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253 results for “regional level”
Figure 1. A in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 1. A view of the impact of the wind waves on the newly dry bottom at the shoreline in the center of Aktau on 20 October 2022. Photo by Andrey Kostianoy.
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity. in Generically Speaking, A Survey On Neogene Rodent Diversity At The Genus Level In The Now Database
Text-fig. 1. Result of cumulative random counting of MN 5 localities in central Europe and the Iberian Peninsula. Ten simulations were run for each area. a. Results of the count including the average in bold, showing the clearly lower diversity in IB. b. The average lines standardized, showing similar patterns in the two areas. Note that in the simulation around thirty localities were needed to capture 80 % of the regional diversity.
Text-fig. 4. Synchrotron radiation X-ray tomographic microscopy orthoslices of flowers of Lambertiflora virginiense gen. et sp. nov. from the Early Cretaceous Puddledock locality, Virginia, USA (holotype, PP53796, Puddledock sample 081). White dots – tepals, yellow dots – stamens or staminodes, red dot – central conical gynoecial region. a) Flower in longitudinal section showing elongated overlapping tepals, remains of probable poorly developed stamens or staminodes and probable poorly developed carpels on the central conical gynoecial region of the receptacle (orthoslice yz0454); b) Flower in transverse section showing rhomboidal bases of 30 tepals, nine poorly developed stamens or staminodes, and the central poorly differentiated gynoecial region of the receptacle (orthoslice xy1160); c) Flower in transverse section at the level of the floral receptacle showing 30 tepals, nine of the poorly developed stamens or staminodes, and the central poorly differentiated gynoecial region of the receptacle (orthoslice xy1250). Scale bars = 250 µm (a–c). in Multiparted, Apocarpous Flowers From The Early Cretaceous Of Eastern North America And Portugal
Text-fig. 4. Synchrotron radiation X-ray tomographic microscopy orthoslices of flowers of Lambertiflora virginiense gen. et sp. nov. from the Early Cretaceous Puddledock locality, Virginia, USA (holotype, PP53796, Puddledock sample 081). White dots – tepals, yellow dots – stamens or staminodes, red dot – central conical gynoecial region. a) Flower in longitudinal section showing elongated overlapping tepals, remains of probable poorly developed stamens or staminodes and probable poorly developed carpels on the central conical gynoecial region of the receptacle (orthoslice yz0454); b) Flower in transverse section showing rhomboidal bases of 30 tepals, nine poorly developed stamens or staminodes, and the central poorly differentiated gynoecial region of the receptacle (orthoslice xy1160); c) Flower in transverse section at the level of the floral receptacle showing 30 tepals, nine of the poorly developed stamens or staminodes, and the central poorly differentiated gynoecial region of the receptacle (orthoslice xy1250). Scale bars = 250 µm (a–c).
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity.
PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. Orignal PERCEIVE survey data (STATA file)</p> <p>2. description of survey questions, descriptive results (word file)</p> <p>3. EU Deliverable document with descriptive analysis of survey questions</p> <p> </p> <p>***please cite the following when using the microdata:</p> <p>Bauhr, M., & Charron, N. (2020). The EU as a savior and a saint? Corruption and public support for redistribution. <em>Journal of European Public Policy</em>, <em>27</em>(4), 509-527.</p> <p>https://www.tandfonline.com/doi/full/10.1080/13501763.2019.1578816</p>
Farm and regional levels' database used to test the effectiveness of slope and distance from buildings in approximating the pastoral site-use intensity of alpine pastures
<p>The excel file contains the two databases used in the paper “Slope and distance from buildings are easy-to-retrieve proxies for estimating livestock site-use intensity in alpine summer pastures” to test the effectiveness of slope and distance from buildings in approximating the pastoral site-use intensity of alpine pastures.</p> <p>The database in the ‘farm level’ sheet has been used to assess if slope and distance from buildings were good predictors of site-use intensity at farm level, i.e. the number of GPS locations counted within sample units was modelled as a function of the two proxies. Moreover, this database has been used to evaluate if the expected transition of Vegetation Ecological Groups (VEGs) from the shrub-encroached to the nitrophilous ones corresponded to a real site-use intensity gradient as represented by the stocking rates measured through GPS locations, i.e. by modelling the total number of GPS locations within sample units in function VEGs.</p> <p>The database in the ‘Regional level’ sheet has been used to evaluate if the five VEGs were effectively discriminated by distance from buildings and slope. Two models were performed by specifying either slope and distance from buildings as response variables and VEG as fixed factor.</p>
