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

The most important key figures about population, households, population growth, births, deaths, migration, marriages, marriage dissolutions and change of nationality of the Dutch population.

CBS is in transition towards a new classification of the population by origin. Greater emphasis is now placed on where a person was born, aside from where that person’s parents were born. The term ‘migration background’ is no longer used in this regard. The main categories western/non-western are being replaced by categories based on continents and a few countries that share a specific migration history with the Netherlands. The new classification is being implemented gradually in tables and publications on population by origin.

Data available from: 1899

Status of the figures: The 2023 figures on stillbirths and perinatal mortality are provisional, the other figures in the table are final.

Changes as of 23 December 2024: Figures with regard to population growth for 2023 and figures of the population on 1 January 2024 have been added. The provisional figures on the number of stillbirths and perinatal mortality for 2023 do not include children who were born at a gestational age that is unknown. These cases were included in the final figures for previous years. However, the provisional figures show a relatively larger number of children born at an unknown gestational age. Based on an internal analysis for 2022, it appears that in the majority of these cases, the child was born at less than 24 weeks. To ensure that the provisional 2023 figures do not overestimate the number of stillborn children born at a gestational age of over 24 weeks, children born at an unknown gestational age have now been excluded.

Changes as of 15 December 2023: None, this is a new table. This table succeeds the table Population; households and population dynamics; 1899-2019. See section 3. The following changes have been made: - The underlying topic folders regarding 'migration background' have been replaced by 'Born in the Netherlands' and 'Born abroad'; - The origin countries Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan and Turkey have been assigned to the continent of Asia (previously Europe).

When will the new figures be published? The figures for the population development in 2023 and the population on 1 January 2024 will be published in the second quarter of 2024.

European countries are experiencing population decline and the tacit assumption in most analyses is that the decline may have detrimental welfare effects. In this paper we use a survey among the population in the Netherlands to discover whether population decline is always met with fear. A number of results stand out: population size preferences differ by geographic proximity: at a global level the majority of respondents favors a (global) population decline, but closer to home one supports a stationary population. Population decline is clearly not always met with fear: 31 percent would like the population to decline at the national level and they generally perceive decline to be accompanied by immaterial welfare gains (improvement environment) as well as material welfare losses (tax increases, economic stagnation). In addition to these driving forces it appears that the attitude towards immigrants is a very strong determinant at all geographical levels: immigrants seem to be a stronger fear factor than population decline. The data was collected from a Dutch household panel.

Data on European cities were collected in the Urban Audit and in the Large City Audit project. The projects' ultimate goal is to contribute towards the improvement of the quality of urban life: it supports the exchange of experience among European cities; it helps to identify best practices; it facilitates benchmarking at the European level and provides information on the dynamics within the cities and with their surroundings.

At the city level, the Urban Audit contains more than 130 variables and more than 50 indicators. These indicators are derived from the variables collected by the European Statistical System.

The data is published in 20 tables within 2 main groups, plus a perception survey table:

Cities and greater cities (urb_cgc)

Population on 1 January by age groups and sex - cities and greater cities (urb_cpop1)
Population structure - cities and greater cities (urb_cpopstr)
Population by citizenship and country of birth - cities and greater cities (urb_cpopcb)
Fertility and mortality - cities and greater cities (urb_cfermor)

Living conditions - cities and greater cities (urb_clivcon)

Education - cities and greater cities (urb_ceduc)

Culture and tourism - cities and greater cities (urb_ctour)
Labour market - cities and greater cities (urb_clma)
Economy and finance - cities and greater cities (urb_cecfi)
Transport - cities and greater cities (urb_ctran)
Environment - cities and greater cities (urb_cenv)

Functional Urban Area (urb_luz)

Population on 1 January by age groups and sex - Functional Urban Area (urb_lpop1)
Population structure - Functional Urban Area (urb_lpopstr)
Population by citizenship and country of birth - Functional Urban Area (urb_lpopcb)
Fertility and mortality - Functional Urban Area (urb_lfermor)
Living conditions - Functional Urban Area (urb_llivcon)
Education - Functional Urban Area (urb_leduc)
Labour market - Functional Urban Area (urb_llma)
Transport - Functional Urban Area (urb_ltran)
Environment - Functional Urban Area (urb_lenv)
Perception survey results (urb_percep)

