This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).

Sampling design

In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.

Fieldwork

The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.

Weights

In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.

Transformations

All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.

Treatment of missing values

In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

The purpose of the study: assess knowledge of the Israeli, United Kingdom, Polish, French, Norwegian, Swedish and German population about Lithuania and its inhabitants. Major investigated questions: respondents were asked whether they heard about Lithuania, Latvia and Estonia, whether they know much about these countries and could name their capitals, and finally whether they would like to visit these countries. Further, the questions were only related to Lithuania. It was wanted to know how much the respondents know about Lithuania and with which region they would most likely associate this country. Those who think that Lithuania is not worth a trip or who have doubts about visiting Lithuania were asked to give their reasons in group of questions. After a group of questions, respondents that formerly visited Lithuania were asked to answer what made them visit Lithuania, what they liked and what they did not like about the country. When asked to imagine that they were planning to visit a European country, and after being asked a group of questions, it was wanted to know what would have the most influence on such a decision. Respondents were asked to rate whether Lithuania's membership in the EU, NATO and the OECD was a positive or negative thing. Next, respondents rated the groups of statements about Lithuania. It was clarified whether they had seen the campaign "Lithuania. Real is beautiful". They were asked to answer which of the listed tourist attractions or activities would be interesting for them if they were to visit another country. It was investigated which positive and negative descriptions best describe Lithuanians. At the end of the survey, questions were asked about how often respondents travel abroad (including all types of travel: work, weekends, holidays) and who usually travels abroad with them. Socio-demographic characteristics: gender, age, place of residence, education, household income, occupation.

A global database of Direct Marketing Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future. Leverage up-to-date audience targeting population trends for market research, audience targeting, and sales territory mapping.

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This comparative project (UK, Japan, Germany, US & New Zealand) examined how governments prepare citizens for collapse in the Critical National Infrastructure (CNI); how they model collapse and population response; case studies of CNI collapse (with particular reference to health and education) and the globalisation of CNI policy. It was funded by the Economic and Social Research Council under grant reference ES/K000233/1. It considered:- 1. How is the critical infrastructure defined and operationalised in different national contexts? How is population response defined, modelled and refined in the light of crisis? 2. What are the most important comparative differences between countries with regard to differences in mass population response to critical infrastructure collapse? 3. To what degree are factors such as differences in national levels of trust, degrees of educational or income inequality, social capital or welfare system differences particularly in the education and health systems significant in understanding differential population response to critical infrastructure collapse? 4. How can a comparative understanding of mass population response to critical infrastructure collapse help us to prepare for future crisis? Research design and methodology Methodologically the study was focused on national systems in developed countries. The focus was on different 'welfare regimes' being broadly liberal market economies (the UK, US and New Zealand) and broadly centralised market economies (Germany and Japan). The data arising from the project was of various types including interviews, focus groups, archival data and documentary evidence. The 'National Infrastructure' is seldom out of the news. Although the infrastructure is not always easy to define it includes things such as utilities (water, energy, gas), transportation systems and communications. We often hear about real or perceived threats to the infrastructure. In this research we will construct 'timelines' of infrastructure protection policy and mass population response to see exactly how and why policy changes in countries over time. We will select a range of countries to represent different political and social factors (US, UK, New Zealand, Japan and Germany). The analysis of these timelines will suggest why national infrastructure policy changes over time. We will then test our results using case studies of actual disasters and expert groups of policy makers across countries. Ultimately this will help us to understand national infrastructure protection changes over time, what drives such changes and the different ways in which countries prepare themselves for infrastructure threats. In addition, through a series of 'leadership activities' the research will bring together researchers in different academic disciplines and people from the public, private and third sectors. The methodology used was to enable an understanding of how countries had developed strategies of mass population response to critical infrastructure failure. The methods were:- 1. Archival research using data in country archives from 1945 to the present day on population response (planned and actual to disasters) 2. Focus groups and interviews with selected experts to enable us to further understand the data in (1). 3. Case studies of actual infrastructure failures - summary notes were prepared from documentary evidence on disasters.

