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6,170 results for “Europeans”
Dataset: Environmental benchmarks for European Cement Industry
<p>This dataset contains the information relative to the article "Environemntal benchmarks for European cement industry".</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.spc.2024.01.020" target="_blank" rel="noopener">Reference paper</a></p> <p><a href="https://www.researchgate.net/publication/377796848_Environmental_benchmarks_for_the_European_cement_industry" target="_blank" rel="noopener">ResearchGate link</a></p>
Collection of figures to explore intra-regime weather variability of North Atlantic-European year-round weather regimes as Supplementary Dataset for Gerighausen et al. (2024)
<p>This is a supplementary dataset accompanying the publication <strong>Gerighausen et al. (2024) </strong>submitted to Meteorological Applications. It contains a collection of browsable figures, complementing selected regimes, seasons, and countries in the paper. The figures are provided as a zipped archive. The ZIP-File (1.2 GB) contains 4 subfolders and 4 auxiliary files as described in <strong>readme.md </strong>in the main folder. Once downloaded and unpacked, the .html navigation panels can be used in any browser to navigate through the plots. </p> <p>Data and methods used to generate the figures are explained in Gerighausen et al. (2024). In brief the analysis is based on ERA5 reanalysis 1979-2021 at 1° grid spacing and 6h temporal resolution aggregated to daily data. Anomalies are computed with respect to a 31-day running mean climatology. The figures are explained in the table below and in the navigation panel.</p> <p><strong>Gerighausen</strong>, J., J. Dorrington, M. Osman, and C. M. Grams, <strong>2024</strong>: Quantifying intra-regime weather variability for energy applications, <em>submitted to Meteorological Applications.</em> <a href="https://doi.org/10.48550/arXiv.2408.04302">doi:10.48550/arXiv.2408.04302</a></p>
Storm Database Files for CLIMK–WINDS: A New Database of Extreme European Winter Windstorms
<p>This database is comprised of the four netCDF files containing the 50 most extreme European winter windstorms identified within the four input sources, with one netCDF file per source: ERA5 reanalysis, CCLM_ERA5_EUR-11 regional climate model simulation, COSMO-REA6 reanalysis, and CCLM_ERA5_CEU-3 regional climate model. This database was created by Clare Marie Flynn and its creation is described in the following paper: Flynn, C. M., Moemken, J., Pinto, J., Schutte, M., and Messori, G.: CLIMK–WINDS: A New Database of Extreme European Winter Windstorms, under review for final submission, Earth System Science Data, 2025.</p>
Ground temperature time series in European mountain permafrost
<p>RELATED PUBLICATION</p> <p>This dataset is related to the following publication:</p> <p><strong>Noetzli J., Isaksen, K., Barnett, J., Chrisitiansen, H.H., Delaloye, R., Etzelmueller, B., Farinotti, D., Gallemann, T., Guglielmin, M., Hauck, C., Hilbich, C., Hoelzle, M., Lambiel, C., Magnin, F., Oliva, M., Paro, L, Pogliotti, P., Riedl, C., Schoeneich, P., M., Valt, M., Vieli A., Philliips, M. (2024). Enhanced permafrost warming in Euro­pean mountains in the 21st century. Nature Communications, 15, 10508, <a href="https://doi.org/10.1038/s41467-024-54831-9">https://doi.org/10.1038/s41467-024-54831-9</a>.</strong></p> <p><strong>==> </strong></p> <p><strong>For information on the measurements, selection criteria, processing information and data providers please refer to the methods, data availability and acknowledgements sections of the related publication ! </strong></p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTENT</p> <p>The dataset includes monthly and annual time series of ground temperatures measured in 64 boreholes in European mountain permafrost areas and corresponding metadata.</p> <p>Temporal coverage: at least 10 years until 2022</p> <p>Spatial coverage: European mountain regions (Svalbard, Scandinavia, Iceland, European Alps, Sierra Nevada)</p> <p>Depth of measurements: at least 10 m; for all boreholes data of the sensors closest to 5, 10 and 20 m depth are included</p> <p>Monthly means are calculated from daily values and annual values are derived from monthly mean values.