In 2023, malignant neoplasms were the leading cause of death among the male population in Japan at around ***** thousand cases. This number accounted for approximately **** percent of about ***** thousand death cases of men recorded in the country during that year. Heart diseases, excluding hypertensive, followed with a share of around ** percent. Malignant neoplasmsIn recent years, malignant neoplasms have been the leading cause of death for both female and male populations in Japan. The most frequent cause of cancerous tumor related deaths has continued to be lung cancer for both men and women. As smoking and passive smoking are some of the main causes of lung cancer, the health ministry in Japan set the goal of reducing the smoking rate of adults from around ** to ** percent by 2022. To minimize the risk of passive smoking, the government also amended the Health Promotion Act and prohibited smoking in public facilities, offices, most restaurants, and public areas starting from April 2020. SuicideOne of the leading causes of death specific to men in Japan was suicide. In the last decade, the number of suicides committed by men in Japan remained roughly double the number of those committed by women. While close to half of the suicides in Japan were committed due to health reasons in previous years, the number of suicides owning to work-related problems has also become a serious social issue in the current Japanese society. One of the reason behind it is said to be the working condition of employees in Japan with a severe workload. The government has been aiming to reduce working hours and overtime to improve the working conditions of workers in Japan.

Heart disease is currently the leading cause of death in the United States. In 2022, COVID-19 was the fourth leading cause of death in the United States, accounting for almost six percent of all deaths that year. The leading causes of death worldwide are similar to those in the United States. However, diarrheal diseases and neonatal conditions are major causes of death worldwide, but are not among the leading causes in the United States. Instead, accidents and chronic liver disease have a larger impact in the United States.

Racial differences

In the United States, there exist slight differences in leading causes of death depending on race and ethnicity. For example, assault, or homicide, accounts for around three percent of all deaths among the Black population but is not even among the leading causes of death for other races and ethnicities. However, heart disease and cancer are still the leading causes of death for all races and ethnicities.

Leading causes of death among men vs women

Similarly, there are also differences in the leading causes of death in the U.S. between men and women. For example, among men, intentional self-harm accounts for around two percent of all deaths but is not among the leading causes of death among women. On the other hand, influenza and pneumonia account for more deaths among women than men.

BackgroundOur current understanding of Asian American mortality patterns has been distorted by the historical aggregation of diverse Asian subgroups on death certificates, masking important differences in the leading causes of death across subgroups. In this analysis, we aim to fill an important knowledge gap in Asian American health by reporting leading causes of mortality by disaggregated Asian American subgroups.Methods and FindingsWe examined national mortality records for the six largest Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) and non-Hispanic Whites (NHWs) from 2003-2011, and ranked the leading causes of death. We calculated all-cause and cause-specific age-adjusted rates, temporal trends with annual percent changes, and rate ratios by race/ethnicity and sex. Rankings revealed that as an aggregated group, cancer was the leading cause of death for Asian Americans. When disaggregated, there was notable heterogeneity. Among women, cancer was the leading cause of death for every group except Asian Indians. In men, cancer was the leading cause of death among Chinese, Korean, and Vietnamese men, while heart disease was the leading cause of death among Asian Indians, Filipino and Japanese men. The proportion of death due to heart disease for Asian Indian males was nearly double that of cancer (31% vs. 18%). Temporal trends showed increased mortality of cancer and diabetes in Asian Indians and Vietnamese; increased stroke mortality in Asian Indians; increased suicide mortality in Koreans; and increased mortality from Alzheimer’s disease for all racial/ethnic groups from 2003-2011. All-cause rate ratios revealed that overall mortality is lower in Asian Americans compared to NHWs.ConclusionsOur findings show heterogeneity in the leading causes of death among Asian American subgroups. Additional research should focus on culturally competent and cost-effective approaches to prevent and treat specific diseases among these growing diverse populations.

In 2021, approximately ****** male Filipinos succumbed to death due to ischaemic heart diseases. Ischaemic heart disease was also the leading cause of death among female population in the Philippines.

