The EURIO (EUropean Research Information Ontology) Knowledge Graph is the knowledge graph containing CORDIS data about research projects funded by the H2020 and FP7 framework programmes. The EURIO Knowledge Graph can be accessed via its SPARQL endpoint at this link: https://cordis.europa.eu/datalab/sparql-endpoint/en. This dataset provides both a database dump of the EURIO Knowledge Graph and subsets of the EURIO Knowledge Graph in the form of named graphs.

The schema defining the structure of the named graphs is the EURIO ontology, available at https://op.europa.eu/en/web/eu-vocabularies/eurio. All files are available in the following formats: RDF, TTL, N-Quads, JSONLD, and N-Triples. For other formats (xlsx,csv etc ;…), please refer to these links: https://data.europa.eu/data/datasets/cordish2020projects and https://data.europa.eu/data/datasets/cordisfp7projects

The file EURIO Knowledge Graph contains a database dump of all CORDIS data about research projects funded under the H2020 and FP7 framework programmes. The file Project contains all projects funded under the H2020 and FP7 framework programmes. The file Organisation contains all organisations funded under the H2020 and FP7 framework programmes.

Reference data (countries, funding schemes/types of action, etc....) can be found in this dataset https://data.europa.eu/euodp/en/data/dataset/cordisref-data, while the EuroSciVoc taxonomy can be freely downloaded or browsed on the EuVocabularies website at this link: https://op.europa.eu/en/web/eu-vocabularies/dataset/-/resource?uri=http://publications.europa.eu/resource/dataset/euroscivoc

European Union Energy Consumption: EU 28 data was reported at 80,127.600 TOE th in 2016. This records a decrease from the previous number of 80,486.100 TOE th for 2015. European Union Energy Consumption: EU 28 data is updated yearly, averaging 86,421.800 TOE th from Dec 1990 (Median) to 2016, with 27 observations. The data reached an all-time high of 91,019.400 TOE th in 2005 and a record low of 77,796.400 TOE th in 2014. European Union Energy Consumption: EU 28 data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.RB004: Energy Consumption.

This dataset provides an overview of the European Union CO2 and GHG emissions - national total: represents the sum of total emissions, without LULUCF, with indirect CO2 and with international aviation.For more explanations on terminology please visit Eurostat metadata and the glossary available here: http://ec.europa.eu/eurostat/statistics-explained/index.php/Category:Energy_glossary For complementary notes please see the DG ENER energy statistical pocketbook 2020 at: https://ec.europa.eu/energy/en/data-analysis/energy-statistical-pocketbookFor more detailed info and updates please consult Eurostat online database at: http://ec.europa.eu/eurostat/data/database For updates or more detailed CO2/GHG emissions data please consult EEA/UNFCCC at: https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16

The BABEL Polish Database is a speech database that was produced by a research consortium funded by the European Union under the COPERNICUS programme (COPERNICUS Project 1304). The project began in March 1995 and was completed in December 1998. The objective was to create a database of languages of Central and Eastern Europe in parallel to the EUROM1 databases produced by the SAM Project (funded by the ESPRIT programme).

The BABEL consortium included six partners from Central and Eastern Europe (who had the major responsibility of planning and carrying out the recording and labelling) and six from Western Europe (whose role was mainly to advise and in some cases to act as host to BABEL researchers). The five databases collected within the project concern the Bulgarian, Estonian, Hungarian, Polish, and Romanian languages.

The Polish database consists of the basic "common" set which is: • The Many Talker Set: 30 males, 30 females; each to read 100 numbers, 3 connected passages and 5 “filler” sentences (or 4 passages if no fillers needed). • The Few Talker Set: 5 males, 5 females, normally selected from the above group: each to read 5 blocks of 100 numbers, 15 passages and 25 filler sentences ( or 20 passages if fillers not needed), and 5 lists of syllables. • The Very Few Talker Set: 1 male, 1 female, selected from many-talker set: 5 blocks of syllables, with and without carrier sentences.

Abstract

EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labour, education and health observations only apply to persons 16 and older. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The 7th version of the 2008 Cross-Sectional User Database (UDB) as released in July 2015 is documented here.