</p> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>DATA COMPILATION</p> <p>The data were compiled to derive 10-year and 20-year warming rates in European mountain permafrost in the study by Noetzli et al. (in review, see above). Data were collected from national permafrost observation networks as well as from individual institutions (e.g, universities, environmental agencies).</p> <p>The aquisition of long time series over decades requires long-term committment from the responsible institutions to maintain instruments and to collect and curate the data. Details on the data source for each time series can be found in the metadata file as well as in the related publication. The main data sources by country are given in the list below.</p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>Data source (institution or national network)</strong></td> </tr> <tr> <td>Austria</td> <td>GeoSphere Austria</td> </tr> <tr> <td>France</td> <td>Réseau français d'observation du permafrost (PermaFrance, <a href="https://wslch365-my.sharepoint.com/personal/jeannette_noetzli_slf_ch/Documents/PermafrostEurope/permafrance.osug.fr">permafrance.osug.fr</a>)</td> </tr> <tr> <td>Germany</td> <td>Bavarian Environment Agency</td> </tr> <tr> <td>Iceland</td> <td>University of Oslo</td> </tr> <tr> <td>Italy</td> <td>ARPA Piemonte, ARPA Valle d'Aosta, ARPA Veneto, University of Insubria</td> </tr> <tr> <td>Norway</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Spain</td> <td>Universitat de Barcelona</td> </tr> <tr> <td>Svalbard</td> <td>Norwegian Permafrost Monitoring Network (<a href="https://cryo.met.no/">cryo.met.no</a> and <a href="http://sios-svalbard.org/">sios-svalbard.org</a>)</td> </tr> <tr> <td>Sweden</td> <td>University of Stockholm</td> </tr> <tr> <td>Switzerland</td> <td>Swiss Permafrost Monitoring Network PERMOS (<a href="http://www.permos.ch">http://www.permos.ch</a>)</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>FILES AND FORMAT</p> <p>This data set includes three csv-files: <br>1) metadata with information on the measurement location and data provider<br>2) monthly ground temperature time series and <br>3) annual ground temperature time series. </p> <p>The variables in the three files are described below. Data files are in long data format.</p> <p><strong>File 1 – borehole_overview.csv<br></strong>Key information on the boreholes, responsible institutions and contact persons.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Name</td> <td>Name of the borehole (as used in the related study)</td> </tr> <tr> <td>Country</td> <td>Alpha-2 code</td> </tr> <tr> <td>Region</td> <td>Larger region</td> </tr> <tr> <td>First_year</td> <td>First year of data</td> </tr> <tr> <td>Elevation [m asl.]</td> <td>Elevation of the borehole</td> </tr> <tr> <td>Lat [° N]</td> <td>Latitude</td> </tr> <tr> <td>Lon [° E]</td> <td>Longitude</td> </tr> <tr> <td>Depth [m]</td> <td>Total depth of the borehole</td> </tr> <tr> <td>DZAA [m]</td> <td>Depth of the Zero Annual Amplitude (uppermost sensor with annual amplitude ≤0.1)</td> </tr> <tr> <td>Phase lag</td> <td>Phase lag at 10 m depth compared to surface in months</td> </tr> <tr> <td>Morphology</td> <td>Main morphology of the site</td> </tr> <tr> <td>Surface_cover</td> <td>Main surface cover at the site</td> </tr> <tr> <td>Lithology</td> <td>Main lithology of the site</td> </tr> <tr> <td>Ice_content</td> <td>Basic classification by ground ice content at the site (no ice, ice-poor, ice-bearing, ice-rich), see publication for details</td> </tr> <tr> <td>Institution</td> <td>Responsible institution (in the year 2024)</td> </tr> <tr> <td>Contact_person</td> <td>Contact person (in the year 2024)</td> </tr> <tr> <td>Special_remarks</td> <td>Remarks on location, e.g. horizontal borehole</td> </tr> </tbody> </table> <p> </p> <p><strong>File 2 – permafrost_temperatures_european_mountains_monthly_2022.csv<br></strong>Time series of monthly mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY-MM-DD]</td> <td>Date</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Monthly mean ground temperature (aggregated from daily values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum daily ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum daily ground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of daily values available to calculate monthly mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p><strong>File 3 – permafrost_temperatures_european_mountains_annual_2022.csv<br></strong>Time series of annual mean ground temperatures at ca. 5, 10 and 20 m depth for 64 boreholes in European mountain permafrost until 2022.