Data has been collected on two spatial levels in the Urban Audit:

• The City (C) according to the administrative definition, as the basic level,
• The Functional Urban Area (FUA) being an approximation of the functional urban zone centered around the city

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

The empirical dataset is derived from a survey carried out on 25 estates in 14 cities in nine different European countries: France (Lyon), Germany (Berlin), Hungary (Budapest and Nyiregyha´za), Italy (Milan), the Netherlands (Amsterdam and Utrecht), Poland (Warsaw), Slovenia (Ljubljana and Koper), Spain (Barcelona and Madrid), and Sweden (Jo¨nko¨ping and Stockholm). The survey was part of the EU RESTATE project (Musterd & Van Kempen, 2005). A similar survey was constructed for all 25 estates.

The survey was carried out between February and June 2004. In each case, a random sample was drawn, usually from the whole estate. For some estates, address lists were used as the basis for the sample; in other cases, the researchers first had to take a complete inventory of addresses themselves (for some deviations from this general trend and for an overview of response rates, see Musterd & Van Kempen, 2005). In most cities, survey teams were hired to carry out the survey. They worked under the supervision of the RESTATE partners. Briefings were organised to instruct the survey teams. In some cases (for example, in Amsterdam and Utrecht), interviewers were recruited from specific ethnic groups in order to increase the response rate among, for example, the Turkish and Moroccan residents on the estates. In other cases, family members translated questions during a face-to-face interview. The interviewers with an immigrant background were hired in those estates where this made sense. In some estates it was not necessary to do this because the number of immigrants was (close to) zero (as in most cases in CE Europe).

The questionnaire could be completed by the respondents themselves, but also by the interviewers in a face-to-face interview.

Data and Representativeness

The data file contains 4756 respondents. Nearly all respondents indicated their satisfaction with the dwelling and the estate. Originally, the data file also contained cases from the UK.

However, UK respondents were excluded from the analyses because of doubts about the reliability of the answers to the ethnic minority questions. This left 25 estates in nine countries. In general, older people and original populations are somewhat over-represented, while younger people and immigrant populations are relatively under-represented, despite the fact that in estates with a large minority population surveyors were also employed from minority ethnic groups. For younger people, this discrepancy probably derives from the extent of their activities outside the home, making them more difficult to reach. The under-representation of the immigrant population is presumably related to language and cultural differences. For more detailed information on the representation of population in each case, reference is made to the reports of the researchers in the different countries which can be downloaded from the programme website. All country reports indicate that despite these over- and under-representations, the survey results are valuable for the analyses of their own individual situation.

This dataset is the result of a team effort lead by Professor Ronald van Kempen, Utrecht University with funding from the EU Fifth Framework.

BackgroundSocioeconomic inequalities in alcohol-related mortality have been documented in several European countries, but it is unknown whether the magnitude of these inequalities differs between countries and whether these inequalities increase or decrease over time.Methods and FindingsWe collected and harmonized data on mortality from four alcohol-related causes (alcoholic psychosis, dependence, and abuse; alcoholic cardiomyopathy; alcoholic liver cirrhosis; and accidental poisoning by alcohol) by age, sex, education level, and occupational class in 20 European populations from 17 different countries, both for a recent period and for previous points in time, using data from mortality registers. Mortality was age-standardized using the European Standard Population, and measures for both relative and absolute inequality between low and high socioeconomic groups (as measured by educational level and occupational class) were calculated.Rates of alcohol-related mortality are higher in lower educational and occupational groups in all countries. Both relative and absolute inequalities are largest in Eastern Europe, and Finland and Denmark also have very large absolute inequalities in alcohol-related mortality. For example, for educational inequality among Finnish men, the relative index of inequality is 3.6 (95% CI 3.3–4.0) and the slope index of inequality is 112.5 (95% CI 106.2–118.8) deaths per 100,000 person-years. Over time, the relative inequality in alcohol-related mortality has increased in many countries, but the main change is a strong rise of absolute inequality in several countries in Eastern Europe (Hungary, Lithuania, Estonia) and Northern Europe (Finland, Denmark) because of a rapid rise in alcohol-related mortality in lower socioeconomic groups. In some of these countries, alcohol-related causes now account for 10% or more of the socioeconomic inequality in total mortality.Because our study relies on routinely collected underlying causes of death, it is likely that our results underestimate the true extent of the problem.ConclusionsAlcohol-related conditions play an important role in generating inequalities in total mortality in many European countries. Countering increases in alcohol-related mortality in lower socioeconomic groups is essential for reducing inequalities in mortality. Studies of why such increases have not occurred in countries like France, Switzerland, Spain, and Italy can help in developing evidence-based policies in other European countries.

