Over the past 24 years, there were constantly more men than women living on the planet. Of the 8.06 billion people living on the Earth in 2024, 4.09 billion were men and 4.05 billion were women. One-quarter of the world's total population in 2024 was below 15 years.

About this dataset

Context

The dataset tabulates the population of England by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for England. The dataset can be utilized to understand the population distribution of England by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in England. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for England.

Key observations

Largest age group (population): Male # 40-44 years (154) | Female # 0-4 years (183). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the England population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the England is shown in the following column.
• Population (Female): The female population in the England is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in England for each age group.

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 England Population by Gender. You can refer the same here

Worldwide, the male population is slightly higher than the female population, although this varies by country. As of 2024, Hong Kong has the highest share of women worldwide with almost ** percent. Moldova followed behind with around ** percent. Among the countries with the largest share of women in the total population, several were former Soviet states or were located in Eastern Europe. By contrast, Qatar, the United Arab Emirates, and Oman had some of the highest proportions of men in their populations.

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.

About this dataset

Context

The dataset tabulates the population of Tennessee by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Tennessee across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.93% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Tennessee is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Tennessee 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 Tennessee Population by Race & Ethnicity. You can refer the same here

In 1950, when Estonia's population was estimated at 1.1 million people, approximately 57 percent of the population was female, while 43 percent was male; this equated to a difference of more than 160,000 people. In the past century, as with many former-Soviet states, Estonia has consistently had one of the most disproportionate gender ratios in the world. The reason for this was due to the large number of men who were killed in wars during the first half of the twentieth century, which was particularly high across the Soviet Union, as well as a much higher life expectancy among women. The difference in the number of men and women in Estonia has gradually decreased over the past seven decades, but in 2020, there are still 70,000 more females than males, in a population of 1.3 million people; this equates to total shares of roughly 53 percent and 47 percent of the total population respectively.

About this dataset

Context

The dataset tabulates the population of Ontario by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Ontario. The dataset can be utilized to understand the population distribution of Ontario by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Ontario. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Ontario.

Key observations

Largest age group (population): Male # 30-34 years (7,947) | Female # 25-29 years (8,143). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Ontario population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Ontario is shown in the following column.
• Population (Female): The female population in the Ontario is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Ontario for each age group.

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 Ontario Population by Gender. You can refer the same here

As of February 2025, 50.2 percent of social media users in the United States were women, and 49.8 percent of users were men. In 2024, there were an estimated 304 million social media users in the country.

Actual value and historical data chart for United States Population Female Percent Of Total

The gender or sex ratio in China has been a contentious issue since the introduction of the one-child policy in 1979, intended to limit the population of the country. Although the policy is no longer in place, the population gender difference throughout the country is still evident. In 2023, fifteen to nineteen-year-old children had the largest gender disparity of 115.3 males to every 100 females. Gender imbalance While the difference of gender at birth has been decreasing in the country over the past decade, China still boasts the world’s most skewed sex ratio at birth at around 110 males born for every 100 females as of 2023. That means there are about 31 million more men in the country than women. This imbalance likely came from the country’s traditional preference for male children to continue the family lineage, in combination with the population control policies enforced. Where does that leave the population? The surplus of young, single men across the country poses a risk for China in many different socio-economic areas. Some of the roll-on effects include males overrepresenting specific labor markets, savings rates increasing, consumption reducing and violent crime increasing across the country. However, the adult mortality rate in China, that is, the probability of a 15-year-old dying before reaching age 60, was significantly higher for men than for women. For the Chinese population over 60 years of age, the gender ratio is in favor of women, with more females outliving their male counterparts.

Actual value and historical data chart for World Population Female Percent Of Total

About this dataset

Context

The dataset tabulates the population of Cleveland by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Cleveland. The dataset can be utilized to understand the population distribution of Cleveland by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Cleveland. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Cleveland.

Key observations

Largest age group (population): Male # 25-29 years (16,343) | Female # 25-29 years (16,950). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Cleveland population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Cleveland is shown in the following column.
• Population (Female): The female population in the Cleveland is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Cleveland for each age group.

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 Cleveland Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Connecticut by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Connecticut. The dataset can be utilized to understand the population distribution of Connecticut by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Connecticut. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Connecticut.

