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17 results for “EU regions”
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>
Dataset of regional NRSS available for producing BBFs in the EU
<p>The data files contain information regarding the calculation of nutrient rich side stream (NRSS) quantities and their nutrient contents. The data is produced within WP1 of LEX4BIO project.</p> <p>The full description of data sources and calculations is available: https://lex4bio.eu/wp-content/uploads/2022/09/LEX4BIO_D1.1_WP1-1.pdf</p> <p>Data summaries are available: https://px.luke.fi/PxWeb/pxweb/en/maatalous/maatalous__biomassa/</p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
PERCEIVE WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>The data set will contain all the shareable data collected and generated through the different tasks of WP5, that are interdependent. In particular, in Task5.1 we collected a bibliography, which is already included in the dataset. In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. During Task5.3 we analyzed these data through Mallet software to elicit topics, as sets of words that co-occur together. The results of the linear regression analysis will be in this data set as well. The results of the analysis will consist of tables of texts and of numerical data.</p> <p>Collected data are only partly available online, therefore our generated data will have a unique value, as there is no comparable public source of data. Data will be helpful for all student and practitioners willing to understand how the concepts of Cohesion Policies, Europe and European identity are shaped in the public sphere.</p>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
FADN data on the support under the CAP delimited for LAU2 (NUTS2) regions in the EU Member States for the 2007-2013 programming period
<p> </p> <p>Ready to use FADN dataset on the support under the CAP in 2007-2013 delimited for LAU2 (NUTS2) regions in the EU Member States.</p> <p>Investigation of the interaction between Cohesion and Rural Policies requires analysing comparable data. However, the CAP data are usually collected at the national level. The FADN database is the only data source for analysing the impact of agricultural policy instruments on the economic situation of farms. However, the regional breakdown of FADN data in some countries does not correspond to the NUTS2 breakdown for which cohesion policy is defined.</p> <p>The provided FADN data delimitation uses a methodology that takes into account the range of impact and features specific to a given region. Because the research shows a very strong relationship between the amount of support under the CAP and the number and size of farms on a given area, this criterion was used to delimit FADN data for particular LAU2 (NUTS2) regions, while maintaining the allocation to individual measures.</p> <p>FADN data aggregated (averaged) to the level of FADN regions and economic size classes were used. Each FADN region has been assigned a corresponding NUTS2 region (or regions) according to the classification in 2010 in which the full census of the farm structure survey was carried out. The delimitation of FADN data to NUTS2 regions was based on weights constructed on the basis of Eurostat data on utilised agricultural area and number of holdings in 2010. In each economic size class, each FADN region consisted of the sum of the NUTS2 regions weighted by the utilised agricultural area. The result of each FADN variable was the sum of its values in each economic size class, weighted by the total number of holdings in each class.</p> <p>This database has served as a basis for two articles, one validating the assumptions of the NUTS2 (LAU) delimitation of the FADN regions and the other using the database to compare synergies and trade-offs between cohesion policy and the common agricultural policy.</p>
PERCEIVE: WP5: The multiplicity of shared meanings of EU and Cohesion Regional and Urban Policy at different discursive levels
<p>This data set contains all the shareable data collected and generated through the different tasks of WP5, which are interdependent.</p> <p>In particular, in Task5.1 we collected a bibliography, which is the basis for our theoretical work.</p> <p>In Task5.2 we collected a large collection of data from different documentary sources and media: EU policies and reports, descriptions and reports created by Local Managing Authorities, newspaper articles, tweets, Facebook posts referred to EU CP policies. We don’t have the permission to share these data (as they are protected by copyright), but all the sources are described in Deliverable 5.2, which is public (see <a href="http://doi.org/10.6092/unibo/amsacta/5726">http://doi.org/10.6092/unibo/amsacta/5726</a> or <a href="http://doi.org/10.5281/zenodo.1318184">http://doi.org/10.5281/zenodo.1318184</a>).</p> <p>During Task5.3 we analyzed the textual content of data listed in Task5.2, to construct a database of discursive topics in Task5.4. Data set includes the description of topics (results of topic modeling), clusters of topics obtained both interpretively and algorithmically, and the relevant data regarding sentiment and semantic analyses.</p> <p>Task5.5 regards a statistical analysis linking public discourse and different definitions of being Europeans on the one hand with European identification on the other hand. The data set contains the measures of variables used to run the regression test, and the results of the test in tabular form.</p>