Changes since the last version: in the .csv export there was a naming problem. - visit_concert: This is a standard CAP variables about visiting frequencies, in numeric form. - fct_visit_concert: This is a standard CAP variables about visiting frequencies, in categorical form. - is_visit_concert: binary variable, 0 if the person had not visited concerts in the previous 12 months. - artistic_activity_played_music: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable. - fct_artistic_activity_played_music: The artistic_activity_played_music in categorical representation. - artistic_activity_sung: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music. - fct_artistic_activity_sung: The artistic_activity_sung variable in categorical representation. - age_exact: The respondent’s age as an integer number. - country_code: an ISO country code - geo: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.] - age_education: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, age_education and is_student. - is_student: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute age in this case to age_education, but we will show why this is not a good strategy. - w, w1: Post-stratification weights for the 15+ years old population of each country. Use w1 for averages of geo entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use w when treating the United Kingdom and Germany as one territory. - wex: Projected weight variable. For weighted average values, use w, w1, for projections on the population size, i.e., use with sums, use wex. - id: The identifier of the original survey. - rowid`: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.

Abstract

In 2012, 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 3rd version of the 2012 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; 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

In 2019, there were approximately 302,020 British citizens living in Spain, with a further 293,061 in Ireland and 176,672 in France. By comparison, there were only 604 British people living in Slovenia, the fewest of any European Union member state. As a member of the European Union, British citizens had the right to live and work in any EU member state. Although these rights were lost for most British citizens after the UK left the EU in 2020, Britons already living in EU states were able to largely retain their previous rights of residence. EU citizens living in the UK EU citizens living in the UK face the same dilemma that British nationals did regarding their legal status after Brexit. In the same year, there were 902,000 Polish citizens, 404,000 Romanians, and 322,000 people from the Republic of Ireland living in the UK in that year, along with almost two million EU citizens from the other 24 EU member states. To retain their rights after Brexit, EU citizens living in the UK were able to apply for the EU settlement scheme. As of 2025, there have been around 8.4 million applications to this scheme, with Romanian and Polish nationals the most common nationality at 1.87 million applications, and 1.27 million applications respectively. Is support for Brexit waning in 2024? As of 2025, the share of people in the UK who think leaving the EU was the wrong decision stood at 56 percent, compared with 31 percent who think it was the correct choice. In general, support for Brexit has declined since April 2021, when 46 percent of people supported Brexit, compared with 43 percent who regretted it. What people think Britain's relationship with the EU should be is, however, still unclear. A survey from November 2023 indicated that just 31 percent thought the UK should rejoin the EU, with a further 11 percent supporting rejoining the single market but not the EU. Only ten percent of respondents were satisfied with the current relationship, while nine percent wished to reduce ties even further.

Attrition in all databases among A) cases and B) controls.

Model relatedness between the original study and each of the validation databases.

Measures of predictive performance of the model at ~80% sensitivity.

