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7 results for “NUTS regions”
Internationalization in the Baltic Regional Accounts: A NUTS 3 Region Dataset
<p>This dataset is a NUTS 3 Region Dataset, corresponding to the Baltic regional accounts. The Baltic countries contain 21 NUTS 3 regions, distributed over 11 in Lithuania, 6 in Latvia and 5 in Estonia. Our dataset encompasses internationalization variables seldom available at regional level, involving trade, FDI, and migration. In addition, the dataset contains domestic variables on fixed capital, labor participation, price indexes, value added, information production and sharing, demographics, the population's well-being, and physical settlement features. All variables cover at least 2007 to 2015 and mostly up to 2019, but some variables cover a longer period from 1991 to 2020. By sharing the dataset, we hope to foster spatial research on internationalization and the spatial economy in the Baltics. When industry-specification is relevant and available, we report data across 11 industries, corresponding the classification applied in Eurostat and OECD's regional statistics. The dataset builds on a combination of micro and macro data sources. The collection and processing of the data is documented in the affiliated article: "<i>Holmen, R. B., Gavoille, N., Masso, J., & Burinskas, A. (2023). Internationalization in the Baltic Regional Accounts: A NUTS 3 Region Dataset, Data Descriptor, Data</i>". The dataset is provided in both Excel format and Stata format.</p>
ERA-NUTS: meteorological time-series based on C3S ERA5 for European regions (1980-2021)
<p><strong># ERA-NUTS (1980-2021)</strong></p> <p>This dataset contains a set of time-series of meteorological variables based on <a href="https://climate.copernicus.eu/climate-reanalysis">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</p> <p>This data has been generated with the aim of providing hourly time-series of the <strong>meteorological variables</strong> commonly used for power system modelling and, more in general, studies on energy systems.</p> <p>An example of the analysis that can be performed with ERA-NUTS is shown <a href="https://youtu.be/zVeF8Dv6jlE">in this video</a>.</p> <p><strong>Important</strong>: <em>this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href="mailto:matteo.de-felice@ec.europa.eu">sending an email</a> or opening an Issue in the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a>.</em></p> <p><strong>## Data</strong><br> The time-series have hourly/daily/monthly frequency and are aggregated following the <a href="https://ec.europa.eu/eurostat/web/nuts/background">NUTS 2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).</p> <p>This dataset contains NUTS0/1/2 time-series for the following variables obtained from the <strong>ERA5 reanalysis data</strong> (in brackets the name of the variable on the Copernicus Data Store and its unit measure):</p> <p> - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br> - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br> - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br> - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br> - <strong>sd</strong>: Snow depth (`sd`, meters)<br> <br> There are also a set of derived variables:<br> - <strong>ws10</strong>: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)<br> - <strong>ws100</strong>: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)<br> - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br> - <strong>RH</strong>: Relative Humidity (computed following Lawrence, BAMS 2005 and Alduchov & Eskridge, 1996)<br> - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href="https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm">EUROSTAT definition</a>.</p> <p>For each variable we have <strong>367 440 hourly samples</strong> (from 01-01-1980 00:00:00 to 31-12-2021 23:00:00) for <strong>34/115/309 regions</strong> (NUTS 0/1/2).<br> <br> The data is provided in two formats:</p> <p> - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` to minimise the size of the files.<br> - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)<br> <br> All the CSV files are stored in a zipped file for each variable.</p> <p><strong>## Methodology</strong></p> <p>The time-series have been generated using the following workflow:</p> <p> 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form">ERA5 hourly data on single levels from 1979 to present</a> dataset<br> 2. The data is read in R with the <a href="http://www.meteo.unican.es/climate4R">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href="https://github.com/matteodefelice/panas">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.<br> 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br> 4. The NetCDF are created using `xarray` in Python 3.8.</p> <p><strong>## Example notebooks</strong></p> <p>In the folder `notebooks` on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href="https://github.com/kavvkon/enlopy">enlopy</a> package.</p> <p>There are currently two notebooks:</p> <p> - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br> - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href="https://jupyter.org/">jupyter</a>]() and <a href="https://ipywidgets.readthedocs.io/en/stable/">ipywidgets</a>.</p> <p>The notebook `exploring-ERA-NUTS` is also available rendered as HTML.<br> <br> <strong>## Additional files</strong></p> <p>In the folder `additional files`on the <a href="https://github.com/energy-modelling-toolkit/era-nuts-code">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.</p> <p><strong>## License</strong></p> <p>This dataset is released under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0 license</a>.</p> <p><strong>## Changelog</strong></p> <p><strong>2022-04-08 </strong>Added Relative Humidity (RH)<br> <strong>2022-03-07 </strong>Added the missing month in CDD/HDD <br> <strong>2022-02-08 </strong>Updated the wind speed and temperature data due to missing months. </p> <p> </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>
Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model
<p>This is the supplementary material to the masters thesis:</p> <p>"NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model"</p> <p><strong>LICENSE</strong></p> <p>All output data provided is under Creative Commons Attribution 4.0 International Public License. All Python scripts provided are under Apache License, Version 2.0. Refer to the ‘Input data documentation’ file for source and data license information for the input data. For license information, refer to the LICENSE files.</p> <p><strong>DATASET DESCRIPTION</strong></p> <p>The supplementary material is organized into four different subdirectories:</p> <ul> <li>The subdirectory ‘Industrial processes’ contains the input data, Python scripts, and output data for the estimation of NUTS-3 load shifting potential of suitable electrically powered industrial processes (cement milling, mechanical pulping, paper production, air separation).</li> <li>The subdirectory ‘Process heat’ contains the input data, Python scripts, and output data for the estimation of NUTS-3 load shifting potential of industrial process heat applications that are powered by electricity.</li> <li>The subdirectory ‘Future projections’ contains the input data, Python scripts, and output data for the estimation of NUTS-0 annual average industrial processes load shifting potential in the future up until 2050.</li> <li>The subdirectory ‘Other tables for reference’ contains data tables that are not used as input data to the Python scripts, but which may serve as useful further reference for the reader. These tables are the original or intermediate tables from which the input data tables were created.</li> </ul> <p>For more information refer to the README file.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the masters thesis "NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model", available here: <a href="https://elib.dlr.de/134116/">https://elib.dlr.de/134116/</a></p> <p>In case of questions please contact: bruno.schyska@dlr.de or wilko.heitkoetter@dlr.de</p> <p> </p>
Dataset: 'Protected Designations of Origin' (PDO) per NUTS-3 regions plus PDO-scores and social-ecological indicators (v2)
<p>The dataset contains the original research data belonging to the research article "EU-wide mapping of ‘Protected Designations of Origin’ food products (PDOs) reveals correlations with social-ecological landscape values" (Flinzberger et al. 2022: <a href="https://doi.org/10.1007/s13593-022-00778-4">https://doi.org/10.1007/s13593-022-00778-4</a>).</p> <p>Based on the mapping of 638 food products registered within the EU as 'Protected Designation of Origin' (PDO), the first excel file <em>"EU28_all_PDO_products_per_NUTS3.xlsx"</em> shows the distribution of PDO-labeled products across the EU28 NUTS-3 regions. All NUTS-3 regions are listed once for each PDO product they contain (as by 30 June 2020).</p> <p>The second excel file <em>"PDO_scores_and_social_ecological_indicator_data.xlsx"</em> includes the accumulated numbers of PDOs per NUTS-3 region (called PDO score) as registered by 30 June 2020. The PDO score is presented for all food products combined, and for four major food categories separately. Further, the excel file contains all aggregated values of social-ecological indicators (including their sources), used for the correlation analysis between PDO scores and social-ecological characteristics in the above-mentioned article.</p> <p>Code explanations (meta data) can be found on the second sheet of each excel file.</p> <p>- - - </p> <p>second file updated to v2</p>
Meteorological indicator for selected European NUTS 3 regions
<p>13 datasets have been concatenated into one dataset for the purpose to predict future temperature scenario from 13 European countries.</p> <p>The 13 datasets have been published by Angelova, Denitsa; Blanco, Norman (2020), “Meteorological indicator dataset for selected European NUTS 3 regions ”, Mendeley Data, V3, doi: 10.17632/sf9x4h5jfk.3</p> <p>Further details at https://data.mendeley.com/datasets/sf9x4h5jfk/3</p>
Potential estimation of agrivoltaics for every NUTS-2 region in Europe
<p>This data is part of a paper, which will be submitted to <strong>Progress in Photovoltaics</strong> with a title '<strong>Comparative analysis of PV configurations for agrivoltaic systems in Europe</strong>' .The data consist of potential estimation of agrivoltaics in European countries considering NUTS-2 region.</p>
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