Dataset and code: One-tenth of EU's biomethane potential combined with carbon capture and storage can shift the region's ammonia production to net-zero
<h2>Overview</h2> <p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p> <div> <h2>Repository structure</h2> </div> <p>The data folder includes:</p> <ul> <li><code>inventories.xlsx</code> contains the LCI datasets for biomethane and ammonia production formatted for use with <a href="https://github.com/brightway-lca">Brightway</a>.</li> <li><code>sustainable_biomethane_potential_Europe.xlsx</code> contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.</li> <li><code>ammonia_production_europe.xlsx</code> contains ammonia production levels in the EU in 2021.</li> <li><code>SA_methane leakage_for presample.xlsx</code> contains data to perform sensitivity analysis on the methane leakage with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>SA_upgrading technology_presamples.xlsx</code> contains data to perform sensitivity analysis on upgrading technologies with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>results</code> folder within data contains csv files with the results, which are used in <code>05_visualization.ipynb</code> for analysis and visualization purposes.</li> </ul> <p>The notebooks folder includes:</p> <ul> <li><code>01_project_setup.ipynb</code> sets up a new Brightway project and imports the ecoinvent database.</li> <li><code>02_lci.ipynb</code> imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).</li> <li><code>03_lcia.ipynb</code> calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).</li> <li><code>04_sensitivity_analysis.ipynb</code> performs the sensitivity analysis.</li> <li><code>05_visualization.ipynb</code> imports all results and generates the figures presented in the scientific article.</li> </ul> <p>The src folder contains supporting functions required to regionalize LCIs and perform the calculations.</p> <div> <h2>How to get propertary data</h2> </div> <p>Some of the LCI datasets in the <code>inventories.xlsx</code> file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.</p> <h2>Contact</h2> <p>Robert Istrate: i.r.istrate@cml.leidenuniv.nl</p>
PERCEIVE. WP4_ T4-5_Citizens vs Practitioners views of the EU Cohesion Policy in 9 PERCEIVE case-study regions
<p>This dataset contains data related to the papers: Tudor MM, Florian V, Rosu, SE, Kruzslicika M (2019) "Citizens’ vs practitioners’ perception on EU regional approach", published by Rural areas and Development & Tudor MM, Florian V (2019) "Percepția cetățenilor vs. percepția practicienilor cu privire la politica de coeziune a UE", published in proceedings of "Piețele Agricole și Spațiul Rural în Contextul Modernizării și Simplificării Politicii Agricole Comune". The dataset was built collecting data from different sources: the dataset “<em>PERCEIVE: WP1: Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries: Survey at citizen level and data relative to regional performance of the Cohesion Policy and institutional quality</em>” (<a href="https://doi.org/10.5281/zenodo.3346047">https://doi.org/10.5281/zenodo.3346047</a>); the dataset “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews) (<a href="http://amsacta.unibo.it/6215/">http://amsacta.unibo.it/6215/</a>).</p>
PERCEIVE. WP3_ T3-1_ Communication strategies effectiveness in the context of EU Cohesion Policy - Romanian case study – Sud Est NUTS II region
<p>This dataset contains data related to the papers: Tudor MM, Florian V, (2018) "Impactul și eficacitatea strategiilor de comunicare în contextul politicii de coeziune a Uniunii Europene. Studiu de caz - România", published in proceedings of "Economia agroalimentară și dezvoltarea rurală din perspectiva integrării europene". The dataset was built collecting data from the dataset “<em>PERCEIVE. WP1. Framework for comparative analysis of the perception of Cohesion Policy and identification with the European Union at citizen level in different European countries. Task1.2. Focus group with Cohesion Policy practitioners</em>” (focus groups and other interviews) (<a href="http://amsacta.unibo.it/6215/">http://amsacta.unibo.it/6215/</a>). Data used in the above-mentioned paper regarding the Section II from the Focus Group protocol and is related with Romanian PERCEIVE case-study region – Sud EST (RO22).</p>
Comprehensive Collection of EU-27 crops statistics: a harmonized regional dataset of area and production.
