Table legend: SD = standard deviation; FSIQ = Full Scale Intelligence Quotient.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. See methodologyhttps://www.census.gov/programs-surveys/international-programs/about/idb.html

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the county level..The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age;

B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by County Date of Coverage: 2016-2020

This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains aggregated household and individual sociodemographic data at the block-level from the 2005 and 2018 National Demographic Census. The data were downloaded from the National Administrative Department of Statistics (DANE). The data were not validated independently. Census data were provided at the level of persons, households, dwellings and spatial data (city blocks). To relate non-spatial and spatial data, city block codes (22 characters) were generated by concatenating the department code (2 characters), municipality (3 characters), class (1 character), rural sector (3 characters),rural section (2 characters), population center (3 characters), urban sector (4 characters), urban section (2 characters) and city block (2 characters).These codes were linked to the persons database. The 2005 and 2018 educational level and employment status census data had two additional categories with no clear definition in the census documentation ("Not applicable" and "Not reported"). Those categories were merged into the "Not reported" category. The 2005 and 2018 census data were merged into one dataset with the following attributes: city block code, census year, sex, educational level, and employment status, combining the multiple categories of socioeconomic variables.

Context

This dataset contains medical insurance cost information for 1338 individuals. It includes demographic and health-related variables such as age, sex, BMI, number of children, smoking status, and residential region in the US. The target variable is charges, which represents the medical insurance cost billed to the individual. This data set might have some missing values.

Uses Of Data Set

• The dataset is commonly used for:

• Regression modeling

• Health economics research

• Insurance pricing analysis

• Machine learning education and tutorials

Columns

Age: Age of primary beneficiary (int)

sex: Gender of beneficiary (male, female)

Bmi: Body Mass Index

children: Number of children covered by health insurance (int)

smoker: Smoking status of the beneficiary (yes, no)

Region: Residential region in the US (northeast, northwest, southeast, southwest)

charges: Medical insurance cost billed to the beneficiary (float)

Potential Uses

• Build predictive models for medical costs
• Explore how smoking and BMI impact charges
• Teach students about regression and feature engineering
• Analyze healthcare affordability trends

This table concerns jobs of foreign-born employees within the age range of 18 up to 74 years. A distinction is made between employees who are registered as a resident in the Dutch population register (BRP; formerly known as the GBA) and those not registered as a resident in the BRP. Furthermore, the table can be broken down into origin background, gender, age, hourly wage class, employment contract type, and the Dutch standard industrial classification (SBI 2008). All employees registered as resident were at least 18 years old when they immigrated to the Netherlands. Likewise, the non-resident employees were at least 18 years old at the start of their stay in the Netherlands. The variable ‘country of origin’ is included as a background variable. Because the target population consists of both resident and non-resident employees, it is not always possible to directly derive the origin background. Missing data in this respect are imputed using information on someone’s country of permanent residence or someone’s nationality. Data available from: 2010. Status of the figures: Data from 2010 up to and including 2023 are final. Changes as of 28 March 2025: The figures for 2023 are adjusted. The method for determining the population has been improved for the reference period 2023. This means that approximately 1% of the total number of jobs held by foreign-born employees are now included. When will new figures be published? New figures for 2024 will be published in the fourth quarter of 2025.

Descriptive statistics of dependent, main independent, and extraneous (socio-demographic) variables.

aOral benign conditions.bOral HK/EH+OED cases.cOral SCCA.

This dataset is for a study that sought to investigate the influence of socio-demographic variables on perception of undergraduates on efficacy of cyber counselling platforms in managing mental health in a distressed Nigerian economy. The study, which used a sample of 800 undergraduates, was guided by six research questions and six hypotheses. A researchers-developed instrument titled “Questionnaire on Perceptions towards Cyber Counselling Platforms” (QPCCP) was used in generating the published data. The QPCCP had two sections, A and B. Section A of the instrument gathered demographic information about the respondents, while Section B had 7 items which sought to establish the perception of the undergraduates on cyber counselling platforms. The Section B of the QPCCP was constructed using Likert-like scale response options which included Strongly Agree (SA), Agree (A), Disagree (D) and Strongly Disagree (SD). These responses were scored 4, 3, 2 and 1 respectively for positively worded items, while reverse scoring of 1, 2, 3, and 4 was done for negatively worded items. Out of the seven items in the Part II, five items were positively worded while item number 5 and 7 were negatively worded.

The datasheet is presented using the coding of the items on the questionnaire. Respondents from Federal university were coded 1 while those from state university were coded 2. Males and females were coded 1 and 2 respectively; while the ages of the respondents were coded 1, 2, 3 and 4, for 17-20years, 21-25years, 26-30years, and above 30years respectively. The respondents’ levels of study were coded 1 for Year 1, 2 for Year 2, 3 for Year 3, and 4 for Year 4. On area of discipline of the respondents, 1, 2, 3 and 4 were used for Sciences, Education, Arts and Social Sciences respectively. Items 1 to 7 on the datasheet represent the seven items on section B of the questionnaire.

The results of the study indicated that sex, year of study, area of discipline and age of the students had significant influence, while ownership of university showed no significant influence, on the perception of undergraduates towards cyber counselling.

Data sets included in the analysis.

Absolute Mobility, Air Pollution, and Demographic Characteristics of 70,185 US Census Tracts. Absolute Mobility from the Opportunity Atlas dataset. Demographic variables from the Census and ACS. Air pollution data from Colmer et al. 2023. Meterological variables from Daymet.

Coronary Heart Disease (CHD) is the leading cause of death in the United States, affecting over 20.5 million adults. Previous studies link health behaviors – such as dietary behavior, physical activity, smoking, and alcohol consumption – to CHD risk. These studies typically use surveys and interviews, which, despite their benefits, are resource-intensive and limited by small sample sizes. Using large-scale national level anonymized smartphone-based location data, our study examines whether health behaviors that are proxy measured by place visitation are associated with CHD prevalence across US census tracts. This study utilized data from multiple sources, including demographic and socioeconomic characteristics, health outcomes, and smartphone-based place visitation data. Health behavior measures were derived from aggregated smartphone location data at the census tract level, focusing on categories such as food retails, drinking places, and physical activity locations. Three sets of regression analyses were conducted: one using only demographic variables, the second including socioeconomic variables, and another incorporating the derived health behavior measures. Linear and spatial regression analyses were employed to assess the relationship between neighborhood-level CHD prevalence and these behaviors. Findings indicate a significant association between health behaviors that are proxy measured by place visitation data and the prevalence of CHD at the neighborhood level. The models incorporating these behaviors demonstrated improved fitness and highlighted specific behavioral factors such as increased visits to physical activity facilities and healthy food retail associated with lower CHD rates. Conversely, higher visits to less healthy food retail were associated with increased CHD rates. Smartphone-based visitation data offers a novel method to assess health behaviors at a large scale, providing valuable insights for targeting CHD interventions more effectively at the neighborhood level. This approach could enhance our understanding and management of CHD, informing public health strategies and interventions to mitigate this major health challenge.

Associations between socio demographic variables and prevalence of STIs.

Demographic variables derived from the present samples of Study 1 and Study 2.

The Annual Population Survey (APS) household datasets are produced annually and are available from 2004 (Special Licence) and 2006 (End User Licence). They allow production of family and household labour market statistics at local areas and for small sub-groups of the population across the UK. The household data comprise key variables from the Labour Force Survey (LFS) and the APS 'person' datasets. The APS household datasets include all the variables on the LFS and APS person datasets, except for the income variables. They also include key family and household-level derived variables. These variables allow for an analysis of the combined economic activity status of the family or household. In addition, they also include more detailed geographical, industry, occupation, health and age variables.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

End User Licence and Secure Access APS data
Users should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to:

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Distribution of demographic variables and health measures by frailty status.

How Are You, Slovakia? Content coding Online interviews - CAWI Adult inhabitants of Slovakia (18+) with access to the internet The survey used a quota sample from the MNFORCE online panel. The sample was designed as representative for the following socio-demographic variables: gender, age, county (kraj), size of settlement and education of respondent. Only population with access to the internet is covered by the survey. This means that mostly older persons without internet access are missing from the sample.

The table PA-Demographic-2025-08-13 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 8528618 rows across 698 variables.

This layer shows employment data in Tucson by neighborhood, aggregated from block level data for 2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.