As of the third quarter of 2024, the majority of Reddit users were male, accounting for 59.8 percent of its audience base. Overall, women accounted for roughly 39.1 percent of the website users. Additionally, most of Reddit's desktop users were based in the United States.

As of June 2024, 28 percent of male respondents in the United States stated that they used Reddit, compared to 20 percent of their female counterpart. Reddit is a social networking and online forum company. The platform is organized in thematic groups, also called subreddits.

There are three files in this fileset: 1. An r script to produce 3 charts and examine some very basic information about the data. 2. A file with 3 charts that the r script produces. 3. The dataset used by the r script. The charts are outtakes (+ 1 extra) from a market analysis the InnoRenew CoE performed. These show the age and gender demographics of Slovenia's forest sector and it's sub-sectors along with a few related sectors.

The charts make it easy to see the gender imbalance, and the age makeup of the Slovenia's forest sector. All sub-sectors are heavily skewed towards male workers. The age breakdown for the overall forest sector (in our analysis this includes primary wood production, paper, furniture, civil engineering and construction, and architecture -- this clearly includes non-wood activities, but that's okay for our purposes) is fairly balanced. However, most of these young workers are isolated in architecture, while other forest sector fields are very skewed towards older workers. If companies have a hard time time recruiting younger, new workers, they will face a labour shortages in the near future as their current labour force begins to retire.

PANDORA is the first large-scale dataset of Reddit comments labeled with three personality models (including the well-established Big 5 model) and demographics (age, gender, and location) for more than 10k users.

This statistic shows the share of individuals who used Reddit in Sweden in Q3 2020, by age group. The age group with the largest share of users were 16 to 25 year olds, where ** percent of internet users used Reddit.

These files provide anonymized source data and tabular data on gender, age, and country of corresponding authors (submitting author) of American Geophysical Union (AGU) journals from January 2018 through June 2020. These datasets supplement an iposter presented at Japan Geosciences Union- American Geophysical Union joint 2020 meeting and supplement the corresponding preprint submission to ESSOAR.

Transport for NSW provides projections of population and dwellings at the small area (Travel Zone or TZ) level for NSW. The latest version is Travel Zone Projections 2024 (TZP24), released in January 2025.\r \r TZP24 replaces the previously published TZP22.\r \r The projections are developed to support a strategic view of NSW and are aligned with the NSW Government Common Planning Assumptions .\r \r The TZP24 Population & Dwellings Projections dataset covers the following variables:\r \r * Estimated Resident Population\r \r * Structural Private Dwellings (Regional NSW only)\r \r * Population in Occupied Private Dwellings, by 5-year Age categories & by Sex\r \r * Population in Non-Private Dwellings\r \r The projections in this release, TZP24, are presented annually from 2021 to 2031 and 5-yearly from 2031 to 2066, and are in TZ21 geography.\r \r Please note, TZP24 is based on best available data as at early 2024, and the projections incorporate results of the National Census conducted by the ABS in August 2021.\r \r Key Data Inputs used in TZP24:\r \r * 2024 NSW Population Projections – NSW Department of Planning, Housing & Infrastructure\r \r * 2021 Census data - Australian Bureau of Statistics (including dwellings by occupancy, total dwellings by Mesh Block, household sizes, private dwellings by occupancy, population age and gender, persons by place of usual residence)\r \r For a summary of the TZP24 projection method please refer to the TZP24 Factsheet .\r \r For more detail on the projection process please refer to the TZP24 Technical Guide . \r \r Additional land use information for workforce and employment as well as Travel Zone 2021 boundaries for NSW (TZ21) and concordance files are also available for download on the Open Data Hub.\r \r Visualisations of the population projections are available on the Transport for NSW Website under Data and research/Reference Information .\r \r Cautions\r \r The TZP24 dataset represents one view of the future aligned with the NSW Government Common Planning Assumptions and population and employment projections.\r \r The projections are not based on specific assumptions about future new transport infrastructure but do take into account known land-use developments underway or planned, and strategic plans.\r \r *\tTZP24 is a strategic state-wide dataset and caution should be exercised when considering results at detailed breakdowns.\r \r *\tThe TZP24 outputs represent a point in time set of projections (as at early 2024).\r \r *\tThe projections are not government targets.\r \r *\tTravel Zone (TZ) level outputs are projections only and should be used as a guide. As with all small area data, aggregating of travel zone projections to higher geographies leads to more robust results.\r \r *\tAs a general rule, TZ-level projections are illustrative of a possible future only.\r \r *\tMore specific advice about data reliability for the specific variables projected is provided in the “Read Me” page of the Excel format summary spreadsheets on the TfNSW Open Data Hub.\r \r *\tCaution is advised when comparing TZP24 with the previous set of projections (TZP22) due to addition of new data sources for the most recent years, and adjustments to methodology.\r \r Further cautions and notes can be found in the TZP24 Technical Guide\r \r Important note: \r \r The Department of Planning, Housing & Infrastructure (DPHI) published the 2024 NSW Population Projections in November 2024. As per DPHI’s published projections, the following variables are excluded from the published TZP24 Population and Dwellings Projections:\r \r *\tStructural Private Dwellings for Travel Zones in 43 councils across Greater Sydney, Illawarra-Shoalhaven, Central Coast, Lower Hunter and Greater Newcastle\r \r *\tOccupied Private Dwellings for Travel Zones in NSW.\r \r Furthermore, in TZP24, the Structural Private Dwellings variable aligns with the 2024 Implied Dwelling projections while the Occupied Private Dwellings variable aligns with the 2024 Households projections at SA2 level prepared by DPHI.\r \r The above variables are available upon request by contacting model.selection@transport.nsw.gov.au - Attention Place Forecasting.

