About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States 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 United States Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Live Oak population by age. The dataset can be utilized to understand the age distribution and demographics of Live Oak.

Content

The dataset constitues the following three datasets

• Live Oak, TX Age Group Population Dataset: A complete breakdown of Live Oak age demographics from 0 to 85 years, distributed across 18 age groups
• Live Oak, TX Age Cohorts Dataset: Children, Working Adults, and Seniors in Live Oak - Population and Percentage Analysis
• Live Oak, TX Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

US Stroke Mortality in Adults Over 35

State, County, and Gender Data

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains stroke mortality data among US adults (35+) by state/territory and county. Learn more about the health of people within your own state or region, across genders and ethnicities. Reliable statistics even for small counties can be seen, thanks to 3-year averages, age-standardization, and spatial smoothing. Data sources such as the National Vital Statistics System give you all the data you need to get a detailed sense of your population's total cardiovascular health. With interactive maps created from this data also provided covering heart disease risks, death rates and hospital bed availability across each location in America, you can now gain a powerful perspective on how effective healthcare initiatives are making an impact in those who live there. Study up on the real cardiovascular conditions plaguing those around us today to make a real change in public health!

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How to use the dataset

This dataset contains stroke mortality data among US adults (35+) by state/territory and county. This data can be useful in helping identify areas where stroke mortality is high, and interventions to reduce mortality should be taken into account.

To access the dataset, you need to download it from Kaggle. The dataset consists of 18 columns including year, location description, geographic level, source of data, class of data values provided, topic of discussion with regard to stroke mortality rates (age-standardized), labels for stratification categories and stratifications used within the given age group when performing this analysis. The last 3 columns consist of geographical coordinates for each location (Y_lat & X_lon) as well as an overall georeferenced column (Georeferenced Column).

Once you have downloaded the dataset there are a few ways you can go about using it:

• You can perform a descriptive analysis on any particular column using methods such as summary statistics or distributions graphs;
• You can create your own maps or other visual representation based on the latitude/longitude columns;
• You could look at differences between states and counties/areas within states by subsetting out certain areas;
• Using statistical testing methods you could create inferential analyses that may lead to insights on why some areas seem more prone to higher levels of stroke mortality than others

Research Ideas

• Track county-level stroke mortality trends among US adults (35+) over time.
• Identify regions of higher stroke mortality risk and use that information to inform targeted, preventative health policies and interventions.
• Analyze differences in stroke mortality rates by gender, race/ethnicity, or geographic location to identify potential disparities in care access or outcomes for certain demographic groups

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------| | Year | Year of the data. (Integer) | | LocationAbbr | Abbreviation of the state or territory. (String) | | LocationDesc | Name of the state or territory. (String) | | GeographicLevel | Level of geographic detail. (String) | | DataSource | Source of the data. (String) | | Class | Classification of the data. (String) | | Topic | Topic of the data. (String) | | Data_Value | Numeric value associated with the topic. (Float) | | Data_Value_Unit | Unit used to express the data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Symbol associated with the data value footnote. (String) | | StratificationCategory1 | First category of stratification. (String) | | Stratification1 | First stratifica...

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

Data accompanies manuscript. Title: Impact of heat on respiratory hospitalizations among older adults living in 120 large US urban areas. This dataset is associated with the following publication: O'Lenick, C., S. Cleland, L. Neas, M. Turner, E. Mcinroe, K. Hill, A. Ghio, M. Rebuli, i. Jaspers, and A. Rappold. Impact of Heat on Respiratory Hospitalizations among Older Adults in 120 Large US Urban Areas. Annals of the American Thoracic Society. American Thoracic Society, New York, NY, USA, 22(3): 367-377, (2025).

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

The U.S. Religious Landscape Survey completed telephone interviews with a nationally representative sample of 35,556 adults living in continental United States households. The survey was conducted by Princeton Survey Research Associates International (PSRAI). This extensive survey by the Pew Forum on Religion & Public Life details the religious makeup, religious beliefs and practices as well as social and political attitudes of the American public.

Information on this page was adapted from the Pew Forum's methodology report for this survey.

About this dataset

Context

The dataset tabulates the Country Life Acres population by age. The dataset can be utilized to understand the age distribution and demographics of Country Life Acres.

