Context

This dataset provides comprehensive global demographic and socioeconomic indicators for each country, compiled for the year 2024. It includes data on population sizes, growth rates, fertility rates, migration, urbanization, and other critical factors that influence global social and economic trends.

Key Features:

Country Name: The name of each country or region included in the dataset.

Population (2024): Estimated total population of each country for the year 2024, measured in millions or billions.

Population Growth Rate: The annual percentage change in population from one year to the next. It highlights whether the population is growing or declining.

Urbanization Percentage: The proportion of the population living in urban areas, indicating trends in urban migration and the shift from rural to urban living.

Fertility Rate: The average number of children born per woman of childbearing age, a key indicator of population reproduction levels.

Median Age: The median age of the population, reflecting the age distribution and helping to assess population aging or youthfulness.

Life Expectancy at Birth: The average number of years a newborn is expected to live, assuming current mortality rates persist.

Infant Mortality Rate: The number of deaths of infants under one year of age per 1,000 live births, a key indicator of healthcare quality and access.

GDP (Gross Domestic Product): The total monetary or market value of all the goods and services produced within a country’s borders in a given time period (usually measured annually in USD).

GDP per Capita: GDP divided by the total population, reflecting the average economic output per person and serving as a measure of the average income or economic standard of living.

Human Development Index (HDI): A composite index that considers life expectancy, education, and income per capita to provide an overall measure of human development.

Applications of the Dataset: Policy and Development Analysis: Governments, international organizations, and think tanks can use this data to craft development policies, allocate resources, and address issues such as urbanization, aging populations, and fertility rates.

Economic Forecasting and Analysis: Economists and financial institutions can leverage this data for macroeconomic analysis, forecasting, and investment decisions, especially using indicators like GDP, GDP per capita, and HDI.

Social and Health Research: Public health organizations can track health indicators like life expectancy, infant mortality rates, and fertility rates to guide public health interventions and strategies.

Education and Demography: Educators and researchers in the fields of demography, sociology, and global studies can use this dataset to analyze population trends, migration patterns, and social changes across the globe.

Sources of Data:

The data is sourced from reputable international organizations including the United Nations, the World Bank, the World Health Organization (WHO), the International Monetary Fund (IMF), and other national statistical agencies.

Data Use & Limitations:

Use: This dataset is intended for general research, educational, and analytical purposes. It provides a snapshot of global demographic trends and socioeconomic conditions as of 2024. Limitations: While the data is collected from reliable sources, estimates for certain countries may vary slightly due to differing methods of data collection or reporting across regions. Additionally, as some countries may not have updated data for 2024, projections or estimates have been used where necessary.

The dataset is a part of the survey conducted in Ho Chi Minh City and Dong Nai the Southeast region of Vietnam in 2020 to collect information for research on fertility. The main research purpose is to identify the socioeconomic determinants of low fertility in the Southeast. In total 808 individuals in the main reproductive age were interviewed, including 382 cases from Dong Nai and 426 cases from Ho Chi Minh City, or 273 unmarried persons and 535 married women. Information about family size desires and socio-demographic characteristics of 535 married men were asked when interviewing their spouses. As such, the survey collected information on the family size desires of 1343 individuals. The dataset has been converted to SPSS format (version 26.0). For data analysis, the dataset need to be weighted (WEI variable) as individuals were not selected with equal probability.

Demographic analysis examines and measures the dimensions and dynamics of populations; it can cover whole societies or groups defined by criteria such as education, nationality, religion, and ethnicity. Educational institutions usually treat demography as a field of sociology, though there are a number of independent demography departments. These methods have primarily been developed to study human populations, but are extended to a variety of areas where researchers want to know how populations of social actors can change across time through processes of birth, death, and migration. In the context of human biological populations, demographic analysis uses administrative records to develop an independent estimate of the population

Four tables of ACS demographic profiles for 2012 to 2016 at the PUMA level. Four profiles include demographics, economic, housing and sociological. Column headers in this database are abbreviated. Please see the data dictionary (shown in worksheet entitled “Dictionary”) for an explanation of these abbreviated headers.

