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)

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

Climate change is one of the biggest challenges facing the world. As the EU continues to act to meet its climate objectives, it is important to understand the views, expectations and behaviour of its citizens concerning climate change. This Eurobarometer survey measures these and compares them with the previous poll on climate change carried out in 2015.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Table of INEBase Population by Gender and country of nationality (Main countries). Annual. Censo de Población

This table contains figures on the number of companies (absolute and relative) that import and export goods or services. These are companies that are active in the Business Economy. In addition, figures are available about the trade value, expressed in millions of euros. The data can be broken down into a few size groups, the independence of the company and the main countries (in terms of trade value) with which trade has been conducted. For trade in services, no breakdown by country is available for countries outside the EU. This table has been developed for the 'State of SMEs' programme. Data available from: 2015. Status of the figures: The figures in the table up to and including 2019 are final, those for 2020 and later are provisional. Changes as of October 7, 2022: The data for 2019 and 2020 have been adjusted on the basis of subsequent information. The data for 2021 has been added. When will new numbers come out? New figures are expected in mid-2023.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

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.

All tourism trips made by residents, aged 15 or over, for personal or professional/business purpose, with at least 1 overnight stay.

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.

For many countries lat/lng are determined with an algorithm that searches the place names in the main geonames database using administrative divisions and numerical vicinity of the postal codes as factors in the disambiguation of place names. For postal codes and place name for which no corresponding toponym in the main geonames database could be found an average lat/lng of 'neighbouring' postal codes is calculated. Please let us know if you find any errors in the data set. ThanksFor Canada we have only the first letters of the full postal codes (for copyright reasons)For Ireland we have only the first letters of the full postal codes (for copyright reasons)For Malta we have only the first letters of the full postal codes (for copyright reasons)The Argentina data file contains 4-digit postal codes which were replaced with a new system in 1999.For Brazil only major postal codes are available (only the codes ending with -000 and the major code per municipality).For India the lat/lng accuracy is not yet comparable to other countries.Update frequency: 1 month

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 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.

Dataset for content analysis published in "Hornikx, J., Meurs, F. van, Janssen, A., & Heuvel, J. van den (2020). How brands highlight country of origin in magazine advertising: A content analysis. Journal of Global Marketing, 33 (1), 34-45."

*Abstract (taken from publication)
Aichner (2014) proposes a classification of ways in which brands communicate their country of origin (COO). The current, exploratory study is the first to empirically investigate the frequency with which brands employ such COO markers in magazine advertisements. An analysis of about 750 ads from the British, Dutch, and Spanish editions of Cosmopolitan showed that the prototypical ‘made in’ marker was rarely used, and that ‘COO embedded in company name’ and ‘use of COO language’ were most frequently employed. In all, 36% of the total number of ads contained at least one COO marker, underlining the importance of the COO construct.

*Methodology (taken from publication)

Sample
The use of COO markers in advertising was examined in print advertisements from three different countries to increase the robustness of the findings. Given the exploratory nature of this study, two practical selection criteria guided our country choice: the three countries included both smaller and larger countries in Europe, and they represented languages that the team was familiar with in order to reliably code the advertisements on the relevant variables. The three European countries selected were the Netherlands, Spain, and the United Kingdom. The dataset for the UK was discarded for testing H1 about the use of English as a foreign language, as will be explained in more detail in the coding procedure.

The magazine Cosmopolitan was chosen as the source of advertisements. The choice for one specific magazine title reduces the generalizability of the findings (i.e., limited to the corresponding products and target consumers), but this magazine was chosen intentionally because an informal analysis suggested that it carried advertising for a large number of product categories that are considered ethnic products, such as cosmetics, watches, and shoes (Usunier & Cestre, 2007). This suggestion was corroborated in the main analysis: the majority of the ads in the corpus referred to a product that Usunier and Cestre (2007) classify as ethnic products. Table 2 provides a description of the product categories and brands referred to in the advertisements. Ethnic products have a prototypical COO in the minds of consumers (e.g., cosmetics – France), which makes it likely that the COOs are highlighted through the use of COO markers.

Cosmopolitan is an international magazine that has different local editions in the three countries. The magazine, which is targeted at younger women (18–35 years old), reaches more than three million young women per month through its online, social and print platforms in the Netherlands (Hearst Netherlands, 2016), has about 517,000 readers per month in Spain (PrNoticias, 2016) and about 1.18 million readers per month in the UK (Hearst Magazine U.K., 2016).