Geographic coverage

The survey covers following countries: Austria, Belgium, Bulgaria, Czech Republic, Denmark, Germany, Estonia, Greece, Spain, France, Ireland, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Iceland, Norway.

Small parts of the national territory amounting to no more than 2% of the national population and the national territories listed below may be excluded from EU-SILC: France - French Overseas Departments and territories; Netherlands - The West Frisian Islands with the exception of Texel; Ireland - All offshore islands with the exception of Achill, Bull, Cruit, Gorumna, Inishnee, Lettermore, Lettermullan and Valentia; United kingdom - Scotland north of the Caledonian Canal, the Scilly Islands.

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the cross-sectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Related Materials.

Mode of data collection

Mixed

In its policy, the European Union intervenes when necessary to prevent conflict or in response to emerging or actual crises. In certain cases, EU intervention can take the form of restrictive measures or 'sanctions'. The application of financial sanctions and more precisely the freezing of assets constitutes an obligation for both the public and private sector. In this regard, a particular responsibility falls on credit and financial institutions, since they are involved in the bulk of financial transfers.

In order to facilitate the application of financial sanctions, the European Banking Federation, the European Savings Banks Group, the European Association of Co-operative Banks, the European Association of Public Banks ("the EU Credit Sector Federations") and the European Commission recognised the need for an EU consolidated list of persons, groups and entities subject to financial sanctions and more precisely the freezing of assets. The Credit Sector Federations set up an initial database containing the consolidated list. The European Commission subsequently took over this database and is responsible for its maintenance and for keeping the consolidated list of sanctions up-to-date. In this respect, the Service for Foreign Policy Instruments (FPI) of the European Commission launched a new Web page in June 2017, where the consolidated lists of financial sanctions consisting in freezing of assets are published in different formats (see link below).

Disclaimer: While every effort is made to ensure that the database and the consolidated list correctly reproduce all relevant data of the officially adopted texts published in the Official Journal of the European Union, neither the Commission nor the EU Credit Sector Federations accepts any liability for possible omissions of relevant data or mistakes, and nor for any use the database or of the consolidated list. Only the information published in the Official Journal of the EU is deemed authentic.

