includes data for all the plots and maps I created for my paper publication entitled "Improvement of Mars surface snow albedo modeling in LMD Mars GCM with SNICAR".
  9. The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms

    Work
    Title: The KSU-UMD Dataset for Benchmarking for Audio Forensic Algorithms
    Creator: Hafiz Malik and Muhammad Khurran Khan, King Saud University
    Description: Details of the microphone used for data collection, acoustic environment in which data was collected, and naming convention used are provided here. 1 - Microphones Used: The microphones used to collect this dataset belong to 7 different trademarks. Table (1) illustrates the number of used Mics of different trademarks and models. Table 1: Trademarks and models of Mics Mic Trademark Mic Model # of Mics Shure SM-58 3 Electro-Voice RE-20 2 Sennheiser MD-421 3 AKG C 451 2 AKG C 3000 B 2 Neumann KM184 2 Coles 4038 2 The t.bone MB88U 6 Total 22 2- Environment Description: A brief description of the 6 environments in which the dataset was collected is presented here: (i) Soundproof room: a small room (nearly 1.5m × 1.5m × 2m), which is closed and completely isolated. With an exception of a small window in the front side of the room which is made of glass, all the walls of the room are made of wood and covered by a layer of sponge from the inner side, and the floor is covered by carpet. (ii) Class room: standard class room (6m × 5m × 3m). (iii) Lab: small lab (4m × 4m × 3m). All the walls are made of glasses and the floor is covered by carpet. The lab contains 9 computers. (iv) Stairs: is in the second floor. The place of recording is 3m × 5m (v) Parking: is the college parking. (vi) Garden: is an open space outside the buildings. 3- Naming Convention: This set of rules were followed as a naming convention to give each file in the dataset a unique name: (i) The file name is 19 characters long, and consists of 5 sections separated by underscores. (ii) The first section is of 3 characters indicates the Microphone trademark. (iii) The second section of 4 characters indicates the microphone model as in table (2). (iv) The third section of 2 characters indicates a specific microphone within a set of microphones of the same trademark and model, since we have more than one microphone of the same trademark and model. (v) The fourth section of 2 characters indicates the environment, where Soundproof room --> 01 Class room --> 02 Lab --> 03 Stairs --> 04 Parking --> 05 Garden --> 06 (vi) The fifth section of 2 characters indicates the language, where Arabic --> 01 English --> 02 Chinese --> 03 Indonesian --> 04 (vii) The sixth section of 2 characters indicates the speaker. Table 2: Microphones Naming Criteria Original Mic Trademark and model --> Naming Convenient Shure SM-58 --> SHU_0058 Electro-Voice RE-20 --> ELE_0020 Sennheiser MD-421 --> SEN_0421 AKG C 451 --> AKG_0451 AKG C 3000 B --> AKG_3000 Neumann KM184 --> NEU_0184 Coles 4038 --> COL_4038 The t.bone MB88U --> TBO_0088 For example: SEN_0421_02_01_02_03 is an English file recorded by speaker number 3 in the soundproof room using microphone number 2 of Sennheiser MD-421
  10. Influence of invasive quagga mussels, phosphorus loads, and climate on spatial and temporal patterns of productivity in Lake Michigan: A biophysical modeling study

    Work
    Title: Influence of invasive quagga mussels, phosphorus loads, and climate on spatial and temporal patterns of productivity in Lake Michigan: A biophysical modeling study
    Creator: Rowe, Mark D.
    Description: Animation files show the 12-month “baseline” simulations for 2000, 2005, and 2010 (see Table 1 of the paper cited above). temp_1_animation.wmv: Surface temperature Chl_1_animation.wmv: Surface chlorophyll-a PO4_1_animation.wmv: Surface total dissolved phosphorus Detritus_1_animation.wmv: Surface detritus concentration (particulate organic carbon, excluding phytoplankton and zooplankton). Zooplankton_1_animation.wmv: Surface zooplankton carbon concentration MRATION_1_animation.wmv: Rate of food assimilated by mussels, according to the product f_a F_A P in Equation 2, expressed as mg phytoplankton carbon per mg mussel biomass carbon per day × 100%. BIO_M_1_animation.wmv: Simulated mussel biomass in mg ash-free-dry-mass m^-2