The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

About Violence Against Women & Girls

The Demographic and Health Surveys (DHS) Program exists to advance the global understanding of health and population trends in developing countries.

The UN describes violence against women and girls (VAWG) as: “One of the most widespread, persistent, and devastating human rights violations in our world today. It remains largely unreported due to the impunity, silence, stigma, and shame surrounding it.”

In general terms, it manifests itself in physical, sexual, and psychological forms, encompassing: • intimate partner violence (battering, psychological abuse, marital rape, femicide) • sexual violence and harassment (rape, forced sexual acts, unwanted sexual advances, child sexual abuse, forced marriage, street harassment, stalking, cyber-harassment), human trafficking (slavery, sexual exploitation) • female genital mutilation • child marriage

About The Data

The data was taken from a survey of men and women in African, Asian, and South American countries, exploring the attitudes and perceived justifications given for committing acts of violence against women. The data also explores different sociodemographic groups that the respondents belong to, including: Education Level, Marital status, Employment, and Age group.

It is, therefore, critical that the countries where these views are widespread, prioritize public awareness campaigns, and access to education for women and girls, to communicate that violence against women and girls is never acceptable or justifiable.

Question | Respondents were asked if they agreed with the following statements: - A husband is justified in hitting or beating his wife if she burns the food - A husband is justified in hitting or beating his wife if she argues with him - A husband is justified in hitting or beating his wife if she goes out without telling him - A husband is justified in hitting or beating his wife if she neglects the children - A husband is justified in hitting or beating his wife if she refuses to have sex with him - A husband is justified in hitting or beating his wife for at least one specific reason

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By Bastian Herre, Pablo Arriagada, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell and Max Roser.

About dataset:

Women’s rights are human rights that all women have. But in practice, these rights are often not protected to the same extent as the rights of men.

Among others, women’s rights include: physical integrity rights, such as being free from violence and making choices over their own body; social rights, such as going to school and participating in public life; economic rights, such as owning property, working a job of their choice, and being paid equally for it; and political rights, such as voting for and holding public office.

The protection of these rights allows women to live the lives they want and to thrive in them.

On this page, you can find data on how the protection of women’s rights has changed over time, and how it differs across countries.

There are 6 dataset in here.

1- Female to male ratio of time devoted to unpaid care work. 2- Share of women in top income groups atkinson casarico voitchovsky 2018. 3- Ratio of female to male labor force participation rates ilo wdi. 4- Female to male ratio of time devoted to unpaid care work. 5- Maternal mortality 6- Gender gap in average wages ilo

In each one, there are some topics and variables that we can analysis and visualize them.

Excel Age-Range creator for Office for National Statistics (ONS) Mid year population estimates (MYE) covering each year between 1999 and 2016

These files take into account the revised estimates for 2002-2010 released in April 2013 down to Local Authority level and the post 2011 estimates based on the Census results. Scotland and Northern Ireland data has not been revised, so Great Britain and United Kingdom totals comprise the original data for these plus revised England and Wales figures.

This Excel based tool enables users to query the single year of age raw data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Simply select the lower and upper age range for both males and females and the spreadsheet will return the total population for the range. Please adhere to the terms and conditions of supply contained within the file.

Tip: You can copy and paste the rows you are interested in to another worksheet by using the filters at the top of the columns and then select all by pressing Ctrl+A. Then simply copy and paste the cells to a new location.

ONS Mid year population estimates

Open Excel tool (London Boroughs, Regions and National, 1999-2016)

Also available is a custom-age tool for all geographies in the UK. Open the tool for all UK geographies (local authority and above) for: 2010, 2011, 2012, 2013, 2014 and 2015.

This full MYE dataset by single year of age (SYA) age and gender is available as a Datastore package here.

Ward Level Population estimates

Single year of age population tool for 2002 to 2015 for all wards in London.

New 2014 Ward boundary estimates

Ward boundary changes in May 2014 only affected three London boroughs - Hackney, Kensington and Chelsea, and Tower Hamlets. The estimates between 2001-2013 have been calculated by the GLA by taking the proportion of a the old ward that falls within the new ward based on the proportion of population living in each area at the 2011 Census. Therefore, these estimates are purely indicative and are not official statistics and not endorsed by ONS. From 2014 onwards, ONS began publishing official estimates for the new ward boundaries. Download here.

Worldpop Human 2020 population by Age and Gender.