<p><strong>Description</strong>: This dataset provides a detailed overview of data related to agricultural production and cultivated area in the European Union with 27 member states (EU27) at the regional level. The objective of this work is to provide a homogeneous dataset to analyze crop trends across European regions.</p> <p>The data covers a wide range of crops, including cereals, vegetables, fruits, and other categories relevant to European agriculture. The information is disaggregated by NUTS regions (Nomenclature of Territorial Units for Statistics).</p> <p>The data used in this dataset comes from Eurostat and national statistics from the member states, and has been harmonized to ensure consistency and reliability. The accompanying documentation provides more details on the harmonization methodology and a complete list of the national statistics websites visited.</p> <p><strong>Keywords</strong>: Agriculture, EU27, Agricultural production, Cultivated area, Regional data, Dataset, Crops, NUTS, Eurostat, National statistics.</p> <p><strong>License</strong>: [CC BY]</p>
LIVERUR - Database for for elaboration of the Report on existing business models in EU countries and regions
<p>Database for for elaboration of the Report on existing business models in EU countries and regions within the WP2: Conceptualization of existing rural business models in EU and regional areas.</p>
Forecast of the competitiveness of EU regions in the conditions of climate change
<p>The database contains forecasts of the values of climate change competitiveness (Regional Climate Change Competitiveness Index) of EU regions (NUTS2) and countries. The calculations were made for the period 2022-2032 and for period 2022-2100 using the ARIMA method.</p> <p>The Regional Climate Change Competitiveness Index is a mean to evaluate the ability of a region to use factors of competitiveness for the formation of a competitive position of the region under climate change conditions.The structure of the index stems from the premise that it constitutes a function of pillars that can be grouped into six broad sub-indexes: Basic, Natural, Efficiency, Innovation, Sectoral, and Social.Twenty-eight pillars were used in order to determine the main index. The higher the value of Regional Climate Change Competitiveness Index, the higher the level of regional competitiveness. For detailed methodology see: Karman, A.; Miszczuk, A.; Bronisz, U. Regional Climate Change Competitiveness—Modelling Approach. Energies 2021, 14, 3704. https://doi.org/10.3390/en14123704.</p> <p>The forecast can be used by regional authorities to model regional policy (policy-mix, tools, measures) against the climate change.</p> <p>Funding: National Science Centre Poland, „Modelling of climate change impacts on regional competitiveness” 2019/35/B/HS5/01548</p>
The evaluation of climate change competitiveness via DEA models and Shannon's entropy: EU regions case
<p>Supplementary materials for the article titled “The evaluation of climate change competitiveness via DEA models and Shannon’s entropy: EU regions case”.</p> <p><span>The data was collected under project provided by the National Science Centre, Poland; Grant No. 2019/35/B/HS5/01548.</span></p> <div> <div> <div> <p> </p> </div> </div> </div> <p> </p>
PERCEIVE: WP2: Evaluation of EU citizens' perception of the EU project: Task2.1: Data relative to regional performance of the Cohesion Policy and institutional quality
<p>1. PERCEIVE regional panel datasets - secondary data collected from Eurostat, EU Commission on Strutural Fund Expenditures and quality of government for NUTS 1, 2 and 3 regions from 1990-2015, (STATA files). See codebook for more detail about variables</p> <p>2. Flash Eurobarometer survey data on "Awarness of EU Regional Policy" and questionaires (STATA files)</p> <p>3. Standard Eurobaromter survey data, annual, from 2000-2016 and questionaires (STATA files)</p> <p>4. Expenditure data on EU Structural Funds, latest three budget periods (2000-2020) (Excel file)</p> <p>5. codebook for all regional data</p>
EUREKA regional users tests for the pilot EU-FarmBook
ZIP archive containing background info, script and questions used by EUREKA partners who conducted the user-test interviews in the 4 EU macroregions, plus their reports (the results) in an Excel file.
EUREKA regional users tests for the pilot EU-FarmBook
ZIP archive containing background info, script and questions used by EUREKA partners who conducted the user-test interviews in the 4 EU macroregions, plus their reports (the results) in an Excel file.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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.