Grandmothers provide key care to their grandchildren in both contemporary and historic human populations. The length of the grandmother-grandchild relationship provides a basis for such interactions, but its variation and determinants have rarely been studied in different contexts, despite changes in age-specific mortality and fertility rates likely having affected grandmotherhood patterns across the demographic transition. Understanding how often and long grandmothers have been available for their grandchildren in different conditions may help explain the large differences between grandmaternal effects found in different societies, and is vital for developing theories concerning the evolution of menopause, post-reproductive longevity, and family living. Using an extensive genealogical dataset from Finland spanning the demographic transition, we quantify the length of grandmotherhood and its determinants from 1790–1959. We found that shared time between grandmothers and grandchildren was consistently low before the demographic transition, only increasing greatly during the 20th century. Whilst reduced childhood mortality and increasing adult longevity had a role in this change, grandmaternal age at birth remained consistent across the study period. Our findings further understanding of the temporal context of grandmother-grandchild relationships, and emphasise the need to consider the demography of grandmotherhood in a number of disciplines, including biology (e.g. evolution of the family), sociology (e.g. changing family structures), population health (e.g. changing age structures), and economics (e.g. workforce retention).

This dataset provides a comprehensive overview of the demographic trends and population statistics of India. It includes various aspects of the population, such as total population figures, gender distribution, religious composition, linguistic diversity, and age group breakdowns. The dataset aims to facilitate research and analysis in the fields of sociology, economics, and public policy by offering valuable insights into the demographic dynamics of India.

Key Features: - Census Data: Detailed population statistics based on census years, including total population, male and female counts, and differences between genders. - Religious Demographics: Information on the population distribution among different religions, along with percentages. - Language Distribution: Data on the number of speakers for various languages in India and their corresponding percentages. - Vital Statistics: Key indicators such as live births, deaths, natural changes, crude birth rates, and total fertility rates. - Age Distribution: Breakdown of the population by age group, including gender-specific counts and percentages.

Purpose: This dataset serves as a valuable resource for researchers, policymakers, and educators interested in understanding the demographic landscape of India. It can be used for various analyses, including population growth trends, gender ratios, and the impact of cultural diversity on the social fabric of the nation.

Four tables of ACS demographic profiles for 2012 to 2016 at the NTA level. Four profiles include demographics, economic, housing and sociological. Column headers in this database are abbreviated. Please see the data dictionary (shown in worksheet entitled “Dictionary”) for an explanation of these abbreviated headers. All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

If you use these data cite the following paper:

- Pappalardo et al., (2019) A public data set of spatio-temporal match events in soccer competitions, Nature Scientific Data 6:236, https://www.nature.com/articles/s41597-019-0247-7

This dataset describes all the soccer teams in seven prominent soccer competitions (Italian, Spanish, German, French and English first divisions, World Cup 2018, European Cup 2016). It consists of the following fields:
- city: the city where the team is located. For national teams it is the capital of the country;
- name: the common name of the team;
- area: information about the geographic area associated with the team;
- wyId: the identifier of the team, assigned by Wyscout;
- officialName: the official name of the team (e.g., Juventus FC);
- type: the type of the team. It is "club" for teams in the competitions for clubs and "national" for the teams in international competitions;

Abbreviation: CCI, charlson comorbidity index.

The sample was drawn by means of the central population register (CPR) of Statistics Sweden. CPR contains basic demographic and social data on every individual born on the 15th of any month, any year, and irrespective of place of birth or place of residence. Thus CPR forms, in effect, a 3.3 probability sample of the entire Swedish population. From CPR were drawn all men born in any of the years 1899, 1902, 1905, and so on, down to and including 1923. Thus there are nine birth cohorts, spaced with three-year intervals. Information about occupation in the present (son's) generation was taken from CPR. The method for gathering information on occupation in the previous (father's) generation was a different one. In CPR parish of birth (if in Sweden) and date of birth is always stated. Consequently every person can be located in the copies of the parish birth registers filed in Stockholm, and in these registers the father's occupation is stated (if the father is known). Other data collected from the CPR: place of birth and current place of residence, marital status, age of the parents, and information on income based on the tax assessments.

The dataset consists of a merged and sorted women DRCDHS dataset 2013/14 and Stata codes used for performing the analyses. It relates to the study entitled "Between Stability and Strain: Unraveling the Links Between Fertility, IPV, and Marital Dynamics in the DRC". This study particularly explores the interplay between fertility, intimate partner violence (IPV), and marital stability in a context marked by high fertility rates, prevalent IPV, and entrenched gender norms. Employing a multilevel probit model, our findings reveal that fertility, relationship quality, and economic independence are key determinants of marital stability. Yet, this stability often conceals dysfunctional relationships that hinder social and economic progress. These insights provide a foundation for targeted policies that strengthen family cohesion and promote women's empowerment in the DRC.

