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19,493 results for “Populism”
GerPS-onto-populated
<p>This is the result of automatically populating the <a href="https://w3id.org/GerPS-onto/ontology">GerPS-onto</a> .</p> <p>The ontology is exemplary populated with instances from a specific <a href="https://fimportal.de/detail/L/99006028261000">German public service</a>.</p> <p>More details available on: <a href="https://github.com/fusion-jena/GerPS-onto">Github</a></p> <p> </p>
Data from: "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (Cervus canadensis) population"
<p>Raw and processed datasets used for analysis in "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (<em>Cervus canadensis</em>) population" by Hooven et al., published in <em>Mammalian Biology</em>. Datasets are as follows:</p> <p>Pregnancy.csv - Raw dataset detailing year and date of capture, individual identifier, and measured intrinsic variables, along with confirmed or predicted pregnancy/calf viability status.</p> <p>Pregnancy_final_mass.csv - Raw dataset after body mass estimation for individuals that were not weighed. </p> <p>all_confirmed_preg.csv - Subset of raw data for all individuals with confirmed pregnancy status (via lab PSPB assay).</p> <p>preg_ageclass.csv - Subset of all_confirmed_preg dataset including all individuals with general age classification (e.g., adult or subadult).</p> <p>preg_numeric.csv - Subset of all_confirmed_preg dataset including all individuals with numeric age value (from incisiform canine cementum annuli).</p> <p>all_fns.csv - Subset of dataset including all individuals with confirmed or predicted fetal/early neonatal survival ("offspring viability") status.</p> <p>fns_ageclass.csv - Subset of all_fns.csv including all individuals with general age classification.</p> <p>fns_numeric.csv - Subset of all_fns.csv including all individuals with numeric age values.</p> <p>parameter_est.csv - Parameter estimates from top-performing generalized linear mixed models for both pregnancy and offpsinrg viability, for plotting.</p>
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020
<p>The presented human thermal bioclimate dataset was created in the frame of the <a href="https://theheatalarm.wordpress.com/">HEAT-ALARM</a> ("Development of a heat-health warning system in Greece") research project.</p> <p><strong>Initially developed for Greece</strong>, it consists of hourly values of population-weighted mPET (modified physiologically equivalent temperature), simulated by the RayMan Pro model for the period 1991-2020 and for 10 population subsets in 72 regional units and combinations thereof, which are based on the NUTS-3 (Nomenclature of Territorial Units for Statistics-3) classification in Greece, using the Copernicus European Regional Reanalysis (CERRA) at 5.5 km spatial resolution. The dataset also includes the main environmental drivers of mPET (e.g. temperature) at the same spatiotemporal resolution.</p> <p>In the framework of <strong>replicating</strong> the original dataset, the current version includes population-weighted values of mPET and its environmental drivers for six populations in five districts of <strong>Cyprus</strong> at the LAU-1 (Local Administrative Units-1) level, covering the period from 1991 to 2020. </p> <p>The code used to produce the presented data is available at: <a href="https://doi.org/10.5281/zenodo.10793067">https://doi.org/10.5281/zenodo.10793067</a>. It can be used to replicate the dataset not only directly in Greece, but also in any other country included in the CERRA domain after appropriate adjustments, as in the case of Cyprus above.</p> <p><em>Compared to the previous version of the dataset for Greece, this version includes vapor pressure (VP) instead of relative humidity (see README.txt for more details), as VP is more relevant for human-biometerological and health-related studies.</em><em> </em></p> <p><strong>References</strong></p> <p>Giannaros, C., Agathangelidis, I., Galanaki, E. <em>et al.</em> Hourly values of an advanced human-biometeorological index for diverse populations from 1991 to 2020 in Greece. <em>Sci Data</em> <strong>11</strong>, 76 (2024). <a href="https://doi.org/10.1038/s41597-024-02923-y">https://doi.org/10.1038/s41597-024-02923-y</a> </p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster
<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> <p>This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST):</p> <p>"Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster"</p> <p>The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article</p> <h2>Dataset description</h2> <p>The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying.</p> <h2>Data files</h2> <p>Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following:</p> <ul> <li><strong>Ref_planar.hdf5: </strong>Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li><strong>2Te_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li><strong>2Ie_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li><strong>No_coll_planar.hdf5: </strong>Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li><strong>Ref_axisym.hdf5: </strong>Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li><strong>fcol_2.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li><strong>fcol_5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li><strong>fcol_7.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li><strong>fcol_10_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> <p>In each of these files the data is organized in a series of subfolders:</p> <ul> <li><strong>Electrons_prim: </strong>Contains the steady-state properties of primary electrons</li> <li><strong>Electrons_trap: </strong>Contains the steady-state properties of trapped electrons</li> <li><strong>Ions_fast: </strong>Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li><strong>Ions_slow: </strong>Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li><strong>Time_dependent: </strong>Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> <p>The data files found in the outer simulation folder are:</p> <ul> <li><strong>xs:</strong> Physical x coordinates [cm]</li> <li><strong>zs:</strong> Physical z coordinates [cm]</li> <li><strong>phi: </strong>electric potential [V]</li> <li><strong>rho_el: </strong>space charge density [C/m³]</li> <li><strong>nn: </strong>Total neutral density [1/m³]</li> </ul> <p>The data files for each particle population are:</p> <ul> <li><strong>n: </strong>Plasma (ion) density [1/m³]</li> <li><strong>f_x: </strong>Particle flux along x [1/(m² s)]</li> <li><strong>f_y: </strong>Particle flux along y [1/(m² s)]</li> <li><strong>f_z: </strong>Particle flux along z [1/(m² s)]</li> <li><strong>p_xx: </strong>xx component of the pressure tensor [J/m³]</li> <li><strong>p_yy: </strong>yy component of the pressure tensor [J/m³]</li> <li><strong>p_zz: </strong>zz component of the pressure tensor [J/m³]</li> </ul> <p>The data files in the time dependent folder are:</p> <ul> <li><strong>t: </strong>Time coordinates [s]</li> <li><strong>phi_W: </strong>Potential of the vacuum chamber walls [V]</li> <li><strong>phi_max:</strong> Maximum value of the potential in the plume [V]</li> <li><strong>nte_frac: </strong>Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li><strong>nu_te_ela: </strong>globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_te_ion: </strong>globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_te_exc: </strong>globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_te_cou: </strong>globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li><strong>nu_pe_ela: </strong>globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_pe_ion: </strong>globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_pe_exc: </strong>globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_pe_cou: </strong>globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul> <p> </p> <p>Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged.</p> <h2>Citation</h2> <p>Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience:</p> <p>@dataset{sim_data_guai25b,<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},<br> month = November,<br> year = 2024,<br> publisher = {Zenodo},<br> version = {1.0.1},<br> doi = {10.5281/zenodo.14165272},<br> url = {https://doi.org/10.5281/zenodo.12751281}<br>}</p> <p>The journal article associated with this data-set shall also be cited as follows:</p> <p>@article{guai25b,<br> doi = {10.1088/1361-6595/adc482},<br> year = {2025},<br> month = {mar},<br> publisher = {IOP Publishing},<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Electron populations and neutralization process in the plume of a gridded ion thruster},<br> journal = {Plasma Sources Science and Technology },<br>}</p> <p> </p> <p><br><br></p> <h2>Acknowledgments</h2> <p>This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA</p>
DATASET: TESTING NICHE EQUIVALENCE IN AMPHIDROMOUS FISH POPULATIONS
<p>Presence records of<em> Galaxias maculatus</em> were collected from 12 locations across five river basins in central-southern Chile during March, May, August, and November of 2019 as part of a study on fish sampling and processing (Ramírez-Álvarez et al. 2022. doi.org/10.1038/s41598-022-06936-8)</p> <p>Isotopic niches defined using a standard ellipse area (SEAc) in isotopic space, represented by a 2D ellipsoidal space (δ13C - δ15N) (see Supporting Information: Standard ellipse area functions - Ramírez-Álvarez et al. 2024. doi:10.1007/s10750-024-05738-5) - Empirical Bayesian Kriging</p> <p>Database of varibles used for niche modelling: isotopic niche and seven abiotic variables selected from 23 predictor variables: (1) 19 climate variables representing 1950–2000 climate averages from WorldClim (http://www.worldclim.org/). (2) Four spatially continuous topographic and