• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally the researchers chose to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, researchers combined some subcategories. First, they were interested in total tax revenue, as well as the shares of total revenue coming from direct and indirect taxes. Further, they measured two sub-categories of direct taxation, namely taxes on property and income. For indirect taxes, they separated excises, consumption, and customs.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Irani... Visit https://dataone.org/datasets/sha256%3Aaa1b4aae69c3399c96bfbf946da54abd8f7642332d12ccd150c42ad400e9699b for complete metadata about this dataset.

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

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.

Data describing the development and survival of gypsy moths (Lymantria dispar L. (Lepidoptera: Erebidae)) from all three subspecies on 13 North American conifers and 3 broad leaf hosts were collected (Keena and Richards 2020). Populations from the United States and Greece served as the Lymantria dispar dispar controls for comparison with the Asian strains from the L. d. asiatica (populations from China, Russia, and South Korea) and L. d. japonica (population from Japan) subspecies. The hosts compared were Acer rubrum, Betula populifolia, Quercus velutina, Pinus strobus, Pseudotsuga menziesii, Abies balsamea, Abies concolor, Larix occidentalis, Picea glauca, Picea pungens, Pinus ponderosa, Pinus taeda, Pinus palustris, Pinus rigida, Tsuga canadensis, and Juniperus virginiana.Survival and developmental data (either to 14 day or to adult with reproductive traits also evaluated) are important for assessing whether there is variation between and/or within a subspecies in host utilization. Host utilization information is critical to managers for estimating the hosts at risk and potential geographic range for Asian gypsy moths from different geographic origins in North America. Since the lists of hosts that Asian gypsy moth is known to feed on in other countries is long and no broad evaluation of North American hosts has been done, without data like these it is difficult to evaluate how the hosts at risk in North America to the Asian and established gypsy moths may differ.For more information about these data, see Keena and Richards (2020, https://doi.org/10.3390/insects11040260).

These data were originally published on 04/17/2020. Minor metadata updates were made on 07/22/2022 and 04/25/2023.

The Gross Domestic Product per capita in Japan was last recorded at 37144.91 US dollars in 2024. The GDP per Capita in Japan is equivalent to 294 percent of the world's average. This dataset provides - Japan GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