In April 2020 Eurostat set up an exceptional data collection on total weekly deaths, in order to support the policy and research efforts related to Covid-19. With this data collection, Eurostat's target was to provide quickly statistics that show the changing situation of the total number of weekly deaths from early 2020 onwards.

The available data on the total weekly deaths are transmitted by the National Statistical Institutes to Eurostat on voluntary basis. Data are collected cross classified by sex, 5-year age-groups and NUTS3 region (NUTS2021). The age breakdown by 5-year age group is the most significant and should be considered by the reporting countries as the main option; when that is not possible, data may be provided with less granularity. Similar with the regional structure, data granularity varies with the country.

Eurostat requested from the National Statistical Institutes the transmission of a back time series of weekly deaths for as many year as possible, recommending as starting point the year 2000. Shorter time series, imposed by data availability, are transmitted by some countries. A long enough time series is necessary for temporal comparisons and statistical modelling.

A note on Ireland: Data from Ireland were not included in the first phase of the weekly deaths data collection: official timely data were not available because deaths can be registered up to three months after the date of death. Because of the COVID-19 pandemic, the Central Statistics Office of Ireland began to explore experimental ways of obtaining up-to-date mortality data, finding a strong correlation between death notices published on RIP.ie and official mortality statistics. Recently, CSO Ireland started publishing a time series covering the period from October 2019 until the most recent weeks, using death notices (see CSO website). For the purpose of this release, Eurostat compared the new 2020-2021 web-scraped series with a 2016-2019 baseline established using official data. CSO is periodically assessing the quality of these data.

The purpose of Eurostat’s online tables in the folder Weekly deaths - special data collection (demomwk) is to make available to users information on the weekly number of deaths disaggregated by sex, 5 years age group and NUTS3 regions over the last 20 years, depending on the availability in each country covered in Eurostat demographic statistics data collections. In order to ensure the highest timeliness possible, data are made available as reported by the countries, and work is ongoing in order to improve data quality and user friendliness.

Starting in 2025, the weekly deaths data is collected on a quarterly basis. The database updates are expected by mid-June (release of monthly data for 1st quarter of the year), mid-September (2nd quarter), mid-December (3rd quarter), and mid-February (4th quarter).

The 'LFS main indicators' section presents a selection of the main statistics on the labour market. They encompass indicators of activity, employment and unemployment. Those indicators are based on the results of the European Labour Force Survey (EU-LFS), in few cases integrated with data sources like national accounts employment or registered unemployment. As a result of the application of adjustments, corrections and reconciliation of EU Labour Force Survey (EU-LFS) data, the 'LFS main indicators' is the most complete and reliable collection of employment and unemployment data available in the sub-domain 'Employment and unemployment'.

The EU-LFS data used for 'LFS main indicators' are, where necessary, adjusted and enriched in various ways, in accordance with the specificities of an indicator. The most common adjustments cover:

• estimation of missing values, i.e. in case of missing quarters, annual results and EU aggregates are estimated using interpolations of EU Labour Force Survey data with reference to the available quarter(s).
• for all quarterly indicators seasonally adjusted data are available.
• correction of the main breaks in the LFS series.

Those adjustments may produce some differences between data published under 'LFS main indicators' and 'LFS series – detailed quarterly/annual survey results', particularly for back data. For the most recent years, the different series converge, due to the implementation of a continuous quarterly survey and the improved quality of the data.

This page focuses on the 'LFS main indicators' in general. There are special pages for indicators that are listed below:

• Monthly Unemployment: une_rt_m.

Quarterly and annual unemployment figures are derived in line with all other LFS Main Indciators, and no longer aggregated from monthly unemployment series.

