The PDB is a database of U.S. housing, demographic, socioeconomic and operational statistics based on select 2010 Decennial Census and select 5-year American Community Survey (ACS) estimates. Data are provided at the census tract level of geography. These data can be used for many purposes, including survey field operations planning.

According to a survey of global consumers, the share of respondents reporting to feel extremely comfortable with mobile apps accessing their personal data has almost doubled since 2021. In comparison, the number of users reporting to feel "very comfortable" with personal data sharing on mobile apps has decreased from 15.4 in 2021 to 12.7 in 2022.

About this dataset

Context

The dataset tabulates the population of Scalp Level by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Scalp Level. The dataset can be utilized to understand the population distribution of Scalp Level by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Scalp Level. 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 Scalp Level.

Key observations

Largest age group (population): Male # 25-29 years (42) | Female # 50-54 years (34). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Scalp Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Scalp Level is shown in the following column.
• Population (Female): The female population in the Scalp Level 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 Scalp Level 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 Scalp Level Population by Gender. You can refer the same here

The dataset collection in question is a compilation of related data tables sourced from the website of Tilastokeskus (Statistics Finland) in Finland. The data present in the collection is organized in a tabular format comprising of rows and columns, each holding related data. The collection includes several tables, each of which represents different years, providing a temporal view of the data. The description provided by the data source, Tilastokeskuksen palvelurajapinta (Statistics Finland's service interface), suggests that the data is likely to be statistical in nature and could be related to regional statistics, given the nature of the source. This dataset is licensed under CC BY 4.0 (Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/deed.fi).

Between 1992 and 2017, global mean sea levels rose by 17.8 millimeters. This is mainly due to the melting of inland glaciers on Greenland, which accounted for 10.6 millimeters of the sea level rise. The melting of glaciers is not the only threat to sea level rise, the warming of oceans as a result of increasing global temperatures is causing the existing sea water to expand slightly.

The rate at which Earth is losing its icecaps is accelerating. In the 2010s, the average rate of loss was 475 billion metric tons of ice per year, which is significantly higher than the 1990s rate of 81 billion metric tons of ice per year. The estimates for global sea level rise by 2100 is around 0.5 meters.

Statistics Norway conducted comprehensive Income and Property Statistics in 1958, 1962, 1967, 1970, 1973, 1976, 1979 and 1982. From 1984 Statistics Norway went over to a system of annual surveys, the change was mainly on sample size and selection issues, and little of what information is collected. The information is largely determined by what is available in public tax data. Income surveys are not considered as a regular sample survey, they are based on a sample drawn from the tax agency's records. This means that foreign nationals are included if they are registered in the Central Register, and they will normally be if they have work and residence permit. Children born during the year is included regardless of date of birth and the same goes for people who died during the year.

The purpose of the Income and Property Statistics has been to assess the income situation for the whole population and for different groups. A main point is to generate statistics on cost households, i.e. households who live and eat together, and to provide an overview of the distribution of persons and households by income size, socio-economic grouping, household type, geography, etc. Another main point is to collect income and wealth data as background forthe Income and Property Statistics. In the years after 1992, after the reform of the rax system, it was considered important to gather information in order to study the effects of the reform, also for self-employed, and a larger syrvey was integrated of such matters in particular. Information on all forms of income, wealth, tax, disposable income for individuals and households are collected. Some information is linked from other registers, for example information on marital status and family composition, while information on household composition are obtained through interviews. Family is a narrower term than household, a family may consist of single, unmarried father or mother with children, or married couples with or without children. A household on the other hand, includes all people who live and eat together and can therefore consist of several families. Experience shows, however, that approximately 90% of all households consist of only one family (see Notater 98/11 SSB 1998: Inntekts- og formuesundersøkelsen 1995). Tax-free income, such as benefits of a distinctly social character, gifts and prizes fall outside due to tax rules. Rules for the percent of property tax assessment, valuation of benefits in kind, personal withdrawals, depreciation etc, also creates some problems. In addition to the pure tax information information on such as education, occupation and income for individuals and for the household: the composition, type and who is the main income earner is invluded. This data is linked to, among else, data from the Education Register, the State Educational Loan Fund, Housing, Social Affairs (social assistance) and the National Insurance Administration.

This collection provides annual data on jail populations across the nation and examines the "spillover" on local jails resulting from the dramatic growth in federal and state prison populations. These data are used to track growth in the number of jails and their capacities nationally, changes in the demographics of the jail population (including sex, race, and adult or juvenile status), supervision status of persons held, prevalence of crowding issues, and a count of non-United States citizens within the jail population.

