EudraGMDP is the name for the Union database referred to in article 111(6) of Directive 2001/83/EC and article 80(6) of Directive 2001/82/EC.

It contains the following information:

• Manufacturing and import authorisations
• Good Manufacturing Practice (GMP) certificates.
• Statements of non-compliance with GMP
• GMP inspection planning in third countries
• Wholesale Distribution Authorisations
• Good Distribution Certificates (GDP)
• Statements of non-compliance with GDP
• Registration of manufacturers, importers and distributors of active substances for human use located in the EEA

Trade Balance: swda: EU 27E: Brazil: Food and Live Animals for Food data was reported at -1.214 EUR bn in Feb 2025. This records a decrease from the previous number of -0.935 EUR bn for Jan 2025. Trade Balance: swda: EU 27E: Brazil: Food and Live Animals for Food data is updated monthly, averaging -0.625 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of -0.353 EUR bn in Feb 2003 and a record low of -1.343 EUR bn in Aug 2022. Trade Balance: swda: EU 27E: Brazil: Food and Live Animals for Food data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA028: Eurostat: Trade Statistics: By SITC: European Union: Brazil.

European Union Trade Balance: swda: EU 27E: Extra EU: Food, Live Animals for Food data was reported at 2.665 EUR bn in Feb 2025. This records an increase from the previous number of 2.549 EUR bn for Jan 2025. European Union Trade Balance: swda: EU 27E: Extra EU: Food, Live Animals for Food data is updated monthly, averaging 1.516 EUR bn from Jan 2002 (Median) to Feb 2025, with 278 observations. The data reached an all-time high of 2.665 EUR bn in Feb 2025 and a record low of 0.812 EUR bn in Dec 2002. European Union Trade Balance: swda: EU 27E: Extra EU: Food, Live Animals for Food data remains active status in CEIC and is reported by Eurostat. The data is categorized under Global Database’s European Union – Table EU.JA020: Eurostat: Trade Statistics: By SITC: European Union: European Countries Outside of EU.

EU-Hydro is a dataset for all EEA38 countries and the United Kingdom providing photo-interpreted river network, consistent of surface interpretation of water bodies (lakes and wide rivers), and a drainage model (also called Drainage Network), derived from EU-DEM, with catchments and drainage lines and nodes. The EU-Hydro dataset is distributed in separate files (river network and drainage network) for each of the 35 major basins of the EEA38 + UK area, in GDB and GPKG formats. The production of EU-Hydro and the derived layers was coordinated by the European Environment Agency in the frame of the EU Copernicus programme.

Content

The Archive Index Database (AIDB), a Microsoft Access database that tracks the relationship between the model inputs and the results, tracks the status of the simulations, and stores all of the default information and parameters applied by model when creating a new project.

The EU-AIDB incorporates 1034 spatial units resulting from the intersection of 204 European administrative regions, and ecological boundaries representing 35 climatic units. It also contains updated parameters for 192 of the main tree species reported by the National Forest Inventories of each EU country.

Context

This is a dataset from Joint Research Centre hosted by the EU Open Data Portal. The Open Data Portal is found here and they update their information according the frequency that the data is collected. Explore Joint Research Centre data using Kaggle and all of the data sources available through the Joint Research Centre organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the EU ODP API and Kaggle's API.

This dataset is distributed under the following license: Legal Notice

Content

Parliamentary questions are questions addressed by Members of the European Parliament to other European Union Institutions and bodies. They are a direct form of parliamentary scrutiny of other EU institutions and bodies.

There are three categories of parliamentary questions:

• Questions for oral answer dealt with during plenary sittings, and included in the day's debates. They may be followed by a resolution (Rule 128)

• Questions for Question Time asked during the period set aside for questions during plenary sittings (Rule 129)

• Written questions with a request for a written answer (Rule 130)

A written declaration is a text of a maximum of 200 words relating exclusively on a matter falling within the competence of the European Union. They do not, however, bind Parliament, that is, they cannot be considered as an act of the Parliament representing its position, but only those of its authors and signatories.

