Priemerný vek pri úmrtí (v rokoch). Vážený aritmetický priemer počtu rokov pri úmrtí. Údaje sú dostupné od roku 1993 a sú aktualizované ročne - okresná úroveň. Zdroj: Štatistický úrad Slovenskej republiky, http://datacube.statistics.sk/

---English--- Mean age at death (years). Weighted arithmetic mean of deaths. Data available since 1993 and updated annually - district level Source: Statistical Office of the Slovak Republic

This data set shows crude birth rate in Malaysia. The rates are per 1,000 population. More Info : https://www.statistics.gov.my

Výsledky volieb do Národnej rady SR 2010

This data set shows the number of unemployed persons by sex for all states in Malaysia. The statistics is derived from Labour Force Survey (LFS) which is conducted every month using household approach and refers to those between the working age of 15-64 years old.

The unemployed are classified into two groups that is the actively unemployed and inactively unemployed. The actively unemployed include all persons who did not work during the reference week but were available for work and were actively looking for work during the reference week. Inactively unemployed persons include the following categories: a. persons who did not look for work because they believed no work was available or that they were not qualified; b. persons who would have looked for work if they had not been temporarily ill or had it not been for bad weather; c. persons who were waiting for result of job applications; and d. persons who had looked for work prior to the reference week.

W.P. Labuan is gazzeted as a Federal Territory in 1984 while W.P. Putrajaya is gazzeted as a Federal Territory in 2001. The statistics for W.P. Putrajaya for 2001-2010 is treated as part of Selangor. Statistics for W.P. Putrajaya is available separately since 2011 onwards.

LFS was not conducted during the years 1991 and 1994.

More info: https://www.statistics.gov.my

This dataset is a polygon coverage of counties limited to the extent of the Pond Creek coal bed resource areas and attributed with statistics on the thickness of the Pond Creek coal zone, its elevation, and overburden thickness, in feet. The file has been generalized from detailed geologic coverages found elsewhere in Professional Paper 1625-C.

Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

This dataset is the definitive version of annually released statistical area 2 (SA2) boundaries concorded to higher geographies for 2021 as defined by Stats NZ. this version contains 2,259 categories.

This statistical area 2 higher geographies file is a correspondence, or concordance, which relates SA2s to larger geographic areas or 'higher geographies'. The higher geographies contained in this concordance are: territorial authority (TA) and regional council (REGC). For more information on the individual higher geographies, refer to each geography’s metadata.

SA2s were introduced as part of the Statistical Standard for Geographic Areas 2018 (SSGA2018) which replaced the New Zealand Standard Areas Classification (NZSAC1992). The SA2 geography replaces the (NZSAC1992) area unit geography.

Names are provided with and without tohutō/macrons, as applicable. Column names for those without macrons are suffixed ‘ascii’. For further information on individual higher geographies, refer to each geography’s metadata.

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

Digital boundary data became freely available on 1 July 2007.

This lookup table relates to the web service 2018 Census individual part 2 by SA1. The web service contains data from the 2018 Census only, no data from previous censuses has been included.

The individual (part 2) dataset is displayed by statistical area 1 geography and contains information on: • Religious affiliation (total responses) • Cigarette smoking behaviour • Difficulty seeing even if wearing glasses • Difficulty hearing even if using a hearing aid • Difficulty walking or climbing steps • Difficulty remembering or concentrating • Difficulty washing all over or dressing • Difficulty communicating using your usual language for example being understood by others • Legally registered relationship status • Partnership status in current relationship • Individual home ownership • Number of children born • Highest qualification • Study participation • Total personal income (grouped), including median total personal income • Sources of personal income (total responses) • Main means of travel to education, by usual residence address (2018 only) • Main means of travel to education, by educational institution address (2018 only)

The data uses fixed random rounding to protect confidentiality. Some counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of ‘-999’ indicate suppressed data, and values of ‘Null’ indicate data not collected.

For further information on this dataset please refer to the Statistical area 1 dataset for 2018 Census webpage - footnotes for individual part 2, Excel workbooks, and CSV files are available to download. Data quality ratings for 2018 Census variables, summarising the quality rating and priority levels for 2018 Census variables, are available.

For information on the statistical area 1 geography please refer to the Statistical standard for geographic areas 2018.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

About this dataset

Context

The dataset tabulates the population of Pike town by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Pike town across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.36% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Pike town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Pike town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pike town Population by Gender. You can refer the same here

Head count of SES employees by portfolio, region and age group. *This data is no longer being updated. For more information please refer to https://www.forgov.qld.gov.au/recruitment-performance-and-c…Show full descriptionHead count of SES employees by portfolio, region and age group. *This data is no longer being updated. For more information please refer to https://www.forgov.qld.gov.au/recruitment-performance-and-career/workforce-planning/workforce-statistics-and-tools/workforce-statistics

About this dataset

Context

The dataset tabulates the population of Iron Mountain by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Iron Mountain. The dataset can be utilized to understand the population distribution of Iron Mountain by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Iron Mountain. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Iron Mountain.