• Duration of working life - annual data: lfsi_dwl_a;
• Population in jobless households - annual data: lfsi_jhh_a;
• Labour market transitions - LFS longitudinal data: lfsi_long.

The entry of the new Framework regulation on Social Statistics (IESS) in 2021 created changes in the LFS Main Indicators. Most countries expected breaks for a number of series derived from LFS microdata, therefore Eurostat and participating countries launched a joint break correction exercise to produce comparable data before and under IESS. The 'LFS main indicators' section therefore contains two type of datasets depending on the underlying regulation. The first type of datasets are historical series under the pre-IESS regulation, and include the suffix ‘_h’ for historical series at the end of the table titles. Historical series will remain accessible and are continued until 2020Q4 LFS microdata revisions of previously released EU-LFS series. Reasons for revisions are for example weight revisions due to revised weighting routines, or census revisions. The second type of datasets are new tables that are filled with data under IESS from 2021Q1 on. These tables also include the break-corrected 2009Q1-2020Q4 data that are produced in the break correction exercise. If countries send longer complete time series than starting in 2009, that data will also be used and published. Until fully back-estimated series in line with IESS are available for all countries, EU and EA aggregates were based on the data that is available at the time and was flagged with a break flag. Fully break-free EU and EA aggregates were published for the first time in February 2022. More information can be found on the EU-LFS Breaks in Time Series (Statistics Explained) webpage.

General information on the EU-LFS can be found in the ESMS page for 'Employment and unemployment (LFS)', see link in related metadata. Detailed information on the main features, the legal basis, the methodology and the data as well as on the historical development of the EU-LFS is available on the EU-LFS (Statistics Explained) webpage.

This dataset is a research outcome of a European Research Council, Starting Grant funded (Grant Number 679097, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, UrbanOccupationsOETR) project. It contains a mid-nineteenth-century urban Ottoman population micro dataset for the city of Bursa.

In recent decades, a "big microdata revolution" has revolutionized access to transcribed historical census data for social science research. Despite this, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained absent from the big microdata ecosystem due to their prolonged inaccessibility. In fact, like other modernizing states in the nineteenth century, the Ottoman Empire started to enumerate its population in population registers (nüfus defterleri) in 1830, which recorded only males of all ages for conscription and taxation purposes. These registers were completed and updated in two waves, one in 1830-1838 and the other in the 1839-1865 period. Following this experience, the Empire implemented its first modern census, which included females, in 1881/1882 for more comprehensive statistical and governance reasons to converge with European census-taking practices and account for the increasing participation of females in economic and social spheres.

The pre-census population registers were opened to researchers in 2011. There are around 11.000 registers today. The microdata of the late Ottoman censuses is still not available. Still, unfortunately, the majority of the existing literature using the population registers superficially utilized and failed to tabulate the microdata. Most works using these valuable sources contented with transcribing the microdata from Ottoman to Latin script and presenting their data in raw and unstyled fashion without publishing them in a separate repository.

Our dataset marks the inaugural release of complete population data for an Ottoman urban center, the city of Bursa, derived from the 1839 population registers. It presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. To ensure that our dataset is more accessible, we are also releasing the dataset in Microsoft Excel format.

The city of Bursa, a major cosmopolitan commercial hub in modern northwestern Turkey, is selected from the larger UrbanOccupationsOETR project database as an exemplary case to represent the volume, value, variety, and veracity of the population data. Furthermore, since urban areas are usually the most densely populated locations that attract the most migration in any country, they are attractive locations for multifold reasons in historical demography. Bursa is not the only urban location in the UrbanOccupationsOETR database. As it focused on urbanization and occupational structural change, it collected the population microdata on major urban centers (chosen as primary locations) and towns (denoted as secondary locations), which pioneered the economic development of post-Ottoman nation-states. What makes the city of Bursa’s data more advantageous than other cities is that it has been cleaned and validated multiple times and used elsewhere for demographic and economic analyses.