Database of US Regional Sea-level Rise Assessment Reports (Current for 2021)
<p>Database of regional sea-level rise assessment reports in the U.S. The data set includes nearly 400 projections from 31 reports for 54 locations in the U.S. and Puerto Rico, and accompanies the publication "Evaluating Knowledge Gaps in Sea-level Rise Assessments from the United States", Garner et al., <em>Earth's Future</em>. The data set is comprised of the most recent published assessment reports for each location (deadline of December 31<sup>st</sup>, 2021). Fields included in the database are listed below. </p> <p>Though substantial effort was made to ensure that all available and relevant assessment reports were included in the database, it is perhaps inevitable that a small number of reports were overlooked and may not be included here. </p> <p>1) Title of the Assessment Report</p> <p>2) Region of focus for the projection</p> <p>3) Broader geographical region for the projection (U.S. Northeast, U.S. South, or U.S. West)</p> <p>4) Latitude of the projection</p> <p>5) Longitude of the projection</p> <p>6) Lead Author of the report</p> <p>7) Sectors with which the authors are affiliated</p> <p>8) Third-party report flag (Yes = not locally produced, No = locally produced)</p> <p>9) Year the report was published</p> <p>10) Year the previous iteration of the report was published, if applicable</p> <p>11) Methodology of the projection</p> <p>12) Emission scenario used for the project</p> <p>13) Baseline year for the projection</p> <p>14) End year for the projection</p> <p>15) Lower estimate of sea-level rise</p> <p>16) Definition of the lower estimate of sea-level rise</p> <p>17) Central estimate of sea-level rise</p> <p>18) Definition of the central estimate of sea-level rise</p> <p>19) Upper estimate of sea-level rise</p> <p>20) Definition of the upper estimate of sea-level rise</p> <p>21) Vertical Land Motion (Yes = included, No = excluded)</p> <p>22) Land Water Storage (Yes = included, No = excluded)</p> <p>23) Greenland Ice Sheet (Yes = included, No = excluded)</p> <p>24) Antarctic Ice Sheet (Yes = included, No = excluded)</p> <p>25) Glaciers (Yes = included, No = excluded)</p> <p>26) Thermal Expansion (Yes = included, No = excluded)</p> <p>27) Ocean Dynamics (Yes = included, No = excluded)</p> <p>28) Link to the report containing the projection</p> <p>29) Notes relevant to the projection's database entry</p>
A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3' UTR region, denoting expression levels, are associated with Atopic Dermatitis
<p>The publication "A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3’ UTR region, denoting expression levels, are associated with Atopic Dermatitis" contains analysis from two different cohorts: Genetics in Atopic Dermatitis (GAD), which is the main dataset, and Pediatric Eczema Elective Registry (PEER), which is the replication cohort. Included herein are the HLA Class II genotypes for both the GAD and PEER cohorts at 2-field resolution, which forms the basis for the analysis included in the publication. (DOI: 10.3389/fgene.2023.1004138)</p>
Figure |. Hystrignathus splendidus sp. n. female. A Esophageal region, lateral view. B Cephalic end, internal view C Cephalic end, external view D Spines at level of the end of procorpus E Tail, lateral view F Vulva, lateral view G Egg. H Genital tract I Habitus, lateral view. in Two new species of nematode (Oxyurida, Hystrignathidae) parasites of Passalus interstitialis Escholtz, 1829 (Coleoptera, Passalidae) from Cuba and a new locality for Longior similis Morffe, Garcia & Ventosa, 2009
Figure |. Hystrignathus splendidus sp. n. female. A Esophageal region, lateral view. B Cephalic end, internal view C Cephalic end, external view D Spines at level of the end of procorpus E Tail, lateral view F Vulva, lateral view G Egg. H Genital tract I Habitus, lateral view.
Mesofauna and macrofauna densities at species/group level from 2008 to 2020 in three regions in Germany
Open the record for dataset details and reuse information.
ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for the Beijing-Tianjin-Hebei Region
<p>ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). This dataset is generated from MODIS/Terra+Aqua MAIAC AOD products together with other auxiliary data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using the linear mixed effect (LME) model. </p> <p>This is the MODIS/Terra+Aqua monthly 1 km ground-level PM<sub>2.5</sub> dataset in the Beijing-Tianjin-Hebei region from 2000 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.85 and a root-mean-square error (RMSE) of 21.49 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use this dataset for related scientific research, please cite the corresponding reference (Xue et al., 2021, JCP):</p> <p>Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J. Spatiotemporal PM<sub>2.5</sub> variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region, <em>Journal of Cleaner Production</em>, 2021, 279, 123742. https://doi.org/10.1016/j.jclepro.2020.123742</p> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Supporting data to 'Correlations between sea-level components are driven by regional climate change'
<p>Dataset supporting the initial submission of the manuscript 'Correlations between sea-level components are driven by regional climate change'</p>
Data from: How sea-level change mediates genetic divergence in coastal species across regions with varying tectonic and sediment processes
Plate tectonics and sediment processes control regional continental shelf topography. We examine the genetic consequences of how glacial-associated sea-level change interacted with variable near-shore topography since the last glaciation. We reconstructed the size and distribution of areas suitable for tidal estuary formation from the Last Glacial Maximum, ~20 thousand years ago, to present from San Francisco, California, USA (~38 °N) to Reforma, Sinaloa, Mexico (~25 °N). We assessed range-wide genetic structure and diversity of three co-distributed tidal estuarine fishes (California Killifish, Shadow Goby, Longjaw Mudsucker) along ~4,600 km using mitochondrial control region and cytB sequence, and 16–20 microsatellite loci from a total of 524 individuals. Results show that glacial-associated sea-level change limited estuarine habitat to few, widely separated refugia at glacial lowstand, and present-day genetic clades were sourced from specific refugia. Habitat increased during postglacial sea-level rise and refugial populations admixed in newly formed habitats. Continental shelves with active tectonics and/or low sediment supply were steep and hosted fewer, smaller refugia with more genetically differentiated populations than on broader shelves. Approximate Bayesian computation favored the refuge-recolonization scenarios from habitat models over isolation by distance and seaway alternatives, indicating isolation at lowstand is a major diversification mechanism among estuarine (and perhaps other) coastal species. Because sea-level change is a global phenomenon, we suggest this top-down physical control of extirpation-isolation-recolonization may be an important driver of genetic diversification in coastal taxa inhabiting other topographically complex coasts globally during the Mid- to Late Pleistocene and deeper timescales.
Population-level variant frequencies in segmental duplication regions
<p>Variant frequencies in segmental duplication regions analyzed by <a href="https://github.com/PacificBiosciences/paraphase">Paraphase</a> in five ancestral populations.</p>
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>
Data from: Stable isotopes in the different trophic levels reveal regional divergence in dietary habits among Tibetan pastoralists
<p>Understanding geographical variation and driving mechanisms of the dietary habits of Tibetan pastoralists is a key to improving their health status under climate change and globalization. Characterization of diet via isotopic signatures along the length of the local food chain could provide this information. We analyzed δ13C and δ15N of soil, plants, animals and pastoralists at different geographical sites ranging in a gradient of easily accessible to remote areas. The high δ15N values in soil and plants were not recovered in animals and pastoralists, indicating use of external feed and food resources respectively. The mean δ13C (-22.0‰) and δ15N values (6.9‰) of pastoralists indicated diets consisting mainly of native C3 plants and animal products. Pastoralists living in the remotest areas maintained more traditional dietary habits based on native animal products. However, dietary changes of pastoralists were triggered by external food resources and alterations in ecological chain of easily accessible areas.</p>
A chromosome-level genome assembly of the highly heterozygous sea urchin Echinometra sp. EZ reveals adaptation in the regulatory regions of stress response genes
