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208 results for “EPA”
Water, Soil, Floc, Plant Total Phosphorus, Total Carbon, and Bulk Density data (FCE) from Everglades Protection Area (EPA) from 2004 to 2016
These data are a compillation of data from multiple sources including South Florida Water Management District (SFWMD) DBhydro web database, United States Environment Protection Agency Regional, Environmental Monitoring and Assessment (REMAP), Everglades Soil Mapping (ESM), and Florida Coastal Everglades Long Term Ecological Research (FCE-LTER). The matrix of these data were compiled for soil, surface water, floc, and plants where the nutrients are counted for total phosphorus, total carbon, and bulk density. When downloading the data from DBhydro, only regularly collected samples (SAMP) were included these data. As per DBhydro metadata, the regular samples were collected monthly by grab method throughout the year from 2004 to 2016 for SFWMD monitoring stations across the EPA. All flagged and field quality controlled values were excluded to avoid the duplication of data. In order to maintain the quality assurance/ quality control (QA/QC) the method detection limit for water TP was fixed at 2 µg/L by the SFWMD. This data set were used to assess the decadal trend of TP concentration in surface water and soil in EPA. Available data from 2004 to 2014 was collected for soils and from 2004 to 2016 for water to understand a decade of trends. Both Geographic Information System (GIS) and statistical data analysis were applied to determine changes in water quality and soil chemistry. These data are the basis for Shishir Sarker's Master's thesis.
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
EPA Eastern Lake Survey original data for the Upper Midwest Region 1984
Overton, W. S., P. Kanciruk, L. A. Hook, J. M. Eilers, D. H. Landers, D. F. BRAKKE, R. A. Linthurst, and M. D. DeHaan. 1986. Characteristics of lakes in the Eastern United States. Vol. 2. Lakes sampled and descriptive statistics for physical and chemical variables. US EPA 600/4-86/007B. 369 p. The Eastern Lake Survey-Phase I (ELS-I), conducted in the fall of 1984, was the first part of a long-term effort by the U.S. Environmental Protection Agency known as the National Surface Water Survey. It was designed to synoptically quantify the acid-base status of surface waters in the United States in areas expected to exhibit low buffering capacity. The effort was in support of the National Acid Precipitation Assessment Program (NAPAP). The survey involved a three-month field effort in which 1612 probability sample lakes and 186 special interest lakes in the northeast, southeast, and upper midwest regions of the United States were sampled. This dataset includes data on 592 lakes in Michigan, Minnesota and Wisconsin. Number of sites: 592
Geographically paired lake-reservoir dataset derived from the 2007 USA EPA National Lakes Assessment
Climate change poses a significant threat to lake and reservoir ecosystems, though the exact nature of these threats may differ between lakes and reservoirs. To assess differences between lakes and reservoirs that may influence their response to climate change, we compared catchment and waterbody attributes of 132 geographically paired lakes and reservoirs from the 2007 United States Environmental Protection Agencys National Lakes Assessment (NLA) dataset. The data include the NLA IDs of each waterbody and their elevation, catchment area, surface area, perimeter, maximum depth, residence time, Secchi disk depth, surface temperature, and bottom temperature. Residence time data was collected from estimates generated by Brooks, J.R., J.J. Gibson, S.J. Birks, M.H. Weber, K.D. Rodecap, J.L. Stoddard. 2014. Stable isotope estimates of evaporation: inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnology and Oceanography 59(6):2150-2165.
EPA Integrated Planning Model (IPM) National Electric Energy Data System (NEEDS) database
EPA is making the latest power sector modeling platform available, including the associated input data and modeling assumptions, outputs, and documentation.
Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI)
<h2><strong>About</strong></h2><p>The gridded EPA U.S. anthropogenic methane greenhouse gas inventory (gridded methane GHGI) includes spatially and temporally resolved (gridded) maps of annual anthropogenic methane emissions (0.1°×0.1°) for the contiguous United States (CONUS). Total gridded methane emissions for each emission source sector are consistent with national annual U.S. anthropogenic methane emissions reported in the U.S. EPA <a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks"><i>Inventory of U.S. Greenhouse Gas Emissions and Sinks</i></a> (U.S. GHGI). More information is available on the <a href="https://www.epa.gov/ghgemissions/gridded-methane-emissions">U.S. EPA website</a>. </p><p>This repository accompanies the peer-reviewed manuscript <a href="https://pubs.acs.org/doi/10.1021/acs.est.3c05138"><i>Maasakkers, et al., 2023</i></a>. Data in this repository are an update to the gridded GHGI version 1, previously described in <a href="https://pubs.acs.org/doi/10.1021/acs.est.6b02878"><i>Maasakkers, et al., 2016</i></a> and available on the <a href="https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions">U.S. EPA website</a>. </p><h4><strong>This repository contains two data products:</strong></h4><ol><li><strong>Gridded GHGI v2 (main product; 2 file types). </strong>Gridded annual U.S. anthropogenic methane emissions for 2012-2018 for 26 source categories (gridded GHGI). This dataset is developed to be consistent with the national U.S. GHGI published in 2020 (<i>U.S. EPA, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2020. U.S. Environmental Protection Agency, 2020, EPA 430-R-22-003, </i><a href="https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018"><i>https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks-1990-2018</i></a>).<br><br>This dataset includes 2 file types: <br>a. Annual methane emission fluxes for 26 inventory source categories. Files contain one year of emissions per source category and include a time dimension variable to make the data suitable (COARDS-compliant) for atmospheric models.<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br><i> - Gridded_GHGI_Methane_v2_YYYY.nc</i><br><br>b. Monthly emission scaling factors for inventory source categories with strong interannual variability (see 'Data Details' below). To use these factors to calculate absolute monthly methane emission fluxes, multiply the scaling factors for each relevant source category by the corresponding emission fluxes in the annual flux files.<br> (Dimensions: latitude x longitude x month; units: dimensionless): <br> - <i>Gridded_GHGI_Methane_v2_Monthly_Scale_Factors_YYYY.nc</i><br> </li><li><strong>Gridded GHGI v2 Express Extension (1 file type).</strong> The v2 Express Extension includes gridded annual U.S. anthropogenic methane emissions for 2012-2020 for 27 source categories (one additional source category compared to the main v2 dataset above). This dataset is developed to be consistent with total methane emissions from the U.S. GHGI published in 2022 (<i>EPA (2022) Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020. U.S. Environmental Protection Agency, EPA 430-R-22-003. </i><a href="https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020"><i>https://www.epa.gov/ghgemissions/draft-inventory-us-greenhouse-gas-emissionsand-sinks-1990-2020</i></a><i>)</i>. <br><br><i>**Note**:</i><strong> </strong>This dataset is <strong>not</strong> a full update to the main gridded GHGI v2 product. To quickly incorporate more recent national methane emission estimates into gridded products, national methane emissions from a more recent U.S. GHGI were spatially allocated (i.e., gridded) using the annual source-specific spatial emission patterns developed for the 2012-2018 main v2 product. Emissions for years 2019 and 2020 were allocated using 2018 spatial patterns.<br><br>This dataset includes 1 file type:<br>a. Annual emission files<br> (Dimensions: latitude x longitude x time; units: molecules CH4 cm-2 s-1):<br> - <i>Express_Extension_Gridded_GHGI_Methane_v2_YYYY.nc</i></li></ol><p><i>--------------------------------------------------</i></p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with EPA (POSES 1-4)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_EPA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of eicosapentaenoic acid (EPA) in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 4 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 3 times, hence there are 3 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Agriculture_2022_WAM-EPA-Ireland-2024
<p><span>2024 Excel workbook for the Agriculture sector under the With Additional Measures (WAM) scenario for 1990–2022, as compiled by the Environmental Protection Agency (Ireland) in support of Ireland's annual GHG inventory and projection submissions. The workbook also includes projected time series for data up to 2050, based on Teagasc modelling using economic projections of agricultural production.</span></p>
EPA Emissions Inventory 2014
<p>This dataset contains data from the <a href="https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-data">EPA National Emissions Inventory from 2014</a>, separated by sector, in shapefile format. Each line within each file contains the amount of each pollutant (VOC, NOx, SOx, NH3, or PM2.5) in micrograms per second and the coordinates for which that emission is located (X,Y).</p>
EPA Pedra do Cavalo its surrounding (by MapBiomas)
<p>This video show a EPA of <em>Pedra do Cavalo</em> its sourrodings from 1992 at 2021 (by year), by MapBiomas classification.</p>
Agriculture_2021_WAM_EPA (2023)
<p>2023 Excel workbook for the Agriculture sector With Additional Measures (WAM) for 1990–2021, as compiled by the Environmental Protection Agency (Ireland) in support of Ireland's annual GHG inventory and projection submissions. The workbook also includes projected time series for data up to 2050 based on Teagasc modelling based on economic projections of agricultural production.</p>
PRevention Using EPA Against coloREctal Cancer
ClinicalTrials.gov study NCT04216251. IPD Sharing: YES. Countries: 1. Publications: 0.