The Ottoman population registers of 1830 and 1839 classified the population under the commonly and officially recognized ethnoreligious identities- Muslim, Orthodox Christian, Armenian, Catholic, Jewish, and (Muslim and non-Muslim) Roma. Muslim and non-Muslim populations were counted in separate registers. The registers were organized along spatial and temporal lines. The standard unit of the register was the quarter (mahalle) in urban and village (karye) in rural settings. Within these register units, populated public and non-household spaces such as inns, dervish lodges, monasteries, madrasas, coffeehouses, bakeries, mills, pastures (of nomads), and large private farms (çiftlik) were recorded separately.

The household (menzil/hane) was the unit of entry, which sometimes took different forms depending on the context, such as the room for inns and the tent for nomads. Each household recorded its members on a horizontal line. The variables of male individuals inhabiting them were self-reported biographical information (names, titles/family names, ages, and occupations), physical description (height and facial hair), relationships with other household members (kinship, tenancy, and employment ties), infirmities, and military and poll tax status, including the reasons for exemption, military post, and poll tax category (high-ala, medium-evsat, and small-edna). Households with no inhabitants were differentiated. At the same time, if a resident was known to be absent during registration due to reasons such as military service or migration, he was recorded in his household with a note stating that reason. If he was missing and appeared later, he was added to the household during updates with a note like “not recorded previously” (e.g., hin-i tahrirde taşrada olub) or “newly recorded” (tahrir-mande).

In addition to the permanent residents of a given location, migrant/temporary non-local (yabancı) residents such as laborers, inn-stayers, and unskilled bachelors (bî-kâr) were recorded along with their place of origin and for how long they had been in the migrated place. Non-Muslim migrants were registered with information regarding the last location where they got their poll tax certificate and if they would make their next poll tax payment in the migrated location.

Updates were made mainly to births, deaths, migrations, and military and poll tax status. No other variables, such as age, were renewed except for occupations in a limited number of cases. Updates are easily identifiable since they were written in siyakat, a special Ottoman chancery shorthand script, and occasionally in red ink. Births were specified with newborns’ names added next to the father’s entry. Deaths were updated by crossing out the deceased person’s record. Migrations were added with a description of the migrated place (including the military branch if the person was conscripted). Military and poll tax status was updated by crossing out the old category and adding the new one next to it. Updates were usually expressed in hijri years, sometimes in month-year, and rarely in day-month-year fashion. It is important to note that since updates were made once every few months, these dates may reflect their registration date rather than giving the exact time of the events. Equally crucial is that many events, especially births, were not reported, so their quality is limited.

Modeled after the way information was contained in the population registers, this relational database has two tables, “tblHouse” and “tblIndividual.” Each table categorizes and standardizes the register variables. To make the data easier to use, the dataset also includes a query “Query_InnerJoin” that combines all the variables from each table in a separate sheet.

Given Bursa’s important place in Ottoman history, our dataset serves as a large and crucial resource for comprehending historical societal, economic, and demographic trends within the Empire in the early stages of globalization. The dataset has 8391 household entries (HouseID) and 19,186 individual (IndivID) entries. This data includes the population registered in all of Bursa’s quarters and other location categories in 1839 and the updates until and including 1843 (Figure 2). The ethno-religious breakdown of the total population is 12462 Muslims (65%), 3315 Armenians (17%), 2466 Orthodox Christians (13%), 749 Jews (4%), and 194 Catholics (1%).

To broaden access and use of our data and bring it more in line with findability, accessibility, interoperability, and reusability (FAIR) data guidelines, the variables of “tblHouse” and “tblIndividual” are sorted into general categories and described in detail in the following tables. As the variables indicate, the dataset and population registers, in general, could serve to formulate unprecedented, hitherto impossible research questions related to major demographic dynamics, i.e., household size and composition, ethnoreligious differences, population density, occupational structure, intergenerational mobility and status transfer, mortality, fertility, migration, age-heaping/human capital, conscription, settlement patterns within and across urban locations, onomastics, toponymy, etc.

Table 1: Categories and descriptions of the variables of tblHouse

EPIC-Oxford is the Oxford component of the European Prospective Investigation into Cancer and Nutrition (EPIC), a large multi-centre cohort study with participants enrolled from 10 European countries. The EPIC-Oxford study began in the 1990s and follows the health of 65,000 men and women living throughout the UK, many of whom are vegetarian. The main objective of EPIC Oxford is to examine how diet influences the risk of cancer, particularly for the most common types of cancer in Britain, as well as the risks of other chronic diseases.

