The estimated resident population (ERP) is the official measure of the Australian population. This dataset contains annual ERP by country of birth, age and sex at the Australia level. At the state/territory level it is available for Census years only.

Australia Population: Growth data was reported at 2.371 % in 2023. This records an increase from the previous number of 1.273 % for 2022. Australia Population: Growth data is updated yearly, averaging 1.447 % from Dec 1961 (Median) to 2023, with 63 observations. The data reached an all-time high of 3.380 % in 1971 and a record low of 0.141 % in 2021. Australia Population: Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage . Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.;Derived from total population. Population source: (1) United Nations Population Division. World Population Prospects: 2022 Revision, (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.;Weighted average;

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.

Nice underwater photo of Nautilus in American Samoa

The Gross Domestic Product per capita in Australia was last recorded at 61211.90 US dollars in 2024. The GDP per Capita in Australia is equivalent to 485 percent of the world's average. This dataset provides - Australia GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Sweetpotato (Ipomoea batatas) plays a critical role in food security and is the most important root crop worldwide following potatoes and cassava. In the United States (US), it is valued at over $700 million USD. There are two sweetpotato germplasm collections (Plant Genetic Resources Conservation Unit and US Vegetable Laboratory) maintained by the USDA, ARS for sweetpotato crop improvement. To date, no genome-wide assessment of genetic diversity within these collections has been reported in the published literature. In our study, population structure and genetic diversity of 417 USDA sweetpotato accessions originating from 8 broad geographical regions (Africa, Australia, Caribbean, Central America, Far East, North America, Pacific Islands, and South America) were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species. Population structure using Bayesian clustering analyses (STRUCTURE) with 32,784 segregating SNPs grouped the accessions into four genetic groups and indicated a high degree of mixed ancestry. A neighbor-joining cladogram and principal components analysis based on a pairwise genetic distance matrix of the accessions supported the population structure analysis. Pairwise FST values between broad geographical regions based on the origin of accessions ranged from 0.017 (Far East – Pacific Islands) to 0.110 (Australia – South America) and supported the clustering of accessions based on genetic distance. The markers developed for use with this collection of accessions provide an important genomic resource for the sweetpotato community, and contribute to our understanding of the genetic diversity present within the US sweetpotato collection and the species. Resources in this dataset:Resource Title: Supplementary Material. File Name: Web Page, url: https://www.frontiersin.org/articles/10.3389/fpls.2018.01166/full#supplementary-material FIGURE S1 | QC Boxplot showing distribution of quality scores of raw reads in a multiplexed library containing 96 Ipomoea batatas accessions. Buffer sequence lie within the first 8 base calls, while variable barcodes (6–9 bp) lie at position 14–17 bp. FIGURE S2 | Proportion raw reads matching both reference subgenomes (6x genotypes) and those specific to each of the subgenomes (4x and 2x genotypes derived Ipomoea trifida and I. triloba, respectively). FIGURE S3 | Boxplot shows relatively uniform read depth across individual samples and genomic loci after de-multiplexing pool samples. Only genotypes with 6 alleles/dose are shown here. FIGURE S4 | Bar plots of Bayesian assignment probabilities for each Ipomoea batatas accession analyzed with segregating 32,784 SNPs using the program STRUCTURE for K = 4. The x-axis indicates accession and the y-axis indicates the assignment probability of that accession to each of the four clusters. Each vertical line represents an individual’s probability of belonging to one of K clusters (represented by different colors) or a combination of if ancestry is mixed. The asterisk (∗) indicates the cultivar Porto Rico, which is a foundational line of the sweetpotato industry in the US. The plus sign (+) indicates that this accession was used as parental material in the mass selection populations developed by Jones et al. (1991). The USDA, ARS, US Vegetable Laboratory (USVL) W-lines and USVL-lines originate from the mass selection populations. Information for all accessions is found in Supplementary Table S1. FIGURE S5 | Linkage disequilibrium estimates (r2) of all genome-wide marker pairs plotted against corresponding interval between marker pairs. Curve (blue line) based on game smoothing method function shows distribution of all data points. Top and middle plot based on genotype data with allelic dosage information, while bottom plot is based on diploidized genotypes. TABLE S1 | Information of Ipomoea batatas accessions analyzed by GBSpoly. TABLE S2 | Pairwise genetic distance matrix between Ipomoea batatas accessions. TABLE S3 | Information for individual SNPs used for data analyses. DATASET S1 | Structure data file for Ipomoea batatas accessions.