This database that can be used for macro-level analysis of road accidents on interurban roads in Europe. Through the variables it contains, road accidents can be explained using variables related to economic resources invested in roads, traffic, road network, socioeconomic characteristics, legislative measures and meteorology. This repository contains the data used for the analysis carried out in the papers: 1. Calvo-Poyo F., Navarro-Moreno J., de Oña J. (2020) Road Investment and Traffic Safety: An International Study. Sustainability 12:6332. https://doi.org/10.3390/su12166332 2. Navarro-Moreno J., Calvo-Poyo F., de Oña J. (2022) Influence of road investment and maintenance expenses on injured traffic crashes in European roads. Int J Sustain Transp 1–11. https://doi.org/10.1080/15568318.2022.2082344 3. Navarro-Moreno, J., Calvo-Poyo, F., de Oña, J. (2022) Investment in roads and traffic safety: linked to economic development? A European comparison. Environ. Sci. Pollut. Res. https://doi.org/10.1007/s11356-022-22567 The file with the database is available in excel. DATA SOURCES The database presents data from 1998 up to 2016 from 20 european countries: Austria, Belgium, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden and United Kingdom. Crash data were obtained from the United Nations Economic Commission for Europe (UNECE) [2], which offers enough level of disaggregation between crashes occurring inside versus outside built-up areas. With reference to the data on economic resources invested in roadways, deserving mention –given its extensive coverage—is the database of the Organisation for Economic Cooperation and Development (OECD), managed by the International Transport Forum (ITF) [1], which collects data on investment in the construction of roads and expenditure on their maintenance, following the definitions of the United Nations System of National Accounts (2008 SNA). Despite some data gaps, the time series present consistency from one country to the next. Moreover, to confirm the consistency and complete missing data, diverse additional sources, mainly the national Transport Ministries of the respective countries were consulted. All the monetary values were converted to constant prices in 2015 using the OECD price index. To obtain the rest of the variables in the database, as well as to ensure consistency in the time series and complete missing data, the following national and international sources were consulted: Eurostat [3] Directorate-General for Mobility and Transport (DG MOVE). European Union [4] The World Bank [5] World Health Organization (WHO) [6] European Transport Safety Council (ETSC) [7] European Road Safety Observatory (ERSO) [8] European Climatic Energy Mixes (ECEM) of the Copernicus Climate Change [9] EU BestPoint-Project [10] Ministerstvo dopravy, República Checa [11] Bundesministerium für Verkehr und digitale Infrastruktur, Alemania [12] Ministerie van Infrastructuur en Waterstaat, Países Bajos [13] National Statistics Office, Malta [14] Ministério da Economia e Transição Digital, Portugal [15] Ministerio de Fomento, España [16] Trafikverket, Suecia [17] Ministère de l’environnement de l’énergie et de la mer, Francia [18] Ministero delle Infrastrutture e dei Trasporti, Italia [19–25] Statistisk sentralbyrå, Noruega [26-29] Instituto Nacional de Estatística, Portugal [30] Infraestruturas de Portugal S.A., Portugal [31–35] Road Safety Authority (RSA), Ireland [36] DATA BASE DESCRIPTION The database was made trying to combine the longest possible time period with the maximum number of countries with complete dataset (some countries like Lithuania, Luxemburg, Malta and Norway were eliminated from the definitive dataset owing to a lack of data or breaks in the time series of records). Taking into account the above, the definitive database is made up of 19 variables, and contains data from 20 countries during the period between 1998 and 2016. Table 1 shows the coding of the variables, as well as their definition and unit of measure. Table. Database metadata Code Variable and unit fatal_pc_km Fatalities per billion passenger-km fatal_mIn Fatalities per million inhabitants accid_adj_pc_km Accidents per billion passenger-km p_km Billions of passenger-km croad_inv_km Investment in roads construction per kilometer, €/km (2015 constant prices) croad_maint_km Expenditure on roads maintenance per kilometer €/km (2015 constant prices) prop_motorwa Proportion of motorways over the total road network (%) populat Population, in millions of inhabitants unemploy Unemployment rate (%) petro_car Consumption of gasolina and petrol derivatives (tons), per tourism alcohol Alcohol