The research project is a subproject of the research association “Strengthening of integration potentials within a modern society” (Scientific head: Prof. Dr. Wilhelm Heitmeyer, Bielefeld) which contains 17 subprojects and is supported by the ministry of education and research. In almost all the economically highly developed countries violent crime increased significantly in the second part of the last century - in contrast to the long term trend of decline of individual (non-governmental) violence since the beginning of modern times. The authors develop an explanatory approach for these facts which is inspired mainly by Norbert Elias´s civilization theory and Emil Durkheim´s theory on society. Detailed time series on the development of different forms of violent crime are presented and set in relation with certain aspects of economic and social structural changes in three countries and also refer to the changes in integration of modern societies. The analysis deals especially with effectivity and legitimacy of the governmental monopoly of violence, the public beneficial security and power system, forms of building social capital, economic and social inequality, precarity of employment, different aspects of increasing economization of society, changes in family structures and usage of mass media and modern communication technologies. Register of tables in HISTAT: A: Crime statistics A.01 Frequency of types of crimes in different countries (1953-2000) A.02 Suspects by crimes of 100.000 inhabitants of Germany, England and Sweden (1955-1998) A.03 Murders, manslaughter and intentional injuries by other persons by sex of 100.000 persons after the statistics of causes of death (1953-2000) A.04 Clearance rate by types of crimes in Germany, England and Sweden (1953-1997) A.05 Prisoners of 100.000 inhabitants of Germany, Great Britain and Sweden (1950-2000) B: Key indicators for economic development in Germany, Great Britain, Sweden and the USA B1: Data on the overall economic framework B1.01 Percent changes in the real GDP per capita in purchasing power parities (1956-1987) B1.02 Percent changes in GDP per capita in prices from 2000 (1955-1998) B1.03 GDP of Germany, Sweden and the United Kingdom in purchasing power parities in percent og the US GDP (1950-1992) B1.04 Labor productivity index for different countries, base: USA 1996 = 100 (1950-1999) B1.05 GDP per hour of labor in different countries in EKS-$ from 1999 (1950-2003) B1.06 Foreign trade - exports and imports in percent of the GDP of different countries (1949-2003) B1.07 GDP, wages and Unit-Labor-Cost in different countries (1960-2003) B2: Unemployment B2.01 Standardized unemployment rate in different countries with regard to the entire working population (1960-2003) B2.02 Share of long-term unemployed of the total number of unemployed in different countries in percent (1992-2004) B2.03 Youth unemployment in different countries in percent (1970-2004) B2.04 Unemployment rate in percent by sex in different countries (1963-2000) B3: Employment B3.01 Employment rate in percent in different countries (1960-2000) B3.02 Share of fixed-term employees and persons in dependent employment in percent in different countries (1983-2004) B3.03 Share of part-time employees by sex compared to the entire working population in different countries (1973-2000) B3.04 Share of un-voluntarily part-time employees by sex in different countries (1983-2003) B3.05 Share of contract workers in different countries in percent of the entire working population (1975-2002) B3.06 Share of self-employed persons in different countries in percent of the entire working population (1970-2004) B3.07 Shift worker rate in different countries in percent (1992-2005) B3.08 Yearly working hours per employee in different countries (1950-2004) B3.09 Employment by sectors in different countries (1950-2003) B3.10 Share of employees in public civil services in percent of the population between 15 and 64 years in different countries (1960-1999) B3.11 Female population, female employees and female workers in percent of the population between 16 and 64 years in different countries (1960-2000) B3.12 Employees, self-employed persons in percent of the entire working population in different countries (1960-2000) B4: Taxes and duties B4.01 Taxes and social security contributions in percent of the GDP (1965-2002) B4.02 Social expenditure in percent of the GDP (1965-2002) B4.03 Social expenditure in percent of the GDP (1960-2000) B4.04 Public expenditure in percent of the GDP in different countries (1960-2003) B4.05 Education expenditure in percent of GDP (1950-2001) B5: Debt B5.01 Insolvencies in Germany and England (1960-2004) B5.02 Insolvencies with regard to total population in different countries (1950-2002) B5.03 Consumer credits in different countries (1960-2002) C: Income distribution in Germany, Great Britain and Sweden C.01 Income inequality in different countries Einkommensungleicheit in verschiedenen Ländern (1949-2000) C.02 Income inequality after different indices and calculations in different countries (1969-2000) C.03 Redistribution: Decline in Gini-Index through transfers and taxes in percent in different countries (1969-2000) C.04 Redistribution: Decline in Gini-Index through transfers and taxes in percent with a population structure as in the United Kingdom in 1969 in different countries (1969-2000) C.05 Redistribution efficiency: Decline in Gini-/ Atkinson-Index through transfers and the share of social expenditure of the GDP in different countries (1969-2000) C.06 Index for concentration of transfers in different countries (1981-2000) C.07 Distribution of wealth in West-Germany (1953-1998) C.08 Distribution of wealth in the United Kingdom (1950-2000) C.09 Distribution of wealth in Sweden (1951-1999) C.10 Relative income poverty in different countries (1969-2000) C.11 Reduction of poverty in different countries (1969-2000) C.12 Neocorporalism index in different countries (1960-1994) D: Perception of safety D.01 Satisfaction with democracy in different countries (1976-2004) D.02 Revenues and employees in the private security sector in different countries (1950-2001) D.03 Decommodification-Score in different countries (1971-2002) E: Demographics E.01 Birth rates: Birth per 1000 women between 15 and 49 years in different countries (1951-2001) E.02 Fertility rate in different countries (1950-2004) E.03 Marriages per 100.000 persons in different countries (1950-2003) E.04 Share of foreigners of the entire population in different countries (1951-2002) E.05 Internal migration in different countries (1952-2001)