EPIC-Europe was initiated in 1992. It involves over 500,000 people from 23 centres in 10 European countries. It is coordinated by the World Health Organization International Agency for Research on Cancer in Lyon, and supported by the European Union and national funding agencies.

EPIC-Oxford is one of two EPIC cohorts in the UK, the other is EPIC-Norfolk.

For further details on the study design, recruitment, data collection and other aspects of the EPIC-Oxford study, please visit https://www.ceu.ox.ac.uk/research/epic-oxford-1

A multitude of smart phone and more traditional tools are used with increasing frequency by volunteers on roads for long-term monitoring of wildlife. Data collected by volunteers on roads has recently indicated large-scale declines of some widespread amphibians in Western Europe. However, it is unclear how representative such data are or not in relation to the actual species distribution. Spatial biases could skew results towards more urbanised areas and consequently produce incorrect or partial trend estimations at regional or national scales. Our objective was to compare and verify potential spatial biases of road based data using distribution datasets of different origins. As a case study, we used the common toad (Bufo bufo), a fast-declining species and the main amphibian targeted by conservation action on roads in Europe. We calculated ecological niche models with the built used Maxent models to compare road survey data obtained from the UK flagship, 35 year-long “Toads on Roads” project, containing almost 2 million amphibian records, in Great Britain with models using national-scale toad distribution records in Great Britain as well as with models using randomly generated points on roads. Road based distribution models that used data collected by volunteers on roads produced similar results to those obtained from overall species distribution, indicating the lack of selection bias and a high spatial coverage of volunteer-collected data on roads. Toads were present in most parts of the country but were generally absent from mountainous areas and, despite the high availability of potential recorders, showed nearly complete absence in large urban areas. To our knowledge, this is the first study that comparatively evaluates species distribution models created using datasets of different origin in order to verify the influence of potential spatial bias of data collected by volunteers on roads. We show that for countries with high road density road network coverage, such as Great Britain, road based data collected by volunteers represent a robust dataset in terms of coverage and a critical citizen science contribution to conservation.

The 6th Vintage of CompNet dataset represents an unbalanced panel dataset which covers 19 European countries. This provides researchers with a dataset for cross-country studies that includes a rich set of indicators from five different fields: productivity, finance, labour, competition and trade. CompNet variables and indicators are available for two samples: “full” and “20E”. The full sample intended to cover the period 1999-2016 for most of the countries in the sample. However, actual data availability reduces this time span to 2003-2015 for the majority of the participating countries. In some countries, firms are legally obliged to report their balance sheet data only when certain thresholds are met. For example, in Poland only firms with more than 10 employees have to report their accountings. To provide a more homogeneous sample across countries, CompNet therefore constructed also the 20E sample, including only firms that have at least 20 employees for the same time span. Content coding Data provider collect firm-level information from balance sheet and administrative statistical registries. We run a harmonized protocol across each firm-level data set to construct our indicators. Self-administered questionnaire In most cases, data rely on business registers of national banks or statistical offices, complemented with other firm-level sources, either to enrich firm coverage, or to include additional information, as, for instance, trade values. Across all countries, the target population of the firm-level datasets is narrowed down to consistently include non-financial corporations with employees. The country coverage of this vintage contains up to 19 countries, including the six biggest EU economies (Germany, France, Italy, Spain, Netherlands and Poland).

One of the most valuable aquaculture fish in Europe is the rainbow trout, Oncorhynchus mykiss, but the profitability of trout production is threatened by a highly lethal infectious disease, viral hemorrhagic septicemia (VHS), caused by the VHS virus (VHSV). For the past few decades, the subgenogroup Ia of VHSV has been the main cause of VHS outbreaks in European freshwater-farmed rainbow trout. Little is currently known, however, about the phylogenetic radiation of this Ia lineage into subordinate Ia clades and their subsequent geographical spread routes. We investigated this topic using the largest Ia-isolate dataset ever compiled, comprising 651 complete G gene sequences: 209 GenBank Ia isolates and 442 Ia isolates from this study. The sequences come from 11 European countries and cover the period 1971–2015. Based on this dataset, we documented the extensive spread of the Ia population and the strong mixing of Ia isolates, assumed to be the result of the Europe-wide trout trade. For example, the Ia lineage underwent a radiation into nine Ia clades, most of which are difficult to allocate to a specific geographic distribution. Furthermore, we found indications for two rapid, large-scale population growth events, and identified three polytomies among the Ia clades, both of which possibly indicate a rapid radiation. However, only about 4% of Ia haplotypes (out of 398) occur in more than one European country. This apparently conflicting finding regarding the Europe-wide spread and mixing of Ia isolates can be explained by the high mutation rate of VHSV. Accordingly, the mean period of occurrence of a single Ia haplotype was less than a full year, and we found a substitution rate of up to 7.813 × 10−4 nucleotides per site per year. Finally, we documented significant differences between Germany and Denmark regarding their VHS epidemiology, apparently due to those countries’ individual handling of VHS.