Summary statistics of business dynamism taken from the Longitudinal Business Database (LBD), UK.

SID23 - Highest Level of Education of individuals aged 25-59 years. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Highest Level of Education of individuals aged 25-59 years...

The Annual Survey of Jails (ASJ) is the only data collection effort that provides an annual source of data on local jails and jail inmates. Data on the size of the jail population and selected inmate characteristics are obtained every five to six years from the Census of Jails. In each of the years between the full censuses, a sample survey of jails is conducted to estimate baseline characteristics of the nation's jails and inmates housed in these jails. The 2008 Annual Survey of Jails is the 21st such survey in a series begun in 1982. The ASJ supplies data on characteristics of jails such as admissions and releases, growth in the number of jail facilities, changes in their rated capacities and level of occupancy, growth in the population supervised in the community, changes in methods of community supervision, and crowding issues. The ASJ also provides information on changes in the demographics of the jail population, supervision status of persons held, and a count of non-citizens in custody. The data presented in this study were collected in the Annual Survey of Jails, 2008. These data are used to track growth in the number of jails and the capacities nationally, changes in the demographics of the jail population and supervision status of persons held, the prevalence of crowding issues, and a count of non-United States citizens within the jail population. The data are intended for a variety of users, including federal and state agencies, local officials in conjunction with jail administrators, researchers, planners, and the public. The reference date for the survey is June 30, 2008.

Recorded crime for Police Force Areas. The data are rolling 12-month totals, with points at the end of each financial year between year ending March 2003 to March 2007 and at the end of each quarter from June 2007.

IDWR maintains a groundwater level database containing data primarily collected by IDWR, but also includes data gathered by the USGS, USBR, and other public and private entities. Please reach out to these other entities to obtain their full complete record, as not all values are present in this database (IDWR can provide a full list of data contributors upon request). IDWR staff manually measure the "depth to water" in wells throughout Idaho. Pressure transducers in many wells provide near-continuous water level measurements. IDWR strives to create complete and accurate data and may revise these data when indicated.

“Groundwater Level Data: All Historic Data” includes all well data managed in IDWR’s internal database, regardless of current well status. For example, historic data from discontinued, abandoned, or inactive wells are contained in this dataset. IDWR’s water level data are also hosted in the Groundwater Data Portal (https://idwr-groundwater-data.idaho.gov/), which displays only actively monitored wells.

The three files included in this download are 1) discrete (manual) depth to water measurements 2) continuous* (pressure transducer) depth to water measurements, and 3) the associated well metadata.

*The continuous measurements data have been condensed to display only the shallowest daily pressure transducer measurements. Complete datasets are available upon request.

List of student's achievement in the learning area of Maths by Indigenous status and year level.

This dataset is no longer being updated. For more information about Learning Outcomes go to https://www.qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

M2SMNXSLV (or statM_2d_slv_Nx) is a 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of monthly mean of daily statistics, such as daily mean (or daily minimum and maximum) air temperature at 2-meter, and maximum precipitation rate during the period. The collection also includes the variance of parameters. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file. MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list. Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may email the question on data access to the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov), or the question on science to the MERRA-2 science team (merra-questions@lists.nasa.gov).

China Number of Region at County Level: City at County Level data was reported at 388.000 Unit in 2020. This records an increase from the previous number of 387.000 Unit for 2019. China Number of Region at County Level: City at County Level data is updated yearly, averaging 368.000 Unit from Dec 1978 (Median) to 2020, with 43 observations. The data reached an all-time high of 445.000 Unit in 1996 and a record low of 92.000 Unit in 1978. China Number of Region at County Level: City at County Level data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GJ: No of Region at County Level.

State, 2016 –2020; County, 2020. The report includes both state and county level water fluoridation data generated from the Water Fluoridation Reporting System (WFRS). State level statistics include data from the biennial report originally published at https://www.cdc.gov/fluoridation/statistics/reference_stats.htm. State and county data include percentage of people, number of people, and number of water systems receiving fluoridated water. County level data is not displayed for all states. Participation in sharing county level data is voluntary and state programs determine if data will be shown.

The Life Insurance Institution-level Statistics publication contains individual insurer level information about financial performance, position, and capital base and solvency data. The Life Insurance Institution-level Statistics publication contains individual insurer level information about financial performance, position, and capital base and solvency data.