The leading causes of death among the white population of the United States are cardiovascular diseases and cancer. Cardiovascular diseases and cancer accounted for a combined **** percent of all deaths among this population in 2022. In 2020 and 2021, COVID-19 was the third leading cause of death among white people. Disparities in causes of death In the United States, there exist disparities in the leading causes of death based on race and ethnicity. For example, chronic liver disease and cirrhosis is the ***** leading cause of death among the white population and the ****** among the Hispanic population, but is not among the ten leading causes for Black people. On the other hand, homicide is the ******* leading cause of death among the Black population, but is not among the 10 leading causes for whites or Hispanics. However, cardiovascular diseases and cancer by far account for the highest share of deaths for every race and ethnicity. Diseases of despair The American Indian and Alaska Native population in the United States has the highest rates of death from suicide, drug overdose, and alcohol. Together, these three behavior-related conditions are often referred to as diseases of despair. Asians have by far the lowest rates of death due to drug overdose and alcohol, as well as slightly lower rates of suicide.

This dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

United States US: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 6.000 Ratio in 2017. This records a decrease from the previous number of 6.200 Ratio for 2015. United States US: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 6.800 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 10.400 Ratio in 1990 and a record low of 6.000 Ratio in 2017. United States US: Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Azerbaijan Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 14.200 Ratio in 2023. This records a decrease from the previous number of 15.000 Ratio for 2022. Azerbaijan Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 53.000 Ratio from Dec 1982 (Median) to 2023, with 42 observations. The data reached an all-time high of 89.800 Ratio in 1982 and a record low of 14.200 Ratio in 2023. Azerbaijan Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Social: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Saudi Arabia SA: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 11.900 Ratio in 2016. This records a decrease from the previous number of 12.300 Ratio for 2015. Saudi Arabia SA: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 14.500 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 38.300 Ratio in 1990 and a record low of 11.900 Ratio in 2016. Saudi Arabia SA: Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

African American males in the United States are much more likely to die from homicide than white males. In 2016, the death rate by homicide for African American males was ** per 100,000 population, compared to a rate of just *** per 100,000 population for white males. African American males are twice as likely to die from firearm-related injuries than white males, with handguns involved in the largest share of homicides in the U.S. Homicide as a leading cause of death While the leading causes of death for black and white residents in the U.S. are similar in many ways, there are two distinct differences. Homicide is not in the leading 10 causes of death among whites, but it is the ******* leading cause of death for blacks, accounting for around ***** percent of all deaths in this group. However, suicide is the ***** leading cause of death among whites, while it is not included in the ** leading causes of death for blacks. Death rates Overall, the death rate in the United States is higher among non-Hispanic whites than any other ethnicity. Furthermore, males across all ethnicities in the U.S. have higher death rates than females. The *** leading causes of death for every ethnicity in the U.S. are cancer and heart disease.

About this dataset

Context

The dataset tabulates the population of United States by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for United States. The dataset can be utilized to understand the population distribution of United States by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in United States. 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 United States.

Key observations

Largest age group (population): Male # 30-34 years (11.65 million) | Female # 30-34 years (11.41 million). 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 United States population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the United States is shown in the following column.
• Population (Female): The female population in the United States 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 United States 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 United States Population by Gender. You can refer the same here

The leading causes of death in the United States have changed significantly from the year 1900 to the present. Leading causes of death in 1900, such as tuberculosis, gastrointestinal infections, and diphtheria have seen huge decreases in death rates and are no longer among the leading causes of death in the United States. However, other diseases such as heart disease and cancer have seen increased death rates. Vaccinations One major factor contributing to the decrease in death rates for many diseases since the year 1900 is the introduction of vaccinations. The decrease seen in the rates of death due to pneumonia and influenza is a prime example of this. In 1900, pneumonia and influenza were the leading causes of death, with around *** deaths per 100,000 population. However, in 2023 pneumonia and influenza were not even among the ten leading causes of death. Cancer One disease that has seen a large increase in death rates since 1900 is cancer. Cancer currently accounts for almost ** percent of all deaths in the United States, with death rates among men higher than those for women. The deadliest form of cancer for both men and women is cancer of the lung and bronchus. Some of the most common avoidable risk factors for cancer include smoking, drinking alcohol, sun exposure, and obesity.

This dataset displays data from the 2005 Census of Japan. It displays data on Aged Single Persons throughout prefectures in Japan. This dataset specifically breaks up the age ranges into 6 different categories (65 and older, 65-69, 70-74, 75-79, 80-84 85 and older) for males, females, and both sexes. This data comes from Japan's Ministry of Internal Affairs and Communication's Statistics Bureau.

