Statistical table of the number of cases by region, age group, and gender since 2003 (Disease name: Novel A-type Influenza, Date type: Onset date, Case type: Confirmed case, Source of infection: Domestic, Imported from overseas)

A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

This dataset is a compilation of the data export tables available on WUEdata for the 2020 Urban Water Management Plans (UWMPs).

• "2020_UWMP_Tables.xlsx" contains each of the 99 tables as a separate workbook (Last Updated 2024)
• The worksheets for each table are also individually stored separately as TSV files in "2020_UWMP_TSV_Export.zip" (Last Updated 2023)
• Column definitions, data types, and table locations are included in "2020_Data_Dictionary.xlsx"
• Shortened versions of the table names are used in the XLSX worksheet names and TSV filenames; "Table_Name_Key.xlsx" links the shortened names and original names
• For reference, the 2020 UWMP Guidebook that was provided to water suppliers is included in this dataset

All land and building registration cases within the jurisdiction of this office are included in the statistics, including the applicants.

Statistics on Domestic Households - Table 130-06607 : Domestic households by type of household and monthly household income

Supply, use and input-output tables (SUIOTs) are matrices that provide a detailed picture of how goods and services are supplied and used in an economy. By balancing data from various sources in a consistent framework, they allow compiling a single coherent estimate of gross domestic product (GDP) based on production, expenditure, and income.

SUIOTs comprise four main types of tables:

• Supply table: This table depicts how products (in rows) enter the economy, either through domestic production by industries or imports (in columns). The supply table also includes data on trade and transport margins as well as taxes less subsidies (in columns) to convert total supply from basic prices to purchasers' prices.
• Use table: This table depicts how products (in rows) are used by the economy, either for intermediate consumption by industries, or for final uses (both in columns). Final uses are disaggregated into 1) final consumption expenditure by households, general government, and non-profit institutions serving households, 2) gross capital formation and 3) exports. The use table contains additional rows to show, for each industry, how gross value added is generated, distinguishing compensation of employees, other taxes less subsidies on production, consumption of fixed capital, and net operating surplus and mixed income. The use table can refer to all transactions, domestic transactions or imported products.
• Input-output table: This table captures the supply and use of products in a single matrix, depicting either products or industries in both rows and columns. Similar to use tables, input-output tables can refer to all transactions, domestic transactions or imported products.
• Margin tables: These tables present separately 1) trade and transport margins and 2) taxes less subsidies, by products (in rows) and industries (in columns).

The supply-use system is built upon two fundamental equations:

• total supply = production + imports = intermediate consumption + final consumption + gross capital formation + exports = total use (this equation is satisfied for any given product category)
• value added = production – intermediate consumption = compensation of employees + gross operating surplus and mixed income + other taxes less subsidies on production (this equation is satisfied for any given economic activity).

SUIOTs distinguish 64 product and industry categories. Products are classified according to the statistical classification of products by activity (CPA 2.1). Industries are classified according to the statistical classification of economic activities (NACE Revision 2).

The data are presented in million euro and million national currency.

Eurostat collects and publishes SUIOTs for the 27 European Union (EU) countries, the countries of the European Free Trade Association (EFTA), and the EU enlargement countries. The tables are compiled in line with the European System of Accounts - ESA 2010 and the related transmission programme. Countries should deliver their SUIOTs 36 months after the end of the reference year according to the ESA 2010 transmission programme. This means that, for example, data for the year 2022 must be transmitted by 31 December 2025.

SUIOTs comprise annual and 5-yearly data transmissions. The annual mandatory data transmission includes the supply table at basic prices, including transformation into purchasers' prices (T1500) and the use table in purchasers' prices (T1600), both in current and previous year’s prices. The 5-yearly mandatory data transmissions concerns years ending with ‘0’ or ‘5’ and includes the following tables:

• use table at basic prices (T1610)
• use table for domestic output at basic prices (T1611)
• use table for imports at basic prices (T1612)
• table of trade and transport margins (T1620)
• table of taxes less subsidies on products (T1630)
• symmetric input-output table at basic prices (T1700/50)
• symmetric input-output table for domestic output at basic prices (T1800/50)
• symmetric input-output table for imports at basic prices (T1900/50).

The 5-yearly tables are to be transmitted in current prices. Countries are free to compile their input-output tables in product-by-product or industry-by-industry format. Eurostat encourages voluntary data transmissions. Several countries provide, e.g., 5-yearly tables on an annual basis or in both current and previous year’s prices.

Eurostat publishes annual and 5-yearly tables in current prices from reference year 2010 onward and the annual tables in previous year’s prices from reference year 2015 onward.

Next to publishing SUIOTs for individual countries, Eurostat compiles annually consolidated tables at current prices for the European Union (EU, that is, the 27 member countries as a whole) and the euro area (EA). The consolidated SUIOTs are based on the EU inter-country supply, use and input-output tables (EU inter-country SUIOTs; so-called FIGARO tables). Intra-EU and intra-EA trade are considered as domestic transactions. Imports and exports correspond to the respective trade in goods and services with countries outside of the European Union and the euro area, respectively.