Content

The dataset constitues the following three datasets

• Country Life Acres, MO Age Group Population Dataset: A complete breakdown of Country Life Acres age demographics from 0 to 85 years, distributed across 18 age groups
• Country Life Acres, MO Age Cohorts Dataset: Children, Working Adults, and Seniors in Country Life Acres - Population and Percentage Analysis
• Country Life Acres, MO Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Dementia can be difficult for married couples for many reasons, including the introduction of caregiving burden, loss of intimacy, and financial strain. In this study, we investigated the impact of dementia staging and neuropsychiatric behavioral symptoms on the likelihood of divorce or separation for older adult married couples. For this case-control study, we used data from the National Alzheimer’s Coordinating Center (NACC) Uniform dataset (UDS) versions 2 and 3. This dataset was from 2007 to 2021 and contains standardized clinical information submitted by NIA/NIH Alzheimer’s Disease Research Centers (ADRCs) across the United States (US). This data was from 37 ADRCs. We selected participants who were married or living as married/domestic partners at their initial visit. Cases were defined by a first divorce/separation occurring during the follow-up period, resulting in 291 participants. We selected 5 controls for each married/living as married case and matched on age. Conditional logistic regression estimated the association between overall Neuro Psychiatric Inventory (NPI) score and severity of individual symptoms of the NPI with case/control status, adjusted for education, the CDR® Dementia Staging Instrument score, living situation, symptom informant, sex, and race. Separate analyses were conducted for each symptom. Multiple comparisons were accounted for with the Hochberg method. Later stage of dementia was negatively associated with divorce/separation with an adjusted odds ratio (AOR) = 0.68 (95%CI = 0.50 to 0.93). A higher overall NPI score was positively associated with divorce/separation AOR = 1.08 (95% CI = 1.03 to 1.12,). More severe ratings of agitation/aggression, depression/dysphoria, disinhibition, and elation/euphoria were associated with greater odds of divorce/separation. Among older adults in the US, a later stage of dementia is associated with a lower likelihood of divorce or separation, while having more severe neuropsychiatric behavioral symptoms of agitation/aggression, depression/dysphoria, disinhibition, and elation/euphoria are associated with a higher likelihood of divorce or separation.

The 1995 Survey of Adults on Probation (SAP) was the first national survey to gather information on the individual characteristics of adult probationers. The SAP was a two-part nationally representative survey consisting of a records check based on probation office administrative records and personal interviews with probationers. The records check provided detailed information for 5,867 probationers on current offenses and sentences, criminal histories, levels of supervision and contacts, disciplinary hearings and outcomes, and demographic characteristics. Only adults with a formal sentence to probation who were not considered absconders were included in the records check. Excluded were persons supervised by a federal probation agency, those only on parole, persons on presentence or pretrial diversion, juveniles, and absconders. The records check forms were completed by a probation officer or by another person knowledgeable about probation office records. A subset of the population selected for the records check was selected for a personal interview, resulting in a total of 2,030 completed interviews. The personal interview sample excluded from the records check sample probationers not on active probation (defined as being required to make office visits at any interval), those incarcerated, and those in residential treatment. Respondents were asked about current offense(s) and supervision, criminal history, alcohol and drug use and treatment, mental health treatment, demographic characteristics, and a variety of socioeconomic characteristics such as employment, income, receipt of welfare, housing, number of children and child support, and living conditions while growing up.

This dataset contains data from the LAMARCK controlled exposure study including DNA methylation assessments done before and 24 hours after each exposure, subclinical health outcomes measures, exposure details, and demographic information on the individual participants in the study. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The dataset can be accessed by contacting Dr. Cavin Ward-Caviness (ward-caviness.cavin@epa.gov). Format: The data is tabular data containing information on DNA methylation assessment, lung function, inflammation, controlled exposure conditions, and demographics of the LAMARCK participants. DNA methylation age has also been calculated based on the DNA methylation assessment data. This dataset is associated with the following publication: Weston, W., M. Bind, W. Cascio, R. Devlin, D. Diaz Sanchez, and C. Ward-Caviness. Accelerated aging and altered sub-clinical response to ozone exposure in young, healthy adults. Environmental Epigenetics. Oxford University Press, Cary, NC, USA, 10(1): dvae007, (2024).