</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>bh</td> <td>Name of the borehole</td> </tr> <tr> <td>time [YYYY]</td> <td>Year</td> </tr> <tr> <td>depth [m]</td> <td>Depth of measurement</td> </tr> <tr> <td>temp [°C]</td> <td>Annual mean ground temperature (aggregated from monthly values)</td> </tr> <tr> <td>t_min [°C]</td> <td>Minimum monthly ground temperature of the year</td> </tr> <tr> <td>t_max [°C]</td> <td>Maximum monthlyground temperature of the year</td> </tr> <tr> <td>count</td> <td>Number of monthly values available to calculate annual mean values</td> </tr> <tr> <td>dclass [5, 10 or 20 m]</td> <td>Depth class defined for analyses in related study</td> </tr> </tbody> </table> <p> </p> <p>---------------------------------------------------------------------------------------------------------------------------</p> <p>CONTACT</p> <p>For question related to this dataset please contact the corresponding author: jeannette.noetzli@slf.ch. <br>For questions related to a specific time series, see metadata for contact information.</p>
Current and future European potential vegetation types
<p>This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the <em>ibis.iSDM</em> package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>).<br><br><strong>Uploaded are:</strong></p> <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png)<br>(Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> <p><strong>Variable naming scheme:</strong><br>Current: "pnv_XX_laea_1km.tif"<br>where XX represents the vegetation type<br>Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by<br>Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/"<br>Filenames are labelled by the date (e.g. "2040.tif").<br><br><strong>Metrics and layers names and their interpretation:</strong><br>For current:<br>"mean" = Average Ensemble posterior prediction<br>"sd" = Standard deviation of posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction<br>"mode" = Most commonly encountered value of posterior prediction<br>"cv" = Coefficient of variation of posterior prediction<br><br>For future:<br>"mean" = Average Ensemble posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction</p> <p>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, <p>IPSL-CM6A-LR, </p> <p>MPI-ESM1-2-HR,</p> <p>MRI-ESM2-0,</p> <p>UKESM1-0-LL</p> </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> <p>All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation. </p>
Meta-analysis and gender classification of 914 national and international surveys in six European countries (2000-2023)
<p><span>This data frame presents the results of a quan</span><span>ti</span><span>ta</span><span>ti</span><span>ve content analysis of the occurrence of gender‐based concepts, themes, issues, and solu</span><span>ti</span><span>ons within large‐scale poli</span><span>ti</span><span>cal and sociological survey ques</span><span>ti</span><span>onnaires fielded cross‐na</span><span>ti</span><span>onally in Europe and in six European countries: Denmark, Germany, Hungary, Switzerland and the UK, spanning 2000‐2023. Data was collected by teams from each country between September 2023‐January 2024. Teams collected ques</span><span>ti</span><span>ons in the original language and provided a transla</span><span>ti</span><span>on into English. Analysis was conducted using the translated text. The unit of analysis (‘CODING_UNIT_TEXT’) was the individual 'gender‐related argument' within a survey ques</span><span>ti</span><span>on. This could be the en</span><span>ti</span><span>re survey ques</span><span>ti</span><span>on, a sub‐ques</span><span>ti</span><span>on (in the case of matrix ques</span><span>ti</span><span>ons), or a singular response op</span><span>ti</span><span>on (for mul</span><span>ti</span><span>ple choice ques</span><span>ti</span><span>ons). Coding units were coded in three key domains:(1) Gender concepts, (2) Themes/issues, and (3) Solu</span><span>ti</span><span>ons. Up