The regional breakdown of imports and exports in the SUIOTs for the EU and the EA is based on the country composition of both regions in the most recent reference year, and this composition is applied to the entire time series. This approach ensures consistency over time. However, it deviates from the approach used for the SUIOTs of individual countries, where an evolving country composition reflects the member countries of the EU and the EA in each respective year.

Cylinder displacement range, vehicle types (large cargo, small cargo, large passenger, small passenger), number of vehicles, tax amount

We randomly selected 11 classes from 4 different companies and conducted a sampling survey to investigate the contact frequency among different categories of individuals. Subsequently, we calculated the median contact frequency.

Related Tables / Normalized VersionThis dataset provides demographic information related to arrests made by the Tempe Police Department. Demographic fields include race and ethnicity, age range at the time of arrest, and gender for each party. The data is sourced from the Police Department’s Records Management System (RMS) and supports analysis of patterns related to arrests, enforcement activity, and demographic trends over time. This information is a component of ongoing efforts to promote transparency and provide context for law enforcement within the community.For detailed guidance on interpreting arrest counts and demographic breakdowns, please refer to the User Guide: Understanding the Arrest Demographic Datasets - Related Tables.Why this Dataset is Organized this Way?The related tables such as persons, charges, and locations follow a normalized data model. This structure is often preferred by data professionals for more advanced analysis, filtering, or joining with external datasets.Providing this format supports a wide range of users, from casual data explorers to experienced analysts.Understanding the Arrests Data (as related tables)The related tables represent different parts of the arrest data. Each one focuses on a different type of information, like the officers, individuals arrested, charges, and arrest details.All of these tables connect back to the arrests table, which acts as the central record for each event. This structure is called a normalized model and is often used to manage data in a more efficient way. Visit the User Guide: Understanding the Arrest Demographic Datasets - Related Tables for more details outlining the relationships between the related tables.Data DictionaryAdditional InformationContact Email: PD_DataRequest@tempe.govContact Phone: N/ALink: N/AData Source: Versaterm RMSData Source Type: SQL ServerPreparation Method: Automated processPublish Frequency: DailyPublish Method: Automatic

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APR in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APR ceased the RFC data collection after September 2010.

The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars (USD).

Data are recorded on an end-quarter basis.

All banks operating in Australia complete ARF 231. Between March 2003 and September 2010, only those larger RFCs with sizeable overseas assets and/or liabilities completed RRF 231. Bank and RFC positions are reported in Australian dollars (AUD). Non-AUD denominated positions have been converted to AUD using an appropriate end-quarter exchange rate, so changes in reported data between quarters are due not only to changes in positions but also valuation gains or losses due to exchange rate changes.

There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only consolidated data are reported in this statistical table.

The data in this statistical table summarise the country exposures of Australian-owned banks (and selected RFCs between March 2003 and September 2010). This is a smaller reporting pool than the series reported in statistical table B11.2, which is based on all banks and RFCs reporting ARF/RRF 231 data. The types of assets included here are consistent with those reported in statistical tables B11.1, B11.2 and B12.1, except that the data are consolidated for Australian-owned reporting entities (i.e. includes the claims on countries of all the offices worldwide of entities with head offices in Australia, but excludes positions between different offices of the same group). Consolidated data only include positions with non-residents (in any currency).

Data are shown for a selected group of countries that account for the bulk of the total. Similar data for other countries are also available in statistical tables B13.1.1 and B13.1.2.

Data presented in this statistical table are immediate risk claims (expressed by the BIS as claims on an immediate borrower basis), which cover exposures on an immediate counterparty location basis. Ultimate risk claims are presented in a complementary statistical table B13.2, which cover immediate exposures adjusted (via guarantees and other risk transfers) to reflect the location of the ultimate counterparty/risk.

In the maturity distribution, the shortest maturity bracket includes deposits that are repayable on demand, overdue items and overdrafts.

aInternational claimsa represent cross-border claims in all currencies and foreign officesa local claims in non-local currencies (which would include, for example, USD claims on New Zealand residents by the New Zealand subsidiary of an Australian-owned bank). Also shown are the local currency claims on local residents by the foreign offices of reporting entities (for example, the New Zealand dollar (NZD) claims on New Zealand residents by the New Zealand subsidiary of an Australian-owned bank). These local currency claims are added to international claims to produce foreign claims.

International organisations are included in the aPublic sectora category in the consolidated data (while in the locational data they can be reported as either bank or non-bank depending on the particular organisation). Official monetary authorities (central banks or similar national and international bodies, such as the BIS) are also included in the public sector in the consolidated data (but are treated as banks in the locational data, B12.1 and B12.2). Publicly-owned entities (other than banks) are classed in the aNon- bank private sectora in the consolidated data (and as non-banks in the locational data).

The aNet risk transfera is mainly due to risk transfers into and out of Australia and typically does not sum to zero. In several cases, risk is transferred out of the countries listed and into Australia hence becoming, in effect, domestic exposures (and reducing foreign claims on an ultimate risk basis). Similarly, the risk associated with what were initially domestic exposures has in several cases been transferred, via guarantees and other risk transfers, to other countries (thereby increasing foreign claims on an ultimate risk basis). The total risk transfer amount is not comparable to the risk transfer amount reported for Australia in the data series of statistical table B11.2 as the former covers only Australian-owned entities while the latter is for all reporting entities.

Derivatives are not included in international claims or foreign claims.

Attainment metrics for all qualification cohorts, by institution type and student gender.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police. \r
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The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public. \r
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The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose. \r
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Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.\r
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The Crime Statistics Agency publishes location type data for all offences where a location type was recorded by Victoria Police. There are three main location types; Residential, Community and Other. These types are further broken down into Subdivisions which show an intermediate level of information, and further into Groups which show a finer level of detail\r
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Data Classification - https://www.crimestatistics.vic.gov.au/about-the-data/classifications \r
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Glossary and Data Dictionary - https://www.crimestatistics.vic.gov.au/about-the-data/glossary-and-data-dictionary\r

This is a collection of data tables supporting the LCMAP CONUS Geographic Assessment v1.0. The data used to generate these tables come from the USGS LCMAP reference dataset and the map products released by LCMAP. Tables include annual land cover class composition and annual rate of land cover change metrics developed with a post-stratified estimator. Other tables including annual gross change of specific types of land covers, cumulative metrics of overall geographic footprint of change, frequency of overall geographic footprint of change, overall area estimates of specific class changes, and all unique changes in land cover classes. All tables cover the time period 1985-2016. All values in these tables are presented in percent and/or square kilometers of CONUS and include standard errors (SE). Tables 1a and 1b are annual land-cover class area estimates. Tables 1a and 1b are annual land-cover class area estimates. Table 1a presents the estimated area of each LCMAP land cover class as a percent of CONUS and Table 1b is the same in square kilometers, both with SEs. The area estimates include the following eight LCMAP land covers classes: Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Snow/Ice, and Barren (Brown et al., 2020; Zhu et al., 2014).Table 2 is the estimated overall net change in each of the eight LCMAP land-cover classes: Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). It includes percent and square kilometers of CONUS in 1985 and 2016. The net area that changed between 1985 and 2016 is presented in percent and km2 of CONUS with the standard error of this change. Table 3 represents area estimates for the area that changed a specific number of times during 1985–2016. These areas are presented as both the fraction of LCMAP’s CONUS extent and as the equivalent in square kilometers, with standard errors. The overall footprint of change that occurred between 1985 and 2016 is presented as the percent and km2 of CONUS. Table 4 is the estimated percent and areal extent (km2) of CONUS that experienced either a change, or none, for each year 1986–2016, with associated standard errors. Tables 5a–h present the areal extent estimates (in km2) for specific land-cover class changes with standard error for every year, 1986–2016. The specific land-cover classes include Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). Table 6a presents area estimates (km2) of CONUS for all land-cover class changes with standard error for every year, 1986–2016. Table 6b presents area estimates (km2) of CONUS that did not change for all eight LCMAP land-cover classes with standard error for every year, 1986–2016. The eight LCMAP land-cover classes include Developed, Cropland, Grass/Shrub, Tree Cover, Water, Wetland, Ice/Snow, and Barren (Brown et al., 2020; Zhu et al., 2014). Table 7 presents the estimated area (km2) of CONUS for four groupings of land-cover class changes with standard error for every year, 1986–2016. The eight LCMAP land-cover classes and class codes include Developed (1), Cropland (2), Grass/Shrub (3), Tree Cover (4), Water (5), Wetland (6), Ice/Snow (7), and Barren (8) (Brown et al., 2020; Zhu et al. 2014). The four groups include “Natural Resource Cycles”, “Increases in Developed and Built-up Land”, “Surface Water Expansion/Contraction”, and “Other”. Table 8 presents the cumulative (1985-2016) area of all specific land cover class changes, 56 in total.

Supplementary Data for "Revisiting the discrimination and distribution of S-type granites from zircon trace element compositions" by Nick Roberts et al.

The CMS Program Statistics - Medicare Advantage & Other Health Plan Enrollment tables provide data on characteristics of the population covered by Medicare Advantage & other health plans. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables:MDCR ENROLL AB 15. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Total, Aged, and Disabled Enrollees, Yearly TrendMDCR ENROLL AB 16. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Enrollees, by Age Group, Yearly TrendMDCR ENROLL AB 17. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Enrollees, by Demographic CharacteristicsMDCR ENROLL AB 18. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Enrollees, by Type of Entitlement and Demographic CharacteristicsMDCR ENROLL AB 19. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Total, Aged, and Disabled Enrollees, by Area of ResidenceMDCR ENROLL AB 20. Medicare Advantage and Other Health Plan Enrollment: Part A and/or Part B Enrollees, by Type of Entitlement and Area of ResidenceResources for using and understanding the dataThe data reported in these enrollment tables are based on information gathered from CMS administrative enrollment data for beneficiaries enrolled in Medicare Advantage and Other Health Plans available from the CMS Chronic Conditions Data Warehouse.