PLA2G7 gene product is a secreted enzyme whose activity is associated with coronary heart disease (CHD). The goal of our study is to investigate the contribution of PLA2G7 promoter DNA methylation to the risk of CHD. Using the bisulphite pyrosequencing technology, PLA2G7 methylation was measured among 36 CHD cases and 36 well-matched controls. Our results indicated that there was a significant association between PLA2G7 methylation and CHD (adjusted P = 0.025). Significant gender-specific correlation was observed between age and PLA2G7 methylation (males: adjusted r = −0.365, adjusted P = 0.037; females: adjusted r = 0.373, adjusted P = 0.035). A breakdown analysis by gender showed that PLA2G7 methylation was significantly associated with CHD in females (adjusted P = 0.003) but not in males. A further two-way ANOVA analysis showed there was a significant interaction between gender and status of CHD for PLA2G7 methylation (gender*CHD: P = 6.04E−7). Moreover, PLA2G7 methylation is associated with the levels of total cholesterols (TC, r = 0.462, P = 0.009), triglyceride (TG, r = 0.414, P = 0.02) and Apolipoprotein B (ApoB, r = 0.396, P = 0.028) in females but not in males (adjusted P>0.4). Receiver operating characteristic (ROC) curves showed that PLA2G7 methylation could predict the risk of CHD in females (area under curve (AUC) = 0.912, P = 2.40E−5). Our results suggest that PLA2G7 methylation changes with aging in a gender-specific pattern. The correlation between PLA2G7 methylation and CHD risk in females is independent of other parameters including age, smoking, diabetes and hypertension. PLA2G7 methylation might exert its effects on the risk of CHD by regulating the levels of TC, TG, and ApoB in females. The gender disparities in the PLA2G7 methylation may play a role in the molecular mechanisms underlying the pathophysiology of CHD.

Table of INEBase Most important care received from the main caregiver by gender and disability group. Population six years and over with a disability that receives care. National. Disability, Independence and Dependency Situations Survey

Table of INEBase Difficulties encountered by the main caregiver by gender and number of disabilities. Population six years and over with a disability that receives care. National. Disability, Independence and Dependency Situations Survey

LivWell is a global longitudinal database which provides a range of key indicators related to women’s socioeconomic status, health and well-being, access to basic services, and demographic outcomes. Data are available at the sub-national level for 52 countries and 447 regions. A total of 134 indicators are based on 199 Demographic and Health Surveys for the period 1990-2019, supplemented by extensive information on socioeconomic and climatic conditions in the respective regions for a total of 190 indicators. The resulting data offer various opportunities for policy-relevant research on gender inequality, inclusive development, and demographic trends at the sub-national level.

For a full description, please refer to the article describing the database here: https://www.nature.com/articles/s41597-022-01824-2

The companion repository livwelldata allows to easily use the database in R. The R package can be downloaded following the instructions on the following git repository: https://gitlab.pik-potsdam.de/belmin/livwelldata. The version of the database in the package is the same as in this repository.

The sixth cycle of the Ithaka S+R Faculty Survey queried a random sample of higher education faculty members in the United States to learn about their attitudes and practices related to research, teaching, and communicating. This survey cycle is the first to include medical faculty. Respondents were asked about resource discovery and access; research topics and practices; research dissemination, including data management and preservation; instruction and perceptions of student research skills; and the role and value of the academic library. Demographic variables include the respondent's age, gender, primary academic field, how many years the respondent has worked at his or her current college or university, how many years the respondent has worked in his or her field, and whether the respondent primarily identifies as a researcher, teacher, or somewhere in between.

This dataset contains information about the number and percentage of managers by gender and age, 15 years and over (2006-07 and 2016-17).\r \r (a) Data was calculated as an average of four quarters (August, November, February, May) in the financial year.\r \t\t\t\t\t\r (b) Occupation is classified according to the ABS Australian and New Zealand Standard Classification of Occupations (ANZSCO), 2006 (cat. no. 1220.0).\t\r \r (c) Until recently, ABS policy has been to revise benchmarks for labour force data on a five-yearly basis following final rebasing of population estimates to the latest Census of Population and Housing data. However, labour force population benchmarks are now updated more frequently when preliminary population estimates become available, and again when these preliminary estimates are subsequently revised. For this release of Gender Indicators, Australia, labour force estimates dating back to (and including) 2014-15 have been revised in accordance with this new benchmarking process. Future revisions to benchmarks will then take place every time a new year of labour force data becomes available for publishing in the Gender Indicators publication. Re-benchmarking historical data has not resulted in any material change to unemployment rates, participation rates or employment to population ratios. For more information see ABS Labour Force, Australia, Jun 2016 (cat. no. 6202.0).\t\t\t\t\t\r Source: ABS data available on request, Labour Force Survey\t\t\t\t\t\r