to two Themes/Issues and Solu</span><span>ti</span><span>ons could be coded per coding unit. Several coding categories within the Themes/Issues and Solu</span><span>ti</span><span>ons domains func</span><span>ti</span><span>on hierarchically, where a coder first assigned a higher‐level category and then as many subcategories as applicable. For example, a ques</span><span>ti</span><span>on concerning government‐funded childcare is coded as B1_Economy ‐> B1_4_LabourMarket ‐> B1_4_1_CareWork ‐> B1_4_1_3_Childcare. The corresponding codebook presents the uni</span><span>ti</span><span>sa</span><span>ti</span><span>on process and coding categories in full detail.</span></p>
Gender codification of 412 national (general) and European Parliament elections in six European countries (2003-2021)
<p>This dataset has been produced by applying the Manifesto Gender Analysis (MGA) codebook to 412 national (general) and European Parliament elections in the six countries participating in the UNTWIST project (Denmark, Germany, Hungary, Spain, Switzerland, and the UK) from 2003 to 2021.</p> <p> The Manifesto Gender Analysis coding procedure, developed by WP4 of the UNTWIST consortium, aims to analyse gender-related content in party manifestos. It relies on existing manifestos collected by MARPOR and EM projects from 2003-2021 in six national contexts: Denmark, Germany, Hungary, Spain, Switzerland, and the United Kingdom. The process involves splitting manifestos into quasi-sentences, coding them based on a scheme inspired by previous projects and feminist typology, and completing an expert survey. This method ensures comprehensive analysis and potential scalability through computational methods. </p> <p>The coding procedure involves a series of essential steps, divided in two main activities: the classification of manifestos’ quasi-sentences, and the completion of a survey dedicated to more general concepts which can be gauged by evaluating the content of the entire documents. In the latter case, then, the unit of measure of each coder consists in the manifesto document, whereas in the former the units of measure are quasi-sentences - i.e., arguments denoting a verbal expression of a political idea or issue. Coders are instructed to split sentences containing multiple arguments into quasi-sentences and ensure that each quasi-sentence encapsulates a single political idea or issue. </p> <p>Once the manifestos are split into said units, coders classify the arguments following the MGA coding scheme. The coding scheme (MGA) consists of 5 domains and 25 coding categories, covering various aspects of gender-related issues. Each domain includes an "other" category for relevant statements that do not fit precisely into the defined categories. Apart from coding categories related to specific themes, the coding scheme then includes additional dimensions. The classification process consists of seven steps: (1) assessing whether the quasi-sentence addresses gender-related issues, (2) defining both the domain and coding category, (3) determining whether the quasi-sentence refers to a specific recipient or group based on gender and/or sexual orientation, (4) evaluating intersectionality, (5)<strong> </strong>assigning the sentiment or connotation, (6) determining if it's related to a goal, issue, or policy, and (7) characterising the policy if applicable.</p> <p>After completing the classification of the quasi-sentences in a given manifesto, coders fill in a survey for each manifesto document. The surveys provide information that cannot be directly inferred from the quasi-sentences, focusing on the gender ontology of a manifesto, the degree to which a manifesto entails a binary conception of sexes, the extent to which a manifesto promotes a patriarchal conception of the society, and how much a manifesto promotes heterosexuality as the only normal and socially acceptable sexual orientation of individuals. While the last four characteristics are gauged relying on quasi-interval measures (scales ranging from 0 to 10), the first one, gender ontology, consists in a categorical variable which distinguishes between manifestos with an essentialist ontology – gender and sex are the same and inseparable –, a constructivist ontology – biological sex is mediated through social construction of femininity and masculinity –, and other or undefined ontologies.</p>