Abstract

The Gallup Poll Social Series (GPSS) is a set of public opinion surveys designed to monitor U.S. adults' views on numerous social, economic, and political topics. The topics are arranged thematically across 12 surveys. Gallup administers these surveys during the same month every year and includes the survey's core trend questions in the same order each administration. Using this consistent standard allows for unprecedented analysis of changes in trend data that are not susceptible to question order bias and seasonal effects.

Introduced in 2001, the GPSS is the primary method Gallup uses to update several hundred long-term Gallup trend questions, some dating back to the 1930s. The series also includes many newer questions added to address contemporary issues as they emerge.

The dataset currently includes responses from up to and including 2025.

Methodology

Gallup conducts one GPSS survey per month, with each devoted to a different topic, as follows:

January: Mood of the Nation

February: World Affairs

March: Environment

April: Economy and Finance

May: Values and Beliefs

June: Minority Rights and Relations (discontinued after 2016)

July: Consumption Habits

August: Work and Education

September: Governance

October: Crime

November: Health

December: Lifestyle (conducted 2001-2008)

The core questions of the surveys differ each month, but several questions assessing the state of the nation are standard on all 12: presidential job approval, congressional job approval, satisfaction with the direction of the U.S., assessment of the U.S. job market, and an open-ended measurement of the nation's "most important problem." Additionally, Gallup includes extensive demographic questions on each survey, allowing for in-depth analysis of trends.

Interviews are conducted with U.S. adults aged 18 and older living in all 50 states and the District of Columbia using a dual-frame design, which includes both landline and cellphone numbers. Gallup samples landline and cellphone numbers using random-digit-dial methods. Gallup purchases samples for this study from Dynata. Gallup chooses landline respondents at random within each household based on which member had the next birthday. As of June 2023, each sample of national adults includes a minimum quota of 80% cellphone respondents and 20% landline respondents, with additional minimum quotas by time zone within region. Gallup conducts interviews in Spanish for respondents who are primarily Spanish-speaking.

Gallup interviews a minimum of 1,000 U.S. adults aged 18 and older for each GPSS survey. Samples for the June Minority Rights and Relations survey (conducted periodically between 2001 and 2021) were significantly larger because Gallup oversampled Black and Hispanic adults to allow for reliable estimates among these key subgroups.

Gallup weights samples to correct for unequal selection probability, nonresponse, and double coverage of landline and cellphone users in the two sampling frames. Gallup also weights its final samples to match the U.S. population according to gender, age, race, Hispanic ethnicity, education, region, population density, and phone status (cellphone only, landline only, both, and cellphone mostly).

Demographic weighting targets are based on the most recent Current Population Survey figures for the aged 18 and older U.S. population. Phone status targets are based on the most recent National Health Interview Survey. Population density targets are based on the most recent U.S. Census.

Usage

The year appended to each table name represents when the data was last updated. For example, January: Mood of the Nation - 2025** **has survey data collected up to and including 2025.

For more information about what survey questions were asked over time, see the Supporting Files.

Bulk Data Access

Data access is required to view this section.