consumption, in liters per capita (age > 15) mot_index Motorization index, in cars per 1,000 inhabitants den_populat Population density, inhabitants/km² cgdp Gross Domestic Product (GDP), in € (2015 constant prices) cgdp_cap GDP per capita, in € (2015 constant prices) precipit Average depth of rain water during a year (mm) prop_elder Proportion of people over 65 years (%) dps Demerit Point System, dummy variable (0: no; 1: yes) freight Freight transport, in billions of ton-km ACKNOWLEDGEMENTS This database was carried out in the framework of the project “Inversión en carreteras y seguridad vial: un análisis internacional (INCASE)”, financed by: FEDER/Ministerio de Ciencia, Innovación y Universidades–Agencia Estatal de Investigación/Proyecto RTI2018-101770-B-I00, within Spain´s National Program of R+D+i Oriented to Societal Challenges. Moreover, the authors would like to express their gratitude to the Ministry of Transport, Mobility and Urban Agenda of Spain (MITMA), and the Federal Ministry of Transport and Digital Infrastructure of Germany (BMVI) for providing data for this study. REFERENCES 1. International Transport Forum OECD iLibrary | Transport infrastructure investment and maintenance. 2. United Nations Economic Commission for Europe UNECE Statistical Database Available online: https://w3.unece.org/PXWeb2015/pxweb/en/STAT/STAT_40-TRTRANS/?rxid=18ad5d0d-bd5e-476f-ab7c-40545e802eeb (accessed on Apr 28, 2020). 3. European Commission Database - Eurostat Available online: https://ec.europa.eu/eurostat/data/database (accessed on Apr 28, 2021). 4. Directorate-General for Mobility and Transport. European Commission EU Transport in figures - Statistical Pocketbooks Available online: https://ec.europa.eu/transport/facts-fundings/statistics_en (accessed on Apr 28, 2021). 5. World Bank Group World Bank Open Data | Data Available online: https://data.worldbank.org/ (accessed on Apr 30, 2021). 6. World Health Organization (WHO) WHO Global Information System on Alcohol and Health Available online: https://apps.who.int/gho/data/node.main.GISAH?lang=en (accessed on Apr 29, 2021). 7. European Transport Safety Council (ETSC) Traffic Law Enforcement across the EU - Tackling the Three Main Killers on Europe’s Roads; Brussels, Belgium, 2011; 8. Copernicus Climate Change Service Climate data for the European energy sector from 1979 to 2016 derived from ERA-Interim Available online: https://cds.climate.copernicus.eu/cdsapp#!/dataset/sis-european-energy-sector?tab=overview (accessed on Apr 29, 2021). 9. Klipp, S.; Eichel, K.; Billard, A.; Chalika, E.; Loranc, M.D.; Farrugia, B.; Jost, G.; Møller, M.; Munnelly, M.; Kallberg, V.P.; et al. European Demerit Point Systems : Overview of their main features and expert opinions. EU BestPoint-Project 2011, 1–237. 10. Ministerstvo dopravy Serie: Ročenka dopravy; Ročenka dopravy; Centrum dopravního výzkumu: Prague, Czech Republic; 11. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2003/2004; Hamburg, Germany, 2004; ISBN 3871542946. 12. Bundesministerium für Verkehr und digitale Infrastruktur Verkehr in Zahlen 2018/2019. In Verkehrsdynamik; Flensburg, Germany, 2018 ISBN 9783000612947. 13. Ministerie van Infrastructuur en Waterstaat Rijksjaarverslag 2018 a Infrastructuurfonds; The Hague, Netherlands, 2019; ISBN 0921-7371. 14. Ministerie van Infrastructuur en Milieu Rijksjaarverslag 2014 a Infrastructuurfonds; The Hague, Netherlands, 2015; ISBN 0921- 7371. 15. Ministério da Economia e Transição Digital Base de Dados de Infraestruturas - GEE Available online: https://www.gee.gov.pt/pt/publicacoes/indicadores-e-estatisticas/base-de-dados-de-infraestruturas (accessed on Apr 29, 2021). 16. Ministerio de Fomento. Dirección General de Programación Económica y Presupuestos. Subdirección General de Estudios Económicos y Estadísticas Serie: Anuario estadístico; NIPO 161-13-171-0; Centro de Publicaciones. Secretaría General Técnica. Ministerio de Fomento: Madrid, Spain; 17. Trafikverket The Swedish Transport Administration Annual report: 2017; 2018; ISBN 978-91-7725-272-6. 18. Ministère de l’Équipement, du T. et de la M. Mémento de statistiques des transports 2003; Ministère de l’environnement de l’énergie et de la mer, 2005; 19. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2000; Istituto Poligrafico e Zecca dello Stato: Roma, Italy, 2001; 20. Ministero delle Infrastrutture e dei Trasporti Conto nazionale dei trasporti 1999. 2000. 21. Generale, D.; Informativi, S. delle Infrastrutture e dei Trasporti Anno 2004. 22. Ministero delle Infrastrutture e dei Trasporti Conto Nazionale delle Infrastrutture e dei Trasporti Anno 2001; 2002; 23. Ministero delle Infrastrutture e dei