The Aboriginal & Torres Strait Islander Social Health Atlas data presenting the latest Aboriginal & Torres Strait Islander (ATSI) Social Health Atlas indicators are available by Indigenous Areas, …Show full descriptionThe Aboriginal & Torres Strait Islander Social Health Atlas data presenting the latest Aboriginal & Torres Strait Islander (ATSI) Social Health Atlas indicators are available by Indigenous Areas, including totals for the Capital cities/ Rest of States/Territories, States/ Territories and Australia. Note: The Department of Health has approved for release a set of population estimates by Indigenous status for 2011, and projections to 2016 by Statistical Areas Level 2, Indigenous Region and Primary Health Network. To obtain these data, please contact us. Attribution: Torrens University Australia

Explore macroeconomic statistics and indicators, including GDP, Gross Fixed Capital Formation, National Income, and more. This dataset covers a wide range of countries such as Afghanistan, Albania, Algeria, Australia, Brazil, China, Germany, India, United States, and many more.

GDP, Gross Domestic Product, Capita, GFCF, Gross Fixed Capital Formation, Value, Added, Gross, Output, National, Income, Manufacturing, Agriculture, Population, National Accounts

Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cuba, Cyprus, Czechia, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela, Yemen, Zambia, Zimbabwe

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Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 12.700 % in 2018. This records an increase from the previous number of 12.200 % for 2016. Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 12.200 % from Dec 1981 (Median) to 2018, with 12 observations. The data reached an all-time high of 13.200 % in 1989 and a record low of 11.200 % in 2014. Australia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Australia AU:(GDP) Gross Domestic Productper Person Employed: 2017 PPP data was reported at 98,027.135 Intl $ in 2022. This records an increase from the previous number of 97,275.461 Intl $ for 2021. Australia AU:(GDP) Gross Domestic Productper Person Employed: 2017 PPP data is updated yearly, averaging 87,332.339 Intl $ from Dec 1991 (Median) to 2022, with 32 observations. The data reached an all-time high of 98,186.647 Intl $ in 2020 and a record low of 68,165.606 Intl $ in 1991. Australia AU:(GDP) Gross Domestic Productper Person Employed: 2017 PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Employment and Unemployment. GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2017 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.;World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank.;Weighted average;

This dataset, released August 2017, contains the Australian residents population by their birthplace divided into English speaking (ES) and non-English speaking (NES) countries, 2016. The following countries are designated as ES: Canada, Ireland, New Zealand, South Africa, United Kingdom and the United States of America; the remaining countries are designated as NES. The dataset also includes the population people born overseas and report poor proficiency in English. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Compiled by PHIDU based on the ABS Census of Population and Housing, August 2016.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Australia GDP per Person Employed: 2021 PPP data was reported at 114,625.675 Intl $ in 2023. This records an increase from the previous number of 114,051.350 Intl $ for 2022. Australia GDP per Person Employed: 2021 PPP data is updated yearly, averaging 102,552.399 Intl $ from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 115,044.553 Intl $ in 2020 and a record low of 79,713.086 Intl $ in 1991. Australia GDP per Person Employed: 2021 PPP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Employment and Unemployment. GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2021 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.;World Bank, World Development Indicators database. Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank.;Weighted average;