Key observations

Largest age group (population): Male # 60-64 years (126,340) | Female # 55-59 years (134,487). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Connecticut population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Connecticut is shown in the following column.
• Population (Female): The female population in the Connecticut is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Connecticut for each age group.

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 Connecticut Population by Gender. You can refer the same here

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Context

The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men. There are two distinct numbers regarding the pay gap: non-adjusted versus adjusted pay gap. The latter typically takes into account differences in hours worked, occupations were chosen, education, and job experience. In the United States, for example, the non-adjusted average female's annual salary is 79% of the average male salary, compared to 95% for the adjusted average salary.

The reasons link to legal, social, and economic factors, and extend beyond "equal pay for equal work".

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

This dataset aims to replicate the data used in the famous paper "The Gender Wage Gap: Extent, Trends, and Explanations", which provides new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during the 1980–2010 period.

Citation

fedesoriano. (January 2022). Gender Pay Gap Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/gender-pay-gap-dataset.

Content

There are 2 files in this dataset: a) the Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, and b) the Current Population Survey (CPS) to provide some additional US national data on the gender pay gap.

PSID variables:

NOTES: THE VARIABLES WITH fz ADDED TO THEIR NAME REFER TO EXPERIENCE WHERE WE HAVE FILLED IN SOME ZEROS IN THE MISSING PSID YEARS WITH DATA FROM THE RESPONDENTS’ ANSWERS TO QUESTIONS ABOUT JOBS WORKED ON DURING THESE MISSING YEARS. THE fz variables WERE USED IN THE REGRESSION ANALYSES THE VARIABLES WITH A predict PREFIX REFER TO THE COMPUTATION OF ACTUAL EXPERIENCE ACCUMULATED DURING THE YEARS IN WHICH THE PSID DID NOT SURVEY THE RESPONDENTS. THERE ARE MORE PREDICTED EXPERIENCE LEVELS THAT ARE NEEDED TO IMPUTE EXPERIENCE IN THE MISSING YEARS IN SOME CASES. NOTE THAT THE VARIABLES yrsexpf, yrsexpfsz, etc., INCLUDE THESE COMPUTATIONS, SO THAT IF YOU WANT TO USE FULL TIME OR PART TIME EXPERIENCE, YOU DON’T NEED TO ADD THESE PREDICT VARIABLES IN. THEY ARE INCLUDED IN THE DATA SET TO ILLUSTRATE THE RESULTS OF THE COMPUTATION PROCESS. THE VARIABLES WITH AN orig PREFIX ARE THE ORIGINAL PSID VARIABLES. THESE HAVE BEEN PROCESSED AND IN SOME CASES RENAMED FOR CONVENIENCE. THE hd SUFFIX MEANS THAT THE VARIABLE REFERS TO THE HEAD OF THE FAMILY, AND THE wf SUFFIX MEANS THAT IT REFERS TO THE WIFE OR FEMALE COHABITOR IF THERE IS ONE. AS SHOWN IN THE ACCOMPANYING REGRESSION PROGRAM, THESE orig VARIABLES AREN’T USED DIRECTLY IN THE REGRESSIONS. THERE ARE MORE OF THE ORIGINAL PSID VARIABLES, WHICH WERE USED TO CONSTRUCT THE VARIABLES USED IN THE REGRESSIONS. HD MEANS HEAD AND WF MEANS WIFE OR FEMALE COHABITOR.