The DSS Payment Demographic data set is made up of:\r \r Selected DSS payment data by \r \r * Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)\r \r * Demographic: age, sex and Indigenous/non-Indigenous \r \r * Duration on Payment (Working Age & Pensions)\r \r * Duration on Income Support (Working Age, Carer payment & Disability Support Pension)\r \r * Rate (Working Age & Pensions)\r \r * Earnings (Working Age & Pensions)\r \r * Age Pension assets data \r \r * JobSeeker Payment and Youth Allowance (other) Principal Carers\r \r * Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)\r \r * Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)\r \r * Disability Support Pension by medical condition\r \r * Care Receiver by medical conditions\r \r * Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.\r \r From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment.\r The expanded report will replace the standard report after June 2023.\r \r Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:\r \r • A new contents page to assist users locate the information within the spreadsheet\r \r • Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.\r \r • Additional information on the Employment Earning by ‘Income Free Area’ tab.\r \r \r From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. \r To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change.\r The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.\r \r \r Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.\r \r Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.\r \r SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023. \r \r From December 2021, the following are included in the report:\r \r * selected payments by work capacity, by various demographic breakdowns\r \r * rental type and homeownership\r \r * Family Tax Benefit recipients and children by payment type\r \r * Commonwealth Rent Assistance by proportion eligible for the maximum rate\r \r * an age breakdown for Age Pension recipients\r \r For further information, please see the Glossary.\r \r From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information. \r \r From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.\r \r From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:\r \r * Statistical Area 2 - SA2\r \r * Commonwealth Electoral Division - CED\r \r * Local Government Area - LGA\r \r Pre June 2014 Quarter Data contains:\r \r Selected DSS payment data by \r \r * Geography: state/territory; electorate; postcode and LGA\r \r * Demographic: age, sex and Indigenous/non-Indigenous \r \r Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment\r \r For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. \r Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below: \r \r * Statistical Paper series\r \r Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.\r \r 28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.

Enrolments by student age in SA Government schools from 2013, collected as part of the annual enrolment data collection in Term 3. Student age is as at July 1.\r \r The data provides a breakdown by gender, full-time equivalent enrolments, enrolments by persons, primary and secondary schooling.\r \r Note: in 2014 the Same First Day Policy was implemented. All children are enrolled at the beginning of Term 1 guaranteeing four terms of reception. This led to a reduction in 4 years olds enrolled from 2014.

Data Notes:\r \r * The data only includes students learning a language on average for more than 1 hour per week for 35 or more weeks a year.\r \r * Includes students studying a language through the Secondary College of Languages (formerly Saturday School of Community Languages).\r \r * In 2021, the Language Participation Collection for Years 7-9 students was moved from August to May.\r \r * Programs in Languages other than English for Years K-6 and the Language Participation for Years 7-9 data collections were not conducted in 2022, in line with the department’s commitment to “clear the decks” for schools in Term 2 2022.\r \r Data Source:\r \r * Schools and Students: Statistical Bulletin . Centre for Education Statistics and Evaluation.\r \r

Gender of insolvent debtors is an annual publication. AFSA use the data on gender provided by debtors on the Statement of Affairs. \r \r More information about the data is available from http://www.afsa.gov.au/resources/statistics/socio-economic-statistics/gender/guide-to-gender-of-insolvent-debtors.

This study examines the radicalization of Western women to extremist violence, both through the creation of a moral-situational-action risk model and the examination of their responses to various types of online propaganda. The Moral-Situational-Action Risk Model for Extremist Violence (MSA-RMEV) was developed using situational action theory from criminology and violence risk practice literature. The MSA-RMEV revolves around three domains reflective of propensity, mobilization, and capacity building, geared towards providing a violence risk assessment that can assist the intelligence community in preventing future acts of violence. A sample of women who self-identified as conservative, liberal, and Muslim were exposed to jihadist, alt-right, and alt-left online propaganda. Physiological responses and self-report assessments were recorded. Eye-gaze, pupil dilation, galvanic skin response, heart rate, and facial emotions were documented, along with women's judgment of their emotional, cognitive, and arousal states, while viewing propaganda. Based on their results, women were categorized as high-risk, medium-risk, or low-risk for violence. Additionally, numerous variables were created to identify participant's beliefs and behavior related to radicalization. Beliefs included religiosity, political affiliation, the presence of moral emotions, sacred values, developmental maturity, and militant thinking. Behaviors included group affiliations, extent of involvement in extremist activities, and presence on social media platforms such as Facebook, Reddit, and Twitter. Demographic variables such as age, marital status, number of children, race, ethnicity, country of origin, and educational status were included.

This dataset details Long Term Unemployment in Wales by local authority.

List of student's achievement in the learning area of Maths by gender and year level.\r \r This dataset is no longer being updated. For more information about Learning Outcomes go to https://qed.qld.gov.au/publications/reports/statistics/schooling/learning-outcomes