1600 years of modelled energy production and demand for European Countries (Norway, France, Italy, Spain, and Sweden)
<h3>Citation</h3> <p>When using this dataset, please cite the following paper: van der Most et al. Temporally compounding energy droughts in European electricity systems with hydropower, 10 January 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-3796061/v1].</p> <h3>Description</h3> <p>This dataset contains daily renewable energy production and demand data used in the study "Temporally compounding energy droughts in European electricity systems with hydropower". The dataset includes production data for various renewable energy sources (offshore wind, onshore wind, solar photovoltaics, run-of-river, and hydropower reservoir inflow) and electricity demand. It was generated wit the use of 1600 years of climate model data and a daily renewable electricity production and demand modelling framework. The study focuses on five European countries with significant hydropower capacities: Norway, France, Italy, Spain, and Sweden.</p> <h3>Content</h3> <ul> <li> <p><strong>Energy Production Data</strong>:</p> <ul> <li>Offshore and Onshore Wind Power: Derived from 10 m wind speed data extrapolated to hub height, using power law equations and cubic power curves.</li> <li>Solar Photovoltaics (PV): Based on solar irradiance and temperature-dependent cell efficiency calculations.</li> <li>Hydropower: Includes inflow data for run-of-river and reservoir hydropower systems modelled with routed runoff data</li> <li>Hydropower dispatch is modelled at the national level using a linear optimization approach that aims to minimize the difference between demand and the sum of all renewable energy production over a year, directing the solution to following the load curves.</li> </ul> </li> <li> <p><strong>Energy Demand Data</strong>:</p> <ul> <li>Daily load data from ENTSO-E tranparancy fitted using a logistic smooth transmission regression approach to national mean, population-weighted daily near-surface temperatures from ERA5 reanalysis data.</li> <li>Demand curves account for weekdays and weekends but exclude cultural and socio-economic factors such as holidays.</li> </ul> </li> </ul> <h3>Methodology</h3> <p>The dataset is generated using the KNMI Large Ensemble Time Slice (KNMI-LENTIS) dataset, which includes 160 sets of 10-year physical climate model simulations of present-day climate (2000-2009). The simulations are conducted with the EC-Earth3 global climate model. The energy production and demand data are modeled to assess the impact of meteorological drivers on energy systems, with a focus on identifying periods of high residual loads (energy droughts). The model set-up has been validated with the use of ERA5 data in previous work. </p> <h3>Usage</h3> <p>This dataset is intended for researchers and policymakers interested in studying the impact of climate variability on renewable energy systems. It provides insights into how different meteorological conditions can lead to energy droughts and offers a basis for developing strategies to enhance the resilience of energy systems.</p> <p> </p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: EACEA subset analysis
<p>This dataset was created within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme, Grant Agreement No 649538. Work Package 4 of this project (Exploiting European data and testing the integrated theory of youth active EU citizenship) is focused on the re-analysis of existing European data. This dataset contains a subset of data originally collected within the project “<em>EACEA 2010/03: Youth Participation in Democratic Life</em>”, coordinated by the London School of Economic and Political Science. Specifically, an online questionnaire survey in seven European countries was conducted among young people age 15-30 in 2011. This dataset contains a subset of 22 variables that were employed for the reanalysis within the CATCH-EyoU project.</p>
Data of European University Association (EUA) Open Access Survey 2017-2018
<p>This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>
European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks
<p><strong>European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks</strong></p> <p>ECHO indicators rationale and code definition mapped out in ICD-9 and ICD-10 (for diagnoses) and ICD-9, NOMESCO, OPCS-4, ACHI and Leustungkatalog. </p> <p> </p>
Decadal BIOCLIM estimates based on ISIMIP3b climatic forcing data for the European continent
<p>This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07). <br><br>For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section <a href="https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data" target="_blank" rel="noopener">https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data</a> and <a href="https://doi.org/10.5194/gmd-17-1-2024">Frieler et al. (2024)</a>.</p> <p>The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.<br><br>For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:</p> <p>bioclim01 = Annual Mean Temperature<br>bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))<br>bioclim03 = Isothermality (BIO2/BIO7) (×100)<br>bioclim04 = Temperature Seasonality (standard deviation ×100)<br>bioclim05 = Max Temperature of Warmest Month<br>bioclim06 = Min Temperature of Coldest Month<br>bioclim07 = Temperature Annual Range (BIO5-BIO6)<br>bioclim08 = Mean Temperature of Wettest Quarter<br>bioclim09 = Mean Temperature of Driest Quarter<br>bioclim10 = Mean Temperature of Warmest Quarter<br>bioclim11 = Mean Temperature of Coldest Quarter<br>bioclim12 = Annual Precipitation<br>bioclim13 = Precipitation of Wettest Month<br>bioclim14 = Precipitation of Driest Month<br>bioclim15 = Precipitation Seasonality (Coefficient of Variation)<br>bioclim16 = Precipitation of Wettest Quarter<br>bioclim17 = Precipitation of Driest Quarter<br>bioclim18 = Precipitation of Warmest Quarter<br>bioclim19 = Precipitation of Coldest Quarter<br>huss = Average (arithmetric mean) specific humidity<br>rsds = Average (arithmetric mean) Surface downwelling shortwave radiation<br>sfcwind = Average near-surface wind speed (arithmetric mean)<br><br>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL</td> </tr> <tr> <td>Spatial grain</td> <td>0.5 degree (~50km²)</td> </tr> <tr> <td>Geographic projection</td> <td>WGS 84</td> </tr> <tr> <td>Temporal grain</td> <td>10 year steps</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1850 to 2010 (Historical), 2010 - 2100 (Future)</td> </tr> <tr> <td>Number of variables</td> <td>22</td> </tr> </tbody> </table> <p><br>All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse. </p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
Deep-soil carbon changes at 62 European beech stands in the Vienna Woods, Austria, 1984-2022
This dataset comprises repeated soil, vegetation, and site measurements from long-term forest monitoring in the Vienna Woods (Wienerwald), Austria, part of the UNESCO Biosphere Reserve “Wienerwald” (48.1°–48.3° N, 15.8°–16.3° E). The study focuses on pure, naturally regenerated European beech (Fagus sylvatica) stands, initially sampled in 1984 and resampled in 2012 and 2022 . Elevations range from ~180 to 800 m a.s.l., with mean annual temperatures of 8–9 °C and precipitation of 600–900 mm. Soil samples were collected from three mineral soil depths (0–5 cm, 30–40 cm, and 80–90 cm) following consistent protocols across sampling years. Variables include total, organic, and inorganic carbon, total nitrogen and sulfur, exchangeable base cations (Ca, Mg, K), pH, total Fe and Mn, fine soil mass, bulk density, rock content, soil texture, and root biomass. Stocks were calculated. Leaf nutrient concentrations (C, N, S, P, Ca, Mg, K) were determined in all sampling years. Dendrochronological measurements were conducted to determine growth trends since stand establishment, and stand-level characteristics (tree density, DBH, aboveground biomass, crown vitality, slope, aspect) were recorded. Site-level climate data (mean annual temperature, annual precipitation) from 1961 to present and atmospheric deposition data for N and S (1990, 2012, 2022) were integrated from national and European gridded datasets. The dataset supports long-term assessments of soil carbon and nutrient dynamics, forest productivity, and environmental change impacts in old-growth beech forests. Data collection is complete for the 1984, 2012, and 2022 campaigns; no ongoing sampling is planned.