hydrological variables from the EarthEnv Project adjusted to the HydroSHEDS river network (http://www.earthenv.org/) (Domisch et al. 2015). Selection of variables was accomplished by analysis of covariance and multicollinearity, using the ENMeval R package (Muscarella et al. 2014): (1) principal component analysis (PCA) to explore relationships among all predictors, evaluating the composition of components (component variables) that accounted for ≥65% of variance explained, (2) pairwise comparisons to detect pairs of variables with strong correlations (Pearson correlation coefficients <0.8), groups with a correlation of less than 0.8 were considered independent, and (3) variance inflation factor (VIF) <10, to reduce the effect of collinearity between predictors (Listed below). A VIF greater than 10 indicates collinearity problems in the model. The vifcor and vifstep functions were employed by calculating two different strategies to exclude highly collinear variables using a stepwise procedure (Muscarella et al. 2014).</p> <div><em>Variable description and ecological question associated, selected variables are marked in bold.</em></div> <div><em>Series 1: Temperature, temperature variations and interaction with precipitation.</em></div> <div>bio1: Annual Mean Temperature; Is the temperature usually suitable? </div> <div><strong>bio2: Mean Diurnal Range; Are the days too warm or too cold?</strong></div> <div>bio3: Isothermality; Do temperatures fluctuate greatly over the course of a month? </div> <div>bio4: Temperature Seasonality (standard deviation); Do temperatures fluctuate greatly over the course of a year?</div> <div>bio5: Min Temperature of Coldest Month; Is the maximum temperature too high?</div> <div>bio6: Min Temperature of Coldest Month; Is the temperature constantly too high?</div> <div><strong>bio7: Temperature Annual Range; Do temperatures fluctuate greatly over the course of a year?</strong></div> <div><strong>bio8: Mean Temperature of Wettest Quarter; Is it too cold or too warm during the rainy season?</strong></div> <div>bio9: Mean Temperature of Driest Quarter; Is it too cold or too warm during the dry season?</div> <div>bio10: Mean Temperature of Warmest Quarter; Are the warmer months too cold?</div> <div><strong>bio11: Mean Temperature of Coldest Quarter; Are the colder months too warm?</strong></div> <div><em>Series 2: Precipitation and Rainfall Patterns</em></div> <div>bio12: Annual Precipitation; Does it rain enough in a year?</div> <div>bio13: Precipitation of Wettest Month; Does it rain a lot during the wettest month?</div> <div><strong>bio14: Precipitation of Driest Month; Does it rain poorly during the driest month?</strong></div> <div>bio15: Precipitation Seasonality (Coefficient of Variation); Would rainfall fluctuate much between seasons?</div> <div>bio16: Precipitation of Wettest Quarter; Does it rain a lot in the rainy season?</div> <div>bio17: Precipitation of Driest Quarter; Is rainfall low during dry seasons?</div> <div>bio18: Precipitation of Warmest Quarter; Does it rain enough during the warmer months?</div> <div>bio19: Precipitation of Coldest Quarter; Does it rain enough during the colder months?</div> <div><em>Series 3: Topology and hydrology</em></div> <div><strong>dem: Average elevation; Does altitude play a role as a topological factor?</strong></div> <div><strong>slope_av: Average slope; Is the slope suitable for the accumulation of small ponds?</strong></div> <div>flow_ac_ac: Accumulation flow; Does enough water accumulate or does it drain too quickly?</div> <div>flow_ac_le: Accumulation flow direction; Does the flow direction support the formation of small ponds?</div> <p>Domisch, S., G. Amatulli & W. Jetz, 2015. Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution. Scientific Data 2(1):150073 doi:10.1038/sdata.2015.73.</p> <p>Muscarella, R., P. J. Galante, M. Soley‐Guardia, R. A. Boria, J. M. Kass, M. Uriarte & R. P. Anderson, 2014. ENM eval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in ecology and evolution 5(11):1198-1205</p>
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Wittgenstein Center (WIC) Population and Human Capital Projections - 2023
<p>THIS IS VERSION WIC3.004 (Beta) (<strong>internal version V14,</strong> <strong>the last three versions were called V13, V12 and V11). </strong>This is a release version corresponding to the <a href="https://data.ece.iiasa.ac.at/ssp/#/login?redirect=%2Fworkspaces">SSP-Database 3.0</a> (will be updated) release and <a href="https://dataexplorer.wittgensteincentre.org/wcde-v3/">WIC-Data Explorer 2023</a></p> <p>Short Abstract</p> <p>We update the population and human capital components of the Shared Socio-Economic Pathways (SSPs) at the global level, considering the most recent baseline information. While the long-term assumptions based on extensive analysis and expert solicitations remain unchanged, we modify only the trend component. The first set of SSPs was based on demographic data through 2012. The population structures by age, sex, and education of the base year (2010) have since changed for most countries, mainly due to more recent data and reliable information. The mortality situation has improved in many countries affected by HIV/AIDS and among children in countries with higher mortality. The impact of COVID-19 on demographic trends must be addressed. In many countries, fertility rates have fallen faster than expected. International migration has been irregular and volatile as usual. These changes are reflected in the new update, with some improvements in operationalization.