About

Overview from FAQ:

The Market Access Database is an important operational tool of the European Union's Market Access Strategy, supporting a continuous three-way exchange of information between the EU institutions, Member States and European business. The Market Access Strategy is a key pillar of the EU's Trade Policy which aims to reduce the obstacles faced by European exporters of goods and services. The Market Access Database is a free, interactive, easy to use service providing: Information about Market Access conditions in non-EU countries.

• A systematic way for the European Commission to follow up complaints from businesses about barriers to trade in non-EU countries.
• A means of ensuring that our trading partners are abiding by their international commitments.
• Better input for defining the EU's trade policy objectives on further trade liberalisation in the framework of the World Trade Organisation (e.g. Doha Development Agenda) and new free trade agreements between the EU and preferential partners (e.g. EU - MERCOSUR).

Access

Access to material via search interfaces, including:

• Complaint register
• Applied Tariff Database
• Trade Barriers Database
• SPS Database
• Statistical Database

Openness

Free to be re-used as long as attribution is given. The Copyright notice on the European Commission website states:

Reproduction is authorised, provided the source is acknowledged, save where otherwise stated.

Where prior permission must be obtained for the reproduction or use of textual and multimedia information (sound, images, software, etc.), such permission shall cancel the above-mentioned general permission and shall clearly indicate any restrictions on use.

Access to the Exporter's Guide and Applied Tariffs sections is limited to those within EU countries:

Access to the Sectoral and Trade Barriers Database, SPS Database, Statistics and Studies sections is free. However, information in the Exporter's Guide and Applied Tariffs sections is restricted to users in the Member States of the European Union and Acceding or Candidate Countries. From a technical point of view, if the computer on which your browser is running is not directly connected to the internet via an Internet Service Provider located in one of those countries, then you are prohibited from viewing the Exporter's Guide and Applied Tariffs data for any purpose.

European Union Imports from United States was US$355.76 Billion during 2024, according to the United Nations COMTRADE database on international trade. European Union Imports from United States - data, historical chart and statistics - was last updated on July of 2025.

European Union Exports of lead was US$453 Million during 2024, according to the United Nations COMTRADE database on international trade. European Union Exports of lead - data, historical chart and statistics - was last updated on July of 2025.

Trade Balance: EU 27E: United States: Other Manufactured Products data was reported at 5.255 EUR bn in Feb 2025. This records an increase from the previous number of 4.277 EUR bn for Jan 2025. Trade Balance: EU 27E: United States: Other Manufactured Products data is updated monthly, averaging 2.197 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 5.885 EUR bn in Jul 2022 and a record low of 0.731 EUR bn in Jan 2009. Trade Balance: EU 27E: United States: Other Manufactured Products data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA050: Eurostat: Trade Statistics: By SITC: European Union: United States.

European Union Imports: EU 28: MP: OP: MG: Iron & Steel data was reported at 3.146 EUR bn in Feb 2019. This records a decrease from the previous number of 3.485 EUR bn for Jan 2019. European Union Imports: EU 28: MP: OP: MG: Iron & Steel data is updated monthly, averaging 2.208 EUR bn from Jan 2000 (Median) to Feb 2019, with 230 observations. The data reached an all-time high of 4.517 EUR bn in Sep 2008 and a record low of 0.743 EUR bn in Dec 2001. European Union Imports: EU 28: MP: OP: MG: Iron & Steel data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA003: Eurostat: Trade Statistics: By SITC.

European Union Imports from El Salvador was US$254.32 Million during 2024, according to the United Nations COMTRADE database on international trade. European Union Imports from El Salvador - data, historical chart and statistics - was last updated on July of 2025.

Abstract

In 2007, the EU-SILC instrument covered all EU Member States plus Iceland, Turkey, Norway, Switzerland and Croatia. EU-SILC has become the EU reference source for comparative statistics on income distribution and social exclusion at European level, particularly in the context of the "Program of Community action to encourage cooperation between Member States to combat social exclusion" and for producing structural indicators on social cohesion for the annual spring report to the European Council. The first priority is to be given to the delivery of comparable, timely and high quality cross-sectional data.

There are two types of datasets: 1) Cross-sectional data pertaining to fixed time periods, with variables on income, poverty, social exclusion and living conditions. 2) Longitudinal data pertaining to individual-level changes over time, observed periodically - usually over four years.

Social exclusion and housing-condition information is collected at household level. Income at a detailed component level is collected at personal level, with some components included in the "Household" section. Labor, education and health observations only apply to persons aged 16 and over. EU-SILC was established to provide data on structural indicators of social cohesion (at-risk-of-poverty rate, S80/S20 and gender pay gap) and to provide relevant data for the two 'open methods of coordination' in the field of social inclusion and pensions in Europe.

The sixth revision of the 2007 Cross-Sectional User Database is documented here.

Geographic coverage

National

Analysis unit

• Households;
• Individuals 16 years and older.

Universe

The survey covered all household members over 16 years old. Persons living in collective households and in institutions are generally excluded from the target population.

Kind of data

Sample survey data [ssd]

Sampling procedure

On the basis of various statistical and practical considerations and the precision requirements for the most critical variables, the minimum effective sample sizes to be achieved were defined. Sample size for the longitudinal component refers, for any pair of consecutive years, to the number of households successfully interviewed in the first year in which all or at least a majority of the household members aged 16 or over are successfully interviewed in both the years.

