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97 results for “European countries”
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>
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>
QuantMig microsimulation population projection model and migration scenarios for 31 European countries
<p>This open data deposit contains the data and model code of QuantMig-Mic microsimulation population projection model for 31 European countries and accompanies deliverables D8.3: Model outputs for dissemination and D8.1: Microsimulation projection model.</p> <p>This Zenodo deposit contains datasets of model input (baseline population and immigration database) and output data (demography and components output tables) and model code with parameters of the Baseline scenario (termed Default in the model code) deposited in QuantMig_mic.zip file. To view the code the users must first install MODGEN software (or can view code files in Visual Studio). All scenarios share the same parameters except the immigrant population - to change the immigration assumptions the users can import immigration assumptions for any other scenario from the ImmigDataBase.csv and change it in the immigration module using the MODGEN user interface or using Visual Studio.</p> <p><strong>The file structure and codebook for the data files is included in the cover note file "readme_quantmig_datasets.pdf"</strong></p> <p>Detailed <strong>information about the QuantMig-Mic microsimulation model, its modules and parameters</strong>:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>Instructions how to install MODGEN can be found in:</p> <p>Marois, G. and Potančoková, M. (2022) QuantMig-mic microsimulation tool. QuantMig Project Deliverable D8.1. International Institute for Applied Systems Analysis (IIASA). http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.1%20v1.1.pdf </p> <p>Detailed <strong>information about the QuantMig migration scenarios</strong> can be found in:</p> <p>Marois, G., Potančoková, M., González-Leonardo, M. (2023) QuantMig-Mic microsimulation population projection model. QuantMig Project Deliverable D8.2. International Institute for Applied Systems Analysis (IIASA), Austria. Available at:http://quantmig.eu/res/files/QuantMig_IIASA_Deliverable%20D8.2_forsubmission_21-07-2023_corr.pdf</p> <p>A <strong>guide to the datasets</strong> and the codebook can be found in: <strong>readme_quantmig_datasets.pdf</strong></p> <p><strong>Countries included in the model: </strong></p> <p>Austria, Belgium, Bulgaria, Croatia, Czechia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom</p> <p> </p>
Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries
<p>This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989"><em><strong>"Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries"</strong></em></a> conducted by <em>Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).</em></p> <p>It is being made public both to act as supplementary data for "<em>Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries</em>", Government Information Quarterly*, and in order for other researchers to use these data in their own work. </p> <p>***Methodology***</p> <p>The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems. </p> <p>This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.</p> <p>***Description of the data in this data set***</p> <p>The file "OpenDataIndex_<em>2013_</em>2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:</p> <p>1) Global Open Data Index (GODI) (4 editions)</p> <p>2) Open Data Maturity Report (ODMR) (8 editions)</p> <p>3) Open Data Inventory (ODIN) (6 editions)</p> <p>4) Open Data Barometer (ODB) (5 editions)</p> <p>5) Open, Useful and Re-usable data (OURdata) Index (3 editions)</p> <p>6) Open Government Development Index (OGDI) (2 editions)</p> <p>These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.</p> <p>Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.</p> <p>***Format of the file***<br>.xls, .csv (for the first spreadsheet only)</p> <p>***Licenses or restrictions***<br>CC-BY</p> <p> </p> <p>For more info, see README.txt<br> </p>
Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021
<p>Estimates of numbers of SARS-CoV-2 infections by country and age group over time, current proportions in different immune states, and potential remaining burden of hospitalisations and deaths for 19 European countries from article "Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021"</p>
Photovoltaic time series for European countries and different system configurations
<p>This repository comprises 38 years-long hourly time series representing the photovoltaic (PV) capacity factors in every European country (EU-28 plus Serbia, Bosnia-Herzegovina, Norway, and Switzerland). The term capacity factor is defined as the ratio between the delivered power and the cumulative installed capacity (DC). 3 letter codes (ISO-3166-3) are used to identify the countries. Time series include years from 1979 to 2017.</p> <p>To obtain PV time series irradiance from Climate Forecast System Reanalysis (CFSR) dataset has been converted into electricity generation and aggregated at country level. The PV model used for the conversion is described in the article linked below. Prior to conversion, reanalysis irradiance is bias corrected using satellite-based SARAH dataset and a globally-applicable methodology, which is also described in the article.</p> <p>For every country, four different time series assuming alternative PV configurations, <em>i.e</em>., rooftop, optimum tilt, 2-axis tracking, and delta are provided. To obtain the PV hourly capacity factors for a country, different assumptions on the shares of the alternative configurations can be made and the weighted time series can be aggregated accordingly.