The sample consisted of all advertisements from all monthly issues that appeared in 2016 in the three countries. This whole-year cluster was selected so as to prevent potential seasonal influences (Neuendorf, 2002). In total, the corpus consisted of 745 advertisements, of which 111 were from the Dutch, 367 from the British and 267 from the Spanish Cosmopolitan. Two categories of ads were excluded in the selection process: (1) advertisements for subscription to Cosmopolitan itself, and (2) advertisements that were identical to ads that had appeared in another issue in one of the three countries. As a result, each advertisement was unique.

Coding procedure
For all advertisements, four variables were coded: product type, presence of types of COO markers, COO referred to, and the use of English as a COO marker. In the first place, product type was assessed by the two coders. Coders classified each product to one of the 32 product types. In order to assess the reliability of the codings, ten per cent of the ads were independently coded by a second coder. The interrater reliability of the variable product category was good (κ = .97, p < .000, 97.33% agreement between both coders). Table 2 lists the most frequent product types; the label ‘other’ covers 17 types of product, including charity, education, and furniture.

In the second place, it was recorded whether one or more of the COO markers occurred in a given ad. In the third place, if a marker was identified, it was assessed to which COO the markers referred. Table 1 lists the nine possible COO markers defined by Aichner (2014) and the COOs referred to, with examples taken from the current content analysis. The interrater reliability for the type of COO marker was very good (κ = .80, p < .000, 96.30% agreement between the coders), and the interrater reliability for COO referred to was...

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.

This Data Set is derived from the WP4 of the AgriLink project.

It is part of task T4.4 of Work Package (WP) 4 of the H2020 AgriLink project. AgriLink [Agricultural Knowledge: linking farmers, advisors and researchers to boost innovation] aims at better understanding the role of advisory services in farmers’ decision making and at boosting their contribution to innovation for sustainable development of agriculture. WP4 addresses more specifically the governance of farm advisory services. The objective of the research presented in this report is to understand the institutions that influence how farm advisory services function on the ground, and to discuss implications for the support for sustainable development innovation.

Data were collected in seven European countries: the Czech Republic, France, Greece, Poland, Portugal, Spain and the UK.

Data were collected for a diversity of types of innovation: Market, Technological, Process, and Social Innovation.

The Data set was built based on interviews with farm advisory suppliers.

In total 170 farm advisory suppliers were interviewed.

The table below provides the distribution of interviews according to countries.

Country

Market innovation (NCRO & RETRO)

Technological innovation (TECH)

Process innovation (BIOP & SOIL)

Social innovation (LABO & COMM)

TOTAL

Czech Republic

4

16

20

France

14

11

25

Greece

11

10

21

Poland

6

18

24

Portugal

11

20

31

Spain

9

29

38

UK

7

4

11

TOTAL

34

28

75

33

170

The data has two aims.

First, to characterise farm advisory suppliers, in terms of (table below):

what do they provide?

Who is in control of the supplier?

    What do they provide
    Farmers
    NGO
    Private
    Public
    semi-public
    Total


    Advice and Bookkeeping
    8

    4
    1

    13


    Advice and Digital tech
    1

    3


    4


    Advice and Education
    2
    4
    3
    5

    14


    Advice and Health services


    1
    2

    3


    Advice and Inputs
    1

    14


    15


    Advice and Inputs and Outputs
    15

    5


    20


    Advice and Machinery


    7


    7


    Advice and Outputs
    8
    1
    8
    2

    19


    Advice and Research
    2
    2
    5
    12
    1
    22


    Only advice and training
    16

    26
    10
    1
    53


    Total
    53
    7
    76
    32
    2
    170

Second, we have set a series of variables to characterise the services they provide. The main variables are:

Number of advisors of the organisation

    Number of advisors
    Number of organisations in that group


    [0:5]
    96


    ]10:50]
    35


    ]5:10]
    16


    >50
    19


    n.a.
    4


    Total
    170

Percentage of advisors in the staff of the organisation

    % of advisors
    Number of organisations


    [0:25[
    43


    [25:50[
    17


    [50:75[
    30


    [75:100]
    70


    n.a.
    10


    Total
    170

Share of back-office activities in the staff of the organisation

    Share of back-office (%)
    Number of organisations


    [0:25[
    41


    [25:50[
    26


    [50:75[
    66


    [75:100]
    24


    n.a.
    13


    Total 
    170

Number of farmers client of the supplier per advisor

    Number of clients per organisation
    Number of organisation


    [0:25[
    31


    [25:75[
    43


    [50:75[
    3


    [75:175[
    28


    >175
    36


    n.a.
    29


    Total 
    170

Main advisory method

    Main Advisory method
    Number of organisations


    Group Advice
    19


    IT tool (app, software…)
    2


    n.a.
    1


    One to One Advice
    129


    Phone or web helpdesk
    15


    Publications
    4


    Total
    170

Main funding source

    Main funding source
    Number of organisations


    EU funds
    15


    Fee-for-advice
    46


    Joint trade
    42


    Membership
    11


    Membership fee
    6


    n.a.
    16


    Public funding
    3


    Public funds
    4


    State budget
    27


    Total
    170

More detailed information about the variables collected can be found in the questionnaire that is available in the appendix of the deliverable D4.2 of AgriLink

Abstract copyright UK Data Service and data collection copyright owner.

The Labour Force Survey Annual Eurostat Datasets form the UK component of the European Union Labour Force Survey (EU LFS), and consist of a subset of core variables from the UK Quarterly Labour Force Survey (held at the UK Data Archive under GN 33246), alongside primary and secondary derived variables computed by Eurostat from the core variables supplied. The data comprise seasonal or calendar quarters, depending on the date, and are not directly comparable with the UK QLFS quarters. Although all datasets in this series are called 'annual', from 1999-2008 they only include data for quarter two of the survey year, as the questions were only asked in this quarter. From 2009, the questions were asked all year. Quarterly EU LFS datasets from 1999 onwards are also available (see under GN 33366) and 'ad hoc' modules (run each year to supplement the information from the core EU LFS questionnaire) are available from 2002 onwards (see under GN 33400).

Users should note that the LFS Eurostat datasets available from the UK Data Archive comprise UK data only, and no data from other EU countries are included here. Further information about the EU LFS can be found on the Eurostat EU Labour Force Survey webpage.

The UK Labour Force Survey (LFS) is a unique source of articulated information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. The first LFS was conducted in 1973 and continues to be one of the reasons for carrying out the survey. Eurostat co-ordinates information from labour force surveys in the European Union (EU) member states in order to assist the EU in such matters as the allocation of the Social Fund. Between 1984 and 1991 the survey was carried out annually, and moved to a quarterly cycle (the QLFS) from May 1992. Further information may be found in the main LFS documentation (see link below).

LFS Documentation (main LFS)
Besides the EU LFS documentation (see below), documentation is also available to accompany the main UK LFS datasets available from the Archive. This largely consists of the latest version of each document alongside the appropriate questionnaire for the year concerned. However, LFS documentation volumes are updated periodically by ONS, so users are advised to check the ONS LFS User Guidance pages before commencing analysis.


Main Topics:
Topics covered include household and demographic characteristics, country and region of work, employment and self-employment, employment history, working time, occupations and occupational status, job hunting, job changing, education and training, unemployment and economic activity.

The dataset was produced by the DEMOTEC Horizon project funded by the EU (H2020 GA no.962553). The main purpose of the project was the study of Participatory Budgeting (PB) and other democratic innovations at the local level of governance.

The dataset consists of citizen responses obtained from an online panel from 10 European countries (Germany, France, Spain, the UK, Cyprus, Greece, Ireland, the Netherlands, Poland and Romania), with roughly 3,000 respondents from each country, except Cyprus (~500 respondents).

Responses to a total of 108 questions are included in the dataset; the main variables of interest concerned awareness of, past participation in and future intent to participate in 4 different forms of democratic innovations: PB, referendums, citizen juries and citizen assemblies. A number of other covariates are present in the dataset from demographics, variables relating to national level politics, attitudes toward local authorities etc.

More methodological details are provided in the attached codebook.

Analysis of ‘Total expenditure and average expenditure by country of birth of the main breadwinner (as of 2011). EPF (API identifier: 24962)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-381-24962 on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Total expenditure and average expenditure by country of birth of the main breadwinner (as of 2011). Annual. National. Household Budget Survey (HBS)

--- Original source retains full ownership of the source dataset ---