InMAP Source-Receptor PM2.5 Concentrations from 2014 EPA NEI
<p>These are the PM2.5 concentration datasets computed using the InMAP source-receptor (SR) matrices, with the 2014 EPA National Emissions Inventory (NEI) as inputs.</p> <p>There are 120 total files in this zip file:</p> <ol> <li>19 sectors * 5 pollutants (NH<sub>4</sub>, NO<sub>3</sub>, primary PM<sub>2.5</sub>, SO<sub>4</sub>, SOA) = 95 files, representing the <strong>contribution of each pollutant in each of those sectors to the total PM<sub>2.5</sub> concentration</strong>; these files are directly calculated from the NEI and SR matrices;</li> <li>An additional 19 files representing <strong>each sector's total contribution to the PM<sub>2.5</sub> concentration</strong>, calculated by summing together the contributions from all 5 of the pollutants for each of the 19 sectors,</li> <li>An additional 5 files representing the <strong>overall PM<sub>2.5</sub> concentration across all sectors</strong>, calculated by summing together the contributions from all of the 19 sectors, for each of the 5 pollutants,</li> <li>An additional 1 file representing the <strong>overall total PM<sub>2.5</sub> concentration across all sectors and pollutants</strong>, calculated by summing together the contributions of all 19 sectors and all 5 pollutants.</li> </ol> <p>Every file is saved as a .mat file and can be loaded into MATLAB using the load() function. Each .mat file has dimensions of 52411x1, where each value represents a PM<sub>2.5</sub> concentration in one of the 52411 InMAP grid cells. You will need to download the InMAP grid shapefile in order to project and map this data.</p>
Figure 5 in Gall-inducing arthropods in a Neotropical savanna area in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil): effects of plant species richness and super-host abundance
Figure 5. Gall morphotypes in host plants in an area of Neotropical savanna in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil). (A-B) Vochysiaceae = Qualea grandiflora, (C-D) Vochysiaceae = Qualea parviflora. of gall morphotypes per host plant species was 1.37. species was significantly influenced both by plant spe- Gall-inducing arthropods belonged to Acari, Diptera, cies richness (p = 0.011) and abundance of super-host Hemiptera and Lepidoptera. The most important gall-in- plants (p = 0.020) (Table 2). We found that galling speducing arthropods were Cecidomyiidae (Diptera) having cies per plant species was negatively affected by plant induced 34 (85.0%) gall morphotypes. In the sequence species richness (Fig. 6) and positively affected by abunwere Eriophyidae (Acari) inducing three (7.5%) mor- dance of super-host plants (Fig. 7). photypes, Psylloidea (Hemiptera) inducing two (5.0%) morphotypes, and Lepidoptera inducing a single (2.5%) morphotype. DISCUSSION The plant families that showed the greatest richness of arthropod galls were Fabaceae, with 16 (40.0%) mor- The number of galling species observed in the area photypes, Vochysiaceae with four (10.0%) and Myrtaceae of EPA of Rio Pandeiros (40 morphotypes) is intermediary (7.5%) with three morphotypes (Table 1). The plant spe- compared to other studies performed in Neotropical sacies Copaifera oblongifolia and Andira humilis Mart. ex vannas (Table 3). Forexample, Urso-Guimarãesetal. (2003) Benth. (Fabaceae) were the most important host spe- recorded only 22 gall morphotypes in cerrado fragments, cies with five and three morphotypes, respectively. All rupestrian field and gallery forest in Delfinópolis, Minas other host plant species had two or one morphotypes Gerais State. In other study, Maia & Fernandes (2004) re- (Table 1). Most of the arthropod galls occurred on leaves corded 137 morphotypes of insect galls in an area of rup- (90.0%), and was lenticular (45.0%), green (52.5%) and estrian fields and cerrado in the Serra de São José, Minas glabrous (82.5%). Gerais. These numbers extremely variable in the diversi- Galling species richness was not affected by none of ty of galling species can be explained by several factors, explanatory variables (Table 2), despite the tendency of among which are different sampling efforts employed in a positive effect of abundance of super-hosts on the gall the studies, as well as variations in the structural characrichness (p = 0.057). Already the galling species per plant teristics and diversity of the studied vegetation. The stan-
Figure 4 in Gall-inducing arthropods in a Neotropical savanna area in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil): effects of plant species richness and super-host abundance
Figure 4. Gall morphotypes in host plants in an area of Neotropical savanna in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil). (A) Fabaceae = Tachigali alba, (B) Malpighiaceae = Malpighiaceae sp., (C) Malvaceae = Eriotheca gracilipes, (D) Myrtaceae = Eugenia dysenterica, (E) Myrtaceae = Eugenia sp., (F) Myrtaceae = Psidium sp., (G) Ochnaceae = Ouratea hexasperma, (H) Ochnaceae = Ouratea spectabilis.
