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.

Top EU Countries with the Largest Restaurants and Mobile Food Services Industry, 2016 Discover more data with ReportLinker!

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

This dataset provides statistics on labour productivity, for large regions (TL2) and small regions (TL3).

Data source and definition

Labour productivity is measured as gross value added per employment at place of work by main economic activity. Regional gross value added and employment data are collected from Eurostat (reg_eco10) for EU countries and via delegates of the OECD Working Party on Territorial Indicators (WPTI), as well as from national statistical offices' websites. In order to allow comparability over time and across countries, labour productivity data in current prices are transformed into constant prices and PPP measures (link).

Definition of regions

Regions are subnational units below national boundaries. OECD countries have two regional levels: large regions (territorial level 2 or TL2) and small regions (territorial level 3 or TL3). The OECD regions are presented in the OECD Territorial grid (pdf) and in the OECD Territorial correspondence table (xlsx).

Use of economic data on small regions

When economic analyses are carried out at the TL3 level, it is advisable to aggregate data at the metropolitan region level when several TL3 regions are associated to the same metropolitan region. Metropolitan regions combine TL3 regions when 50% or more of the regional population live in a functionnal urban areas above 250 000 inhabitants. This approach corrects the distortions created by commuting, see the list of OECD metropolitan regions (xlsx) and the EU methodology (link).

Small regions (TL3) are categorized based on shared characteristics into regional typologies. See the economic indicators aggregated by territorial typology at country level on the access to City typology (link) and by urban-rural typology (link).

Cite this dataset

OECD Regions and Cities databases http://oe.cd/geostats

Further information

• The interactive web-tool OECD Regions and Cities Statistical Atlas
• The report OECD Regions and Cities at a Glance

Contact: RegionStat@oecd.org

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.

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.

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)

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.

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

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.

Eurovision Contest Winners: A Look at the Data

World's Longest Running Tv Programme

About this dataset

The Eurovision Song Contest is an annual music competition that began in 1956. It is one of the longest-running television programmes in the world and is watched by millions of people every year. The contest's winner is determined using numerous voting techniques, including points awarded by juries or televoters.

Since 2004, the contest has included a televised semi-final::— In 2004 held on the Wednesday before the final:— Between 2005 and 2007 held on the Thursday of Eurovision Week n2 - Since 2008 the contest has included two semi-finals, held on the Tuesday and Thursday before the final.

The Eurovision Song Contest is a truly global event, with countries from all over Europe (and beyond) competing for the coveted prize. Over the years, some truly amazing performers have taken to the stage, entertaining audiences with their catchy songs and stunning stage performances.

So who will be crowned this year's winner? Tune in to find out!

How to use the dataset

This dataset contains information on all of the winners of the Eurovision Song Contest from 1956 to the present day. The data includes the year that the contest was held, the city that hosted it, the winning song and performer, the margin of points between the winning song and runner-up, and the runner-up country.

This dataset can be used to study patterns in Eurovision voting over time, or to compare different winning songs and performers. It could also be used to study how hosting the contest affects a country's chances of winning

Research Ideas

• In order to studyEurovision Song Contest winners, one could use this dataset to train a machine learning model to predict the winner of the contest given a set of features about the song and the performers.
• This dataset could be used to study how different voting methods (e.g. jury vs televoters) impact the outcome of the Eurovision Song Contest.
• This dataset could be used to study trends in music over time by looking at how the style ofwinner songs has changed since the contest began in 1956

Acknowledgements

Data from eurovision_winners.csv was scraped from Wikipedia on April 4, 2020.

The dataset eurovision_winners.csv contains a list of all the winners of the Eurovision Song Contest from 1956 to the present day

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: eurovision_winners.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------------------------| | Year | The year in which the contest was held. (Integer) | | Date | The date on which the contest was held. (String) | | Host City | The city in which the contest was held. (String) | | Winner | The country that won the contest. (String) | | Song | The song that won the contest. (String) | | Performer | The performer of the winning song. (String) | | Points | The number of points that the winning song received. (Integer) | | Margin | The margin of victory (in points) between the winning song and the runner-up song. (Integer) | | Runner-up | The country that placed second in the contest. (String) |

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 ...

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.

ENTSO-E Pan-European Climatic Database (PECD 2021.3) in Parquet format

TL;DR: this is a tidy and friendly version of a subset of the PECD 2021.3 data by ENTSO-E: hourly capacity factors for wind onshore, offshore, solar PV, hourly electricity demand, weekly inflow for reservoir and pumping and daily generation for run-of-river. All the data is provided for >30 climatic years (1982-2019 for wind and solar, 1982-2016 for demand, 1982-2017 for hydropower) and at national and sub-national (>140 zones) level.

