As of the second quarter of 2025, the majority of Reddit users were male, accounting for ** percent of its audience base. 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.

Why?

Last year a redditor created a survey to collect demographic data on the subreddit /r/ForeverAlone. Since then they have deleted their account but they left behind their data set.

Columns are below:

Content

• Timestamp

DateTime

• What is your Gender?

String

• What is your sexual orientation?

String

• How old are you?

DateTime

• What is your level of income?

DateTime

• What is your race?

String

• How would you describe your body/weight?

String

• Are you a virgin?

String

• Is prostitution legal where you live?

String

• Would you pay for sex?

String

• How many friends do you have IRL?

DateTime

• Do you have social anxiety/phobia?

String

• Are you depressed?

String

• What kind of help do you want from others? (Choose all that apply)

String

• Have you attempted suicide?

String

• Employment Status: Are you currently…?

String

• What is your job title?

String

• What is your level of education?

String

• What have you done to try and improve yourself? (Check all that apply)

Reddit: /r/Tinder

Examining User Behaviors and Attitudes

By Reddit [source]

About this dataset

This dataset provides an in-depth exploration of the world of online dating, based on data mined from Reddit's Tinder subreddit. Through analysis of the six columns titled title, score, id, url, comms_num and created (which include information such as social norms and user behaviors related to online dating), this dataset can teach us valuable insights into how people are engaging with digital media and their attitudes towards it. Unveiling potential dangers such as safety risks and scams that can arise from online dating activities is also possible with this data. Its findings are paramount for anyone interested in understanding how relationships develop on a digital platform – both for researchers uncovering the sociotechnical aspects of online dating behavior and for companies seeking further insight into their user's perspectives. All in all, this dataset might just hold all the missing pieces to understanding our current relationship dynamic!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive overview of online dating trends and behaviors observed on Reddit's Tinder subreddit. This data can be used to analyze user opinions, investigate user experiences, and discover online dating trends. To utilize this dataset effectively, there are several steps an individual can take to gain insights from the data:

Research Ideas

• Using the dataset to examine how online dating trends vary geographically and by demographics (gender, age, race etc.)
• Analyzing the language used in posts for insights into user attitudes towards online dating.
• Creating a machine learning model to predict a post's score based on its title, body and other features of the data set can help digital media companies better target their marketing efforts towards more successful posts on Tinder subreddits

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Tinder.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The body of the post. (String) | | timestamp | The timestamp of the post. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Discover truly valuable life tips shared by real humans.

About the Dataset:

Reddit is a treasure trove of genuine life experiences from millions of people. Subreddits like r/lifeProTips and r/YouShouldKnow are well-known for containing some of the best and most practical tips that anyone can apply to their life.

This dataset is a cleaned version of the split reddit dump by u/Watchful1.

Each row in the dataset contains a helpful life tip.

Interesting Task Ideas:

1. Develop a web app that presents users with an interesting tip each day.
2. Explore the data to determine the most popular types of tips.
3. Build a recommendation system that suggests relevant tips based on specific life situations or topics.
4. Develop AI-powered models that generate useful life tips using the examples in the dataset.
5. Analyze popular life topics and their corresponding tips to uncover patterns and common themes.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

Gender Wage Gap in the USA

USA Hispanic-White Wage Gap Dataset

USA Unemployment Rates by Demographics & Race

USA Wage Comparison for College vs. High School

Employment-to-Population Ratio for USA

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.

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.

In the data you can find the age and gender as demographics for 439 participants among Syrian Refugees living in Gaziantep province of Turkey. then you can find M1, M2, M3,....M21 for each item of SCAS-R Social adaptation scale.

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

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.

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

Agencies responsible for the administration and delivery of social security, family assistance, student assistance and related payments and programs publish a range of statistical information online. Relevant agencies include: \r
\r •\tDepartment of Human Services\r
\r •\tDepartment of Social Services \r
\r •\tDepartment of Education and Training\r
\r •\tDepartment of Health \r
\r •\tDepartment of Veterans’ Affairs. \r
\r Published statistical information generally encompasses customer population data for key payments, with a pre-defined drill-down available for relevant demographic (e.g. gender, age) and geographic (e.g. national, state/territory) characteristics.The Department of Human Services also publishes a comprehensive suite of health related statistical information. \r
\r This dataset provides a summary of links to existing sources of statistical information published on a range of government websites, including those noted above and the Australian Institute of Health and Welfare (AIHW). The AIHW provides authoritative information and statistics to promote better health and wellbeing.\r
\r Humanservices.gov.au contains information about accessing statistical information. If not available online, you can request statistical information by emailing statistics@humanservices.gov.au. In providing this service, the Department of Human Services liaises with relevant agencies as required.

The "Expanded Jobseeker Payment and Youth Allowance - monthly profile" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment 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 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 JobSeeker Payment and Youth Allowance (other) recipients in the monthly profile includes:\r \r •\tA monthly time series by rate of payment, providing details of recipients who are current on payment and in receipt of a full, part or zero rate of payment, and those who are suspended from payment (table 2)\r \r •\tBy work capacity status, showing those who have a partial capacity to work and those who have full capacity (table 7)\r \r •\tBy payment duration (table 8)\r \r The “JobSeeker Payment and Youth Allowance recipients – monthly profile” is a monthly report, covering the Income Support payments of JobSeeker Payment and Youth Allowance (other). It also includes data on Youth Allowance (student and apprentice), Sickness Allowance and Bereavement Allowance. The report includes payment recipient numbers by demographics such as age, gender, state, earnings and Statistical Area Level 2.\r

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.

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

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.

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.