Sponsored by the Robert Wood Johnson Foundation, the Active for Life (AFL) initiative investigated how two physical activity programs for adults aged 50 and older, Active Choices (AC) and Active Living Every Day (ALED), worked in community settings. Created by researchers at Stanford University, Active Choices used lifestyle counseling and personalized telephone support to encourage older adults to be physically active. In AFL, this was a 6-month program delivered through one face-to-face meeting followed by up to eight one-on-one telephone counseling calls. Active Living Every Day, which was created by the Cooper Institute and Human Kinetics Inc., also provided lifestyle counseling to promote physical activity, but in a classroom and workbook format. During the first three years of the four-year AFL initiative, ALED was delivered as a 20-week program where participants attended weekly small group meetings, but in the last year it was shortened to 12 weekly meetings. Nine organizations received AFL grants to implement the programs during 2003-2006. Four grantees implemented the one-on-one AC model, while five implemented the group-based ALED model. Data were collected from the AC and ALED sites for both a process and outcomes evaluation. The primary aims of the process evaluation were to (1) monitor the extent to which the grantees demonstrated fidelity to the AC and ALED models in their program implementation, (2) assess staff experiences implementing the programs, and (3) assess participants' impressions of the programs. A quasi-experimental, pre-post study design was used to assess outcomes. Primary aims of the outcomes evaluation were to evaluate the impact of AC and ALED on self-reported physical activity, and to evaluate the impact of the programs on self-reported stress, depressive symptoms, and satisfaction with body function and appearance. Secondary aims of the outcome evaluation were to (1) evaluate the impact of the programs on measures of functional fitness, (2) examine whether changes in self-reported physical activity and functional fitness were moderated by participant characteristics, including age, gender, race, baseline physical activity self-efficacy, and baseline physical activity social support, and (3) examine whether changes in self-reported physical activity were consistent with a mediation model for physical activity self-efficacy and physical activity social support. The collection has 14 data files (datasets). Datasets 1-7 constitute the process evaluation data, and Datasets 8-14 the outcomes evaluation data: Dataset 1 (AC Initial Face-to-Face Sessions Data) contains information about the initial face-to-face AC session: the format, date, and length of the session, whether the 8 steps required in the face-to-face session were completed, what was discussed between the health educator and the participant related to physical activity plans, interests, benefits, and barriers, and the health educator's progress notes. The file contains one record for each AC participant. Dataset 2 (AC Completed Calls Data) comprises information about the completed AC calls, but does not cover the topics discussed on the calls. Recorded information about each call includes the date and length of the call, the health educator's progress notes, and whether the participant was assessed for injury, light activity, moderate activity, exercise goals, or exercise intentions. Each call is represented by a separate record in the data file and, typically, there are multiple records per participant. Dataset 3 (AC Topics Discussed on Completed Calls ) contains information about the topics discussed on each completed AC call, e.g., exercise barriers/benefits, previous exercise experiences, goal setting, long term goals, injury prevention, rewards/reinforcement, social support, progress tracking, and relapse prevention. Each record in the file represents one topic and there are often multiple records per call for each participant. Dataset 4 (AC Aggregate Call Data) aggregates the call data across calls for each AC participant. For example, for a given participant, this dataset shows the total number of calls completed, the number of calls where injury/health problems were assessed, etc. The file contains one record per participant. Dataset 5 (ALED Sessions Data) contains information about each class session for e

This study was undertaken to obtain information on the characteristics of gun ownership, gun-carrying practices, and weapons-related incidents in the United States -- specifically, gun use and other weapons used in self-defense against humans and animals. Data were gathered using a national random-digit-dial telephone survey. The respondents were comprised of 1,905 randomly-selected adults aged 18 and older living in the 50 United States. All interviews were completed between May 28 and July 2, 1996. The sample was designed to be a representative sample of households, not of individuals, so researchers did not interview more than one adult from each household. To start the interview, six qualifying questions were asked, dealing with (1) gun ownership, (2) gun-carrying practices, (3) gun display against the respondent, (4) gun use in self-defense against animals, (5) gun use in self-defense against people, and (6) other weapons used in self-defense. A "yes" response to a qualifying question led to a series of additional questions on the same topic as the qualifying question. Part 1, Survey Data, contains the coded data obtained during the interviews, and Part 2, Open-Ended-Verbatim Responses, consists of the answers to open-ended questions provided by the respondents. Information collected for Part 1 covers how many firearms were owned by household members, types of firearms owned (handguns, revolvers, pistols, fully automatic weapons, and assault weapons), whether the respondent personally owned a gun, reasons for owning a gun, type of gun carried, whether the gun was ever kept loaded, kept concealed, used for personal protection, or used for work, and whether the respondent had a permit to carry the gun. Additional questions focused on incidents in which a gun was displayed in a hostile manner against the respondent, including the number of times such an incident took place, the location of the event in which the gun was displayed against the respondent, whether the police were contacted, whether the individual displaying the gun was known to the respondent, whether the incident was a burglary, robbery, or other planned assault, and the number of shots fired during the incident. Variables concerning gun use by the respondent in self-defense against an animal include the number of times the respondent used a gun in this manner and whether the respondent was hunting at the time of the incident. Other variables in Part 1 deal with gun use in self-defense against people, such as the location of the event, if the other individual knew the respondent had a gun, the type of gun used, any injuries to the respondent or to the individual that required medical attention or hospitalization, whether the incident was reported to the police, whether there were any arrests, whether other weapons were used in self-defense, the type of other weapon used, location of the incident in which the other weapon was used, and whether the respondent was working as a police officer or security guard or was in the military at the time of the event. Demographic variables in Part 1 include the gender, race, age, household income, and type of community (city, suburb, or rural) in which the respondent lived. Open-ended questions asked during the interview comprise the variables in Part 2. Responses include descriptions of where the respondent was when he or she displayed a gun (in self-defense or otherwise), specific reasons why the respondent displayed a gun, how the other individual reacted when the respondent displayed the gun, how the individual knew the respondent had a gun, whether the police were contacted for specific self-defense events, and if not, why not.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

