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97 results for “European countries”
Tables, Figures and Country Datasets complementing the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2017
<p>All summary tables and figures produced for the European Union Summary Report on Zoonoses and Food-borne Outbreaks 2017 are provided.</p> <p>The Appendix file (Excel file) allows the user to filter by chapter the corresponding summary tables and figures with their abbreviated file name and titles.</p> <p>Lastly, all country data are published as supporting information to this report.</p>
The effects of e-government evaluation, trust and the digital divide in the levels of e-government use in European countries
<p>Despite the significant amounts of public investment devoted to enhancing e-government over the last ten years, citizens’ use of this service is still limited, posing a challenge to national governments. Using a regression analysis applied to panel data derived from 27 European countries for the period from 2010 to 2018, our work confirms that supply-side e-government performance evaluations, the level of citizen trust in the government, income per capita and education are determinants of the level of citizens’ use of e-government. Furthermore, the results of the cluster analysis suggest that, over the study period, in the group of countries with highest e-government use rates, the variables under study exhibit more favourable relative values.</p>
The versatility of pulses: Are consumption and consumer perceptions in different European countries related to the actual climate impact of different pulse types? Author links open overlay panel
<p>Pulses support sustainable production and consumption. Their culinary versatility creates a wide range of possibilities for new products, bridging consumers’ preparation barriers. However, this potential is often intangible for consumers who have little knowledge about plant-based foods. Based on an online survey in Denmark, Germany, Poland, Spain, and the United Kingdom (<em>N</em> = 4,226), this study aimed to investigate consumer utilization and perception of pulses as a versatile, low-carbon food relative to objective life cycle assessment (LCA) measures of 12 pulse types. The most popular pulse types, with specific preferences across countries, were lentils, kidney beans, and chickpeas, typically consumed at home and purchased in dried or canned form. Respondents associated pulses with being healthy and natural, but sustainability was not an essential attribute related to the perception of pulses. LCA revealed a low environmental impact caused by pulse production and consumption, with marginal variations between types and produce. Respondents were unaware of the nuances in the environmental impact of different pulse types, generally perceiving uncommon pulses to be relatively more sustainable than others. In conclusion, a low consumption combined with a misconception of pulses’ environmental impact may demand different promotional strategies including clear communication to inform consumers.</p>
Figure 1 in Albania: another European country with the occurrence of Buchonomyia thienemanni Fittkau, 1955
Figure 1. Shkumbin River, view of the sampling site of B. thienemanni. Photo: K. Trnková
A Study Evaluating Implementation Strategies for Cabotegravir (CAB)+ Rilpivirine (RPV) Long-acting (LA) Injectables for Human Immunodeficiency Virus (HIV)-1 Treatment in European Countries
ClinicalTrials.gov study NCT04399551. IPD Sharing: YES. Countries: 5. Publications: 2.
Fourier Open-label Extension Study in Subjects With Clinically Evident Cardiovascular Disease in Selected European Countries
ClinicalTrials.gov study NCT03080935. IPD Sharing: YES. Countries: 7. Publications: 3.
Pre-exposure Prophylaxis Implementation in Central-Eastern European Countries
ClinicalTrials.gov study NCT05323123. IPD Sharing: NO. Countries: 2. Publications: 1.
Genotype data of 1970 Pedunculate oak trees (Quercus robur L.) in 13 European countries at 381 gene loci covering the nuclear and organelle genome
Open the record for dataset details and reuse information.
Food aid in four European countries: Assessing the price and content of charitable food aid packages
<table> <tbody> <tr> <td> <table> <tbody> <tr> <td><strong>Description</strong></td> <td>The dataset is the result of a study on the content and monetary value of food aid packages distributed by local food aid organisations in four European cities: Antwerp, Barcelona, Budapest and Helsinki. Concretely, three food aid organisations per city, who fulfilled the inclusion criteria of the study, were randomly selected. In each organisation, two types of data were collected: 1) a structured interview with the head or a well-informed volunteer, and 2) four visits in which the content of the food aid packages was registered by a participating researcher. After this, food basket data was used to monetize the food aid products, and where necessary this was supplemented by available online pricing data of supermarkets in the four countries. The interviews were conducted at the location of the organisations and a few organisations also provided some of the information via e-mail or telephone. Although the questions were objective/factual about the history and functioning of the organisations only, some of the answers contain estimations of the interviewee in case when no objective information or data was available. The interview questions were set up in English and discussed with the involved researchers, but the questions were translated and the interviews were conducted in respectively Dutch, Hungarian, Finnish and Spanish. For analysis, the interview answers back to English. The registration of the content of the food aid packages included writing down relevant information about that product (such as volume, expiration date etc.) and to whom the product was given. The researchers collected this information in a harmonised way by making use of a uniformly constructed Microsoft Excel template.</td> </tr> <tr> <td><strong>Personal data yes/no</strong></td> <td>no</td> </tr> <tr> <td><strong>Type(s) of data and data format</strong></td> <td>Interview, content and prices of food aid packages data, 13 excel files</td> </tr> <tr> <td><strong>Temporal and special coverage</strong></td> <td>The data was collected between February 2022 and May 2022, in Antwerp (Belgium), Barcelona (Spain), Budapest (Hungary) and Helsinki (Finland).