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Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines
<p><strong>Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines</strong></p> <p><strong>Versions:</strong></p> <p>Version 1.1 This one:</p> <ul> <li>updated region names</li> </ul> <p>Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a></p> <p>This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines.</p> <p>The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a></p> <p>An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a></p> <p>Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a></p> <p><strong>Authors</strong></p> <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> <p><strong>Contents</strong></p> <p>This data and code set contains the following directories:</p> <p><em>Results</em></p> <p>The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report.</p> <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> <p>These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code.</p> <p><em>Code</em></p> <p>The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories.</p> <p>To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing:</p> <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> <p>This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104.</p> <p>Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory.</p> <p>The folder contains the following files:</p> <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> <p><em>Data</em></p> <p>The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following:</p> <p>Directories:</p> <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> <p>Files:</p> <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> <p><em>GMT</em></p> <p>This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.</p>
Subset of global model sea level data for "Challenges, Advances and Opportunities in Regional Sea Level Projections: the Role of Ocean-shelf Dynamics"
<p>Monthly sea surface height above the geoid data in NW European seas from six global simulations using the NEMO ocean model (https://www.nemo-ocean.eu/) for 1990 to 2009</p> <p><strong>ORCA0083_DFS_NWS_ssh_1990_2009, ORCA025_DFS_NWS_ssh_1990_2009, ORCA1_DFS_NWS_ssh_1990_2009,</strong> are the N006 simulation set created by Andrew Coward and the NOC Marine Systems Modelling team as used by:</p> <p>Baker et al 2022 Biological Carbon Pump Sequestration Efficiency in the North Atlantic: A Leaky or a Long-Term Sink? Global Biogeochemical Cycles <a href="https://doi.org/10.1029/2021GB007286">https://doi.org/10.1029/2021GB007286</a>,</p> <p>Wilson, C. <em>et al.</em> 2021 Significant variability of structure and predictability of Arctic Ocean surface pathways affects basinwide connectivity. <em>Commun. Earth Environ.</em> <strong>2</strong>, 164. <a href="https://doi.org/10.1038/s43247-021-00237-0">https://doi.org/10.1038/s43247-021-00237-0</a> (2021).</p> <p>These simulations are forced by the Drakkar Forcing Set 5.2 (DFS) and initialised at 1958, with a nominal 1/12, 1/4 and 1 degree resolution. See references for further model details.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009, ORCA025_JRA_tides_NWS_ssh_1990_2009, ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009, </strong>are new simulations produced by Chris Wilson, James Harle and the Shelf Enabled NEMO team. All are forced by the JRA reanalysis, initialised in 1976.</p> <p><strong>ORCA025_JRA_NWS_ssh_1990_2009</strong> is a reference run based on GO9, an evolution of the Joint Marine Modelling Programme configuration described by Storkey et al 2018 UK Global Ocean GO6 and GO7: a traceable hierarchy of model resolutions, Geoscientific Model Development https://gmd.copernicus.org/articles/11/3187/2018/</p> <p><strong>ORCA025_JRA_tides_NWS_ssh_1990_2009</strong> adds explicit tides to this.</p> <p><strong>ORCA025_JRA_ShelfPhysics_NWS_ssh_1990_2009</strong> adds tides, Generic Length Scale Mixing and Multi-envelope vertical coordinates</p> <p>Details of these simulations can be found here:</p> <p>https://github.com/NOC-MSM/SE-NEMO </p>
PERCEIVE: WP2: Evaluation of EU citizens' perception of the EU project: Task2.1: Data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables</p> <p>2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)</p> <p>3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)</p> <p>4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)</p> <p>5. codebook for all regional data</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Surface hourly measurement data of O3, NO2 and PM2.5 for "Large modeling uncertainty in projecting decadal surface ozone changes over urban and industrial regions of China"
<p>Surface hourly measurement data of O3, NO2 and PM2.5 during summer of 2017.</p> <p>In the .csv files, the first column contains the ID for each measurement site. "lon", "lat" are longitude and latitude, respectively.</p> <p>Date format is "YYYYMMDD_hour".</p>
Forest net biome exchange and carbon stock projections by the regions of mainland Finland
<p>Forest net biome exchange and carbon stock projections with uncertainty by regions of the mainland Finland over period 2015-2050. Projections under three climate scenarios, RCP2.6, RCP4.5, and RCP8.5, and four harvest scenarios - BaseHarv (historical average harvest level of the years 2015-2021), slightly more intensive harvests MaxHarv (1.2 x BaseHarv), lower harvest intensity LowHarv (0.6 x BaseHarv) and no harvests after the year 2021 NoHarv - for each administrative region (NUTS3), and as aggregated from all the regions to the whole country, are given. The files contain mean value and 2.5%, 5%, 25%, 75%, 95% and 97.5% quantiles of the average Net Biome emissions (file <em>NBEave.xlsx</em>) and total carbon stock (file <em>Cstockave.xlsx</em>). The forest areas (forest land and poorly productive forest land, excluding undrained peatlands), and areas of mineral soils and drained organic soils for each region and the whole mainland of Finland are given in file <em>areas.xlsx</em>.</p> <p>Description of the used data and methods are given in article:<br> Junttila, V., Minunno, F., Peltoniemi, M. <em>et al.</em> Quantification of forest carbon flux and stock uncertainties under climate change and their use in regionally explicit decision making: Case study in Finland. <em>Ambio</em> (2023). <a href="https://doi.org/10.1007/s13280-023-01906-4">https://doi.org/10.1007/s13280-023-01906-4</a></p>
Data from: Projected land use changes will cause water quality degradation at drinking water intakes across a regional watershed
Open the record for dataset details and reuse information.