Data for "Competing for capitals: the great fragmentation of the firm and varieties of FDI attraction profiles in the European Union""
<p>Dataset for https://www.tandfonline.com/doi/full/10.1080/09692290.2020.1737564</p> <p> </p> <p>Code available at https://osf.io/q6x97/</p>
MoTiV: a Dataset of European User Mobility for Behavioral-Data
<p>Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.</p>
Background data: Untangling the effects of multiple human stressors and their impacts on fish assemblages in European running waters
<p>This dataset presents some backkground data from the EFI+ database. Related work addresses human stressors and their impacts on fish assemblages at pan-European scale by analysing single and multiple stressors and their interactions. Based on an extensive dataset with 3105 fish sampling sites, patterns of stressors, their combination and nature of interactions, i.e. synergistic, antagonistic and additive were investigated. </p> <p>Data were derived within the EU-project "Improvement and Spatial extension of the European Fish Index (EFI+)". EFI+, an EU FP6 research project from 2007-2009 was designed to gain new knowledge and to further develop and improve new biological assessment methods to meet needs of the Water Framework Directive (WFD). </p> <p>Background data are available for boxplots and barplots shown in the related research article in STOTEN.</p>
Supplementary data to: "A European Monsoon-like climate in a Warmhouse World"
<p>Supplementary information belonging to the manuscript titled "A European Monsoon-like climate in a Warmhouse World" by Nick van Horebeek and colleagues, containing the following information:</p> <ul> <li>"Campanile_D47_sample_data_calc.csv" - A file containing all clumped isotope measurements carried out for this study</li> <li>"Campanile_D47_season_data_calc.xlsx" - A file containing seasonal means and uncertainties of temperature and d18Osw based on clumped isotope measurements carried out for this study</li> <li>"Campanile_d18O_season_data_calc.csv" - A file containing all incrementally sampled oxygen and carbon isotope values with seasonal characterization.</li> <li>"Intra-growthline_variability_edit.png" - An image showing the variability in d18O values repeatedly measured in the same location in the shell</li> <li>"Campanile_Winter_growth_stop_images.zip" - A folder containing all shell images used in the publication</li> <li>"Campanile_Data_figure_S1.xlsx" - A file containing the dataset needed to produce the supplementary figure showing variability in oxygen isotope values (S1).</li> <li>"SI_Campanile_d18O_d13C_depth_rev1.png" - A plot showing the variability in d18O and d13C values along the shell</li> <li>"Campanile_clumped_season_plot_rev3.r" - Script used to process clumped isotope data for seasonal statistics and plotting</li> <li>"Campanile_clumped_d18O_plots_rev2.r" - Script used to process and plot seasonal statistics and isotope data + uncertainty against shell age.</li> </ul>
The European Energy Vision 2060 (EU EnVis-2060): Scenario Parametrization
<h3>Description</h3> <p>This repository contains the scenario parametrization for the European Energy Vision 2060 (EU EnVis-2060) scenarios, which have been created by the European research projects Man0EUvRE (funded by the CETPartnership) and iDesignRES (funded by the European Commission). The data is formatted in the IAMC data format (see <a href="https://pyam-iamc.readthedocs.io/en/stable/data.html">https://pyam-iamc.readthedocs.io/en/stable/data.html</a>). </p> <p>The underlying raw data, including all sources and assumptions used for each data point can be found at the Global Energy System Model (GENeSYS-MOD) data repository (see <a href="https://github.com/GENeSYS-MOD/GENeSYS_MOD.data">https://github.com/GENeSYS-MOD/GENeSYS_MOD.data</a>). </p> <p> </p> <p>Alongside the scenario parametrization, there is also included a short report about the qualitative storylines, the workflow, and some key assumptions as part of Deliverable 1.2 of the Man0EUvRE project, as well as the Q2Q (qualitative to quantitative) matrix used in the process of the parametrization.