</p> <p>Changes in version "V14":</p> <p> We found a bug affecting the education distribution sex (interchanged) in 37 countries (with UN country code): 32 Argentina, 44 Bahamas, 48 Bahrain, 64 Bhutan, 76 Brazil, 108 Burundi, 132 Cabo Verde, 156 China, 158 China, Taiwan Province of China, 170 Colombia, 214 Dominican Republic, 218 Ecuador, 226 Equatorial Guinea, 288 Ghana, 296 Kiribati, 360 Indonesia, 392 Japan, 400 Jordan, 410 Republic of Korea, 414 Kuwait, 496 Mongolia, 528 Netherlands, 558 Nicaragua, 583 Micronesia (Fed. States of), 591 Panama, 626 Timor-Leste, 630 Puerto Rico, 634 Qatar, 643 Russian Federation, 662 Saint Lucia, 702 Singapore, 764 Thailand, 784 United Arab Emirates, 788 Tunisia, 792 Turkey, 840 United States of America, 894 Zambia. The impact on the total population is minimal.</p>
CitiesGOER: Globally Observed Environmental Data for 52,602 Cities with a Population ≥ 5000
<p><strong>CitiesGOER</strong> is a database that provides environmental data for 52,602 cities and 48 environmental variables, including 38 bioclimatic variables, 8 soil variables and 2 topographic variables. Data were extracted from the same 30 arc-seconds global grid layers that were prepared when making the <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database that is available from <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparations of these layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. CitiesGOER was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p>The identities and coordinates of cities were sourced from a data set with information for cities with a population size larger than 1000 that was created by <a href="https://public.opendatasoft.com/explore/?sort=modified">Opendatasoft</a> and made available from <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/table/?disjunctive.cou_name_en&sort=name</a>. The data was downloaded on 22-JULY-2023 and afterwards filtered for cities with a population of 5000 or above. Cities where information on the country was missing were removed. The coordinates of cities were used to extract the environmental data via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.6-47) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Version 2023.08 provided median values from 23 Global Climate Models (GCMs) for Shared Socio-Economic Pathway (SSP) 1-2.6 and from 18 GCMs for SSP 3-7.0, both for the 2050s (2041-2060). Similar methods were used to calculate these median values as in the case studies for the TreeGOER manuscript (calculations were partially done via the <a href="https://rdrr.io/cran/BiodiversityR/man/ensemble.envirem.html">BiodiversityR::ensemble.envirem.run</a> function and with downscaled bioclimatic and monthly climate 2.5 arc-minutes <a href="https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html">future grid layers available from WorldClim 2.1</a>).</p> <p>Version 2023.09 used similar methods as for previous versions to provide median values from 13 GCMs for the 2090s (2081-2100) for SSP 5-8.5.</p> <p>The locations of the 52,602 cities are mapped in one of the series available from the <strong>TreeGOER Global Zones</strong> atlas that can be obtained from <a href="https://doi.org/10.5281/zenodo.8252756">https://doi.org/10.5281/zenodo.8252756</a>.</p> <p>Version 2024.10 includes a new data set that documents the location of the city locations in <strong>Holdridge Life Zones</strong>. Information is given for historical (1901-1920), contemporary (1979-2013) and future (2061-2080; separately for RCP 4.5 and RCP 8.5) climates inferred from global raster layers that are <a href="https://datadryad.org/stash/dataset/doi:10.5061/dryad.41ns1rnff">available for download from DRYAD</a> and were created for the following article: Elsen et al. 2022. <strong>Accelerated shifts in terrestrial life zones under rapid climate change.</strong> <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a>. Version 2024.10 further includes Holdridge Life Zones for the climates that were available from the previous versions, calculating biotemperatures and life zones with similar methods as used by Holdridge (<a href="https://www.jstor.org/stable/1675393?seq=1">1947</a>; <a href="https://app.ingemmet.gob.pe/biblioteca/pdf/Amb-56.pdf">1967</a>) and Elsen et al. (<a href="https://doi.org/10.1111/gcb.15962">2022</a>) (for future climates, median values were determined first for monthly maximum and minimum temperatures across GCMs ). The distributions of the 48,129 species documented in TreeGOER across the Holdridge Life Zones are given in this Zenodo archive: <a href="https://zenodo.org/records/14020914">https://zenodo.org/records/14020914</a>.</p> <p>Version 2024.11 includes a new data set that documents the location of the city locations in <strong>Köppen-Geiger climate zones</strong>. Information is given for historical (1901-1930, 1931-1960, 1961-1990) and future (2041-2070 and 2071-2099) climates, with for the future climates seven scenarios each (SSP 1-1.9, SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, SSP 4-3.4, SSP 4-6.0 and SSP 5-8.5). This data set was created from 30 arc-second raster layers available via: Beck, H.E., McVicar, T.R., Vergopolan, N. et al. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a> </p> <p>Version 2025.03 includes extra columns for the baseline, 2050s and 2090s datasets that partially correspond to climate zones used in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database. One of these zones are the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">Whittaker biome types</a>, available as a polygon from the <a href="https://rawgit.com/valentinitnelav/plotbiomes/master/html/Whittaker_biomes_dataset.html">plotbiomes</a> package (see also <a href="https://www.davidzeleny.net/wiki/lib/exe/fetch.php/vegecol:materials:ricklefs_bioms_chapter_5.pdf">here</a>). Whittaker biome types were extracted with similar R scripts as described by <a href="https://rpubs.com/Roeland-KINDT/1275232">Kindt 2025</a> (these were also used to calculate environmental ranges of TreeGOER species, as archived <a href="https://zenodo.org/records/14908944">here</a>).</p> <p>Version 2025.03 further includes information for the baseline climate on the steady state water table depth, obtained from a 30 arc-seconds raster layer calculated by the GLOBGM v1.0 model (Verkaik et al. <a href="https://gmd.copernicus.org/articles/17/275/2024/">2024</a>). Also included was the elevation, obtained from the same WorldClim 2.1 raster layer used to prepare TreeGOER.</p> <p> </p> <p>As an alternative to CitiesGOER, the <strong>ClimateForecasts</strong> database (<a href="https://zenodo.org/records/10776414">https://zenodo.org/records/10776414</a>) documents the environmental conditions at the locations of 15,504 weather stations. ClimateForecasts was integrated in the <a href="https://worldagroforestry.org/output/globalusefulnativetrees"><strong>GlobalUsefulNativeTrees</strong> database</a> (see <a href="https://doi.org/10.1038/s41598-023-39552-1">Kindt et al. 2023</a>).</p> <p> </p> <p>When using CitiesGOER in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. <a href="https://doi.org/10.5194/soil-7-217-2021">https://doi.org/10.5194/soil-7-217-2021</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> <li>Opendatasoft (2023) Geonames - All Cities with a population > 1000. <a href="https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name">https://public.opendatasoft.com/explore/dataset/geonames-all-cities-with-a-population-1000/information/?disjunctive.cou_name_en&sort=name</a> (accessed 22-JULY-2023)</li> </ul> <p>When using information from the Holdridge Life Zones, also cite:</p> <ul> <li>Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K.-V., Reside, A. E., & Watson, J. E. M. (2022). Accelerated shifts in terrestrial life zones under rapid climate change. <em>Global Change Biology</em>, 28, 918–935. <a href="https://doi.org/10.1111/gcb.15962">https://doi.org/10.1111/gcb.15962</a></li> </ul> <p>When using information from Köppen-Geiger climate zones, also cite:</p> <ul> <li>Beck, H.E., McVicar, T.R., Vergopolan, N., Berg, A., Lutsko, N.J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A.I. and Miralles, D.G. 2023. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Sci Data 10, 724. <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a></li> </ul> <p>When using information on the Whittaker biome types, also cite:</p> <ul> <li>Ricklefs, R. E., Relyea, R. (2018). Ecology: The Economy of Nature. United States: W.H. Freeman.</li> <li>Whittaker, R. H. (1970). Communities and ecosystems.</li> <li>Valentin Ștefan, & Sam Levin. (2018). plotbiomes: R package for plotting Whittaker biomes with ggplot2 (v1.0.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7145245">https://doi.org/10.5281/zenodo.7145245</a></li> </ul> <p>When using information on the steady state water table depth, also cite:</p> <ul> <li>Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H., Lin, H. X., & Bierkens, M. F. (2024). GLOBGM v1. 0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model. Geoscientific Model Development, 17(1), 275-300. <a href="https://gmd.copernicus.org/articles/17/275/2024/">https://gmd.copernicus.org/articles/17/275/2024/</a></li> </ul> <p> </p> <p>The development of <strong>CitiesGOER</strong> was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative</strong> through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project. Development of version 2024.10 was further supported by the <strong>Green Climate Fund</strong> through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em> project, by the <strong>Bezos Earth Fund</strong> to the <em>Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p>