National IPV/SV Survey

Examining Intimate Partner and Sexual Violence in the United States

By Health [source]

About this dataset

This NISVS Intimate and Sexual Violence Survey dataset captures detailed information on intimate partner violence (IPV) and sexual violence experiences among U.S. adults aged 18 years or older. It contains statistics on the prevalence of lifetime IPV, as well as additional data on the types of intimate partner violence (psychological aggression, physical assault, sexual assault), and details more recent IPV experience; including experiencing sexual coercion or being forced to do sexual acts against one's will. Additionally, it records measures of the frequency of experiencing different forms psychological abuse; such as feeling scared by a partner's use or threat of force and feeling like one had no power over important issues in their life due to an abusive partner. Further characteristics such as respondent demographics are also included in the dataset to further explore disparities around incidence of IPV and SVP experience across various populations. With a better understanding about these trends, policy makers can take targeted action towards creating responsible legislation that addresses this pervasive problem in our society today

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How to use the dataset

This dataset provides data from the National Intimate Partner and Sexual Violence Survey (NISVS), which collects information about intimate partner violence (IPV) and sexual violence (SV). The survey was conducted by the Centers for Disease Control and Prevention from 2010-2012. This dataset includes key statistics from the surveys, with an emphasis on IPV, SV, and other types of intimate partner violence.

To get started with this dataset, take a look at the variables included in it. There are several types of variables – demographic characteristics like age; report tables that include numbers associated with different forms of IPV or SV; summary charts that show percentages associated with certain activities or events; and full reports detailing information on various topics related to IPV/SV.

For more detailed analysis, you can explore specific characteristics in different subsets of this data. For example, you might look at patterns by gender, race/ethnicity or geography. Or you could compare the frequency of certain forms of abuse over time across different groups who participated in the survey. You could also use visualizations like line graphs or scatter plots to reveal patterns between variables that were not immediately obvious when looking at only raw numbers or percentages in tables.

When analyzing results using this dataset you’ll want to be sure to take into account any potential limitations due to sampling methods used when collecting this data as well as any issues caused by variations across states’ reporting standards depending on their understanding of specific terms included in their formulation of reporting questions for participation in this survey project. It is important to keep these potential sources for variations alive when creating research questions and hypothesis associated with your own analyses related to choosing appropriate indicators as partof developing effective measuresfor comparison purposesacross differentpartici-pants participatinginthisdataset environment .

Ultimately though we hope that this dataset serves as a helpful resource when evaluating trends surrounding IPV/SV both nationally among all states included alongwith representativelocally relevantpopu- lation groups in order to promote awareness needed around monitoring preventative measures which deter people from engaginginbehaviors leading toparticularoutcomes identified through aninterpretationofthisdataset source material available via Kaggle platform interface provided hereinby subjectexpertiseand resources available within academic literature used foranalyzingdata captured within NISVS Inti/Sexualviolence Surv collection according prescribed analytical protocols suggested here- within forthiguidebooklet offeredtosupportsuchinquiries related

Research Ideas

• Examining county-level variations of national intimate partner and sexual violence incidents over time.
• Analyzing the demographics of individuals affected by such violence to better understand marginalized identities and communities at risk.
• Investigating how intimate partner and sexual violence intersects with other risk factors, including income, location, age, gender identity, race/ethnicity etc., in order to identify potential disparities in service a...