Context

This dataset looks at the demographics of World of Warcraft players–gender, age, sexuality, etc.–and how they play the game–role, race, class–to see if there is an association between any of them. Specifically, I was interested in how gender and sexuality affects the gender of the character they play, but there are many other things to look at. This data was gathered through a Google Forms survey, which was then posted on Reddit, Tumblr, Twitter, and my WoW guild's Discord server.

Content

There are 14 columns, 100 rows (not including the titles). 12 of those columns were gathered from a Google Forms survey, and the last two were added by hand. They are:

• Timestamp: Useless. Just when the survey was completed.
• Gender: The gender of the player.
• Sexuality: The sexuality of the player.
• Age: Age of the player.
• Country: Country the player lives in.
• Main: The gender of character the player mains
• Faction: The faction the player mains.
• Server: The server(s) the player mains.
• Role: The role(s) the player mains (DPS, Healer, Tank, or any combination therein).
• Class: The class(es) the player mains. This was a question where the respondent check any number of boxes, so there are many different ways it could be answered. Hard to analyze.
• Race: The race(s) the player mains. Same as class.
• Max: The number of 110s (max level) the player has. Only numerical variable in the dataset.
• Attracted: The gender the player is attracted to.
• Type: The "type" of person the player is. Combines gender and sexuality ("gay woman", "bi male", etc.)

Acknowledgements

This dataset belongs to me. I created the survey and compiled the data. However, I would like to thank stormwind-keep on Tumblr and earth2gem on Twitter for helping me get the survey out to a broader audience.

Inspiration

I already ran a bunch of my own analyses using R, but I could not find a good way to analyze the Class and Race variables. If anyone can figure that one out, please do.

This survey aims to assess the perceptions of EU citizens about gender-based violence. Among others, it explores the following topics: • Opinions about and attitudes towards gender-based violence; • Perceptions of the prevalence of domestic violence and sexual harassment; • Personal knowledge of a victim of domestic violence, and to whom people speak in the case of knowledge of domestic violence; • Whether a range of acts of gender-based violence are wrong and are, or should be, illegal.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Name: Data used to rate the relevance of each dimension necessary for a Holistic Environmental Policy Assessment. Summary: This dataset contains answers from a panel of experts and the public to rate the relevance of each dimension on a scale of 0 (Nor relevant at all) to 100 (Extremely relevant). License: CC-BY-SA Acknowledge: These data have been collected in the framework of the DECIPHER project. This project has received funding from the European Union’s Horizon Europe programme under grant agreement No. 101056898. Disclaimer: Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Collection Date: 2024-1 / 2024-04 Publication Date: 22/04/2025 DOI: 10.5281/zenodo.13909413 Other repositories: - Author: University of Deusto Objective of collection: This data was originally collected to prioritise the dimensions to be further used for Environmental Policy Assessment and IAMs enlarged scope. Description: Data Files (CSV) decipher-public.csv : Public participants' general survey results in the framework of the Decipher project, including socio demographic characteristics and overall perception of each dimension necessary for a Holistic Environmental Policy Assessment. decipher-risk.csv : Contains individual survey responses regarding prioritisation of dimensions in risk situations. Includes demographic and opinion data from a targeted sample. decipher-experts.csv : Experts’ opinions collected on risk topics through surveys in the framework of Decipher Project, targeting professionals in relevant fields. decipher-modelers.csv: Answers given by the developers of models about the characteristics of the models and dimensions covered by them. prolific_export_risk.csv : Exported survey data from Prolific, focusing specifically on ratings in risk situations. Includes response times, demographic details, and survey metadata. prolific_export_public_{1,2}.csv : Public survey exports from Prolific, gathering prioritisation of dimensions necessary for environmental policy assessment. curated.csv : Final cleaned and harmonized dataset combining multiple survey sources. Designed for direct statistical analysis with standardized variable names. Scripts files (R) decipher-modelers.R: Script to assess the answers given modelers about the characteristics of the models. joint.R: Script to clean and joint the RAW answers from the different surveys to retrieve overall perception of each dimension necessary for a Holistic Environmental Policy Assessment. Report Files decipher-modelers.pdf: Diagram with the result of the full-Country.html : Full interactive report showing dimension prioritisation broken down by participant country. full-Gender.html : Visualization report displaying differences in dimension prioritisation by gender. full-Education.html : Detailed breakdown of dimension prioritisation results based on education level. full-Work.html : Report focusing on participant occupational categories and associated dimension prioritisation. full-Income.html : Analysis report showing how income level correlates with dimension prioritisation. full-PS.html : Report analyzing Political Sensitivity scores across all participants. full-type.html : Visualization report comparing participant dimensions prioritisation (public vs experts) in normal and risk situations. full-joint-Country.html : Joint analysis report integrating multiple dimensions of country-based dimension prioritisation in normal and risk situations. Combines demographic and response patterns. full-joint-Gender.html : Combined gender-based analysis across datasets, exploring intersections of demographic factors and dimensions prioritisation in normal and risk situations. full-joint-Education.html : Education-focused report merging various datasets to show consistent or divergent patterns of dimensions prioritisation in normal and risk awareness. full-joint-Work.html : Cross-dataset analysis of occupational groups and their dimensions prioritisation in normal and risk situation full-joint-Income.html : Income-stratified joint analysis, merging public and expert datasets to find common trends and significant differences during dimensions prioritisation in normal and risks situations. full-joint-PS.html : Comprehensive Political Sensitivity score report from merged datasets, highlighting general patterns and subgroup variations in normal and risk situations. 5 star: ⭐⭐⭐ Preprocessing steps: The data has been re-coded and cleaned using the scripts provided. Reuse: NA Update policy: No more updates are planned. Ethics and legal aspects: Names of the persons involved have been removed. Technical aspects: Other: