This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GDP PER CAPITA PPP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The presented dataset contains the centroid distance matrix for the different levels of the European Union's (EU) Nomenclature of Territorial Units for Statistics (NUTS) regions in meters, as well as their code, name, level, and country identifier. Centroids are calculated based on the largest contiguous shape of regions. To support EU-related spatial, regional, and geographical studies, an R function is also attached that compiles the aforementioned dataset for the selected (or all) NUTS levels while complementing it with the geometrical data and centroids of regions. Optionally, this R function displays centroids on a map of Europe to ease the verification of their positions.

Please cite as: • (Data in Brief article)

Top EU Countries with the Largest Computer Services Industry, 2016 Discover more data with ReportLinker!

Empowering’ university policies improve our economies, states the recent report of Empower European Universities called The State of University Policy for progress in Europe. This report assesses the contribution of higher education policies to higher education performance and economic innovation. The main findings are summarized in a policy report, the technical report explains the data we have used and method, the country reports provide a snapshot of each one of the 32 countries.Higher education contributes to economic innovation. This study measures and compares the extent to which national governments’ policies foster this contribution across Europe. The study stresses the relevance of policies which are ‘empowering’ for higher education institutions, or in other words provide them with appropriate resources and regulatory environments.The assessment relies on quantitative scores, based on the contribution of policies regarding funding and autonomy to higher education performance in education, research and economic innovation, using non-arbitrary weights and eighteen policy indicators across 32 European countries. A large number of countries belong to a ‘middle group’ in our overall assessment, indicating a relative cohesion in Europe. Yet, substantial variations exist in terms of higher education policy in Europe, each European country having room for policy improvement.

The following text was abstracted from Bruce Gittings' Digital Elevation Data Catalogue: 'http://www.geo.ed.ac.uk/home/ded.html'. The catalogue is a comprehensive source of information on digital elevation data and should be retrieved in its entirety for additional information.

The European 1:1M database now includes the European Union (EU) plus Scandanavia & Eastern Europe. Cost is #355 per small country to #492 for large countries. Prices for the whole of Europe are also available.

Ireland is now part of the Europe 1:1M database, although actually captured at 1:500K and previously named Ireland 1:500K database.

Discounts are normally available for educational establishments. For research and teaching (excluding commercial research) the data can be obtained at very low prices through CHEST at Manchester University Computing Centre (Tel: 061 275 6099). Higher education users in ALL European countries excluding the former Warsaw Pact area (for the time being) may obtain data through CHEST following a new deal.

Launched by the Council of Europe in 1987, the Cultural Routes demonstrate, through time and space travel, that the heritage of different European countries contributes to the common cultural heritage.

France is today the country of Europe crossed by the largest number of cultural routes of the Council of Europe, with 31 routes listed out of 48 certified in Europe. To know more.

The European Values Study is a large-scale, cross-national and longitudinal survey research program on how Europeans think about family, work, religion, politics, and society. Repeated every nine years in an increasing number of countries, the survey provides insights into the ideas, beliefs, preferences, attitudes, values, and opinions of citizens all over Europe.

As previous waves conducted in 1981, 1990, 1999, 2008, the fifth EVS wave maintains a persistent focus on a broad range of values. Questions are highly comparable across waves and regions, making EVS suitable for research aimed at studying trends over time.

The new wave has seen a strengthening of the methodological standards. The full release of the EVS 2017 includes data and documentation of altogether 37 participating countries. For more information, please go to the EVS website.

Morale, religious, societal, political, work, and family values of Europeans.

Topics: 1. Perceptions of life: importance of work, family, friends and acquaintances, leisure time, politics and religion; happiness; self-assessment of own health; memberships in voluntary organisations (religious or church organisations, cultural activities, trade unions, political parties or groups, environment, ecology, animal rights, professional associations, sports, recreation, or other groups, none); active or inactive membership of humanitarian or charitable organisation, consumer organisation, self-help group or mutual aid; voluntary work in the last six months; tolerance towards minorities (people of a different race, heavy drinkers, immigrants, foreign workers, drug addicts, homosexuals, Christians, Muslims, Jews, and gypsies - social distance); trust in people; estimation of people´s fair and helpful behavior; internal or external control; satisfaction with life; importance of educational goals: desirable qualities of children.

1. Work: attitude towards work (job needed to develop talents, receiving money without working is humiliating, people turn lazy not working, work is a duty towards society, work always comes first); importance of selected aspects of occupational work; give priority to nationals over foreigners as well as men over women in jobs.

2. Religion and morale: religious denomination; current and former religious denomination; current frequency of church attendance and at the age of 12; self-assessment of religiousness; belief in God, life after death, hell, heaven, and re-incarnation; personal god vs. spirit or life force; importance of God in one´s life (10-point-scale); frequency of prayers; morale attitudes (scale: claiming state benefits without entitlement, cheating on taxes, taking soft drugs, accepting a bribe, homosexuality, abortion, divorce, euthanasia, suicide, paying cash to avoid taxes, casual sex, avoiding fare on public transport, prostitution, in-vitro fertilization, political violence, death penalty).

3. Family: trust in family; most important criteria for a successful marriage or partnership (faithfulness, adequate income, good housing, sharing household chores, children, time for friends and personal hobbies); marriage is an outdated institution; attitude towards traditional understanding of one´s role of man and woman in occupation and family (gender roles); homosexual couples are as good parents as other couples; duty towards society to have children; responsibility of adult children for their parents when they are in need of long-term care; to make own parents proud is a main goal in life.

4. Politics and society: political interest; political participation; preference for individual freedom or social equality; self-assessment on a left-right continuum (10-point-scale) (left-right self-placement); individual vs. state responsibility for providing; take any job vs. right to refuse job when unemployed; competition good vs. harmful for people; equal incomes vs. incentives for individual effort; private vs. government ownership of business and industry; postmaterialism (scale); most important aims of the country for the next ten years; willingness to fight for the country; expectation of future development (less importance placed on work and greater respect for authority); trust in institutions; essential characteristics of democracy; importance of democracy for the respondent; rating democracy in own country; satisfaction with the political system in the country; preferred type of political system (strong leader, expert decisions, army should ...

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.

BackgroundSocioeconomic inequalities in alcohol-related mortality have been documented in several European countries, but it is unknown whether the magnitude of these inequalities differs between countries and whether these inequalities increase or decrease over time.Methods and FindingsWe collected and harmonized data on mortality from four alcohol-related causes (alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy; alcoholic liver cirrhosis; and accidental poisoning by alcohol) by age, sex, education level, and occupational class in 20 European populations from 17 different countries, both for a recent period and for previous points in time, using data from mortality registers. Mortality was age-standardized using the European Standard Population, and measures for both relative and absolute inequality between low and high socioeconomic groups (as measured by educational level and occupational class) were calculated.Rates of alcohol-related mortality are higher in lower educational and occupational groups in all countries. Both relative and absolute inequalities are largest in Eastern Europe, and Finland and Denmark also have very large absolute inequalities in alcohol-related mortality. For example, for educational inequality among Finnish men, the relative index of inequality is 3.6 (95% CI 3.3–4.0) and the slope index of inequality is 112.5 (95% CI 106.2–118.8) deaths per 100,000 person-years. Over time, the relative inequality in alcohol-related mortality has increased in many countries, but the main change is a strong rise of absolute inequality in several countries in Eastern Europe (Hungary, Lithuania, Estonia) and Northern Europe (Finland, Denmark) because of a rapid rise in alcohol-related mortality in lower socioeconomic groups. In some of these countries, alcohol-related causes now account for 10% or more of the socioeconomic inequality in total mortality.Because our study relies on routinely collected underlying causes of death, it is likely that our results underestimate the true extent of the problem.ConclusionsAlcohol-related conditions play an important role in generating inequalities in total mortality in many European countries. Countering increases in alcohol-related mortality in lower socioeconomic groups is essential for reducing inequalities in mortality. Studies of why such increases have not occurred in countries like France, Switzerland, Spain, and Italy can help in developing evidence-based policies in other European countries.

