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. For the United States, total population available from 1950-2060, and other demographic variables available from 1980-2060. See methodology at https://www.census.gov/programs-surveys/international-programs/about/idb.html

Demographic variables of 7500 Polling Booth Catchments (PBCs) in Australia. The SA1s at the 2011 Census of Population and Housing were spatially allocated to a nearest polling booth location to form polling booth catchments within each of the 150 Electoral Divisions. The 150 booth catchments layers were then merged into one Australia booth catchments layer. The demographic variables were derived from 2011 census.

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. 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.

The BC Demographic Survey: DIP Linkage Rates resources provide information on data linkage between the 2023 BC Demographic Survey and other available datasets in the Data Innovation Program (DIP). Overall linkage rates are provided for each DIP administrative dataset, which indicate the percentage of records that can be linked to records from the BC Demographic Survey. The summary and detail resources look at specific comparisons between a DIP dataset’s demographic variables (if present) and those from the survey, which is done at a high-level, as well as comparing the variable values themselves. This information will assist researchers exploring issues related to anti-racism in government in understanding how the Survey data can supplement their planned research using other administrative datasets in DIP. The data provided in this catalogue is also viewable in an accompanying dashboard: https://bcstats.shinyapps.io/bc-demographic-survey-dip-data-linkage-rates

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the State 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 StateDate of Coverage: 2016-2020

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

This dataset reports an analyzed version of the Chilean Family Expenditures Survey with variables created from the article: Madeira, C., The impact of the Chilean pension withdrawals during the Covid pandemic on the future savings rate, Journal of International Money and Finance, forthcoming, 102650 (2022). https://doi.org/10.1016/j.jimonfin.2022.102650 .

The data contains 33,538 households from the 1997, 2007, 2012 and 2017 waves. The variables include Household identifier variables and population weights, Demographic variables (gender, age, education, spouse occupation, couple, child and senior persons), Work and income variables, Savings rates and consumption flows variables, Ratios of household wealth as a fraction of permanent household income, Betas for the linear correlation between unemployment risk and income volatility of the different 538 worker types with the aggregate consumption kernel pricing returns and the pension fund returns.

The applied model that was calibrated from the raw data is explained in detail in the online file “Methodology.pdf”. The codes used to create the variables are explained in detail in the file README_JIMF_Codes_Summary.docx and CODES_JIMF.zip includes all the 45 Stata software codes used in the article. The file Data_summary.docx summarizes the dataset.

All the methods (in Stata do-files), theoretical methodology, and the datasets are published online with the Mendeley Data.

The population survey was conducted from 1999 to 2015 using phone CATI interviews (LINK Institute, Zurich). From 2019, it was realised in cooperation with Statistik Stadt Zürich and Urban Development in Mixed Mode Online/Paper.

A representative sample was interviewed. The population includes all persons of age who have resided in the city of Zurich for at least one year (at the time of sampling) and registered with Swiss citizenship, establishment permit or residence permit B. Also, weekly stays are included.

From 1999 to 2015, German, Italian, Spanish, Serbian-Croatian-Bosnian, Portuguese and English were offered as an interview language. In the surveys from 2019 onwards, French was also offered.

Important note: For data protection reasons, the data set published here does not include all variables of the original questionnaire. Furthermore, the dataset was enriched with two socio-demographic variables from the population register of the City of Zurich: the city districts (‘A1BVSKreis01’) and the official sex (‘A1BVSSex01’) of the interviewed persons. Since the sample has been enlarged from 2019, further socio-demographic variables can be included without affecting data protection. There is therefore a additional dataset for the years 2019 to 2023. This dataset contains the following sociodemographic variables: the urban quarters (‘A1BVSQuar03’), the age (‘A1BVSAlterV03’) in three age categories and the official sex (‘A1BVSSex01’) of the respondents.

Dataset Description

This dataset contains a simulated collection of 1,00000 patient records designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modelling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables.

Dataset Structure

1. Dataset Volume

   • Size: 10,000 records. • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups.

2. Variables and Categories

A. Sociodemographic Variables

1. Age:
•  Continuous variable in years.
•  Range: 18–80 years.
•  Mean ± SD: 49.37 ± 12.81.
2. Sex:
•  Categorical variable.
•  Values: Male, Female.
3. Education:
•  Categorical variable.
•  Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa.
4. Occupation:
•  Categorical variable.
•  Values: Service, Business, Agriculture, Retired, Unemployed, Housewife.
5. Monthly Income:
•  Categorical variable in Bangladeshi Taka.
•  Values: <5000, 5001–10000, 10001–15000, >15000.
6. Residence:
•  Categorical variable.
•  Values: Urban, Sub-urban, Rural.