Abstract: Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males and all females as well as total populations. Filenames are are follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = minimum age; OO= Maximum age

The study of human body shape using classical anthropometric techniques is often problematic due to several error sources. Instead, 3D models and representations provide more accurate registrations, which are stable across acquisitions, and enable more precise, systematic, and fast measuring capabilities. Thus, the same person can be scanned several times and precise differential measurements can be established in an accurate manner. Here we present {\tt 3DPatBody}, a dataset including 3D body scans, with their corresponding 3D point clouds and anthropometric measurements, from a sample of a Patagonian population (female=211, male=87, other=1). The sample is of scientific interest since it is representative of a phenotype characterized by both its biomedical meaning as a descriptor of overweight and obesity, and its population-specific nature related to ancestry and/or local environmental factors. The acquired 3D models were used to compare shape variables against classical anthropometric data. The shape indicators proved to be accurate predictors of classical indices, also adding geometric characteristics that reflect more properly the shape of the body under study.

Age-specific (per year) numbers of human females, males, mortality and fertility rates. Ages 1-100. Number and mortalities from WHO-CHOICE project (www.who.int/choice) and fertility derived from U.S. Census Bureau International Data Base (www.census.gov/population/international/data/idb).

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Introduction

This dataset simulates realistic daily activity patterns of a large synthetic population. It was generated to model how people distribute their time across various activities (e.g., sleeping, working, commuting, eating, shopping, socializing). The design incorporates demographic attributes such as age, employment status, family situation, and urban/rural setting, ensuring variability and realism.

The dataset is valuable for applications such as: - Smart city planning (e.g., transport demand, energy usage) - Behavioral simulations (agent-based models, epidemiological spread) - AI/ML training for time-use prediction, recommendation, or optimization tasks - Visualization of population-level daily rhythms

Dataset Structure

The dataset is split into three main components:

people.csv - Contains demographic and household attributes of the synthetic population (12,000 individuals). - Columns: - person_id: Unique identifier - age: Age in years - gender: male / female / other - employment: e.g., employed_full_time, student, retired - remote_worker: Boolean flag for remote-capable workers - shift_worker: Boolean flag for irregular work hours - has_children: Boolean, parent or not - pet_owner: Boolean - city_type: urban / suburban / rural

activities_hourly_full.csv - Hourly-level activity records for each individual across 14 consecutive days. - ~4.0 million rows total. - Columns: - person_id: Link to people.csv - date: Calendar date - day_of_week: 0=Monday, …, 6=Sunda - hour: 0–23 - activity: Activity category (sleep, work, school, commute, eating, leisure, etc.) - location: home, workplace, school, transport, store, gym, outdoors, bar/restaurant, other

hourly_distribution.csv - Aggregated counts of activities by hour-of-day across the entire population. - Useful for checking overall diurnal patterns (e.g., sleep peaks at night, commuting in the morning/evening).

Activity Categories

The dataset includes a broad set of 20 activity types, such as: sleep, work, school, commute, shopping, sport, socializing, leisure, housework, childcare, personal_care, eating, study, healthcare, religious, volunteering, travel, errands, gardening, pet_care.

Each activity is linked to a location category (e.g., sleep → home, work → workplace/remote, commute → transport).

Realism and Modeling Choices

• Sleep patterns vary by age and shift-work status.
• Work & school occur mainly on weekdays, with commutes before/after (unless remote).
• Meals appear at culturally realistic times and can override work/school slots.
• Weekends see more shopping, chores, leisure, and socializing.
• Demographics influence routines:
• Parents have morning/evening childcare.
• Students study evenings.
• Pet owners walk pets.
• Suburban/rural individuals spend more time gardening.

Usage Notes

The dataset is synthetic: no real individuals are represented.

Acknowledgement

Foto von Maria Krasnova auf Unsplash

Subjective sleep quality (mean ± 1 SD) of elderly participants (N = 1992) inhabiting the rural areas of the Sambalpur district and comparison thereof.

Necessary files to reproduce Boehnke and Gay. 2022. "The Missing Men. World War I and Female Labor Force Participation." Journal of Human Resources, 57(4).

HPV: human papillomavirus; O: observed; E: expected; CI: credible interval.aAdjusted for age, type of sample, and type-specific HPV prevalence;bAsa plus sample random effects.

Human cooperation in large groups and between non-kin individuals remains a Darwinian puzzle. Investigations into whether and how sexual selection is involved in the evolution of cooperation represent a new and important research direction. Here, 69 groups of four men or four women recruited from a rural population in Senegal played a sequential public-good game in the presence of out-group observers, either of the same sex or of the opposite sex. At the end of the game, participants could donate part of their gain to the village school in the presence of the same observers. Both contributions to the public good and donations to the school, which reflect different components of cooperativeness, were influenced by the sex of the observers. The results suggest that in this non-Western population, sexual selection acts mainly on men’s cooperative behaviour with non-kin, whereas women’s cooperativeness is mainly influenced by nonsexual social selection.