The study is about public expenditure on education – in current and constant prices – in an international comparison for Germany, France, Spain and Great Britain from 1815 to 1989. Goal of the study: explanation of the internal structures of the education system and showing the relation between the development of the education system and economic growth. Main points: economics of education, relation between development of the education system and economic growth, relation between the development level of a country and the level of education of its population. Structure of the study in HISTAT (topic: education) Tables: - Education expenditure in Germany, France, Great Britain and Spain (1815-1998) - Education expenditure in Japan (1868-1940) - Education expenditure in Spain, in 1000 Peseta (1850-1965) Variables: - Germany: Public expenditure on education in current prices - Germany: Public expenditure on education in constant prices - Great Britain: Public expenditure on education in current prices - Great Britain: Public expenditure on education in constant prices - France: Public expenditure on education in current prices - France: Public expenditure on education in constant prices - Spain: Public expenditure on education in current prices - Spain: Public expenditure on education in constant prices - Japan: Public expenditure on education altogether in thousand Yen - Japan: Expenditure on education of the prefectures in thousand Yen - Japan: Expenditure on education of the cities, towns and villages in thousand Yen - Japan: ALLTOGETHER: Expenditure on education of prefectures, cities, towns and - Japan: Expenditure on education altogether in thousand Yen - Japan: Gross national product in thousand Yen - Japan: National income in thousand Yen - Japan: Public expenditure in thousand Yen - Japan: Expenditure on military in thousand Yen - Japan: Public debt in thousand Yen - Japan: Public investments in thousand Yen - Japan: Other public expenditure in thousand Yen - Japan: Price index 1934-36 = 100 - Japan: Population in thousand Spain: Public expenditure on education in thousand Peseta for: - Management schools, in current prices - Management schools, in constant prices - Primary schools, in current prices - Primary schools, in constant prices - Vocational schools, in current prices - Vocational schools, in constant prices - Secondary schools, in current prices - Secondary schools, in constant prices - Technical colleges, in current prices - Technical colleges, in constant prices - Universities, in current prices - Universities, in constant prices - Special schools, in current prices - Special schools, in constant prices - Expenditure on education altogether, in current prices - Expenditure on education altogether, in constant prices - Public expenditure altogether, in current prices - Public expenditure altogether, in constant prices - Spain: Price index of national income (1958 = 100) - Spain: National Income in thousand Pesetas - Spain: Population altogether

The main variables for this Location dataset are: - Pedestrian influx - Vehicle flow - Resident population - Income level - Business concentration.

Also, the model is enriched with information on the population interested in a specific topic (Like retail store location data), measured from the interaction of users in social networks (Consumer behavior data).

All the variables evaluated in the model are at the spatial grid level, to which it is possible to add existing points of sale and their respective revenue. This additional information makes it possible to estimate the billing of an additional Point of Sale in the best areas identified to locate a specific type of business.

Why should you trust PREDIK Data-Driven? In 2023, we were listed as Datarade's top providers. Why? Our solutions for location data, consumer behavior data, and store location data adapt according to the specific needs of companies. Also, PREDIK methodology focuses on the client and the necessary elements for the success of their projects.

- `visit_concert`: This is a standard CAP variables about visiting frequencies.
- `is_visit_concert`: binary variable, 0 if the person had not visited concerts in the previous 12 months.
- `artistic_activity_played_music`: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable.
- `artistic_activity_sung`: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.
- `age_exact`: The respondent’s age as an integer number.
- `country_code`: an ISO country code
- `geo`: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]
- `age_education`: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, `age_education` and `is_student`.
- `is_student`: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute `age` in this case to `age_education`, but we will show why this is not a good strategy.
- `w`, `w1`: Post-stratification weights for the 15+ years old population of each country. Use `w1` for averages of `geo` entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use `w` when treating the United Kingdom and Germany as one territory.
- `wex`: Projected weight variable. For weighted average values, use `w`, `w1`, for projections on the population size, i.e., use with sums, use `wex`.
- `id`: The identifier of the original survey.
- `rowid``: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.