These are data on variation in host specificity and genetics among 16 populations of an aphid parasitoid, Aphelinus certus, 15 from Asia and one from North America. Host range was the same for all the parasitoid populations, but levels of parasitism varied among aphid species, suggesting adaptation to locally abundant aphids. Differences in host specificity did not correlate with geographical distances among parasitoid populations, suggesting that local adaption is mosaic rather than clinal, with a spatial scale of less than 50 kilometers. Analysis of reduced representation libraries for each population showed genetic differentiation among them. Differences in host specificity correlated with genetic distances among the parasitoid populations. Resources in this dataset:Resource Title: data dictionary for Aphelinus certus population variation. File Name: data_dictionary_Aphelinus_certus.csvResource Description: This is the data dictionary for the other files (Aphelinus_certus_host_use.csv, Aphelinus_certus_culture_data.csv) in this project. Resource Title: Host specificity of Aphelinus certus populations. File Name: Aphelinus_certus_host_use.csvResource Description: Results of no-choice experiments in the laboratory on parasitism, adult emergence rate, and progeny sex ratio for 15 populations of Aphelinus certus from China, Japan, and South Korea and one population from the US. Resource Title: Culture data for Aphelinus certus populations. File Name: Aphelinus_certus_culture_data.csvResource Description: This file gives data on the locations, dates, founding numbers, and collectors for the populations of Aphelinus certus studied in this project. Resource Title: Fst and host use distances among populations of Aphelinus certus. File Name: A_certus_Fst_host_dist.csvResource Description: We used next-generation sequencing of reduced-representation genomic libraries to genotype single nucleotide polymorphisms (SNPs) among the 16 A. certus populations. Libraries were prepared as described in Manching et al. (2017). Briefly, genomic DNA was extracted from pools of wasps from each population using Qiagen DNeasy Blood and Tissue Kits (Qiagen, Valencia, CA), following the standard protocol. The resulting DNA was digested with restriction endonucleases using one rare cutter (NgoMIV with a 6 bp recognition site) and one frequent cutter (CviQI with a 4 bp recognition site) (New England Biolabs, Inc., Ipswich, MA), which together determined the number of unique locations of fragments across the genome and the lengths of these fragments. Custom adaptors, with barcodes for each population that also served to register clusters on the Illumina HiSeq during sequencing, were ligated onto the fragments using T4 ligase (New England Biolabs, Inc., Ipswich, MA). The ligates were pooled and purified using Agencourt AMPure XP beads (Beckman Coulter, Indianapolis, IN). The purified ligate was separated into 10 aliquots that were amplified in separate PCR reactions to both increase copy number at each locus and add more adaptor sequence for sequencing. The adaptors were designed so that the only fragments that amplify would have the rare-common combination of cut sites. After PCR, the products were pooled and then size-selected (300-350 bp) using the BluePippin system (Sage Science, Beverly, MA). After quantification with qPCR, the resulting fragments were sequenced for ~100 nucleotides in single-end reads an Illumina HiSeq 2500 (Illumina, San Diego, CA) at the Delaware Biotechnology Institute. Sequence data were processed with a reduced-representation computational pipeline called RedRep (described in Manching et al. (2017)); the scripts and documentation for the pipeline are available under an open source MIT license at https://res1githubd-o-tcom.vcapture.xyz/UD-CBCB/RedRep. Briefly, sequences were deconvoluted by barcode using custom scripts and the FASTX-Toolkit (version 0.0.14; http://hannonlab.cshl.edu/fastx_toolkit). Custom scripts and CutAdapt (version 1.14; Martin 2011) were then used to remove adapters, trim low quality read ends, and filter out sequences that did not meet minimum length/quality standards or did not meet expectations for the restriction-site sequences. High-quality reads were mapped to the draft genome of A. certus using BWA-MEM program (version 0.7.16a; Li 2013). SNP loci were identified using the GATK HaplotypeCaller (version 3.5-0; McKenna et al. 2010). We filtered the SNP loci for read depth ≥ 50 and then for presence in all populations using BEDtools (version 2.26) and custom scripts written in R (version 3.3.3; R.Core.Team 2017). We tested the relationship between host use distance and genetic distance, as measured by FST. Because A. certus individuals were pooled within populations to make the libraries for sequencing, we used read depths to estimate allele frequencies for SNP loci. We filtered the data for SNP loci that were present in all populations and had read depth ≥ 50, and we used the numbers of individuals in each pool in calculating FST between populations with the calcPopDiff function in the polysat R package (version 1.7-2; Clark 2017). Using Mantel's permutation test, we compared the genetic and parasitism distance matrices (10,000 permutations with the mantel.randtest function in the ade4 R package). Clark, L. V. (2017) polysat version 1.7-2. Tools for polyploid microsatellite analysis. in. Li, H. (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv: 1303.3997v1 [q-bio.GN]. Manching, H., Sengupta, S., Hopper, K. R., Polson, S. W., Ji, Y. and Wisser, R. J. (2017) Phased genotyping-by-sequencing enhances analysis of genetic diversity and reveals divergent copy number variants in maize. Genes Genomes Genetics, 7(7), pp. 2161-2170. Martin, M. (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. . EMBnet.journal, 17, pp. 10-12. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M. and DePristo, M. A. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), pp. 1297-1303. R.Core.Team (2017) R: A language and environment for statistical computing. in: R Foundation for Statistical Computing, Vienna, Austria. https://res1wwwd-o-tR-projectd-o-torg.vcapture.xyz/.

This data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all Luxembourg Wealth Study (LWS) datasets in all waves (as of March 2022). It includes Gini coefficients calculated on: • Disposable Net Worth • Value of Principal residence • Financial AssetsThis project sought to renew the ESRC's invaluable financial support to LIS (formerly the Luxembourg Income Study) for a period of five more years. LIS is an independent, non-profit cross-national data archive and research institute located in Luxembourg. LIS relies on financial contributions from national science foundations, other research institutions and consortia, data-providing agencies, and supranational organisations to support data harmonisation and enable free and unlimited data access to researchers in the participating countries and to students world-wide. LIS' primary activity is to make harmonised household microdata available to researchers, thus enabling cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Users of the Luxembourg Income Study Database and Luxembourg Wealth Study Database come from countries around the globe, including the UK. LIS has four goals: 1) to harmonise microdatasets from high- and middle-income countries that include data on income, wealth, employment, and demography; 2) to provide a secure method for researchers to query data that would otherwise be unavailable due to country-specific privacy restrictions; 3) to create and maintain a remote-execution system that sends research query results quickly back to users at off-site locations; and 4) to enable, facilitate, promote and conduct crossnational comparative research on the social and economic wellbeing of populations across countries. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. LIS currently includes microdata from 46 countries in Europe, the Americas, Africa, Asia and Australasia. LIS contains over 250 datasets, organised into eight time "waves," spanning the years 1968 to 2011. Since 2007, seventeen more countries have been added to LIS, including the BRICS countries (Brazil, Russia, India, China, South Africa), Japan, South Korea and a number of other Latin American countries. LWS contains 20 wealth datasets from 12 countries, including the UK, and covers the period 1994 to 2007. All told, LIS and LWS datasets together cover 86% of world GDP and 64% of world population. Users submit statistical queries to the microdatabases using a Java-based job submission interface or standard email. The databases are especially valuable for primary research in that they offer access to cross-national data at the micro-level - at the level of households and persons. Users are economists, sociologists, political scientists, and policy analysts, among others, and they employ a range of statistical approaches and methods. LIS also provides extensive documentation - metadata - for both LIS and LWS, concerning technical aspects of the survey data, the harmonisation process, and the social institutions of income and wealth provision in participating countries. In the next five years, for which support is sought, LIS will: - expand LIS, adding Waves IX (2013) and X (2016), and add new middle-income countries; - develop LWS, adding another wave of datasets to existing countries; acquire new wealth datasets for 14 more countries in cooperation with the European Central Bank (based on the Household Finance and Consumption Survey); - create a state-of-the-art metadata search and storage system; - maintain international standards in data security and data infrastructure systems; - provide high-quality harmonised household microdata to researchers around the world; - enable interdisciplinary cross-national social science research covering 45+ countries, including the UK; - aim to broaden its reach and impact in academic and non-academic circles through focused communications strategies and collaborations. All surveyed households and their members are included in our estimates of Gini and Atkinson coefficients, percentile ratios, and poverty lines. Poverty lines are calculated based on the total population. Those lines are then used to calculate poverty rates among subgroups (children and the elderly). Thus, when calculating poverty rates, the subgroups vary, but the poverty lines remain constant within any given dataset. The data file includes the Gini coefficient calculated for different wealth welfare aggregates constructed for all LWS datasets in all waves (as of March 2022).

Judgement on questions of security policy as well as of the American and Soviet presence in Europe. Judgement on the American and Soviet way of life and the cultural as well as technical achievements of the countries. Image of the Americans. Anti-Americanism. Topics: Satisfaction with the social system in the Federal Republic; attitude to a change of the social system; countries representing similar as well as different social values than the Federal Republic; countries with great influence on the policies of the Federal Republic; attitude to foreign investments in the Federal Republic; countries with the greatest military threat for the Federal Republic; countries most likely to come to the aid of the Federal Republic in case of a military attack; attitude to Japan, the USA, the United Kingdom and the Soviet Union; change of one´s own opinion on the United States as well as the Soviet Union; national consciousness (scale); country with model function for the life style in the Federal Republic; evaluation of this influence; judgement on current German-American and German-Soviet relations; assessment of German-American economic relations; attitude to German-American relations and perceived consideration of the Americans for German wishes; feeling of personal political effectiveness; approval of selected forms of political participation; attitude to protests and demonstrators against American policies (scale); attitude to American military bases in the Federal Republic; judgement on the relationship of American soldiers to the German civilian population; attitude to the closeness of the relation of the German and American foreign policy; judgement on US influence on German domestic policy; comparison of the USA and the Soviet Union regarding support of human rights in other countries, use of military force for national goals, willingness to negotiate in conflicts, trustworthiness in negotiations, attempting to rule other countries economically, support for poorer countries with foreign aid, intervention in other countries and the desire for peace; judgement on the cultural achievements of the United States in the area music, film, sport, media and literature; judgement on the technical achievements in the areas medicine, space flight, management methods, telecommunication and data processing; judgement on the social, economic and freedom achievements of the American society; personal contact with Americans and positive or negative experiences made here; trust in American defense preparedness for the Federal Republic; attitude to German NATO membership; comparison of Americans and Sowjets regarding their support for post-war Europe, their history and culture and their people; assessment of the support for peace by American or Soviet foreign policy; attitude to military support for the Contras in Nicaragua by the Americans and for the Sandinists by the Soviet Union; judgement on the Soviet presence in Afghanistan and US support for the opposition in Afghanistan; observing human rights in the USA and in the Soviet Union; extent of interest in news about German-American relations; most important and most trustworthy sources of information in the media about these relations; self-classification on a left-right continuum; party preference (Sunday question); behavior at the polls in the last Federal Parliament election 1987. Also encoded was: ZIP (postal) code. Beurteilung sicherheitspolitischer Fragen sowie der amerikanischen und sowjetischen Präsenz in Europa. Beurteilung der amerikanischen und sowjetischen Lebensweise und der kulturellen wie technischen Leistungen der Länder. Image der Amerikaner. Antiamerikanismus. Themen: Zufriedenheit mit der Gesellschaftsform in der Bundesrepublik; Einstellung zu einer Veränderung der Gesellschaftsform; Länder, die ähnliche sowie unterschiedliche gesellschaftliche Werte vertreten wie die Bundesrepublik; Länder mit großen Einfluß auf die Politik der Bundesrepublik; Einstellung zu ausländischen Investitionen in der Bundesrepublik; Länder mit der größten militärischen Bedrohung für die Bundesrepublik; Länder, die der Bundesrepublik im Falle eines militärischen Angriffs am ehesten zur Hilfe kommen würden; Einstellung zu Japan, zu den USA, zum Vereinigten Königreich und zur Sowjetunion; Veränderung der eigenen Meinung zu den Vereinigten Staaten sowie zur Sowjetunion; Nationalbewußtsein (Skala); Land mit der Vorbildfunktion für den Lebensstil in der Bundesrepublik; Bewertung dieses Einflusses; Beurteilung der gegenwärtigen deutsch- amerikanischen und deutsch-sowjetischen Beziehungen; Einschätzung der deutsch-amerikanischen Wirtschaftsbeziehungen; Einstellung zu den deutsch-amerikanischen Beziehungen und empfundene Rücksichtnahme der Amerikaner auf deutsche Wünsche; Empfindung eigener politischer Wirksamkeit; Befürwortung ausgewählter Formen politischer Partizipation; Einstellung zu Protesten und Demonstranten gegen die amerikanische Politik (Skala); Einstellung zu amerikanischen Militärstützpunkten in der Bundesrepublik; Beurteilung des Verhältnisses der amerikanischen Soldaten zur deutschen Zivilbevölkerung; Einstellung zur Enge der Beziehung der deutschen und der amerikanischen Außenpolitik; Beurteilung des US-Einflusses auf die deutsche Innenpolitik; Vergleich der USA und der Sowjetunion bezüglich der Förderung von Menschenrechten in anderen Ländern, der Anwendung militärischer Gewalt für eigene Ziele, der Verhandlungsbereitschaft in Konflikten, der Vertrauenswürdigkeit bei Verhandlungen, des Versuchs wirtschaftlicher Beherrschung anderer Länder, der Unterstützung ärmerer Länder mit Entwicklungshilfe, der Einmischung in andere Länder und des Wunsches nach Frieden; Beurteilung der kulturellen Leistungen der Vereinigten Staaten im Bereich Musik, Film, Sport, Medien und Literatur; Beurteilung der technischen Leistungen in den Bereichen Medizin, Raumfahrt, Managementmethoden, Telekommunikation und Datenverarbeitung; Beurteilung der gesellschaftlichen, wirtschaftlichen und freiheitlichen Leistungen der amerikanischen Gesellschaft; persönlicher Kontakt zu Amerikanern und dabei gemachte positive oder negative Erfahrungen; Vertrauen in die amerikanische Verteidigungsbereitschaft für die Bundesrepublik; Einstellung zur deutschen NATO-Mitgliedschaft; Vergleich der Amerikaner und der Sowjets bezüglich ihrer Unterstützung für das Nachkriegseuropa, ihrer Geschichte und Kultur und ihrer Menschen; Einschätzung der Friedensförderung durch die amerikanische bzw. die sowjetische Außenpolitik; Einstellung zur militärischen Unterstützung für die Kontras in Nikaragua durch die Amerikaner und für die Sandrinisten durch die Sowjetunion; Beurteilung der sowjetischen Präsenz in Afghanistan und der US-Unterstützung für die Opposition in Afghanistan; Wahrung der Menschenrechte in den USA und in der Sowjetunion; Umfang des Interesses an Nachrichten über die deutsch-amerikanischen Beziehungen; wichtigste und vertrauenswürdigste Informationsquellen in den Medien über diese Beziehungen; Selbsteinstufung auf einem Links-Rechts- Kontinuum; Parteipräferenz (Sonntagsfrage); Wahlverhalten bei der letzten Bundestagswahl 1987. Demographie: Alter; Geschlecht; Familienstand; Familienzusammensetzung; Kinderzahl; Alter der Kinder; Konfession; Schulbildung; Beruf; Berufstätigkeit; Befragter ist Haushaltsvorstand; Charakteristika des Haushaltsvorstands; Ortsgröße; Bundesland. Zusätzlich verkodet wurde: Postleitzahl.

Chad TD: GDP: USD: Gross National Income per Capita: Atlas Method data was reported at 670.000 USD in 2023. This records an increase from the previous number of 660.000 USD for 2022. Chad TD: GDP: USD: Gross National Income per Capita: Atlas Method data is updated yearly, averaging 220.000 USD from Dec 1962 (Median) to 2023, with 62 observations. The data reached an all-time high of 940.000 USD in 2014 and a record low of 110.000 USD in 1964. Chad TD: GDP: USD: Gross National Income per Capita: Atlas Method data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Gross Domestic Product: Nominal. GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.;World Bank national accounts data, and OECD National Accounts data files.;Weighted average;

Scathophaga_wings_microsatelitesThe data file contains information on wing shape and size of 28 Scathophaga stercoraria populations from North America, Europe, and Japan, reared at three developmental temperatures (12°C, 18°C and 24°C). Landmark data of 12 x-y coordinates of individual flies are Procrustes transformed and raw centroid sizes are scaled in millimeter. Allelic information on 10 polymorphic microsatellites of 33 populations is provided in an extra sheet.

Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).