Context

This is a dataset from European Parliament hosted by the EU Open Data Portal. The Open Data Portal is found here and they update their information according the amount of data that is brought in. Explore European Parliament data using Kaggle and all of the data sources available through the European Parliament organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the EU ODP API and Kaggle's API.

This dataset is distributed under the following license: Dataset License

Cover photo by Will van Wingerden on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

As part of a study commissioned by the European Union Observatory for Nanomaterials (EUON) to investigate the safe use of nano-sized pigments in consumer products, as well as their safety when used by professionals and workers, a list of nano-sized pigments currently known to be on the EU market was established.

The list consists of 81 substances from the European Chemicals Agency's (ECHA's) chemicals database as well as publications by the Belgian and French national inventories, and the current EU catalogue of nanomaterials used in cosmetic products. Data from the Danish Product Register was also used. Click on the substance names in the list to get more information from ECHA's chemicals database.

The EUON provides information about existing nanomaterials on the EU market. Whether you are developing policies in the area, a consumer or representing industry or a green NGO, the information on the EUON offers interesting reading about the safety, innovation, research and uses of nanomaterials.

The EUON is funded by the European Commission. It is hosted and maintained by the European Chemicals Agency (ECHA).

In 2024, Germany ranked first by revenue in the data center market among the 27 countries presented in the ranking. Germany's revenue amounted to ************* U.S. dollars, while France and Italy, the second and third countries, had records amounting to ************* U.S. dollars and ************ U.S. dollars, respectively.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Data Center.

Graph and download economic data for Population, Total for the European Union (SPPOPTOTLEUU) from 1960 to 2024 about EU, Europe, and population.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from European Parliament hosted by the EU Open Data Portal. The Open Data Portal is found here and they update their information according the amount of data that is brought in. Explore European Parliament data using Kaggle and all of the data sources available through the European Parliament organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the EU ODP API and Kaggle's API.

This dataset is distributed under the following licenses: Legal Notice

Cover photo by Frederic Köberl on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

### Context The dataset is used to explore and visualize the differences between the Agricultural Sectors across European countries. While the Common Agricultural Policy is using a set of priorities and financial mechanisms more or less standardized across EU member states, there are huge differences between the West and the East of Europe in terms of their agricultural sectors. Romania is a particular interesting case, having one of the most fragmented and least productive agricultural sector.

### Content The database contains a selection of Farm Structure Indicators for EU member states (28 countries, including UK). The selected indicators provide an overview of the European Agricultural sector (for a non-expert public) and allow for relevant comparisons between EU countries. Data covers the year 2016, the most recent year for which Farm Structure data was provided by Eurostat. Since multiple countries conducted their Agricultural Census in the period 2020-2021, more recent data is expected to be published soon.

The indicators were selected and downloaded from the Eurostat database. The indicators come from 4 different Eurostat data tables: [ef_m_farmleg], [ef_lf_main], [ef_mp_training], [ef_m_farmang]. Given the different structure of the datasets provided by Eurostat, the indicators were merged into the final database using a series of VLOOKUP functions in Microsoft Excel.

Inspiration

I will be using the data to produce a series of maps (in leaflet() or tmap() in R), exploring cross country differences and trying to answer questions about how "common" really is the European Agriculture.

Key information about EU Private Consumption Expenditure

• EU Private Consumption Expenditure was reported at 2,784.664 USD bn in Jun 2025. This records an increase from the previous number of 2,564.648 USD bn for Mar 2025.
• EU Private Consumption Expenditure data is updated quarterly, averaging 2,094.601 USD bn from Mar 1999 to Jun 2025, with 106 observations.
• The data reached an all-time high of 2,784.664 USD bn in Jun 2025 and a record low of 972.178 USD bn in Dec 2000.
• EU Private Consumption Expenditure data remains in an active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Nominal GDP: Private Consumption Expenditure: USD: Quarterly: Seasonally Adjusted.

The European Medicines Agency (EMA) maintains a public list containing details on all European experts who can be involved in the Agency's work.