B. Clinical Variables

7. Systolic BP:
•  Continuous variable in mmHg.
•  Range: 100–200 mmHg.
•  Mean ± SD: 140 ± 15 mmHg.
8. Diastolic BP:
•  Continuous variable in mmHg.
•  Range: 60–120 mmHg.
•  Mean ± SD: 90 ± 10 mmHg.
9. Elevated Creatinine:
•  Binary variable (\geq 1.4 \, \text{mg/dL}).
•  Values: Yes, No.
10. Diabetes Mellitus:
•  Binary variable.
•  Values: Yes, No.
11. Family History of CVD:
•  Binary variable.
•  Values: Yes, No.
12. Elevated Cholesterol:
•  Binary variable (\geq 200 \, \text{mg/dL}).
•  Values: Yes, No.
13. Smoking:
•  Binary variable.
•  Values: Yes, No.

C. Complications

14. LVH (Left Ventricular Hypertrophy):
•  Binary variable (ECG diagnosis).
•  Values: Yes, No.
15. IHD (Ischemic Heart Disease):
•  Binary variable.
•  Values: Yes, No.
16. CVD (Cerebrovascular Disease):
•  Binary variable.
•  Values: Yes, No.
17. Retinopathy:
•  Binary variable.
•  Values: Yes, No.

D. Treatment and Control

18. Treatment:
•  Categorical variable indicating therapy type.
•  Values: Single Drug, Combination Drugs.
19. Control Status:
•  Binary variable.
•  Values: Controlled, Uncontrolled.

Dataset Applications

1. Predictive Modeling:
•  Develop models to predict blood pressure control status using demographic and clinical data.
2. Risk Analysis:
•  Identify significant factors influencing hypertension control and complications.
3. Severity Scoring:
•  Quantify hypertension severity for patient risk stratification.
4. Complications Prediction:
•  Forecast complications like IHD, LVH, and CVD for early intervention.
5. Treatment Guidance:
•  Analyze therapy efficacy to recommend optimal treatment strategies.

The Current Population Survey Civic Engagement and Volunteering (CEV) Supplement is the most robust longitudinal survey about volunteerism and other forms of civic engagement in the United States. Produced by AmeriCorps in partnership with the U.S. Census Bureau, the CEV takes the pulse of our nation’s civic health every two years. The data on this page was collected in September 2023. The next wave of the CEV will be administered in September 2025. The CEV can generate reliable estimates at the national level, within states and the District of Columbia, and in the largest twelve Metropolitan Statistical Areas to support evidence-based decision making and efforts to understand how people make a difference in communities across the country. Click on "Export" to download and review an excerpt from the 2023 CEV Analytic Codebook that shows the variables available in the analytic CEV datasets produced by AmeriCorps. Click on "Show More" to download and review the following 2023 CEV data and resources provided as attachments: 1) 2023 CEV Dataset Fact Sheet – brief summary of technical aspects of the 2023 CEV dataset. 2) CEV FAQs – answers to frequently asked technical questions about the CEV 3) Constructs and measures in the CEV 4) 2023 CEV Analytic Data and Setup Files – analytic dataset in Stata (.dta), R (.rdata), SPSS (.sav), and Excel (.csv) formats, codebook for analytic dataset, and Stata code (.do) to convert raw dataset to analytic formatting produced by AmeriCorps. These files were updated on January 16, 2025 to correct erroneous missing values for the ssupwgt variable. 5) 2023 CEV Technical Documentation – codebook for raw dataset and full supplement documentation produced by U.S. Census Bureau 6) 2023 CEV Raw Data and Read In Files – raw dataset in Stata (.dta) format, Stata code (.do) and dictionary file (.dct) to read ASCII dataset (.dat) into Stata using layout files (.lis)

The variables in the General Household Survey, 2000-2001: Social Capital Teaching Dataset are a subset taken from the full General Household Survey, 2000-2001 (GHS). For that year of the GHS, a social capital 'trailer' was conducted alongside the main survey, which included questions on respondents' local area, fear of crime, participation and trust. The trailer was funded by the Health Development Agency as part of a larger body of work to further understanding of social capital in terms of its meaning, measurement and links to health within the British population. The variables included here are those from the social capital file and others from the main survey, chosen to reflect different dimensions of social capital in relation to a variety of demographic variables, and some outcome variables such as, health, income and employment.

Further information can be found in the Social capital: introductory user guide.

The second edition of the study (released February 2008) replaced the previous edition (released February 2006). The second edition contains a rescaled weight with a mean of 1 (correcting the previous version) and corrects a systematic error in the data which affected the internal consistency of the social capital module variables in relation to those from the main file. Current users of the data are strongly advised to switch to the second edition of the study.

The full General Household Survey series is held at the UK Data Archive under GN 33090.

The table AL-Voter-History-2025-03-12 is part of the dataset L2 Voter and Demographic Dataset, available at https://stanford.redivis.com/datasets/t6qv-ad1vt3wqf. It contains 3612139 rows across 906 variables.