The Common hamster (Cricetus cricetus) has declined by more than 99% in the westernmost part of its range in Belgium, the Netherlands and the adjacent German federal state of North Rhine-Westphalia (BNN region) in the second half of the 20th century. To detect a decline in genetic variation as a result of inbreeding and genetic drift we have measured the genetic variation in current BNN hamster populations and compared the outcome with the genetic variation in museum samples from the historical, non-fragmented, population.Most of the current populations have lost the majority of their rare alleles and individual animals have become nearly homozygous. The total gene diversity of the remaining small populations combined is not much less than that of the historical population. Hence, the main genetic difference between historical and present is not in terms of total genetic variation or number of alleles in the BNN region, but in the distribution of this variation over the populations.SamplesThis study is based on 250 DNA samples from hamster populations in the BNN region (n=85) and reference populations in France (n=68) and a population in Central Germany (n=97).The samples from the BNN region consisted of 52 samples from the five currently surviving populations in Belgium, the Netherlands and North Rhine–Westphalia and 33 samples from museum specimens.The museum samples consisted of pieces of dried skin, sometimes including hairs, that were taken from museum specimens that were collected or found in the wild in Belgium and the Netherlands during the period 1925-1985; 85% of the specimens were collected before 1970. In total 51 museum specimens from the museums of natural history in Leiden (Naturalis, the Netherlands) and Brussels (KBIN, Belgium) were sampled, but only 33 samples provided sufficient enough DNA for PCR amplification. The technical analysis of museum and current samples is described in detail in Neumann & Jansman (2004). Each museum sample was assigned either to the historical hamster population of Belgium (n=8) or to the historical population of the Netherlands (n=25).GenotypingAll samples were genotyped for a maximum of 11 microsatellite loci: Ccrμ3, Ccrμ4, Ccrμ6, Ccrμ10, Ccrμ11, Ccrμ12, Ccrμ13, Ccrμ15, Ccrμ17, Ccrμ19, and Ccrμ20. However, in our study we used only 9 of the 11 available microsatellites because there were too many missing values at loci Ccrμ6 and/or Ccrμ19, especially in the museum samples. Museum samples with less than 6 known loci were excluded from the analysis. Almost half of the 33 museum samples showed missing values (n=17, 51%), with 8 samples missing 1 locus, 8 samples missing 2 loci and 1 sample missing 3 loci.Each row is a genotype from an individual.The dataset has the following columns:A) Population per country and periodB) Unique individual numberC) Population: historical or recentD-Y) alleles per locus for each individualZ) Specific sample location (in the wild)AA) Populations (assigned to)AB) Origin (wild)AC) Year of collectionAD) Country

It is commonly assumed that the size of a country’s population has nothing to do with the structure of the law. The law of larger jurisdictions is supposedly just as simple or complex as the law of smaller jurisdictions. However, this hypothesis has never been empirically tested. This is surprising in view of the fact that a thriving field of research in linguistics deals with the relationship between language complexity and the size of the speech community. This research shows that grammatical complexity correlates negatively with the size of the speech community: the bigger the community, the simpler the grammar. The aim of this paper, an experiment in numerical comparative law, is to investigate whether the same is true for the law. The question that it seeks to answer is whether smaller jurisdictions have a more complex law than bigger jurisdictions and, if so, how this could be explained. The material is drawn from both constitutional law and private law.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolonged development arc in Sub-Saharan Africa.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.