</p> <p>The license for the AU REatlas photovoltaic time series dataset is: <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a></p> <p>When using this data please make sure you include the following citation:</p> <p><em>M. Victoria and Gorm B. Andresen, Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries, Progress in Photovoltaics: Research and Applications (2019) </em><a href="https://doi.org/10.1002/pip.3126">https://doi.org/10.1002/pip.3126</a></p> <p>More information can be requested from M. Victoria (<a href="mailto:mvp@eng.au.dk">mvp@eng.au.dk</a>) and Gorm B. Andresen (<a href="mailto:gba@eng.au.dk">gba@eng.au.dk</a>).</p> <p>Version 2 assumes tilt angle of 60º for PV panels in delta configuration (in version 1, tilt angle in delta configuration is equal to latitude). The remaining files do not change.</p> <p>Version 3 includes one additional file corresponding to country-wise time series obtained assuming 1 axis-tracker (horizontal axis oriented North-South). In addition, small corrections of the previous time series have been implemented affecting only early hours in the day.<br> </p>
Corresponding spreadsheet to the Paper 'Variability in the assessment of childcare in 30 European countries'
<p>The spreadsheet provides the list of indicators reported by the national experts to assess the quality of child care in the relevant countries along with those gathered from official documents provided by the experts. It has been adopted to the Paper 'Variability in the assessment of childcare in 30 European countries'. </p>
Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity
<p>The repository contains the data and codes supporting the findings of the study:<strong> </strong>Climate targets in European timber-producing countries conflict with goals on forest ecosystem services and biodiversity, which can be found in the zip file "<strong>euclimate_vs_natpolicy-main.zip"</strong>. </p> <p>Further, the repository includes the raw forest simulation data used as input for the multi-objective optimizations and the raw optimization outputs of each study region. The codes to run the national optimization can be retrieved from <a href="http://doi.org/10.5281/zenodo.6631109">https://doi.org/10.5281/zenodo.6631109</a>.</p> <p>Abstract:</p> <p>The European Union (EU) set clear climate change mitigation targets to reach climate neutrality, accounting for forests and their woody biomass resources. We investigated the consequences of increased harvest demands resulting from EU climate targets. We analysed the impacts on national policy objectives for forest ecosystem services and biodiversity through empirical forest simulation and multi-objective optimization methods. We show that key European timber-producing countries – Finland, Sweden, Germany (Bavaria) – cannot fulfil the increased harvest demands linked to the ambitious 1.5°C target. Potentials for harvest increase only exists in the studied region Norway. However, focusing on EU climate targets conflicts with several national policies and causes adverse effects on multiple ecosystem services and biodiversity. We argue that the role of forests and their timber resources in achieving climate targets and societal decarbonization should not be overstated. Our study provides insight for other European countries challenged by conflicting policies and supports policymakers.</p>
Food4Me - personalised nutrition European 9 countries public acceptance survey - Dataset from WP2
<p>This data set consists of a large cross-country survey conducted for the EU funded project Food4Me in 2013. It contains determinants for consumer acceptance and intention to use of personalized nutrition advices based on different levels of specificity and personality of data –self-reported intake, blood analysis and DNA based analysis of optimal nutrition patterns.</p> <p>Data are provided in SPSS and interoperable .CSV format. Surveys and metainformation are added in interoperable .RTF format.</p> <p>The underlying survey is present in 8 languages English, German, Spanish, Norwegian, Dutch, Portuguese, Greek and Polish. Used scales are sourced to the originals in the description file. </p>
Data for ms. Do people really care less about their cats than about their dogs? A comparative study in three European countries
<p>The present dataset is based on a questionnaire which is also part of this package. The enclose questionnaire includes identifiable and relevant variables names (yellow highlighted).</p> <p>Participants were recruited by Norstat, a European-based survey company, with the aim of gaining a representative sample of Austrian, Danish and UK citizens, including pet owners. The survey company administers and hosts online panels comprising citizens from many European countries. We aimed for a sample that is representative in terms of age, gender, and region. Therefore, a stratified sampling principle was set up where individuals within each stratum were randomly invited to participate. The invitations were issued through e-mail that contained a link to the online questionnaire. Data was collected from 11-25<sup>th</sup> of March 2022 in Austria, from 11-24<sup>th</sup> of March 2022 in Denmark and from 8-23<sup>rd</sup> of March 2022 in the UK. The invitation provided information about the background of the study, the participating universities, ethical approval, estimated time for questionnaire completion and further, participants were informed that the completion of the questionnaire was voluntary and anonymous, and that they could exit the survey at any point. Before participants were directed to the survey, they ensured informed consent by confirming that they are over 17 years old, and consent to participate in this survey. </p> <p>Besides the questionnaire the dataset includes a csv and an Excel file consisting of the data that is used in the ms. and an rtf and a pdf file with data variable names/labels, and value labels.</p>
Top-down and bottom-up vegetal and animal product metabolic profiles for 29 european countries (EU27 + United Kingdom + Norway).