This comprehensive dataset provides detailed population statistics for major cities across Pakistan, spanning multiple census years from 1972 to 2023. The dataset includes population figures for each city as recorded in the 1972, 1981, 1998, 2017, and 2023 censuses, along with the percentage change in population between consecutive censuses. The data is organized by city and province, offering valuable insights into urban growth trends, demographic shifts, and regional development over the past five decades.

Features

• City: Name of the city.

• Pop (2023 Census): Population as per the 2023 census, with percentage change from the 2017 census.

• Pop (2017 Census): Population as per the 2017 census, with percentage change from the 1998 census.

• Pop (1998 Census): Population as per the 1998 census, with percentage change from the 1981 census.

• Pop (1981 Census): The Population as of the 1981 census, with a percentage change from the 1972 census.

• Pop (1972 Census): Population as per the 1972 census.

• Province: The province or administrative region where the city is located.

Potential Use Cases

• Urban Planning: Analyze population growth trends to inform infrastructure development and resource allocation.

• Demographic Studies: Study the demographic changes in different regions of Pakistan over time.

• Policy Making: Support evidence-based policy decisions related to housing, education, healthcare, and transportation.

• Academic Research: Utilize the dataset for research in urban studies, sociology, and economics.

Data Source

This dataset's data was collected and compiled from the Wikipedia page titled "List of cities in Pakistan by population." The information on Wikipedia is based on publicly available census data and government records, which have been aggregated and presented in a structured format. While Wikipedia serves as a secondary source, the original data is derived from official census reports conducted by the Pakistan Bureau of Statistics and other governmental bodies.

Acknowledgments We acknowledge Wikipedia for providing a consolidated and accessible source of information on city-wise population data in Pakistan. Additionally, we extend our gratitude to the Pakistan Bureau of Statistics and other government agencies responsible for conducting and publishing the census data, which forms the foundation of this dataset. Their efforts in collecting and maintaining accurate demographic records have made this dataset possible.

Description:

This comprehensive dataset offers an in-depth look at gun-related deaths in the United States from 2012 to 2014, as reported by the Centers for Disease Control and Prevention (CDC). It provides a rich source of information for public health research, policy development, and sociological studies, offering a nuanced understanding of the dynamics and demographics of gun-related fatalities.

Features:

• Year: The year when the death occurred, providing a temporal context.
• Month: The month of the incident, adding more granularity to the timeline.
• Intent: Categorizes the death by intent, such as suicide or homicide, essential for understanding the circumstances.
• Police: Indicates whether a police officer was involved in the death.
• Sex: The gender of the deceased, crucial for demographic analysis.
• Age: The age of the deceased, providing insights into age-related trends.
• Race: The race of the deceased, essential for understanding racial disparities.
• Place: The location of the incident, such as home or street, which can influence the context of the death.
• Education: Educational background of the deceased, offering a socio-economic dimension.

Use Case:

This dataset is highly valuable for tasks such as public health research, policy formulation in gun control, and sociological studies. It can be employed to analyze trends and patterns in gun-related deaths, assist in crafting informed laws and public safety measures, and provide a foundation for educational and awareness initiatives about gun violence and its impact on different demographic groups.

Note: - Entries are organized chronologically, capturing each recorded incident in detail. - The dataset is especially significant for examining year-on-year trends and demographic variances in gun-related fatalities, serving as a critical resource for comprehensive analysis and research.

Introducing a data set that specifically compares females and males can be done in various ways, depending on the purpose and context of the data set. Here's a general introduction that you can use as a starting point:

Title: Female vs Male Data Set: A Comparative Analysis

Introduction:

The "Female vs Male Data Set" is a comprehensive collection of information that aims to provide insights into the similarities and differences between females and males across various domains. This data set has been curated to facilitate analysis and exploration of characteristics, traits, preferences, and other factors that may vary between the two genders.

Dataset Description:

The Female vs Male Data Set comprises a wide range of data points sourced from diverse fields, including demographics, biology, psychology, sociology, economics, education, and more. It encompasses both quantitative and qualitative data, allowing for statistical analysis as well as qualitative interpretations.

The data set covers a multitude of aspects, such as:

Demographic Information: Age, ethnicity, geographical distribution, and other relevant demographic factors that distinguish females and males.

Physiological and Biological Factors: Biological traits, genetic variations, hormonal differences, and anatomical characteristics that are unique or more prevalent in one gender compared to the other.

Social and Cultural Factors: Gender roles, societal expectations, cultural norms, and their impacts on behavior, relationships, and social dynamics between females and males.

Psychological and Personality Traits: Differences or similarities in personality traits, cognitive abilities, emotional patterns, and psychological attributes between females and males.

Educational and Professional Data: Educational attainment, career choices, employment statistics, wage disparities, and other factors related to education and professional domains.

