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777 results for “EU”
Zenodo-communities for EU projects
<div> <div>Dataset of Zenodo communities associated with EU-funded projects. Only communities linked to a single EU project under either Horizon Europe, Horizon 2020, or Framework Programme 7 are included. Earlier Framework Programmes are not included, as Framework Programme 6 ended in 2006, and Zenodo was launched on May 8, 2013. The dataset was extracted from Zenodo on May 22, 2024, and contains data as of that date. It includes 2,724 communities linked to an EU-funded project.</div> </div>
EU-TRHeaDS Conjoint Dataset
<p>The EU-TRHeaDS Conjoint Dataset is a set of 20,920 observations that was gathered, organised and edited in the framework of the research project ‘EU Citizens’ Transnational Rights and Health-related Deservingness at the Street-level - EU-TRHeaDS’ (PI: Roberta Perna), which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101022244.</p> <p>One of EU-TRHeaDS' aims is to investigate which criteria ‘activate’ the category of healthcare (un)deservingness in the context of intra-EU migration among the general public in two EU Member States (Belgium and Spain), and the extent to which these preferences turn into patterns of systematic penalisation towards specific EU nationality groups. It does so by carrying out a conjoint experimental study nested in an online survey run in parallel in Belgium and Spain with a representative sample of the population on the dimensions of gender, age (18 years old), level of education achieved and geographical region of residence. During the four tasks of the experiment, respondents were asked who they would prioritise to access publicly-funded healthcare out of two fictitious patients who differed in four attributes, all randomly assigned: 1) nationality; 2) migration trajectory; 3) responsibility over ill health, and 4) employment status.</p> <p>As a subset of a larger survey on intra-EU mobility and access to healthcare rights, the EU-TRHeaDS Conjoint Dataset specifically includes the socio-demographic variables of the probabilistic sample in each country, the variables of the conjoint experiment and information about the time spent by respondents in completing each of the four experimental tasks.</p> <p>For detailed information and the codebook, see the document 'EU-TRHeaDS_conjoint_Description&Codebook'</p>
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, derived from national census and EU Copernicus datasets
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p>
EU MarcoPolo project | SO2 emission inventory over China
<p>The aposteriori SO<sub>2</sub> emissions for year 2014, in the domain from 102°E to 132°E and from 15°N to 55°N, in a 0.25°x0.25° spatial resolution and monthly temporal resolution, have been provided to the MarcoPolo project and can be found at <a href="http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/">http://users.auth.gr/mariliza/MarcoPolo/SO2_EmissionInventory/</a>. For details on the creation of the inventory refer to <a href="http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf">http://users.auth.gr/mariliza/MarcoPolo/D3.4_SO2_emission_estimates.pdf</a> and for the inclusion of the SO2 emission inventory to the MarcoPolo Emission Database refer to: <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.2_DescriptionMarcoPoloInventory.pdf</a> as well as <a href="http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf">http://users.auth.gr/mariliza/MarcoPolo/D4.3_assessment_impact_updated_emission_inventories_v2.0.pdf</a> .</p> <p>The main reference to this dataset is found here:</p> <p>Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.: Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, https://doi.org/10.5194/amt-11-1817-2018, 2018.</p> <p>The netcdf data files contain the following structure:</p> <ul> <li>Dimensions <ul> <li>lat = 129</li> <li>lon = 121</li> </ul> </li> <li>Attributes <ul> <li>author = "MariLiza Koukouli"</li> <li>contact information = "mariliza@auth.gr"</li> <li>institution = "Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki"</li> <li>time frame = "2014"</li> <li>sector classification = "total emissions"</li> <li>emis_cat_name = "sulphur dioxide emissions"</li> <li>source_type_name = "sulphur dioxide emissions"</li> <li>pollutant_description = "updated sulphur dioxide emissions based on the CHIMERE model running the MEIC emissions and the OMI/Aura observations"</li> <li>unit_emissions = "Mg/month"</li> <li>nodata_value = "-9999.0"</li> </ul> </li> <li>Variables <ul> <li>float emissions(lon, lat)</li> </ul> </li> </ul>
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>
MAGIC Deliverable D6.5: Shale gas development in the EU 10Km radius well grid scenario
<p>Geo data set of escenario of shale gas implementation in Europe. Developed for WP 6 of the <a href="https://magic-nexus.eu/">MAGIC-Nexus project</a>. It derives from a Geomodel of wells and a database of shale gas played developed by the <a href="https://ec.europa.eu/jrc/sites/jrcsh/files/pl1-britze.pdf">EUOGA </a>project. </p> <p><strong>DB Fields------------------------------------------</strong></p> <p>WELLid: Id of the well</p> <p>RBid: Id of the River Basin in which the well is located</p> <p>RBtxtINT: Name of the River Basin - English</p> <p>RBtxt: Name of the River Basin - Country's Name</p> <p>GWid: Groundwater basin ID</p> <p>PADid: ID of the extraction pad</p> <p>Formation: Shale formation</p> <p>Age: of the well </p> <p>Depth_avg: Average depth of the shale (inherited)</p> <p>Mature_avg: Average matureness of the shale (inherited)</p> <p>TOC_avg: Average Organic content of the shale (inherited)</p> <p>ThickGross: Gross Thickness of the shale play in meters (inherited)</p> <p>ThickNet_m: Net Thickness of the shale play in meters (inherited)</p> <p>EUOGA_Basi: Basin of the well according ot the EUOGA project database (inherited)</p> <p>Basin_inde: Id of the shale basin (inherited)</p> <p>NGS_Basin: Id of the BAsin as stated by the national geological service</p> <p>Shale_CP: Shale country </p> <p>RF_Maturit: Reference Maturity</p> <p>RF_Depth: Reference Depth</p> <p>CNTR_CODE, Country code</p> <p>NUTS_NAME: Name of the NUTS region</p> <p>NUTid: ID of the NUTS region</p> <p>x,y Coordinates of the well</p>