Highly parallel genomic selection response in replicated Drosophila melanogaster populations with reduced genetic variation
<p>Many adaptive traits are polygenic and frequently more loci contributing to the phenotype are segregating than needed to express the phenotypic optimum. Experimental evolution with replicated populations adapting to a new controlled environment provides a powerful approach to study polygenic adaptation. Since genetic redundancy often results in non-parallel selection responses among replicates, we propose a modified Evolve and Resequence (E&R) design that maximizes the similarity among replicates. Rather than starting from many founders, we only use two inbred <em>Drosophila melanogaster</em>strains and expose them to a very extreme, hot temperature environment (29°C). After 20 generations, we detect many genomic regions with a strong, highly parallel selection response in 10 evolved replicates. The X chromosome has a more pronounced selection response than the autosomes, which may be attributed to dominance effects. Furthermore, we find that the median selection coefficient for all chromosomes is higher in our two-genotype experiment than in classic E&R studies. Since two random genomes harbor sufficient variation for adaptive responses, we propose that this approach is particularly well-suited for the analysis of polygenic adaptation.</p> <p>See the README.txt file to get a description of the uploaded files. Scripts.zip contains annotated command lines and scripts for the project (see internal README.txt file).</p>
Percentage of Population Who Read Books In European Regions
<p>The indicator is created from the Eurobarometer 79.2 survey’s <a href="https://search.gesis.org/research_data/ZA5688">GESIS datafile</a> using regional subsamples. The regional subsamples were recoded to the NUTS 2016 regional boundary definitions with the <a href="https://regions.dataobservatory.eu/">regions</a> R package. In the larger countries, where only NUTS1 level information was present (for example, in Germany and the United Kingdom), we imputed the NUTS1 territorial average values to the constituent NUTS2 regions.</p> <p>A ‘dirty averaging’ was used to create regional averages, with scale national post-stratification weights to an expected value of 1. Al respondents who read at least one book in the previous 12 months were coded to have read a book.</p> <p>This indicator was used in the<br> Balázs Bodó, Dániel Antal, Zoltán Puha: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242509">Can scholarly pirate libraries bridge the knowledge access gap?</a> An empirical study on the structural conditions of book piracy in global and European academia, in Plos ONE (Published: December 3, 2020.)</p>
Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021
<p>Estimates of numbers of SARS-CoV-2 infections by country and age group over time, current proportions in different immune states, and potential remaining burden of hospitalisations and deaths for 19 European countries from article "Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021"</p>
Data & R-Code for "Weather and food availability additively affect reproductive output in an expanding raptor population"
<p><strong>Abstract</strong></p> <p>The joint effects of interacting environmental factors on key demographic parameters can exacerbate or mitigate the separate factors’ effects on population dynamics. Given ongoing changes in climate and land use, assessing interactions between weather and food availability on reproductive performance is crucial to understand and forecast population dynamics. By conducting a feeding experiment in 4 years with different weather conditions, we were able to disentangle the effects of weather, food availability and their interactions on reproductive parameters in an expanding population of the red kite (<em>Milvus milvus</em>), a conservation-relevant raptor known to be supported by anthropogenic feeding. Brood loss occurred mainly during the incubation phase, and was associated with rainfall and low food availability. In contrast, brood loss during the nestling phase occurred mostly due to low temperatures. Survival of last-hatched nestlings and nestling development was enhanced by food supplementation and reduced by adverse weather conditions. However, we found no support for interactive effects of weather and food availability, suggesting that these factors affect reproduction of red kites additively. The results not only suggest that food-weather interactions are prevented by parental life-history trade-offs, but that food availability and weather conditions are crucial separate determinants of reproductive output, and thus population productivity. Overall, our results suggest that the observed increase in spring temperatures and enhanced anthropogenic food resources have contributed to the elevational expansion and the growth of the study population during the last decades.</p>