THIS DATASET WAS LAST UPDATED AT 8:11 AM EASTERN ON SEPT. 22

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

About this dataset

Context

The dataset tabulates the population of Round Top by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Round Top. The dataset can be utilized to understand the population distribution of Round Top by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Round Top. 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 Round Top.

Key observations

Largest age group (population): Male # 25-29 years (15) | Female # 50-54 years (13). 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

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 Round Top population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Round Top is shown in the following column.
• Population (Female): The female population in the Round Top 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 Round Top 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 Round Top Population by Gender. You can refer the same here

Theoretically, males should increase their ejaculate expenditure when the probability of sperm competition occurring (or risk) is high but decrease ejaculate expenditure as the number of competing ejaculates (or intensity) increases. Here we examine whether male decorated crickets (Gryllodes sigillatus) use cuticular hydrocarbons (CHCs) transferred to females by rival males at mating to assess the risk and intensity of sperm competition and adjust their ejaculate accordingly. Unmated females and those perfumed with CHCs extracted from one, three or five males could be distinguished chemically, providing a reliable cue of the risk and intensity of sperm competition. In agreement with theory, males mating to these females increased sperm number with the risk of sperm competition and decreased sperm number with the intensity of sperm competition. Similarly, as the risk of sperm competition increased, males produced a larger and more attractive spermatophylax (an important non-sperm component of the ejaculate) but there these traits did not vary with the intensity of sperm competition. Our results therefore demonstrate that both sperm and non-sperm components of the male ejaculate respond to the risk and intensity of sperm competition in different ways and that CHCs provide males with an important cue to strategically tailor their ejaculate. This publications consists of two experiments. In Experiment 1, we perfumed virgin females with the cuticular hydrocarbons (CHCs) of one male, three males, five males and measured their CHC profile using GC-MS. We also measured CHCs in a sample of virgin males and virgin females. We used discriminant function analysis to show that the CHC profile of crickets in these treatment groups could be statistically distinguished and therefore provide a male cricket with information on the risk and intensity of sperm competition. In Experiment 2, we applied these same perfuming procedures (no perfuming - control, one male, three males and five males) to virgin females and then allowed them to mate to a naive and virgin male. For each male, we measure their sperm count (using microscopy), dry weight of the spermatophylax (using an electronic balance) and the multivariate attractiveness of the spermatophylax based on the free amino acid composition (using GC-MS).

Description of the data and file structure Microsoft Excel is required to open the data files. We have uploaded 2 files: named "Dryad_Experiment 1" and "Dryad_Experiment 2".

"Dryad_Experiment 1" contains all of the raw data for Experiment 1. The first column ("Treatment") contains the treatment group (virgin_female, virgin_male, one_rival, three rivals and five rivals). Column 2 ("Code") contains the unique numerical code used in a discriminant function analysis. Columns 3 to 17 ("Peaks 1 to 15", each peak is the area under the peak that characterizes one chemical (CHC)) contain the abundance of each CHC peak (full names provided in Table 1 of the manuscript). Columns 18 to 20 ("DF1, DF2 and DF3" - discriminate functions do not have units) contain the saved discriminant functions (with eigenvalues exceeding 1) on these 15 CHC peaks.

"Dryad_Experiment 2" contains all of the raw data for Experiment 1. The first column ("Treatment) contains the treatment group (control, one_rival, three_rivals and five_rivals). The second column ("PW") contains the pronotum width of males (measured in mm using an eyepiece graticule in a dissecting microscope). The third column ("Sperm_count") contains the number of sperm contained in the male's ampulla (measured using a compound microscope). The fourth column ("SPHYLAX_DW") contains the dry weight of the spermatophylax (ng, measured after freeze drying using an electronic balance). The final column ("SPHYLAX_Attractiveness") is the multivariate attractiveness score of the spermatophylax (there are no units of measurement as it is derived from a vector of linear selection gradients) based on the free amino acid composition (determined using GC-MS).

Sharing/Access information Links to other publicly accessible locations of the data:

The data contained on Dryad will be also be made publically available via the Research Data Management System at Western Sydney University.