Health and Wellness: Variances in health outcomes, disease prevalence, risk factors, and responses to treatment between females and males.

Usage and Applications:

The Female vs Male Data Set can be utilized for a wide range of research, analysis, and decision-making purposes. Some potential applications include:

Gender Studies: Conducting in-depth studies on gender differences and gender-related topics. Social Sciences: Exploring the societal impacts of gender and investigating gender inequalities. Marketing and Consumer Behavior: Understanding gender-based preferences and consumption patterns. Health and Medicine: Investigating gender-specific health concerns and developing targeted interventions. Education: Analyzing gender gaps and formulating strategies for educational equality. Policy-making: Informing evidence-based policies and initiatives aimed at gender equity. It's important to note that this data set should be used responsibly and with an understanding that gender is a complex and multifaceted concept. Care should be taken to avoid generalizations and to respect individual variations within each gender.

Disclaimer: The data set does not endorse or perpetuate stereotypes or biases, but rather aims to provide a foundation for further exploration and understanding of gender-related aspects.

By utilizing the Female vs Male Data Set, researchers, analysts, and policymakers can gain valuable insights into the similarities and differences between females and males, leading to a more informed and nuanced understanding of gender dynamics in various fields.

The dataset includes data collected during a nationwide survey conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Kyiv International Institute of Sociology from 6 December 2024 to 9 January 2025.

The survey explores respondents' attitudes towards the balance between freedom, security, material well-being, and the role of the state in managing these aspects. It examines people's willingness to sacrifice their rights for the sake of security or well-being, their views on the economic system and the responsibilities of the state, and the relationship between the state and its citizens. The survey also investigates respondents' opinions on policy priorities, including whether politicians should adhere to the will of the majority or rely on expert opinion. The dataset includes key socio-demographic characteristics of the respondents, such as gender, age, macro-region of residence, type of settlement, education, financial situation, and language of communication.

The survey employed the CAPI method (computer-assisted personal interviewing) to collect responses from 2,580 individuals aged 18 and over, residing in government-controlled areas of Ukraine. The survey did not cover the Donetsk, Sumy, and Kherson regions due to security-related restrictions.

A three-stage random sampling method was used, with quota selection applied at the final stage. The sample structure reflects the demographic composition of the adult population in the surveyed areas as of early 2022, in terms of age, gender, and type of settlement. The theoretical margin of sampling error does not exceed 2.9%, with a confidence level of 95% and a design effect of 1.5.

This dataset contains the original survey data in SPSS (.sav) format, available in both Ukrainian and English. It has also been exported to an Excel file, with the contents of the corresponding XLSX file being identical to the original SAV file. The survey methodology and questionnaire are provided in the Documentation section (PDF files), in both English and Ukrainian. Key findings from the survey, along with a year-on-year comparison, can be found in the Results section (PDF files), also in both English and Ukrainian.

Demographic characteristics of respondents in the eleven countries.

In 2010, cancer deaths accounted for more than 15% of all deaths worldwide, and this fraction is estimated to rise in the coming years. Increased cancer mortality has been observed in immigrant populations, but a comprehensive analysis by country of birth has not been conducted. We followed all individuals living in Sweden between 1961 and 2009 (7,109,327 men and 6,958,714 women), and calculated crude cancer mortality rates and age-standardized rates (ASRs) using the world population for standardization. We observed a downward trend in all-site ASRs over the past two decades in men regardless of country of birth but no such trend was found in women. All-site cancer mortality increased with decreasing levels of education regardless of sex and country of birth (p for trend

Abbreviation: SSRI, selective serotonin reuptake inhibitor; 95% CI, 95% confidence interval.*Definition 3.3 in Table 1 is used.†Information of death in the insurer’s enrollment data is used.‡Adjusted for age and sex by Cox regression model.

Overview of differences in modelling approach and datasets used by UN IGME 2012 and UN IGME 2013, for estimating the U5MR and the number of under-five deaths.

Mortality rate ratios (MRRs) are adjusted for age at follow-up and calendar period at baseline. The reference group is Sweden-born men. MRR values significantly different from 1.0 are highlighted in bold.*Continents, regions, and countries with at least five cases of cancer mortality.aThe former Czechoslovakia includes Czechoslovakia, Slovakia, and the Czech Republic.bThe former Soviet Union includes Belarus, Moldova, Russian Federation, Soviet Union, and Ukraine.cThe former Yugoslavia includes Yugoslavia, Croatia, Macedonia, Serbia, Slovenia, and Montenegro.