UPDATE (19/10/2022): updated the demand files due after fixing a bug in the processing code (the file for 2030 was the same for 2025) and solving an issue caused by a malformed header in the ENTSO-E excel files.

ENTSO-E has released with the latest European Resource Adequacy Assessment (ERAA 2021) all the inputs used in the study. Those inputs include: - Demand dataset: https://eepublicdownloads.azureedge.net/clean-documents/sdc-documents/ERAA/Demand%20Dataset.7z - Climate data: https://eepublicdownloads.entsoe.eu/clean-documents/sdc-documents/ERAA/Climate%20Data.7z

The data files and the methodology are available on the official webpage.

As done for the previous releases (see https://zenodo.org/record/3702418#.YbmhR23MKMo and https://zenodo.org/record/3985078#.Ybmhem3MKMo), the original data - stored in large Excel spreadsheets - have been tidied and formatted in open and friendly formats (CSV for the small tables and Parquet for the large files)

Furthermore, we have carried out a simple country-aggregation for the original data - that uses instead >140 zones.

DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies.

Description

This dataset includes the following files:

• capacities-national-estimates.csv: installed capacity in MW per zone, technology and the two scenarios (2025 and 2030). The files include also the total capacity for each technology per country (sum of all the zones within a country)
• PECD-2021.3-wide-LFSolarPV-2025 and PECD-2021.3-wide-LFSolarPV-2030: tables in Parquet format storing in each row the capacity factor for solar PV for a hour of the year and all the climatic years (1982-2019) for a specific zone. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"
• PECD-2021.3-wide-Onshore-2025 and PECD-2021.3-wide-Onshore-2030: same as above but for wind onshore
• PECD-2021.3-wide-Offshore-2025 and PECD-2021.3-wide-Offshore-2030: same as above but for wind offshore
• PECD-wide-demand_national_estimates-2025 and PECD-wide-demand_national_estimates-2030: hourly electricity demand for all the climatic years for a specific zone. The two files contain the load for the scenarios "National Estimates 2025" and "National Estimates 2030"
• PECD-2021.3-country-LFSolarPV-2025 and PECD-2021.3-country-LFSolarPV-2030: tables in Parquet format storing in each row the capacity factor for country/climatic year and hour of the year. The two files contain the capacity factors for the scenarios "National Estimates 2025" and "National Estimates 2030"
• PECD-2021.3-country-Onshore-2025 and PECD-2021.3-country-Onshore-2030: same as above but for wind onshore
• PECD-2021.3-country-Offshore-2025 and PECD-2021.3-country-Offshore-2030: same as above but for wind offshore
• PECD-country-demand_national_estimates-2025 and PECD-country-demand_national_estimates-2030: same as above but for electricity demand
• PECD_EERA2021_reservoir_pumping.zip: archive with four files per each scenario: 1. table.csv with generation and storage capacities per zone/technology, 2. zone weekly inflow (GWh), 3. table.csv with generation and storage per country/technology and 4. country weekly inflow (GWh)
• PECD_EERA2021_ROR.zip: as for the previous file but the inflow is daily
• plots.zip: archive with 182 png figures with the weekly climatology for all the variables (daily for the electricity demand)

Note

I would like to thank Laurens Stoop for sharing the onshore wind data for the scenario 2030, that was corrupted in the original archive.

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.

​Eurodac (European Asylum Dactyloscopy Database) is a large-scale IT system that helps with the management of European asylum applications since 2003, by storing and processing the digitalised fingerprints of asylum seekers and irregular migrants who have entered a European country. In this way, the system helps to identify new asylum applications against those already registered in the database.

It is used by 31 countries: 27 EU Member States and 4 Associated Countries (Iceland, Liechtenstein, Norway and Switzerland). EU Member States connected as per December 2022 were: Austria, Belgium, Bulgaria, Cyprus, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain and Sweden.

National asylum authorities use Eurodac to store new fingerprints and compare existing records on asylum seekers.

Statistical data on the work of the Eurodac Central System are made public at the end of each year in accordance with the Eurodac Regulation (Article 8(2) of Regulation (EU) No 603/2013). The statistics contain a breakdown of data for each Member State.

The British Geological Survey has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.

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