Data was derived from the following sources:

All uploaded data has been collected from experiments conducted by the authors.

Code/Software R code is provided for the permutation based MANCOVA. All other analyses were conducted using IBM SPSS (version 29.0.0.0).

In Experiment 1, we collected the cuticular hydrocarbon (CHC) profiles of virgin females that had been perfumed with the CHCs of one male, three males and five males, as well as virgin males and virgin females that were not perfumed. We determined the CHC of crickets in these treatments using established GC-MS protocols for this species (Gryllodes sigillatus). This experiment showed that the CHC profile of crickets in these treatments could be statistically distinguished and therefore provide a reliable cue for the risk and intensity of sperm competition.

In Experiment 2, we applied these same perfuming treatments to females (virgin female - control, one male, three males and five males) and then allowed them to mate with a naive virgin male. We measured each males sperm count using microscopy. We also measure the dry weight of the spermatophylax (using an electronic balance) and the attractiveness of the spermatophylax based on the free amino acid composition. We determined the free amino acid composition of the spermaotphylax using established GC-MS protocols for this species.

Extreme differences between the sexes are usually explained by intense sexual selection on male weapons or ornaments. Sexually antagonistic genes, with a positive effect on male traits but a negative effect on female fitness, create a negative inter-sexual correlation for fitness (sexual conflict). However, such antagonism might not be apparent if sexually selected male traits are condition-dependent, and condition elevates female fitness. Here we reveal a surprising positive genetic correlation between male weaponry and female fecundity. Using mite lines that had previously been through 13 generations of selection on male weapons (fighting legs), we investigated correlated evolution in female fecundity. Females from lines under positive selection for weapons (up lines) evolved higher fecundity, despite evolving costly, thicker legs. This is likely because male mites have condition-dependent weaponry that increases our ability to indirectly select on male condition. Alleles with positive effects on condition in both sexes could have generated this correlation because: the up lines evolved a higher proportion of fighters and there were positive correlations between weapon size and the male morph and sex ratios of the offspring. This positive inter-sexual genetic correlation should boost the evolution of male weapons and extreme sex differences.,This file has two tabs. The first one contains the fecundity data (number of eggs laid in 10 days) for females after 13 generations of artificial selection applied on the fighter legs of fighter males of the mite Rhizoglyphus echinopus. Each row contains information for one female mated to two scrambler males, and the columns contain information about (respectively from left to right): replicate selection line; selection direction (up for thicker legs and down for thinner legs); the ID of the line (replicate and direction combined); family ID (the family from which each female was derived in the previous generation); and the number of eggs laid by this female. The other tab has information about sex and morph ratio through the first 9 generations of selection. Each row has information on the offspring of selected sires in each generation, and the columns contain information about (respectively from left to right): generation (1 to 9); replicate selection line; selection direction (as described above); the ID of the line (as described above); the ID of the particular sire; number of fighter males in that sire's offspring, number of females in that sire's offspring, number of scrambler males in that sire's offspring, number of 'intermorph' males (rare males with one scrambler and one fighter leg — they are counted as males for 'sex ratio' but ignored in the information for 'morph ratio') in that sire's offspring; and then descriptive stats based on these numbers (total of adults, total of males, sex ratio and morph ratio).,

This dataset displays data from the 2005 Census of Japan. It displays male population by age, selected age ranges, percentages of age ranges, average average, and median age in the selected prefectures in Japan for the year 2005. Only 30 of the 47 prectures were displayed in the data source. There are also 2 other datasets that break this data up by total and female figures. This data comes from Japan's Ministry of Internal Affairs and Communication's Statistics Bureau.

Israel IL: Mortality Rate: Infant: Male: per 1000 Live Births data was reported at 3.000 Ratio in 2017. This records a decrease from the previous number of 3.200 Ratio for 2015. Israel IL: Mortality Rate: Infant: Male: per 1000 Live Births data is updated yearly, averaging 3.900 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 10.300 Ratio in 1990 and a record low of 3.000 Ratio in 2017. Israel IL: Mortality Rate: Infant: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Israel – Table IL.World Bank: Health Statistics. Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Life expectancy (in years) at birth: Total (Men & Women) in select countries Null value ".." changed to -1