Key observations

Largest age group (population): Male # 25-29 years (416) | Female # 55-59 years (349). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Iron Mountain population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Iron Mountain is shown in the following column.
• Population (Female): The female population in the Iron Mountain is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Iron Mountain for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Iron Mountain Population by Gender. You can refer the same here

In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA 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. APRA 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.

This statistical table contains two data worksheets - one presenting data expressed in Australian dollar (AUD) terms and the other in USD terms.

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.

‘Total banks and RFCs’ is also reported in USD equivalent amounts, using the end-quarter AUD/USD exchange rate from statistical table F11.

The consolidated data reported in this statistical table are on the international exposures of banks (and RFCs between March 2003 and September 2010) operating in Australia. The types of assets included here are consistent with the locational data in statistical table B12.1. However, the consolidated data differ from the locational data in three key ways: foreign currency positions with Australian residents are excluded (whereas they are included in the locational data); claims between different offices of the same institution (e.g. between the head office and its subsidiary) are netted (whereas positions, including intra-group positions, are reported on a gross basis in the locational data); and on-balance sheet derivatives are not included in international claims or foreign claims, but are included separately under ‘Derivatives’ in statistical table B13.2. Foreign-owned reporting entities report on an unconsolidated basis.

The consolidated data are split by type of exposure. ‘International claims’ refers to all cross-border claims plus foreign offices’ local claims on residents in foreign currencies; foreign claims refers to all cross-border claims plus foreign offices’ local claims on residents in both local and foreign currencies; immediate risk claims (expressed by the BIS as claims on an immediate borrower basis) cover claims based on the country where the immediate counterparty resides; and ultimate risk claims cover immediate exposures adjusted (via guarantees and other risk transfers) to reflect the location of the ultimate counterparty/risk.

Foreign offices include the overseas branches, subsidiaries and joint ventures of a bank (or RFC between March 2003 and September 2010).

Risk transfers are those transfers of risk from the country of the immediate borrower to the country of ultimate risk as a result of guarantees, collateral, and where the counterparty is a legally dependent branch of a bank headquartered in another country. The risk reallocation includes loans to Australian borrowers that are guaranteed by foreign entities and therefore represent outward risk transfers from Australia, which increase the ultimate exposure to the country of the guarantor. Similarly, foreign lending that is guaranteed by Australian entities is reported as an inward risk transfer to Australia, which reduces the ultimate exposure to the country of the foreign borrower. The risk reallocation also includes transfers between different economic sectors (banks, public sector and non-bank private sector) in the same country.

Foreign claims on an ultimate risk basis are shown for the following types of reporting entity: Australian-owned banks (i.e. those with their parent entity legally incorporated in Australia); foreign subsidiary banks; branches of foreign banks; RFCs; and Australian-owned entities (i.e. Australian-owned banks and RFCs). The RFC data are only available between March 2003 and September 2010.

‘Foreign claims (ultimate risk basis) – Aust-owned entities’ is also reported in USD equivalent amounts, using the end-quarter AUD/USD exchange rate from statistical table F11.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

Number and percent of women Veterans in fiscal years 2000 to 2023.

Source: VetPop2020

Sheep statistics, supply and disposition of sheep and lambs, Canada and provinces (head x 1,000). Data are available on an annual basis.

This dataset provides a yearly summary of the operational scope of the Primary Health Care Corporation (PHCC) in Qatar. It includes the number of operational health centers, total patient registrations, and total visits from 2020 to 2024. The dataset offers a high-level overview of the growth and utilization of primary healthcare services over time, supporting health sector evaluation and capacity planning.

Dataset obsahuje vrstvu prirodzeného prírastku obyvateľov v jednotlivých obciach Košického samosprávneho kraja. Dataset obsahuje štatistické dáta od roku 1996 a je ročne aktualizovaný. Zdrojom dát sú dáta Štatistického úradu SR. Pre tvorbu datasetu je použitá dátová kocka Prehľad stavu a pohybu obyvateľstva - [om7010rr].

Prirodzený prírastok obyvateľstva je rozdiel medzi počtom živonarodených a počtom zomretých obyvateľov.

Bližšie informácie o zdroji dát: http://datacube.statistics.sk/ https://slovak.statistics.sk/

Names, distributions and status of Queensland plants, algae, fungi, lichens and cyanobacteria, based on the Queensland Herbarium database 'Herbrecs'.
Please refer to Flora census web page for further information. "https://www.qld.gov.au/environment/assets/documents/plants-animals/herbarium/qld-flora-census.pdf" title="Introduction to the Census of the Queensland Flora 2013">'Introduction to the Census of the Queensland Flora 2013' can also be viewed from this site.

This product is monthly Ice Cover Statistics. The dataset was prepared by Dr. Peter Romanov at Cooperative Institute for Climate Studies(CICS) of the University of Maryland for Northern Eurasia Earth Science Partnership Initiative (NEESPI) program. The product includes the monthly ice statistics (frequency of occurrence) for Northern Hemisphere at 1x1 degree spatial resolution. The dataset covers the time period starting January 2000 to November 2014. The data was derived from daily ice cover charts produced at NOAA/NESDIS within Interactive Multisensor Ice Mapping System (IMS).