GEOLAB Project FROSPER: FROst heaving soils-Solar Panel foundations interaction in cold European Regions: an experimental study
<p>The objective of the proposed project is to investigate the<strong> resilience of solar panels foundations in cold climates</strong>. The efficiency of solar panels is overall better in cold climates, when the direct sunlight is available, due to the lower temperature-induced dispersion. For this reason, large solar fields tend to be installed in cold regions, such as northern Europe or Canada. A solar field with <i>e.g.</i>, 10 MW, requires a great number of solar panels (~6000) whose foundations are generally<strong> steel piles</strong> driven into the soil down to a depth of 2÷5 m below the ground surface. In the cold regions, the shallow layers of soil are periodically subjected to <strong>freezing thus to the frost-heaving phenomena</strong>. The latter can increase the <strong>risk of uplift failure mechanism of the pile foundation</strong> compromising the exercise of the entire solar panel row.</p><p>This project aims to investigate the interaction between saturated soils and solar panel foundations under frozen conditions in <strong>scaled centrifuge models</strong>. The steel piles will be at first driven into the saturated soil sample, then a set of the <strong>freezing-thawing cycles</strong> will be reproduced in the centrifuge. <strong>Possible practical interventions to reduce the soil frost-heaving effects</strong> will also be explored, as the use of protective insulating mantel at the ground surface all around the head of the pile.</p><p>This project will represent a breakthrough in the understanding of the solar panel foundations behaviour, allowing a deeper insight into possible uplift failure mechanisms. The motivation of the study lies in the reduction of the costs and in the resilience improvement of critical infrastructures for the generation of energy in the EU territories. </p>
Data for the paper Constraints on regional projections of mean and extreme precipitation under warming
<p>Data for the paper " Constraints on regional projections of mean and extreme precipitation under warming"</p>
Dataset of publicly funded R&D projects in the Valencian region
<p>The excel file contains information on R&D collaborations in the Valencian region, which received funding from AVI or IVACE in the time period 2016-2022. The following open calls are taken into consideration: (1) Strategic projects in cooperation, (2) Consolidation of the business value chain, (3) R&D in cooperation.</p> <p>The data was collected as part of Marie Skłodowska-Curie Policies for Smart Specialisation (POLISS) research network (<a href="https://poliss.eu/" rel="nofollow">https://poliss.eu</a>). This project has received funding from the European’s Union H2020 Research and Innovation Programme under Grant Agreement No 860887.</p>
End of century temperature and precipitation projections (SSP2-4.5 and SSP5-8.5) for 490 grape-growing regions
<p>Spreadsheet with data (mean maximum temperature, mean minimum temperature and precipitation) extracted from WorldClim 2.1 maps (https://www.worldclim.org/) for the location of each grape-growing region, for each month of the year, for the 5 time periods and the projections SSP2-4.5 and SSP5-8.5.</p> <p>This dataset supports the figures created for the review published in Journal of Experimental Botany entitled: </p> <p><strong><span>Toward understanding grapevine responses to climate change: a multistress and holistic approach.</span></strong></p> <p><span><a href="mailto:johannml@unav.es"><span><span>Johann Martínez</span></span></a><a href="mailto:johannml@unav.es"><span><span>-</span></span></a><a href="mailto:johannml@unav.es"><span><span>Lüscher</span></span></a></span><span>*<sup>1</sup>, José Tomás Matus<sup>2</sup>, Eric Gom</span><span>è<span>s<sup>3</sup>, Inmaculada Pascual<sup>1</sup></span></span></p> <p><sup><span>2 </span></sup><span>Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, 46980 Paterna, Valencia, Spain.</span></p> <p><sup><span>3 </span></sup><span>EGFV, Université de Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV, F-33882 Villenave d’Ornon, France</span></p> <p><span>*Corresponding author’s email: </span><span><a href="mailto:johannml@unav.es"><span>johannml@unav.es</span></a></span></p> <p> </p> <p> </p>
Validation Data used for manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model"
<p>those are the processed data that used for model-data comparison in the manuscript "Climate Projections over the Great Lakes Region: Using Two-way Coupling of a Regional Climate Model with a 3-D Lake Model", including Lake Surface Temperature and Lake Surface Ice Cover from Great Lakes Surface Environmental Analysis (GLSEA), Surface Air temperature and Precipitation from Climatic Research Unit (CRU). </p>
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46. in Muridae
Distribution. NE Egypt (coastal region of Sinai), S Israel (Negev Desert), and Palestine. Descriptive notes. Head-body 130-170 mm, tail 120-180 mm, ear 17-22 mm, hindfoot 30-41 mm; weight 125-275 g. A medium-sized jird, Buxton's Jird has tail of about same length as head-body length and partially hairy soles of hindfeet. Bicolored tail ends with well-developed pencil of black hairs. Dorsal pelage is reddish sandy, diffusely speckled with black hairs, and ventral is white. Enlarged tympanic bullae project over back of skull and represent c.35-37% ofskull length. Karyotype 2n = 46.
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570. in Species discovery and diversity in Lobocriconema (Criconematidae: Nematoda) and related plant-parasitic nematodes from North American ecoregions
Few juveniles or males were collected. Only four males from groups 7, 8, 9, and 11, all in clade D, were included in the dataset. The male in Fig. 13E–H conforms to the general morphological description of males in Lobocriconema with an undifferentiated labial region, the absence of a stylet, a degenerate pharyngeal region, a FIGURE 7. SEM images of specimens representing clades D (A–H) and B (I). NID numbers are associated with unique specimens, all are females except image C. A) Lobocriconema sp., face view with conspicuous labial disc surrounded by irregular labial structure, Nine-Mile Prairie, Nebraska, NID 4533. B) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Big Thicket National Preserve, Texas, NID 4560. C) Lobocriconema sp., juvenile, head with visible submedian lobes, body scales with fine terminal projections, Spring Creek Prairie, Nebraska, NID 4514. D) Lobocriconema sp., face view lacking submedian lobes and displaying subcuticular labial structure, Nine-Mile Prairie, Nebraska, NID 4527 E) Lobocriconema sp., cephalic profile with protruding stylet, Nine-Mile Prairie, Nebraska, NID 4529. F) Lobocriconema sp., head profile lacking submedian lobes, Tunica Hills, Louisiana, NID 4574. G) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4533. H) Lobocriconema sp., tail with closed vulva, Nine-Mile Prairie, Nebraska, NID 4526. I) Lobocriconema sp., face view lacking submedian lobes, Great Smoky Mountains National Park, Purchase Knob, NID 4570.
Supplementary material 1 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/ib.4.14916
Relations between the 4 tables of the MS Access database used to store the data of the project and respective fields : Data type: Image
Supplementary material 3 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/ib.4.14916
References considered for the bibliographic records : Data type: (measurement/occurence/multimedia/etc.)
Supplementary material 1 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
Relations between the 4 tables of the MS Access database used to store the data of the project and respective fields : Data type: Image
Supplementary material 3 from: Croce A, Nazzaro R (2017) An atlas of orchids distribution in the Campania region (Italy), a citizen science project for the most charming plant family. Italian Botanist 4: 15-32. https://doi.org/10.3897/italianbotanist.4.14916
References considered for the bibliographic records : Data type: (measurement/occurence/multimedia/etc.)
Future Projections and Life Cycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability
<p>The dataset presents the findings of the study "Future Projections and Lifecycle Assessment of End-of-life Tires to Energy Conversion in Hong Kong: Environmental, Climate and Energy Benefits for Regional Sustainability". The data results are contained in the files "Results_data.xlsx" and "LCIs and LCA results.zip," while the "Figures data.xlsx" file includes the data needed for plotting. </p>
Global and regional projections of the economic burden of Asthma: A value of statistical life approach
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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.
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DANDI Archive for NWB datasets
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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
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