The dataset contains the following indicators :

Table 1a - Population size Population, total and by sex (in thousands) Sex ratio (women/100 men) Table 1b - Composition of the population Percentage of total population under 15 years Percentage of total population aged 60 years and above, by sex Sex ratio in 60+ age group (men/100 women) Table 1c - Population growth and distribution Annual population growth rate Percentage urban population Sex ratio (women/100 men) of international migrants

Table 2a - Life expectancy Life expectancy at birth, by sex Life expectancy at age 60, by sex Table 2b - Maternal mortality and infant mortality Maternal mortality ratio Infant mortality rate Under 5 mortality rate Table 2c - Child-bearing Adolescent fertility rate Total fertility rate Table 2d - Contraceptive prevalence Contraceptive prevalence among married women of childbearing age, any method and modern method Table 2e - HIV/AIDS Estimated number of adults living with HIV/AIDS Women's share of adults living with HIV/AIDS

Table 3a – Persons per room Average number of persons per room by urban/rural area Table 3b – Human settlements Population distribution (%) by urban/rural area Annual rate of population change (%) by urban/rural area Table 3c– Water supply and sanitation Improved Drinking Water Coverage (%) by urban/rural area Improved Sanitation Coverage (%) y urban/rural area

Table 4a - Literacy Adult (15+) literacy rate, by sex Youth (15-24) literacy rate, by sex Table 4b - Primary education Primary net enrolment ratio, by sex Girl's share of primary enrolment Table 4c - Secondary education Secondary net enrolment ratio, by sex Girl's share of secondary enrolment Table 4d - Tertiary education Tertiary gross enrolment ratio, by sex Women's share of tertiary enrolment Table 4e – School life expectancy School life expectancy (primary to tertiary education) by sex

Table 5a – Income and economic activity Adult (15+) economic activity rate, by sex Per capita GDP (US dollars) Table 5b - Part-time employment Percentage of adult employment that is part-time, by sex Women's share of part-time employment Table 5c - Distribution of labour force by status in employment Percentage employees, by sex Percentage employers, by sex Percentage own-account workers, by sex Percentage contributing family workers, by sex Table 5d - Adult unemployment Unemployment rate, by sex

Computed prospective ages for 1950-2100 for all countries and regions based on 2017 Revision of the UN World Population Prospects.Content:1. codebook.pdf contains a brief overview of the dataset, its background and a description of the cases and variables.2. methods.pdf is a (draft but complete) write up of the calculations used to create the dataset.3. 2017_prospective-ages.csv is the human readable form of the prospective age dataset containing the calculated prospective old-age thresholds for 241 countries and regions, for the period 1950-2100, for men, women and both together, as well as the proportions of the population (male, female and total) over these thresholds.This figshare fileset is published directly from the github repository ProspectiveAgeData. For an application of this data see the factsheet on ageing in the Middle East and Northern Africa which will be published in Population Horizons journal.

The arrival of agriculture into Europe during the Neolithic transition brought a significant shift in human lifestyle and subsistence. However, the conditions under which the spread of the new culture and technologies occurred are still debated. Similarly, the roles played by women and men during the Neolithic transition are not well understood, probably due to the fact that mitochondrial DNA (mtDNA) and Y chromosome (NRY) data are usually studied independently rather than within the same statistical framework. Here, we applied an integrative approach, using different model-based inferential techniques, to analyse published datasets from contemporary and ancient European populations. By integrating mtDNA and NRY data into the same admixture approach, we show that both males and females underwent the same admixture history and both support the demic diffusion model of Ammerman and Cavalli-Sforza. Similarly, the patterns of genetic diversity found in extant and ancient populations demonstrate that both modern and ancient mtDNA support the demic diffusion model. They also show that population structure and differential growth between farmers and hunter-gatherers are necessary to explain both types of data. However, we also found some differences between male and female markers, suggesting that the female effective population size was larger than that of the males, probably due to different demographic histories. We argue that these differences are most probably related to the various shifts in cultural practices and lifestyles that followed the Neolithic Transition, such as sedentism, the shift from polygyny to monogamy or the increase of patrilocality.

The data used in this paper are the number of deaths and exposure to risk, which can be obtained directly from the Human Mortality Database. It is provided for both genders, male and female as well as the total population. The data is presented by single age ranging from 0 to 109, and age 110+ denotes those at higher ages for a particular year.