</td> </tr> <tr> <td><strong>Language of files</strong></td> <td>English</td> </tr> <tr> <td><strong>Subject</strong></td> <td>Food aid in 4 European countries, food aid packages, interview data</td> </tr> <tr> <td><strong>Audience</strong></td> <td>Social Sciences</td> </tr> <tr> <td><strong>Rightsholder</strong></td> <td>University of Antwerp, Karen Hermans, phd student, ORCID 0000-0001-9192-2948</td> </tr> <tr> <td><strong>Access rights</strong></td> <td>Restricted access: users may view and download the data by sending an e-mail to <a href="mailto:karen.hermans@uantwerpen.be">karen.hermans@uantwerpen.be</a> and specifying the purpose of the data use and how the data will be used. If the data is to be used for scientific purposes and the data is necessary or useful to reach the objectives, access will be granted. <div> <div> <div> </div> </div> </div> </td> </tr> </tbody> </table> <p> </p> </td> </tr> </tbody> </table>
Future electricity demand time series for European Countries from 2023 to 2100
<p>This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy. To cite this dataset, please cite the published paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.123387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apenergy.2024.123387</a></p> <p>This dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file. </p> <p>The column explanation is as below:</p> <ul> <li>'country_code': the country code in 2 digits</li> <li>'year': the projection year</li> <li>'month': month of the year</li> <li>'day': day of the month</li> <li>'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> </ul>
Input data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all necessary input data to reproduce the results of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
Output data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all output data (results) of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</p>
Oil-gas prices and economic activity in selected European countries – evidence form time and frequency domain causality
<p>Data sets related with research: <strong>Oil-gas prices and economic activity in selected European countries – evidence form time and frequency domain causality</strong>. - file name: Data - Time Series - DEU, NOR, POL</p> <p>results of Breitung-Candelon test</p>
Distribution. Formerly Anatolia, Turkey, then has been introduced into Europe from ancient times and later into many other countries in North and South America, South Africa, Australia, New Zealand, and Fiji Is. The distribution map includes both the native range in Anatolia and the European continent with its old introductions. in Cervidae
Distribution. Formerly Anatolia, Turkey, then has been introduced into Europe from ancient times and later into many other countries in North and South America, South Africa, Australia, New Zealand, and Fiji Is. The distribution map includes both the native range in Anatolia and the European continent with its old introductions.
Investigating the Country of Origin and the Role of the .eu TLD in External Trade of European Union Member States
<p>This dataset includes structured information on various parameters of construction, development and hosting of almost 30.000 existing .eu websites which were collected through means of Web data extraction. This information was analyzed and processed by a detailed algorithm that produced results concerning each website’s most probable country of origin based on a multitude of factors. The dataset is part of the core methodology and discussion sections of the research paper entitled "Investigating the Country of Origin and the Role of the .eu TLD in External Trade of European Union Member States".</p>
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
Analysis of The Influence of Country Sustainability Performance on Stock Returns in The Top 5 Asian and European Countries
<p>This paper aims to investigate the influence of a country's sustainability performance on yearly stock returns in the top 5 Asian and European countries from 2019 to 2022. Sustainability performance is proxied by four variables: ESG score, green bonds, air quality index (AQI), and energy consumption. Stock returns are measured using stock prices from the stock exchange of the respective country. The study employs quantitative research methods, using multiple regression analysis with STATA software to test the hypotheses concerning the relationship between dependent and independent variables, with a total of 40 observations. The results reveal a significant positive relationship between ESG score and AQI with yearly stock returns, whereas energy consumption shows an insignificant positive relationship. In contrast, Green bonds have an insignificant negative relationship with yearly stock returns. These findings are useful for international investors and governments in making informed overseas stock investment decisions and understanding the importance of sustainability performance in improving the stock market condition.</p>
Supplementary material 3 from: Sieber I, Borges P, Burkhard B (2018) Hotspots of biodiversity and ecosystem services: the Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosystem 3: e24719. https://doi.org/10.3897/oneeco.3.e24719
Regional Overview: Mapping and Assessment of Ecosystem Services in the Caribbean EU Outermost Regions and Overseas Countries and Territories
Supplementary material 7 from: Sieber I, Borges P, Burkhard B (2018) Hotspots of biodiversity and ecosystem services: the Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosystem 3: e24719. https://doi.org/10.3897/oneeco.3.e24719
Regional Overview: Mapping and Assessment of Ecosystem Services in the Pacific EU Overseas Countries and Territories
Supplementary material 4 from: Sieber I, Borges P, Burkhard B (2018) Hotspots of biodiversity and ecosystem services: the Outermost Regions and Overseas Countries and Territories of the European Union. One Ecosystem 3: e24719. https://doi.org/10.3897/oneeco.3.e24719
Regional Overview: Mapping and Assessment of Ecosystem Services in the Amazonian EU Outermost Region
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.