</p> <p> </p> <h3>Changelog</h3> <table> <tbody> <tr> <td>Version</td> <td>Date</td> <td>Changes</td> </tr> <tr> <td>3.1</td> <td>08.09.2025</td> <td> <p>Improvements in district heating, technology costs for wind, PV, and electrolyzers. Updated fossil fuel import prices.</p> </td> </tr> <tr> <td>3.0</td> <td>31.07.2025</td> <td> <p>Further refinement of data set, used for <a href="https://doi.org/10.5281/zenodo.16640689">quantification</a> of the scenarios with GENeSYS-MOD (v1.1.0)</p> <p>Data changes are based on partner feedback and further calibration for the European scenarios.</p> </td> </tr> <tr> <td>2.0.1</td> <td>11.03.2025</td> <td> <p>Added newest version of Q2Q matrix</p> </td> </tr> <tr> <td>2.0</td> <td>28.02.2025</td> <td> <p>Significantly overhauled data set, used for <a href="https://doi.org/10.5281/zenodo.14959447">quantification</a> of the scenarios with GENeSYS-MOD (v1.0.1)</p> </td> </tr> <tr> <td>1.0.2</td> <td>11.09.2024</td> <td>Fixed missing hydropower data in capacities due to an error in the conversion script</td> </tr> <tr> <td>1.0.1</td> <td>07.09.2024</td> <td>Fixed missing data in residual capacities</td> </tr> <tr> <td>1.0</td> <td>06.09.2024</td> <td>Initial Upload</td> </tr> </tbody> </table> <p> </p> <h3>Funding</h3> <p>This research was funded by CETPartnership, the European Partnership under Joint Call 2022 for research proposals, co-funded by the European Commission (GA N°101069750) and with the funding organisations listed on the CETPartnership website.</p>
Occurrence cubes at species level for European countries
<p>This package contains aggregated occurrence data ("occurrence cubes") at species level for European countries. These occurrence cubes were generated by grouping species occurrence data from the <a href="https://www.gbif.org/">Global Biodiversity Information Facility (GBIF)</a> by year (year), 1x1km spatial <a href="https://www.eea.europa.eu/en/datahub/datahubitem-view/3c362237-daa4-45e2-8c16-aaadfb1a003b">EEA reference grid</a> cell (eea_cell_code) and species (speciesKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see <a href="https://github.com/trias-project/occ-cube/blob/master/src/3_assign_grid.Rmd#L198-L234">this code</a>). The occurrence cubes can be used as input data for indicators, mapping and species distribution modelling.</p> <p>The occurrence cubes are built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.</li> <li>The code to process the data to cubes is publicly available on GitHub at <a href="https://github.com/trias-project/occ-cube-alien">https://github.com/trias-project/occ-cube</a> (version <a href="https://github.com/trias-project/occ-cube/tree/20240124">20240124</a>).</li> </ul> <h2>Files</h2> <ul> <li><strong>Occurrence cubes at species level per country</strong>: filename format countrycode_species_cube.csv.</li> <li><strong>Taxonomic information for species in a cube</strong>: filename format countrycode_species_info.csv.</li> </ul> <h2>Included countries</h2> <ul> <li><strong>Belgium</strong> (BE): based on <a href="https://doi.org/10.15468/dl.9qx3ba">https://doi.org/10.15468/dl.9qx3ba</a></li> <li><strong>Italy</strong> (IT): based on <a href="https://doi.org/10.15468/dl.jghpm5">https://doi.org/10.15468/dl.jghpm5</a></li> <li><strong>Lithuania</strong> (LT): based on <a href="https://doi.org/10.15468/dl.duegx2">https://doi.org/10.15468/dl.duegx2</a></li> <li><strong>Slovenia</strong> (SI): based on <a href="https://doi.org/10.15468/dl.9eky98">https://doi.org/10.15468/dl.9eky98</a></li> <li><strong>Romania</strong> (RO): based on <a href="https://doi.org/10.15468/dl.b7z5vw">https://doi.org/10.15468/dl.b7z5vw</a></li> <li><strong>Portugal</strong> (PT): based on <a href="https://doi.org/10.15468/dl.b89nr4">https://doi.org/10.15468/dl.b89nr4</a></li> </ul> <p>Occurrence cubes are added on demand. To include occurrence cubes for other European countries, <a href="https://github.com/trias-project/occ-cube/issues">leave an issue</a> or contact the main author, or generate your own cube using the code in <a href="https://github.com/trias-project/occ-cube">this repository</a>.</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.