Database uses data on shorebird counts from around Australia. The majority of the records are from Birdlife’s Birdata database. We supplemented this data with bird surveys within the Coorong, from David Paton, from data available from the South Australian Government (Paton, Paton, and Bailey 2016). Data for some shorebird areas, namely eighty mile beach, Roebuck Bay, Werribee/Avalon, did not have count area level data for a number of recent years (2019-2022), but had aggregated summary data available. Within the database, observations of the number of individuals per species are organized in “count areas”, which are generally one high tide roost, or segments of beach. Count areas are situated within “Shorebird areas”, which are the maximum areas in which individual birds are likely to move during the non-breeding season (Clemens, Herrod, and Weston 2014). The database contains >380,000 records from 448 shorebird areas around the country. Data on individual species generation times was sourced from Birdlife’s Data Zone (‘BirdLife Data Zone’ 2022). For our analysis, we aggregated the data into independent count occasions for each shorebird area within each Australian summer, here termed “season”. To achieve this, we summarized the total number of each species observed at a shorebird area for each month during the summer months (October, November, December, January, and February). First, the database was subset to records that had complete fields for “shorebird area”, “point count ID”, “count”, and “date”. Data were then aggregated to find the max observations per species per count area per month. The max observations per count area per month were then summed per shorebird area. For shorebird areas with counts across multiple months, we used the top two counts per season as input for our data analysis. Finally, we only included shorebird areas that had at least 500 birds observed over the entire time series and had at least one count for at least half of the years in the entire time series (14 years of the 29 years in the time series). Structured, regular monitoring began in 1993, so we used data from 1993-2022. Modelling abundance and population trends - The objective was to estimate abundance and population trends of the targeted species at the national level, using the time-series data described above. Following the successful example of modelling population trends of shorebirds in Australia by Studds et al (2017), we also used N-mixture models, which estimate the abundance of each species at each shorebird area each year, while accounting for imperfect detection of individuals as well as among-area difference, temporal trends, and over-dispersion in abundance. The model allowed us to estimate: (i) the abundance of each species at each shorebird area each year, (ii) the total abundance of each species across all areas each year, and (iii) the nationwide population index of each species, which shows “average” changes in the species abundance across all shorebird areas. As N-mixture models tend to be highly complex with many parameters and thus require much information (i.e., data) for those parameters to be successfully estimated, we developed two types of N-mixture models with varying levels of complexity/assumption: (i) the model assuming that detection probabilities at a given area vary among months within each year, and (ii) the model assuming that detection probabilities at a given area are constant across months within each year. We first fitted model (i) above to all targeted species using the program JAGS (Hornik et al. 2003) through the R2jags package (Su, Yajima, and Edu 2022) in R version 4.2.1 (R Core Team 2015). Model convergence was checked with R-hat values and trace plots. If the model still did not converge, we next fitted model (ii) above and increased the number of iterations until the model converged. If both models did not converge, we fitted a simpler model, which had the same structure but without accounting for the imperfect detection of individuals (i.e., assuming that all individuals are detectable). Using model outputs, we then estimated the rate of change in abundance. For a given time frame (29 years, three generations, 1993-2013, 2013-2022) we calculated growth rates using generalized least squares regression to account for temporal autocorrelation. We then sampled 1000 growth rates from each regression result and calculated the mean and standard deviation based on these samples. We then took the difference in the samples and calculated the probability of the difference being larger than zero using the code: 100*length(difference_in_samples[difference_in_samples <0])/length(difference_in_samples) We used IUCN criteria A2, to assess how the species should be listed based on the estimated declines from our analysis. These thresholds are: 80% - critically endangered 50% - endangered, and 30% - vunerable From the IUCN IUCN Red List of Threatened Species “

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.

BackgroundIn recent decades, there has been a shift to later childbearing in high-income countries. There is limited large-scale evidence of the relationship between maternal age and child outcomes beyond the perinatal period. The objective of this study is to quantify a child’s risk of developmental vulnerability at age five, according to their mother’s age at childbirth.Methods and findingsLinkage of population-level perinatal, hospital, and birth registration datasets to data from the Australian Early Development Census (AEDC) and school enrolments in Australia’s most populous state, New South Wales (NSW), enabled us to follow a cohort of 99,530 children from birth to their first year of school in 2009 or 2012. The study outcome was teacher-reported child development on five domains measured by the AEDC, including physical health and well-being, emotional maturity, social competence, language and cognitive skills, and communication skills and general knowledge. Developmental vulnerability was defined as domain scores below the 2009 AEDC 10th percentile cut point.The mean maternal age at childbirth was 29.6 years (standard deviation [SD], 5.7), with 4,382 children (4.4%) born to mothers aged

Isolates recently collected in 2013–14 from South Australia and old isolates received from different culture collections, universities and institutes were used to determine the population structure of Rathayibacter toxicus.