Data and code for 'Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Turner, A., Heard, G., Hall, A., Wassens, S. (in review). Age structure of amphibian populations with endemic chytridiomycosis, across climatic regions with markedly different infection risk.</p> <p>The data are provided as a series of .csv files, R script and two zip folders of R packages (Surv_mod and VB_mod)</p> <p>1. <strong>Skeleto_dat_ready_Jan2021.csv</strong> Data from frog surveys conducted by Anna Turner</p> <p>2. <strong>Geoffs_data.csv</strong> Data from frog surveys conducted by Geoff Heard</p> <p>3. <strong>Environmental_variables_skeleto.csv</strong> Environmental data collected during surveys </p> <p>4. <strong>sk.dat_July21.csv</strong> Collated data from Anna and Geoff - created by 'Data_collation_for_analysis_2.R' ready for analysis</p> <p>5. <strong>Variables_that_are_highly_correlated_with_each_other_season_wide.csv</strong> Testing for correlation</p> <p>6. <strong>Model_structure_skeleto_2.csv </strong>creates model structure for analysis</p> <p>7. <strong>Model_selection_statistics_June_21.csv </strong>Output from model</p> <p>R code is provided seperately for each of the following components:</p> <p>1. <strong>Data_collation_for_analysis_2.R</strong> Collating data from Anna and Geoffs datasets</p> <p>2. <strong>Skeleto_analysis_5.R - </strong>First uses regression modelling to explore factors correlated with variation in age</p> <p> - Following Scheele et al. (2016) regression models with a poisson distribution</p> <p> - Use bayesian non-linear regression to fit the Von Bertalanffy growth model to size-at-age data</p> <p> - Plots male and female growth curves</p> <p> - Uses catch curve approach to estimate survival from best fitting regression model following Scroggie (2012) but with bayesian implementation</p>
Dataset accompanying Nölke et al. 2022. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Scientific Reports
<p>This repository contains biomass and nitrogen yield data as well as data on diversity effects used by Nölke et al. in an article published in Scientific Reports (2022).</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original research article.</p>
Relative abundance of the CHC extracts of each population replicate's of I. uriae ticks from Iceland after log centered ratio transformation
<p>Relative abundance of the CHC extracts of each population replicate’s of I. uriae ticks from Iceland after log centered ratio transformation.</p>
Pacific salmon population time-series dataset to support Appendix S1: Data and additional information on declines of Pacific Salmon
<p>Dataset used to support the main paper 'Protecting our coast for everyone’s future: Indigenous and scientific knowledge support marine spatial protections proposed by Central Coast First Nations in Pacific Canada' by Reid et al. 2022. Dataset cited in Appendix S1 regarding trends in adult salmon abundances in the Central Coast. The data were as compiled by Will Atlas from the <a href="https://wildsalmoncenter.org/">Wild Salmon Center</a> to describe trends in the abundance of adult salmon returning to the Central Coast, which is the sum of escapement and harvest, as derived from the following sources:</p> <ol> <li>Escapement data from DFO: <a href="https://open.canada.ca/data/en/dataset/c48669a3-045b-400d-b730-48aafe8c5ee6">NuSEDS-New Salmon Escapement Database System - Open Government Portal (canada.ca)</a></li> <li>Harvest rates estimated by Karl English and colleagues and available at: <a href="https://data.salmonwatersheds.ca/data-library/">Salmon Watersheds Program - Data Library</a>.</li> <li>Information on total harvest that is reported in the DFO post season review (DFO 2020).</li> </ol>
Within Population Variability of Coral Heat Tolerance - Images
<p>Image dataset used for a colour analysis of coral branches throughout a long-term marine heatwave emulation experiment using machine learning. Article: "Within population variability in coral heat tolerance indicates climate adaptation potential" by Humanes and Lachs et al. Code to analyse the dataset is found at 10.5281/zenodo.6256164.</p>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
ScienceDex guides
Understand access before you commit
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.