Success.ai’s LinkedIn Company Data for Global Manufacturing Professionals provides a robust dataset designed for businesses aiming to connect with decision-makers and professionals in the manufacturing sector worldwide. Covering roles such as plant managers, supply chain leaders, production heads, and operations executives, this dataset offers verified LinkedIn profiles, work emails, and comprehensive professional insights.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, market research, and partnership strategies are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to thrive in the competitive and evolving manufacturing industry.

Why Choose Success.ai’s LinkedIn Company Data?

1. Verified LinkedIn Profiles and Contact Data

   • Access verified LinkedIn profiles, professional histories, work emails, and phone numbers of manufacturing leaders worldwide.
   • AI-driven validation ensures 99% accuracy, optimizing outreach and reducing communication inefficiencies.

2. Comprehensive Global Coverage

   • Includes professionals from major manufacturing hubs across North America, Europe, Asia-Pacific, and other key regions.
   • Gain insights into regional manufacturing trends, operational practices, and technological advancements.

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   • Real-time updates capture changes in leadership, organizational structures, and market expansions.
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4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access LinkedIn company data for manufacturing professionals across industries and regions.
• Leadership and Professional Insights: Connect with supply chain managers, plant heads, and C-suite executives in the manufacturing sector.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for targeted engagement.
• Professional Histories: Understand career trajectories, expertise, and leadership roles to guide your outreach strategies.

Key Features of the Dataset:

1. Comprehensive Manufacturing Profiles

   • Identify and connect with key players responsible for production, operations, and supply chain management in the manufacturing sector.
   • Target professionals driving innovation, efficiency, and technology adoption.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (automotive, electronics, textiles), geographic location, or job function.
   • Tailor campaigns to align with specific business needs, such as logistics optimization, process improvement, or sustainability initiatives.

3. Regional and Industry Insights

   • Leverage data on global manufacturing trends, regional benchmarks, and emerging technologies to inform your strategies.
   • Refine your approach to align with industry needs and geographic demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Design targeted campaigns to promote manufacturing equipment, technology solutions, or operational tools to industry professionals.
   • Use verified LinkedIn contact data for multi-channel outreach, including email, phone, and digital platforms.

2. Partnership Development and Collaboration

   • Build relationships with manufacturing leaders seeking innovative solutions, supply chain partnerships, or sustainability collaborations.
   • Foster strategic alliances that drive operational efficiency and mutual growth.

3. Market Research and Competitive Analysis

   • Analyze trends in global manufacturing to refine your business strategies, identify growth opportunities, and benchmark against competitors.
   • Identify high-demand solutions and underserved segments in the market.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for critical roles in manufacturing operations, engineering, or supply chain management.
   • Provide workforce optimization tools or talent development platforms tailored to the sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality LinkedIn company data at competitive prices, ensuring strong ROI for your marketing, sales, and partnership initiatives.

2. Seamless Integration

   • Integrate verified LinkedIn company data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, enhancing productivity and streamlining workflows.

3. Data Accuracy with AI Validation
   ...

The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. The following files constitute Round 1: Core Data, Marital/Cohabiting History Data, Social Networks Data, Medications Data, and Sexual Partners Data. Included in the Core file (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, medications and alternative therapies, history of sexual and intimate partnerships and patient-physician communication, in addition to bereavement items. In addition data was collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function and a panel of biomeasures including, weight, waist circumference, height, blood pressure, smell, saliva collection, taste, and a self-administered vaginal swab for female respondents. The Core file also contains a count of the total number of drugs taken, and a variable for each observed therapeutic category, indicating whether the respondent reported taking one or more medications in that category. These variables are derived from the information in the medications file, and thus are guaranteed to be consistent with it. The Marital/Cohabiting History file (Dataset 3) contains one record for each marriage or cohabitation identified in Section 3A of the questionnaire. The Social Networks file (Datasets 4 and 5) contains one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Medications file (Dataset 6) contains one record for each item listed in the medications log (including alternative medicines and nutritional products). Respondents who did not report taking any medications or who refused to participate in this module are not represented in this file. Lastly, the Sexual Partners file (Dataset 7) contains one record for each sexual partner identified in Section 3A of the questionnaire. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required.