Working time, energy throughput and value added embodied in production, consumption and trade by subsectors for the US, the EU, China and rest of the world (2011)
<p>This repository contains the data needed to reproduce the results in:</p> <p>Pérez-Sánchez, L., Velasco-Fernández, R., Giampietro, M., The international division of labor and embodied working time in trade for the US, the EU and China, Ecological Economics. <a href="http://doi.org/10.1016/j.ecolecon.2020.106909">https://doi.org/10.1016/j.ecolecon.2020.1069097</a></p> <p>Sources of data are specified in the dataset (under tab "references")</p> <p> </p>
Database on vacancies in selected non-EU countries
<p>This database is a revised version of the deliverable D3.1 of the Horizon Europe project 'Global Strategy for Skills, Migration and Development' (GS4S). For more information, please see the associated working paper: Locating Shortages in Migrants’ Origin Countries: A Big Data Approach, authored by Friedrich Poeschel. </p>
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>
Accessibility Indicators to services at EU scale - 1km grid indicators
<p>This archive makes available <strong>accessibility indicators at EU scale from populated 1km EU grid to towns and cities at EU scale</strong> (512 million travel time by car calculated between origins and destinations). It follows a reproducible, transparent and updatable framework. It uses <strong>only open source and free routing engines (OSRM)</strong>, based on OpenStreetMap (OSM) network. This routing engine makes possible the creation of travel time indicators for a large set of origins and destinations.</p> <p>The EU towns and cities layer has been recently made available and named by the European Commission. This layer is based on a <a href="https://ec.europa.eu/regional_policy/information-sources/maps/urban-centres-towns_en">common methodology</a> for all Europe. Within GRANULAR activities, we consider the towns and cities layer as <strong>a proxy</strong> to discuss on little and medium commercial centralities in Europe.</p> <p>This methodological framework, <strong>implemented with open source solutions (data and code) only and documented in a reproducible way in R notebooks</strong>, could be easily extended to other origins and destinations, if a relevant layer will be identified in the future.</p> <p>Based on travel time matrix, it is possible to compute a large set of indicators. This archive (see readme at the root folder) <strong>describes the input data used, summarises the data processing and provide information and metadata on output indicators created at 1km grid cells.</strong></p> <p>All the output data is also available. </p> <p> </p>
List of capacity building resources for combating climate mis/disinformation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
2023 EU Transparency Register - Meta-organizations
<p>For my Research Habilitation Dissertation, I extracted all information from the EU transparency Register</p> <p>There were 12,435 registered organizations as of January 2023 (Source: Transparency Register). On this basis, I attempted to distinguish between meta-organizations and non-meta-organizations. One should keep in mind that this database only concerns organizations and individuals that seek to lobby EU institutions. There are much more meta-organizations worldwide, and many are not concerned with lobbying the EU.</p> <p>I downloaded and organized the database. I added a year column and edited the database in order to identify meta-organizations. There are 13 different categories of organizations or individuals that can be registered: 1) “Academic institutions”, 2) “Associations and networks of public authorities”, 3) “Companies & groups”, 4), “Entities, offices or networks established by third countries”, 5) “Law firms”, 6) “Non-governmental organisations, platforms and networks and similar”, 7) “Organisations representing churches and religious communities”, 8) “Other organisations, public or mixed entities”, 9) “Professional consultancies”, 10) “Self-employed individuals”, 11) “Think tanks and research institutions”, 12) “Trade and business associations”, 13) “Trade unions and professional associations”. </p> <p>Sheet 1 shows the different tables I created for my habilitation. </p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></p>
PMa_000105_F_Eu
<u>File Name</u>: PMa_000105_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt,"Blindemansspelletje", eerste helft 18e eeuw, 6 kettindraden per cm, 292x257cm <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry Blindman's buff (Blindemansspelletje) 292x257cm 18th century Made in Oudenaarde <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
PMa_000101_F_Eu
<u>File Name</u>: PMa_000101_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt,"Jacht van Silvio op het everzwijn", eerste helft 18e eeuw, 6 kettindraden per cm, 294x436c <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry The hunting of Silvio 18th century 294x436cm <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
PMa_000106_F_Eu
<u>File Name</u>: PMa_000106_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt, eerste helft 18e eeuw, 6 kettindraden per cm, <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry 18th century <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
PMa_000102_F_Eu
<u>File Name</u>: PMa_000102_F_Eu.jpg <br><u>Sublocation</u>: Château d'Eu <br><u>Location</u>: Eu <br><u>Province</u>: Normandie, Seine-Maritime <br><u>Country</u>: France <br><u>Header</u>: Oudenaards wandtapijt," Mirtillo bekent aan Amarilli zijn liefde", eerste helft 18e eeuw, 6 kettindraden per cm, 294x253cm <br><u>Description</u>: Palace (Château d'Eu) The museum (Musée Louis-Philippe du château d'Eu) Flemish tapestry Mirtillo declares his love to Amarillis (Mirtillo bekent aan Amarilli zijn liefde) 294x253cm 18th century Made in Oudenaarde <br><u>Keywords</u>: Cultural heritage, Eu (Seine-Maritime), Europe, France, Museum/private collection, Normandie, Seine-Maritime, Tapestry, Techniques <br><br><u>Author</u>: Photo: Paul M.R. Maeyaert <br><u>Copyright</u>: Paul M.R. Maeyaert; <br>
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.