The search in the database allows the user to find a list of annual electronic declarations of interest (DoI) for external experts and their curriculum vitae (CV) working with EMA. It indicates the interest level assigned to each DoI by the EMA.

This dataset provides annual indicator data and composite indices for European NUTS2 regions over the period 2010–2022. It includes the components of the Regional Attractiveness Index (RAI), which measures regional attractiveness in the context of the twin transition (green and digital). The database integrates 20+ socioeconomic, environmental, and digital indicators harmonised across regions.

This project has received funding from the H2020 innovation and research program of the European Commission under the Marie Sklodowska-Curie grant no: 846077, entitled “Quality of Service for the Internet Things in Smart Cities via Predictive Networks".

Each file consists of time series data which are the number of bits at each sampling.

• Purpose of the data: These data are provided to aid researchers who aim to develop methods to process IoT sensor traffic data.
• Time and date of creation and availability: These data were collected as part of the QoSIoTSmartCities project in the years 2019 – 2021 and made available on this site.
• Data file size: The data file size appears next to the data file.
• Data Units: All of the units of these data are “bits”

The data produced in the project is useable by third parties with the disclaimer that the Coordinator, the funding agency, and the host institution bear no responsibility whatsoever, legal or otherwise, that result from the re-use of these data sets.

Since the General Data Protection Regulation (GDPR) implementation Europe-wide on the 25th of May 2018, the highest number of personal data breaches as of January 2024 were reported in the Netherlands, a total of around *******. The Netherlands ranked second, with more than ******* personal data breach notifications.

Abstract

In 2013, 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.

This is the 1st version of the 2013 Cross-Sectional User Database as released in July 2015.

Geographic coverage

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

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 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 Related Materials.

Mode of data collection

Mixed

This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html.

The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:

Questionnaire

Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv

Codebook: includes information on all the variables and their coding.

European Seabirds At Sea (ESAS) assembles offshore monitoring data on seabirds and marine mammals. This international database mostly includes data from the North Sea, yet large parts of the northeastern Atlantic Ocean are covered as well. It finds its origin in the 'Seabirds at Sea' project, which was initiated in 1979 following the discovery of major oil potential in the North Sea and an urgent need to gain more knowledge on the occurrence and distribution of seabirds in their offshore habitat. This led to the execution of large-scale ship-based surveys across the North Sea using a standardized data collection method and a first European-wide data assembly in 1991.

Viabundus.eu is a freely accessible online street map of late medieval and early modern northern Europe (1350-1650). Originally conceived as the digitisation of Friedrich Bruns and Hugo Weczerka's Hansische Handelsstraßen (1962) atlas of land roads in the Hanseatic area, the Viabundus map moves beyond that. It includes among others: a database with information about settlements, towns, tolls, staple markets and other information relevant for the pre-modern traveller; a route calculator; a calendar of fairs; and additional land routes as well as water ways.

Viabundus is a work in progress. Version 1.3, released on 17 March 2024, contains a rough digitisation of the land routes from Hansische Handelsstraßen, as well as a thoroughly researched road network for the current-day Netherlands, Denmark, the German states of Lower Saxony, Schleswig-Holstein, Thuringia, Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Hesse and North Rhine-Westfalia, and parts of Poland (Pomerania, Royal Prussia, Greater Poland). The inclusion of other regions is currently being planned. Additions to the dataset will be released as new versions in the future.

The project's homepage viabundus.eu contains a web map application to explore the data. To allow for more advanced spatial and historical analyses, the underlying dataset is available for download under the CC-BY-SA license.

The dataset is designed as a network model and therefore consists of two main elements: 1) a relational database of nodes, i.e. geographical places, with historical information about settlements, towns, tolls, staple markets, fairs, bridges, ferries, harbours and shipping locks; 2) a database with edges, i.e. the geospatial representations of the land and water routes that connected these nodes. The entire database is available in CSV format (with geospatial geometry as WKT); the edges and the outlines of towns in the 16th century are also separately available as geojson and GML files. For more information about the structure of the dataset, theoretical considerations and sources, please consult the enclosed documentation file.