1. intnum68: 1968 INTERVIEW NUMBER
2. pernum68: PERSON NUMBER 68
3. wave: Current Wave of the PSID
4. sex: gender SEX OF INDIVIDUAL (1=male, 2=female)
5. intnum: Wave-specific Interview Number
6. farminc: Farm Income
7. region: regLab Region of Current Interview
8. famwgt: this is the PSID’s family weight, which is used in all analyses
9. relhead: ER34103L this is the relation to the head of household (10=head; 20=legally married wife; 22=cohabiting partner)
10. age: Age
11. employed: ER34116L Whether or not employed or on temp leave (everyone gets a 1 for this variable, since our wage analyses use only the currently employed)
12. sch: schLbl Highest Year of Schooling
13. annhrs: Annual Hours Worked
14. annlabinc: Annual Labor Income
15. occ: 3 Digit Occupation 2000 codes
16. ind: 3 Digit Industry 2000 codes
17. white: White, nonhispanic dummy variable
18. black: Black, nonhispanic dummy variable
19. hisp: Hispanic dummy variable
20. othrace: Other Race dummy variable
21. degree: degreeLbl Agent's Degree Status (0=no college degree; 1=bachelor’s without advanced degree; 2=advanced degree)
22. degupd: degreeLbl Agent's Degree Status (Updated with 2009 values)
23. schupd: schLbl Schooling (updated years of schooling)
24. annwks: Annual Weeks Worked
25. unjob: unJobLbl Union Coverage dummy variable
26. usualhrwk: Usual Hrs Worked Per Week
27. labincbus: Labor Income from...

In 2020, the male-female ratio expressed as men per 100 women in Mexico amounted to approximately *****. Between 1995 and 2020, the figure dropped by around ****, though the decline followed an uneven course rather than a steady trajectory.

Proportion of women and men employed in the National Occupational Classification (NOC) broad occupational categories, current year.

About this dataset

Context

The dataset tabulates the population of Palm Beach County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Palm Beach County. The dataset can be utilized to understand the population distribution of Palm Beach County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Palm Beach County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Palm Beach County.

Key observations

Largest age group (population): Male # 55-59 years (50,401) | Female # 60-64 years (53,567). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Palm Beach County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Palm Beach County is shown in the following column.
• Population (Female): The female population in the Palm Beach County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Palm Beach County for each age group.

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 Palm Beach County Population by Gender. You can refer the same here

As of October 2025, micro-blogging platform X (formerly Twitter) was more popular with men than women, with male audiences accounting for 64.4 percent of global users. Additionally, users between the ages of 25 and 34 were particularly active on X/Twitter, making up more than 37 percent of users worldwide. How many people use? Although X/Twitter holds its status as a mainstream social media site, it falls short in comparison to other well-known platforms in terms of user numbers. As of early 2022, X/Twitter had around 436 million monthly active users, whilst Meta’s Facebook reached almost three billion MAU. Overall, the United States is home to over 105 million X/Twitter users, making up Twitter’s largest audience base, followed by Japan, India, and the United Kingdom, respectively. How is Twitter used? X/Twitter is utilized by its audience for many different purposes. In May 2021, over 80 percent of high-volume X/Twitter users (defined as users who tweet around 20 times per month) in the United States reported using the platform for entertainment, whilst 78 percent said they used it as a way to stay informed. High-volume X/Twitter users were far more likely to use the service as a means of expressing their opinion. Furthermore, in 2022, over half of social media users in the U.S. used Twitter as a news resource.  

In 1889 Romania had just achieved full independence and was enjoying a period of stability and progress. The numbers of men and women remain similar, between 1889 and 1912, however there are 100,000 more men in 1889 and 100,000 more women in 1919. This demographic change came as a result of migration and international conflict.

Between 1912 and 1930 the population of Romania grows by over 10 million people. The main reason for this is the huge territories gained by Romania in the aftermath of the First World War. During the war Romania remained neutral for the first two years, after which it joined the allies, however it was very quickly defeated and overrun by the Central Powers, and in total it lost over 600,000 people as a direct result of the war. With the collapse of the Austro-Hungarian and Russian empires after the war, Romania gained almost double it's territory, which caused the population to soar to 18.1 million in 1930. The population then decreases by 1941 and again by1948, as Romania seceded territory to neighboring countries and lost approximately half a million lives (mostly men) during the war. From 1948 onwards the population begins to grow again, and the difference in the number of men and women falls from 530 thousand in 1948 to 330 thousand in 1990.

Like many other Eastern European countries, there was very limited freedom of movement from Romania during the Cold War, and communist rule was difficult for the Romanian people. The Romanian Revolution in 1898 ended communist rule in the country and the country transitioned to a free-market society and movement from the country was allowed. Since then the population has fallen each year as more and more Romanians move abroad in search of work and opportunities. The number of men is decreasing at a faster rate than the number of women, and by 2020 it is forecasted there will be 530 thousand more women in Romania than men.

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Medical Masks Dataset dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

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