Figure 3 in Gall-inducing arthropods in a Neotropical savanna area in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil): effects of plant species richness and super-host abundance
Figure 3. Gall morphotypes in host plants in an area of Neotropical savanna in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil). (A-D) Fabaceae = Copaifera oblongifolia, (E) Fabaceae = Hymenaea stigonocarpa, (F-G) Fabaceae = Machaerium opacum, (H) Fabaceae = Sclerolobium denudatum.
Figure 2 in Gall-inducing arthropods in a Neotropical savanna area in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil): effects of plant species richness and super-host abundance
Figure 2. Gall morphotypes in host plants in an area of Neotropical savanna in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil). (A) Dilleniaceae = Davilla elliptica, (B) Ebenaceae = Diospyros hispida, (C) Erythroxylaceae = Erythroxylum suberosum, (D-F) Fabaceae = Andira humilis, (G) Fabaceae = Copaifera luetzelburgii, (H) Fabaceae = Copaifera oblongifolia.
Figure 1 in Gall-inducing arthropods in a Neotropical savanna area in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil): effects of plant species richness and super-host abundance
Figure 1. Gall morphotypes in host plants in an area of Neotropical savanna in the EPA of Rio Pandeiros (Bonito de Minas, MG, Brazil). (A) Anacardiaceae =Anacardium humile, (B) Bignoniaceae = Handroanthus ochraceus, (C) Calophyllaceae = Kielmeyera speciosa, (D) Caryocaraceae = Caryocar brasiliense, (E) Combretaceae = Terminalia fagifolia, (F-G) Connaraceae = Connarus suberosus, (H) Dilleniaceae = Davilla elliptica.
2014 EPA National Emissions Inventory allocated to the grid cells of InMAP Source-Receptor Matrix
<p>This dataset is the 2014 EPA National Emissions Inventory (NEI) v1 allocated to the individual grid cells of InMAP Source-Receptor Matrix (<a href="https://zenodo.org/record/2589760#.Yds79GjMI2w">ISRM</a>). The source types is classified by EPA Source Classification Codes (SCCs). The dataset includes emissions of both primary and secondary PM<sub>2.5</sub>. Secondary PM<sub>2.5</sub> includes four precursors: NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC. The detailed description of emission processing is in <a href="https://doi.org/10.1073/pnas.1818859116">Tessum et al. (2019</a>).</p> <p>Each shapefile in the dataset is in the format of input file of <a href="http://spatialmodel.com/inmap/">InMAP</a>/ISRM, which includes the emission amounts of five pollutants (Primary PM<sub>2.5</sub>, NO<sub>x</sub>, SO<sub>x</sub>, NH3, and VOC), stack information (height, diameter, temperature, and velocity), and SCCs. The unit of emissions is <span class="math-tex">\(\mu g/s\)</span>. (If these emissions are used directly with the ISRM, the resulting outputs will be concentrations, in units of <span class="math-tex">\(\mu g/m^3\)</span>.)</p>
S22 | EPACONS | US EPA Consumer Product Suspect List
<p>This is the collection associated with list S22 EPACONS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S22</p> <p>EPACONS</p> <p><strong>US EPA Consumer Product Suspect List </strong></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/010318Update/es7b04781_si_002.xlsx">Original File XLSX</a> (1/03/2018)<br> <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/010318Update/EPA_ConsumerProductSuspects_01032018.xlsx">Merged Suspects XLSX</a> (1/03/2018)<br> <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/010318Update/EPA_ConsumerProductSuspects_01032018.csv">Merged Suspects CSV</a> (1/03/2018)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/epacons">EPACONS List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/010318Update/EPA_ConsumerProductSuspects_InChIKeys_01032018.txt">Merged Consumer Product InChIKeys</a> (1/03/2018)</p> <p>Suspects in supporting information from Phillips <em>et al. </em>2018, DOI: <a href="https://pubs.acs.org/doi/abs/10.1021/acs.est.7b04781">10.1021/acs.est.7b04781</a> - Suspect Screening Analysis of Chemicals in Consumer Products with GCxGC-TOF/MS matched with NIST.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.