A global database of B2B Marketing Data that provides an understanding of population distribution at administrative and zip code level over 55 years, past, present, and future.

Leverage up-to-date audience targeting population trends for market research, audience targeting, and sales territory mapping.

Self-hosted marketing population dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The B2B Marketing Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (B2B Marketing Data/B2B Leads data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

B2B leads data export methodology

Our population data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our B2B Marketing databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

The relationship between the HEXACO personality model and Disintegration – a personality trait encompassing psychotic-like experiences/behaviors - was investigated. In this pre-registered study, we predicted that Disintegration factor would separate from HEXACO. Support for the metric invariance of the seven-factor structure based on HEXACO and Disintegration in three general population samples (UK, Germany, Serbia) consisting of 1105 participants in total was found. As expected, all nine subdimensions of Disintegration formed a strong higher-order factor separate from HEXACO factors. Disintegration appeared to be the most coherent and robust among the seven factors across samples (three nations) and units of analysis (facets and items). These findings further disconfirm a widely held assumption that psychotic-like tendencies are best conceptualized as high Openness. Dataset for: Knežević, G., Lazarević, L. B., Bošnjak, M., & Keller, J. (2022). Proneness to psychotic-like experiences as a basic personality trait complementing the HEXACO model—A preregistered cross-national study. Personality and Mental Health, 16(3), 244-262. https://doi.org/10.1002/pmh.1537

Street Noise-Level Dataset — Health & Insurance Applications

Silencio’s Street Noise-Level Dataset offers health organizations, insurance companies, and wellness researchers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows insurance and health organizations to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

This is a nationally representative cross-sectional survey of Chinese international students in the UK, with a comparison group of UK home students. It is part of a wider study with other surveys in Germany and China. The study population are taught (undergraduate and postgraduate) Chinese students studying in UK universities. Areas covered in the questionnaires: Socio-demographic characteristics and course details; family background (parental education, occupation, household income, siblings); prior education (academic achievement and educational migration); motivations for study abroad and decision-making process; personality traits and values (e.g., risk-taking attitude); study experience in current course; health and wellbeing; future life course aspirations; cosmopolitan vs national orientations.Young people moving away from home to seek 'bright futures' through higher education are a major force in the urbanization of China and the internationalization of global higher education. Chinese students constitute the largest single group of international students in the richer OECD countries of the world, making up 20 percent of the total student migration to these countries. Yet systematic research on a representative sample of these student migrants is lacking, and theoretical frameworks for migration more generally may not always apply to students moving for higher education. Bright Futures is a pioneering study that investigates key dimensions of this educational mobility through large-scale, representative survey research in China, the UK and Germany. We explore this phenomenon in two related aspects: the migration of students from the People's Republic of China to the UK (this data collection) and Germany for higher education, and internal migration for studies within China. This research design enables an unusual set of comparisons, between those who stay and those who migrate, both within China and beyond its borders. We also compare Chinese students in the UK and Germany with domestic students in the two countries. Through such comparisons we are able to address a number of theoretical questions such as selectivity in educational migrations, aspirations beyond returns, the impact of transnationalization of higher education on individual orientations and life-course expectations, and the link between migration and the wellbeing of the highly educated. Bright Futures is a collaborative project, involving researchers from University of Essex, University of Edinburgh, UNED, University of Bielefeld and Tsinghua University. The research was funded by the Economic and Social Research Council (UK), German Research Foundation (Germany) and the National Natural Science Foundation (China). The sample design is a two-stage stratified sample, with universities as the Primary Sampling Units (PSUs). The sample was stratified by university ranking and the size of Chinese students enrolled at the institution to ensure that students from different types of universities were proportionately represented. Within each university that agreed to participate we either sampled all Chinese students in undergraduate and taught postgraduate programmes, or (in universities with a very large population of Chinese students) took a random sample. In each university, we sampled the same number of British home students as Chinese students for comparison. The questionnaire for UK home students is designed to serve as a comparison group to Chinese students. All questionnaires were in the students’ main language, i.e. Chinese or English respectively. The survey was conducted online. The response rate at the student level was approximately 13 percent. Survey fieldwork took place between April 2017 and March 2018. The achieved sample size in the UK is 1,446 Chinese students and 1,678 home students.