For the cross-sectional component, the plans are to achieve the minimum effective sample size of around 131.000 households in the EU as a whole (137.000 including Iceland and Norway). The allocation of the EU sample among countries represents a compromise between two objectives: the production of results at the level of individual countries, and production for the EU as a whole. Requirements for the longitudinal data will be less important. For this component, an effective sample size of around 98.000 households (103.000 including Iceland and Norway) is planned.

Member States using registers for income and other data may use a sample of persons (selected respondents) rather than a sample of complete households in the interview survey. The minimum effective sample size in terms of the number of persons aged 16 or over to be interviewed in detail is in this case taken as 75 % of the figures shown in columns 3 and 4 of the table I, for the cross-sectional and longitudinal components respectively.

The reference is to the effective sample size, which is the size required if the survey were based on simple random sampling (design effect in relation to the 'risk of poverty rate' variable = 1.0). The actual sample sizes will have to be larger to the extent that the design effects exceed 1.0 and to compensate for all kinds of non-response. Furthermore, the sample size refers to the number of valid households which are households for which, and for all members of which, all or nearly all the required information has been obtained. For countries with a sample of persons design, information on income and other data shall be collected for the household of each selected respondent and for all its members.

At the beginning, a cross-sectional representative sample of households is selected. It is divided into say 4 sub-samples, each by itself representative of the whole population and similar in structure to the whole sample. One sub-sample is purely cross-sectional and is not followed up after the first round. Respondents in the second sub-sample are requested to participate in the panel for 2 years, in the third sub-sample for 3 years, and in the fourth for 4 years. From year 2 onwards, one new panel is introduced each year, with request for participation for 4 years. In any one year, the sample consists of 4 sub-samples, which together constitute the cross-sectional sample. In year 1 they are all new samples; in all subsequent years, only one is new sample. In year 2, three are panels in the second year; in year 3, one is a panel in the second year and two in the third year; in subsequent years, one is a panel for the second year, one for the third year, and one for the fourth (final) year.

According to the Commission Regulation on sampling and tracing rules, the selection of the sample will be drawn according to the following requirements:

1. For all components of EU-SILC (whether survey or register based), the crosssectional and longitudinal (initial sample) data shall be based on a nationally representative probability sample of the population residing in private households within the country, irrespective of language, nationality or legal residence status. All private households and all persons aged 16 and over within the household are eligible for the operation.
2. Representative probability samples shall be achieved both for households, which form the basic units of sampling, data collection and data analysis, and for individual persons in the target population.
3. The sampling frame and methods of sample selection shall ensure that every individual and household in the target population is assigned a known and non-zero probability of selection.
4. By way of exception, paragraphs 1 to 3 shall apply in Germany exclusively to the part of the sample based on probability sampling according to Article 8 of the Regulation of the European Parliament and of the Council (EC) No 1177/2003 concerning

Community Statistics on Income and Living Conditions. Article 8 of the EU-SILC Regulation of the European Parliament and of the Council mentions: 1. The cross-sectional and longitudinal data shall be based on nationally representative probability samples. 2. By way of exception to paragraph 1, Germany shall supply cross-sectional data based on a nationally representative probability sample for the first time for the year 2008. For the year 2005, Germany shall supply data for one fourth based on probability sampling and for three fourths based on quota samples, the latter to be progressively replaced by random selection so as to achieve fully representative probability sampling by 2008. For the longitudinal component, Germany shall supply for the year 2006 one third of longitudinal data (data for year 2005 and 2006) based on probability sampling and two thirds based on quota samples. For the year 2007, half of the longitudinal data relating to years 2005, 2006 and 2007 shall be based on probability sampling and half on quota sample. After 2007 all of the longitudinal data shall be based on probability sampling.

Detailed information about sampling is available in Quality Reports in Documentation.

Mode of data collection

Mixed

GISCO (Geographic Information System of the COmmission) is responsible for meeting the European Commission's geographical information needs at three levels: the European Union, its member countries, and its regions.