<p>This repository contains the data needed to reproduce the results in:</p> <p>Cadillo-Benalcazar, J.; Renner, A. Giampietro, M. (2020). A multiscale integrated analysis of the factors characterizing the sustainability of food systems in Europe. Journal of Environmental Management. Volume 271, 1 October 2020, 110944. <a href="https://doi.org/10.1016/j.jenvman.2020.110944">https://doi.org/10.1016/j.jenvman.2020.110944</a></p> <p>Renner, A.; Cadillo-Benalcazar, J.; Benini, L., Giampietro, M. (2020). Environmental pressure of the European agricultural system: Anticipating the biophysical consequences of internalization. Ecosystem services. Special Issue: Agro-futures. Volume 46, December 2020, 101195. <a href="https://doi.org/10.1016/j.ecoser.2020.101195">https://doi.org/10.1016/j.ecoser.2020.101195</a></p>
Top 10 most-played albums on Spotify for various European countries
<p>This dataset presents the Top 10 most listened-to albums on Spotify for five European countries: Spain, France, Italy, the United Kingdom, and Germany. The information has been compiled from data available on the website <a href="https://chartmasters.org/">https://chartmasters.org/</a>. The dataset, in CSV format, includes essential details such as the ranking position, album title, artist, and the total number of plays. Organized to provide an overview, the dataset offers insights into current musical preferences in various European regions, showcasing the most popular albums on the Spotify platform.</p>
Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report
<p>European Food Safety Authority; European Centre for Disease Prevention and Control</p><p>All summary tables and figures produced for the European Union One Health 2022 Zoonoses Report are provided as archives containing Excel files for tables, and as PDF or PNG files for figures.</p><p><strong>All country data connected to this Report are published SEPARATELY on Knowledge Junction - see related identifiers. This is because DATA OWNERSHIP for country data stays with the organisation(s) of the country submitting the data - for further reference see doi:10.2903/sp.efsa.2019.EN-1544.</strong></p><p>Supplementary datasets submitted are given in the related identifier section, however for clarity we give here the information on what they refer to:</p><p><strong>10.5281/zenodo.10255165 </strong>Foodborne outbreaks</p><p><strong>10.5281/zenodo.10256864 </strong>Disease status</p><p><strong>10.5281/zenodo.10255061 </strong>Animal Population</p><p><strong>10.5281/zenodo.10246432 </strong>Prevalence</p><p><i><strong>Sample-based data submitted by specific countries</strong></i></p><p><strong>10.5281/zenodo.10257184 </strong>Finland</p><p><strong>10.5281/zenodo.10257162 </strong>Croatia</p><p><strong>10.5281/zenodo.10257105 </strong>Norway</p><p><strong>10.5281/zenodo.10257034 </strong>Luxembourg</p><p><strong>10.5281/zenodo.10257142 </strong>United Kingdom (Northern Ireland)</p><p><strong>10.5281/zenodo.10257210 </strong>Ireland</p><p><strong>10.5281/zenodo.10257388 </strong>Sweden</p><p>Journal article: 10.2903/j.efsa. 2023.8442 </p><p>Citation</p><p>EFSA (European Food Safety Authority) & ECDC (European Centre for Disease Prevention and Control). (2023). Tables, figures, and country data complementing the European Union One Health Zoonoses 2022 Report [Dataset]. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.10057302&data=05%7C01%7C%7Cd53ce1fd0eeb4b027cb608dbf6528f55%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638374606688640910%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=46n1ois6rCgqRamDnk4%2B42K2XGwwg1T1nvG5ezYqqyg%3D&reserved=0">https://doi.org/10.5281/zenodo.10057302</a></p>
Population of European Countries
<p>Population of European Countries on the first day of the year.</p> <p>This indicator is a reduced and interpreted verison of the Eurostat [<a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_pjan&lang=en">demo_pjan</a>] data asset. It contains the total population for both sexes. Missing values, for example, in Kosovo, are estimated with linear interpolation. Not yet know values are forecasted. <br> <br> </p>
COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries
<p>"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"</p> <p>Dataset for preprint titled <br> "COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe"<br> https://doi.org/10.1101/2021.01.27.21250658 </p> <p>by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA</p> <p>Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.</p> <p> </p> <p>===========<br> Dataset file: <br> 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv</p> <p><br> Dataset graphical preview: <br> 2.0.0.INFOGRAPHIC_CloudFraction_vs_COVID-19_mortality_Europe_March-December_2020.png</p> <p>DATASET:<br> 444 rows (records), with the following fields:</p> <p>"Country" :<br> Country name; 37 European countries included.</p> <p>"Date": <br> Date stamp at the collection time.<br> Data collection was performed in the last day of every month. <br> Date format: YYYY-MM-DD</p> <p>"Month_Key" : <br> Date stamp at the collection time, formatted for easier monthly time series analysis.<br> Date format: YYYY-MM</p> <p>"Month_Fct2020"<br> Date stamp at the collection time,formatted for easier graphing, as a string with names of the months<br> (in English). </p> <p>"Deaths_per_1Mpop" :<br> Monthly mortality from COVID-19 raported in the country, <br> reported as number of COVID-19 deaths per 1 million population of the country, <br> in that particular month / country. <br> NB: it is reported as million population, not patients. </p> <p>"LogDeaths_per_1Mpop" :<br> Log10 transformation of "Deaths_per_1Mpop"</p> <p>"Insolation_Average" :<br> Insolation average (solar irradiance at ground level),<br> in that particular month / country. <br> It is expressed in Watt / square meter of the ground surface. <br> Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, <br> CERES / FLASHFlux team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M )</p> <p>"Cloud_Fraction" :<br> Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),<br> as decimal fraction of the sky obscured by clouds, <br> in that particular month / country. <br> Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,<br> MODIS Atmosphere Science Team, 2020, <br> https://neo.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR<br> (old link: https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR )</p> <p>"CENTR_latitude" and<br> "CENTR_longitude" :<br> Latitude and Longitude of the country centroid, for each country. <br> Data derived from Google LLC, "Dataset publishing language: country centroids",<br> https://developers.google.com/public-data/docs/canonical/countries_csv <br> NOTE: This is identical in every month (obviuously); <br> it is redundantly included for easier monthly sectional analysis of the data. </p> <p>===========</p> <p>Versioning of the dataset: <br> MAJOR: changes yearly; 1 = 2020<br> MINOR: changes if new monthly data is added in that particular year. <br> PATCH: Changes only if errors or minor edits were performed. </p> <p><br> ===========<br> CHANGELOG: </p> <p>Version 2.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_December_2020.csv<br> - CERES/FLASHFLUX data for August-December 2020 became available at new links at nasa.gov<br> - These data were gathered, analyzed and introduced in this dataset (2.0.0). <br> - updated links for CERES/FLASHFLUX and MODIS dataset<br> - added DOI link for preprint<br> - minor edits on text. <br> -Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-all-year_versiunea18d.csv</p> <p><br> Version 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv <br> First version<br> Dataset file source for this version (internal analysis source file):<br> db_covid_all-ANALYSIS.2020-09-22_r10.csv</p>
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Data on the occurrence of Vibrio spp. of public health importance (i.e. Vibrio parahaemolyticus, Vibrio vulnificus, and Vibrio cholerae non-O1/non-O139) in seafood in European countries (Jan 2010-Sept 2023)
<p><span>This file contains data on the occurrence of <em><span>Vibrio</span></em><span> spp. of public health importance (<em>i.e</em>. <em>Vibrio parahaemolyticus</em>, <em>Vibrio vulnificus,</em> and <em>Vibrio cholerae </em>non-O1/non-O139) in seafood produced and/or commercialized in Europe, </span>covering studies published between January 2010 and September 2023. </span><span>The systematic review protocol used to identify and extract the information is available at <a href="../doi/10.5281/zenodo.10282513"><span>https://zenodo.org/doi/10.5281/zenodo.10282513</span></a> .</span></p>
Marriage Proportion, Age Specific Fertility, Births within Marriage ratios for US, Japan, and selected European countries
<p>Dataset to accompany the paper "Marital fertility patterns and nonmarital birth ratios: an integrated approach"</p> <p>Includes:</p> <p>US African American women and White American women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1980, 1985, 1990, 1995, 2000.</p> <p>Selected European country women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1991, 2001, and 2011 (data not available for all countries in all years).</p> <p>Japanese women data on age-specific fertility (5-year groups), age specific marital fertility (5-year groups), proportion of women with a first marriage (5-year groups), and the ratio of births within marriage (5-year groups) as well as calculated values from the paper. For ages 15-44 and years 1950, 1960, 1970, 1980, 1990, 1995, 2000, 2005, and 2010.</p> <p> </p>
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