This dataset provides values for GDP GROWTH RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

The pan-European land cover map of 2015 was produced by combining the large European-wide land survey LUCAS (Land Use/Cover Area frame Survey) and Landsat-8 data. We used annual and seasonal spectral-temporal metrics and environmental features to map 12 land cover and land use classes across Europe (artificial land, seasonal cropland, perennial cropland, broadleaved forest, coniferous forest, mixed forest, shrubland, grassland, barren, water, wetland, and permanent snow/ice). The classification was based on Landsat-8 data acquired over three years (2014-2016). Overall map accuracy was 75.1%. The spatial resolution and minimum mapping unit is 30 x 30 m. The map can be downloaded as a single GeoTiff file of 874Mbyte. The produced pan-European land cover map compared favourably to the existing CORINE (Coordination of Information on the Environment) 2012 land cover dataset. The mapped country-wide area proportions strongly correlated with LUCAS-estimated area proportions (r=0.98). Differences between mapped and LUCAS sample-based area estimates were highest for broadleaved forest (map area was 9% higher). Grassland and seasonal cropland areas were 7% higher than the LUCAS estimate, respectively. In comparison, the correlation between LUCAS and CORINE area proportions was weaker (r=0.84) and varied strongly by country. CORINE substantially overestimated seasonal croplands by 63% and underestimated grassland proportions by 37%. Our study shows that combining current state-of-the-art remote sensing methods with the large LUCAS database imporves pan-European land cover mapping.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

BackgroundCountries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants.Materials and methodsWe used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility (Δβ) between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants.ResultsOur model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Δβ between variants, revealing that: (i) Δβ increased progressively from the Alpha to the Omicron variant; (ii) Δβ showed a high degree of variability within Omicron variants; (iii) a higher Δβ was associated with a later emergence of the variant within a country; (iv) a higher degree of immunization of the population against previous variants was associated with a higher Δβ for the Delta variant; (v) larger countries exhibited smaller Δβ, suggesting regionally diverse outbreaks within the same country; and finally (vi) the model reliably captures the dynamics of competing variants, even in complex scenarios.ConclusionThe use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Δβ, we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.

European Production of Cement by Country, 2023 Discover more data with ReportLinker!

The Relational Export Dataset “RED” provides comparable dyadic trade data between nation-states for the period 1870 - present. This dataset is built in accordance with the analytical focus of the DFG-funded "Collaborative Research Centre 1342 - Global Dynamics of Social Policy" (CRC 1342). In principle, this large-scale project follows an interdependence-centered approach to explain the diffusion of governmental social policies from 1880 to the present. Trade linkages are an explanatory variable in this respect (Windzio et al., 2022). This requires temporally consistent data on interstate linkages for the largest possible sample of countries. So far, there has been no data set that meets these requirements. We, therefore, introduce a dataset which combines trade data from UN Comtrade (Comtrade, 2022), UNCTAD (UNCTAD, 2021), and the Correlates of War (COW) Project (Barbieri and Keshk, 2016). Unlike most databases, the data here does not represent absolute monetary trade volumes in a given currency. Rather, the data depicts the ratio of trade flows between two countries and the total exports of the specific exporting country. Hence, we measure trade in relational terms weighted by the respective importance of trading partners for one another. These relations are estimated from both an export and an import-oriented point of view; in this technical description, however, we focus on the ratios estimated solely with export values.

We describe and analyze patterns in the geographical focus of political science research across more than a century. Using a new database of titles and abstracts from 27,690 publications in eight major political science journals from their inception, we demonstrate that, historically, political scientists concentrated their studies on a limited number of countries situated in North America and Western Europe. While a strong focus on Western countries remains today, we detail how this picture has changed somewhat over recent decades, with political science research becoming increasingly ‘globalized’. Still, several countries have received almost no attention, and geographical citation patterns differ by subfield. For example, we find indications of a greater focus on the United States and large Western European countries in international relations than in comparative politics publications. In extension, we analyze several correlates of a country being the focus of political science research, including the country’s predominant languages, income, population size, democracy level, and conflict experience, and show systematic variation in the geographic focus of research. This unequal focus, we argue, has important implications regarding the applicability of extant descriptive and causal claims, as well as the development of theories in political science.

In 2023, the United Kingdom was the largest digital advertising market in Western Europe with a spending of ** billion euros. Greece was the smallest market, with an expenditure of *** million euros. The ** countries presented in the data set had a spending of nearly ** billion euros altogether.

This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.