<p><em>Echinometra</em> is the most widespread genus of sea urchin and has been the focus of a wide range of studies in ecology, speciation, and reproduction. However, available genetic data for this genus are generally limited to a few select loci. Here, we present a chromosome-level genome assembly based on 10x Genomics, PacBio, and Hi-C sequencing for <em>Echinometra</em> sp. EZ from the Persian/Arabian Gulf. The genome is assembled into 210 scaffolds totaling 817.8 Mb with an N50 of 39.5 Mb. From this assembly we determined that the <em>E</em>. sp. EZ genome consists of 2n = 42 chromosomes. BUSCO analysis showed that 95.3% of BUSCO genes were complete. ab initio and transcript-informed gene modeling and annotation identified 29,<span>405</span> genes, including a conserved Hox cluster. <em>E.</em> sp. EZ can be found in high-temperature and high-salinity environments, and we therefore compared gene families and transcription factors associated with environmental stress response ("defensome") with other echinoid species with similar high-quality genomic resources. While the number of defensome genes was broadly similar for all species, we identified strong signatures of positive selection in non-coding elements near genes involved in environmental response pathways as well as losses of transcriptions factors important for environmental response. These data provide key insights into the biology of <em>E</em>. sp. EZ as well as the diversification of <em>Echinometra</em> more widely and will serve as a useful tool for the community to explore questions in this taxonomic group and beyond.</p>
Wave climate simulations for Denmark - for paper 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'
<p>This wave climate dataset are the results for the paper titled 'Coinciding storm surge and wave setup: A regional assessment of sea level rise impact'.</p> <p>The operational wave forecasting service provided by DMI-WAM uses the WAM Cycle version 4.5.4, a third-generation spectral wave model. DMI-WAM is used for the wave climate simulations. The meteorological forcing used in this study was obtained from the regional climate model DMI-HIRHAM, developed by the Danish Meteorological Institute (DMI). It is a component of the CORDEX (Coordinated Regional Climate Downscaling Experiment) ensemble in Europe. Regarding the selection of the time frame and IPCC scenarios in our study, we adhered to the recommendations provided by municipalities. Municipalities are keenly interested in obtaining near-future wind wave data for the specific purpose of using them for risk management. Therefore, the examination of forthcoming weather extremes in the near future within the context of the high greenhouse gas emission scenario (RCP8.5 scenario) is of significance within this investigation. We conduct simulations that encompass two distinct time periods: the historical period spanning from 1976 to 2005, and the near-future period from 2041 to 2070. We analyse the WAM model results for wave climate under both present climate conditions (1976-2005) and future climate scenarios (2041-2070) under the RCP8.5 scenario. Furthermore, note that while our wave climate simulations provide valuable insights into the dynamics of wind-induced waves, the mean SLR is not explicitly taken into account. The mean SLR component is considered in the storm surge simulations.</p> <p>Description of files:</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.hist.final.max.nc</a> - Maximum sea level, significant wave height, wave length and slope for the historical period.</p> <p><a href="../api/records/11052226/draft/files/sla.swh.slope.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">sla.swh.slope.rcp85.final.max.MSLR35.nc</a> - Maximum sea level, significant wave height, wave length and slope for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.hist.final.max.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.hist.final.max.nc</a> - Maximum wave setup for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wavesetup.rcp85.final.max.MSLR35.nc/content" target="_blank" rel="noopener noreferrer">wavesetup.rcp85.final.max.MSLR35.nc</a> - Maximum wave setup for the RCP8.5 period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.his.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.his.swh.98p.nc</a> - 2% exceedence of significant wave height for the historical period.</p> <p><a href="../api/records/11052226/draft/files/wam.grib.rcp8.swh.98p.nc/content" target="_blank" rel="noopener noreferrer">wam.grib.rcp8.swh.98p.nc</a> - 2% exceedence of significant wave height for the RCP8.5 period.</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>
Month-to-month proportion of LSD-vaccinated animals at regional level (NUTS3) in south-eastern Europe since January 2016 until November 2017 and reported LSD outbreaks
<p>The European Food Safety Authority (EFSA), under request of the European Commission, performed an epidemiological analysis of the lumpy skin disease (LSD) epidemics based on the data collected from the affected and at-risk Member States and non-EU countries in south-eastern Europe. The video at the link below shows the month-to-month proportion of LSD-vaccinated animals at regional level (NUTS3) since January 2016 until November 2017 and reported LSD outbreaks per month (red dots are the new outbreaks each month, grey dots are past outbreaks).</p> <p> </p> <p>*This designation is without prejudice to positions on status and is in line with UNSCR 1244 and the ICJ Opinion on the Kosovo Declaration of Independence.</p>
ScienceDex guides
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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.