In addition to creating statistical and other thematic maps, GISCO manages a database of geographical information, and provides related services to the Commission. Its database contains core geographical data covering the whole of Europe, such as administrative boundaries, and thematic geospatial information, such as population grid data. Some data are available for download by the general public and may be used for non-commercial purposes. For further details and information about any forthcoming new or updated datasets, see http://ec.europa.eu/eurostat/web/gisco/geodata.

This metadata refers to the whole content of GISCO reference database extracted in July 2018, which contains both public datasets and datasets to be used only internally by the EEA. The document GISCO-ConditionsOfUse.pdf provided with the dataset gives information on the copyrighted data sources, the mandatory acknowledgement clauses and re-dissemination rights. The license conditions for EuroGeographic datasets in GISCO are provided in a standalone document "LicenseConditions_EuroGeographics.pdf".

The database is provided in GDB and in SQLITE, with datasets at scales from 1:60M to 1:100K, with reference years spanning until 2016. The database manual, a file with the content of the database, and a document with the naming conventions are also provided with the database. For particular datasets extracted from this database (NUTS 2016 and COUNTRIES 2016) please refer to the associated resources in the EEA SDI catalogue.

NOTE: This metadata file is only for internal EEA purposes and in no case replaces the official metadata provided by Eurostat.

European Union Exports: EU 27E: United States: Crude Mate, Inedible Except Fuels data was reported at 0.403 EUR bn in Feb 2025. This records a decrease from the previous number of 0.434 EUR bn for Jan 2025. European Union Exports: EU 27E: United States: Crude Mate, Inedible Except Fuels data is updated monthly, averaging 0.204 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 0.674 EUR bn in May 2022 and a record low of 0.093 EUR bn in May 2009. European Union Exports: EU 27E: United States: Crude Mate, Inedible Except Fuels data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA050: Eurostat: Trade Statistics: By SITC: European Union: United States.

European Union Money Supply M3: Outstanding data was reported at 16,806.116 EUR bn in Mar 2025. This records an increase from the previous number of 16,778.802 EUR bn for Feb 2025. European Union Money Supply M3: Outstanding data is updated monthly, averaging 4,137.802 EUR bn from Jan 1970 (Median) to Mar 2025, with 663 observations. The data reached an all-time high of 16,825.417 EUR bn in Dec 2024 and a record low of 318.769 EUR bn in Feb 1970. European Union Money Supply M3: Outstanding data remains active status in CEIC and is reported by European Central Bank. The data is categorized under Global Database’s European Union – Table EU.KA002: European Central Bank: Money Supply: Outstanding.

Trade Balance: swda: EU 27E: Taiwan: Chemicals & Rel Products, nes data was reported at 0.275 EUR bn in Feb 2025. This records an increase from the previous number of 0.176 EUR bn for Jan 2025. Trade Balance: swda: EU 27E: Taiwan: Chemicals & Rel Products, nes data is updated monthly, averaging 0.173 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 0.393 EUR bn in Apr 2022 and a record low of 0.059 EUR bn in Jan 2009. Trade Balance: swda: EU 27E: Taiwan: Chemicals & Rel Products, nes data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA046: Eurostat: Trade Statistics: By SITC: European Union: Taiwan.

European Union Trade Balance: EU 27E: Intra EU: Total data was reported at 6.037 EUR bn in Feb 2025. This records a decrease from the previous number of 19.346 EUR bn for Jan 2025. European Union Trade Balance: EU 27E: Intra EU: Total data is updated monthly, averaging 5.869 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 22.005 EUR bn in Jan 2023 and a record low of -13.179 EUR bn in Dec 2021. European Union Trade Balance: EU 27E: Intra EU: Total data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA019: Eurostat: Trade Statistics: By SITC: European Union: Intra European Union.

European Union Imports: EU 27E: China: Crude Materials, Inedible Except Fuels data was reported at 0.347 EUR bn in Feb 2025. This records an increase from the previous number of 0.345 EUR bn for Jan 2025. European Union Imports: EU 27E: China: Crude Materials, Inedible Except Fuels data is updated monthly, averaging 0.208 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 0.425 EUR bn in Nov 2022 and a record low of 0.071 EUR bn in Aug 2002. European Union Imports: EU 27E: China: Crude Materials, Inedible Except Fuels data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA030: Eurostat: Trade Statistics: By SITC: European Union: China.