BackgroundA comprehensive analysis of sex-specific differences in the characteristics, treatment, and outcomes of individuals with end-stage renal disease undergoing dialysis might reveal treatment inequalities and targets to improve sex-specific patient care. Here we describe hemodialysis prevalence and patient characteristics by sex, compare the adult male-to-female mortality rate with data from the general population, and evaluate sex interactions with mortality.Methods and FindingsWe assessed the Human Mortality Database and 206,374 patients receiving hemodialysis from 12 countries (Australia, Belgium, Canada, France, Germany, Italy, Japan, New Zealand, Spain, Sweden, the UK, and the US) participating in the international, prospective Dialysis Outcomes and Practice Patterns Study (DOPPS) between June 1996 and March 2012. Among 35,964 sampled DOPPS patients with full data collection, we studied patient characteristics (descriptively) and mortality (via Cox regression) by sex. In all age groups, more men than women were on hemodialysis (59% versus 41% overall), with large differences observed between countries. The average estimated glomerular filtration rate at hemodialysis initiation was higher in men than women. The male-to-female mortality rate ratio in the general population varied from 1.5 to 2.6 for age groups

This table summarizes the observed patterns of sex-specific differences in demographic parameters reported in a number of recent studies. The first column lists the location of the sampled populations, or indicates whether the study is conducted at a global scale. The second column gives the markers used, and the third column indicates the statistical methods employed. The fourth column provides indications on social organization, available a priori for the populations under study. In the fifth and sixth columns, the authors' interpretations of sex-specific differences in demographic parameters are given, with respect to skewed gene flow and/or effective numbers.aIndications on social organization, marriage rules, etc., as provided by the authors.bThe differences in demographic parameters between males and females, as inferred by the authors, are given in terms of sex-biased gene flow, and skewed effective numbers; the authors' interpretation to the observed pattern is given in parentheses, when available.cSingle nucleotide polymorphisms.dAnalysis of molecular variance [69].eNot available (no detailed information given by the authors concerning social organization, marriage rules, etc.).fShort tandem repeats.gTime to the most recent common ancestor.hmtDNA and NRY were not sampled in the same individuals or populations.iThe authors discussed a possible difference in demographic parameters between males and females, but considered it as negligible.jThe authors did not consider this pattern.kFood-producer populations.lHunter-gatherer populations.mMonte Carlo Markov chain method to estimate population sizes and migration rates [70].nVariance in Reproductive Success.opopulation-mutation parameter.

The polygenic risk score (PRS) is calculated as the weighted sum of an individual’s genotypes and their estimated effect sizes, which is often used to estimate an individual’s genetic susceptibility to complex traits and disorders. It is well known that some complex human traits or disorders have sex differences in trait distributions, disease onset, progression, and treatment response, although the underlying mechanisms causing these sex differences remain largely unknown. PRSs for these traits are often based on Genome-Wide Association Studies (GWAS) data with both male and female samples included, ignoring sex differences. In this study, we present a benchmark study using both simulations with various combinations of genetic correlation and sample size ratios between sexes and real data to investigate whether combining sex-specific PRSs can outperform sex-agnostic PRSs on traits showing sex differences. We consider two types of PRS models in our study: single-population PRS models (PRScs, LDpred2) and multiple-population PRS models (PRScsx). For each trait or disorder, the candidate PRSs were calculated based on sex-specific GWAS data and sex-agnostic GWAS data. The simulation results show that applying LDpred2 or PRScsx to sex-specific GWAS data and then combining sex-specific PRSs leads to the highest prediction accuracy when the genetic correlation between sexes is low and the sample sizes for both sexes are balanced and large. Otherwise, the PRS generated by applying LDpred2 or PRScs to sex-agnostic GWAS data is more appropriate. If the sample sizes between sexes are not too small and very unbalanced, combining LDpred2-based sex-specific PRSs to predict on the sex with a larger sample size and combining PRScsx-based sex-specific PRSs to predict on the sex with a smaller size are the preferred strategies. For real data, we considered 19 traits from Genetic Investigation of ANthropometric Traits (GIANT) consortium studies and UK Biobank with both sex-specific GWAS data and sex-agnostic GWAS data. We found that for waist-to-hip ratio (WHR) related traits, accounting for sex differences and incorporating information from the opposite sex could help improve PRS prediction accuracy. Taken together, our findings in this study provide guidance on how to calculate the best PRS for sex-differentiated traits or disorders, especially as the sample size of GWASs grows in the future.