In January 2018, 798 Hispanic/Latino adults living in the United States were recruited through Qualtrics Panels to complete a survey in English or Spanish. Respondents were diverse in their nativity (e.g., 52% Mexican or Mexican American; 17% Puerto Rican; 8.5% Cuban). The survey included the following measures: -Demographic and Health Information – Demographic and Health Data Questionnaire (DHDQ). This researcher-constructed questionnaire is designed to obtain participant information such as: (a) race/ethnicity, (b) age, (c) gender, (d) sexual orientation, (e) relationship status, (f) household income, (g) generational status, (h) education level, (i) presence of chronic health conditions, (j) self-reported height and weight, (k) overall health status, (l) native language and proficient language(s), (m) number of health care visits in the past year, and (n) perceived weight. -Media and Technology Usage and Attitudes Scale (MTUAS). The Media and Technology Usage and Attitudes Scale is a 60-item scale used to measure the frequency of use from specific forms of media and attitudes toward technology (Rosen, Whaling, Carrier, Cheever, & Rokkum, 2013). The scale consists of eleven media usage subscales and four attitude subscales. For the purposes of this study, only the smartphone usage subscale will be included (9 items). Prompts assessing the frequency of technology use stated: “Please indicate how often you do each of the following…” and asked about smartphone usage habits on a scale from 1(Never) to 10 (All the time). Higher scores are indicative of more technology use. The MTUAS was found to show sufficient proof of reliability for smartphone usage subscale (α = .93). Validity has also been shown through comparisons with measures of daily media usage hours, technology-related anxiety, and the Internet Addiction Test (Rosen et al., 2013). -The Sedentary Behavior Questionnaire (SBQ). The Sedentary Behavior Questionnaire is an 18-item scale designed to assess nine different sedentary behaviors including the use of technological devices, hobbies, and sitting due to transportation and work (Rosenberg et al., 2010). The measure is designed to assess sedentary behaviors over weekdays as well as the weekend and then are multiplied to estimate the sum amounts of sedentary hours during a week/weekend. The scale consisted of nine items with answer choices ranging from 1 (None) to 9 (6 hours or more). The current study will slightly alter the SBQ as some of the items may be dated in regards to the technology. An example is “sitting listening to music on the radio, tapes, or CDs.” The examples used in the items will be reflective of sedentary forms of technology used nowadays. The SBQ has been found to be a reliable measure for sedentary behaviors as intraclass correlation coefficients found that the items were sufficient for both weekday (.64-.90) and weekends (.51-.93). Validity of the measure was also sufficient as partial correlations were used to compare the self-reported ratings of the SBQ to accelerometer measures of activity. The study also found that in comparison to the International Physical Activity Questionnaire and body mass index, there were significant correlations with both male and female samples (Rosenberg et al., 2010). -PHQ-9- English: The Patient Health Questionnaire (PHQ-9). The PHQ-9 is a 9-item instrument that measures depressive symptoms (Kroenke, Spitzer, & Williams, 2001). Instructions on the PHQ-9 are as follows: “Over the last 2 weeks, how often have you been bothered by any of the following problems?” The assessment uses a 4-point Likert-type scale with responses ranging from 0 (not at all) to 3 (nearly every day). Scores for PHQ-9 scale are determined by assigning a score to each response ranging from 0 to 3 and then summing the responses. The PHQ-9 score can range from 0 to 27. Higher scores on the measure indicate higher levels of depressive symptoms. -Health Promoting Behaviors – Health Promoting Lifestyle Profile II (HPLP-II). The HPLP-II is a 52-item inventory designed to measure engagement in behaviors that characterize a health-promoting lifestyle (Walker, Sechrist, Pender, 1995). The HPLPII is comprised of a scale and six subscales, which include Spiritual Growth, Interpersonal Relations, Nutrition, Physical Activity, Health Responsibility, and Stress Management. Only the Nutrition (9 items) and Physical Activity (8 items) subscales will be used for the current study. Instructions on the HPLP-II are to indicate level of engagement in each listed behavior using a Likert-type scale, with responses ranging from 1 (never) to 4 (routinely). Scores for the HPLP-II scale and subscale are determined by calculating means for each. Higher scores on the scale and subscales indicate higher levels of engagement in the assessed health promoti... Visit https://dataone.org/datasets/sha256%3A947312a2e719300f2006c0c8f48294d38a5b6a63ad0f31869ed48ea690048cde for complete metadata about this dataset.