11 datasets found
  1. Global Country Information Dataset 2023

    • kaggle.com
    zip
    Updated Jul 8, 2023
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    Nidula Elgiriyewithana ⚡ (2023). Global Country Information Dataset 2023 [Dataset]. https://www.kaggle.com/datasets/nelgiriyewithana/countries-of-the-world-2023
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    zip(24063 bytes)Available download formats
    Dataset updated
    Jul 8, 2023
    Authors
    Nidula Elgiriyewithana ⚡
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Description

    This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

    DOI

    Key Features

    • Country: Name of the country.
    • Density (P/Km2): Population density measured in persons per square kilometer.
    • Abbreviation: Abbreviation or code representing the country.
    • Agricultural Land (%): Percentage of land area used for agricultural purposes.
    • Land Area (Km2): Total land area of the country in square kilometers.
    • Armed Forces Size: Size of the armed forces in the country.
    • Birth Rate: Number of births per 1,000 population per year.
    • Calling Code: International calling code for the country.
    • Capital/Major City: Name of the capital or major city.
    • CO2 Emissions: Carbon dioxide emissions in tons.
    • CPI: Consumer Price Index, a measure of inflation and purchasing power.
    • CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
    • Currency_Code: Currency code used in the country.
    • Fertility Rate: Average number of children born to a woman during her lifetime.
    • Forested Area (%): Percentage of land area covered by forests.
    • Gasoline_Price: Price of gasoline per liter in local currency.
    • GDP: Gross Domestic Product, the total value of goods and services produced in the country.
    • Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
    • Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
    • Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
    • Largest City: Name of the country's largest city.
    • Life Expectancy: Average number of years a newborn is expected to live.
    • Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
    • Minimum Wage: Minimum wage level in local currency.
    • Official Language: Official language(s) spoken in the country.
    • Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
    • Physicians per Thousand: Number of physicians per thousand people.
    • Population: Total population of the country.
    • Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
    • Tax Revenue (%): Tax revenue as a percentage of GDP.
    • Total Tax Rate: Overall tax burden as a percentage of commercial profits.
    • Unemployment Rate: Percentage of the labor force that is unemployed.
    • Urban Population: Percentage of the population living in urban areas.
    • Latitude: Latitude coordinate of the country's location.
    • Longitude: Longitude coordinate of the country's location.

    Potential Use Cases

    • Analyze population density and land area to study spatial distribution patterns.
    • Investigate the relationship between agricultural land and food security.
    • Examine carbon dioxide emissions and their impact on climate change.
    • Explore correlations between economic indicators such as GDP and various socio-economic factors.
    • Investigate educational enrollment rates and their implications for human capital development.
    • Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
    • Study labor market dynamics through indicators such as labor force participation and unemployment rates.
    • Investigate the role of taxation and its impact on economic development.
    • Explore urbanization trends and their social and environmental consequences.

    Data Source: This dataset was compiled from multiple data sources

    If this was helpful, a vote is appreciated ❤️ Thank you 🙂

  2. TikTok: distribution of global audiences 2025, by age and gender

    • statista.com
    • de.statista.com
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    Statista Research Department, TikTok: distribution of global audiences 2025, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    As of February 2025, it was found that around 14.1 percent of TikTok's global audience were women between the ages of 18 and 24 years, while male users of the same age formed approximately 16.6 percent of the platform's audience. The online audience of the popular social video platform was further composed of 14.6 percent of female users aged between 25 and 34 years, and 20.7 percent of male users in the same age group.

  3. Instagram: distribution of global audiences 2024, by age and gender

    • statista.com
    • de.statista.com
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    Stacy Jo Dixon, Instagram: distribution of global audiences 2024, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

                  Teens and social media
    
                  As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
                  Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.
    
  4. w

    Global Financial Inclusion (Global Findex) Database 2011 - Latvia

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Sep 21, 2021
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    Development Research Group, Finance and Private Sector Development Unit (2021). Global Financial Inclusion (Global Findex) Database 2011 - Latvia [Dataset]. https://microdata.worldbank.org/index.php/catalog/1204
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    Dataset updated
    Sep 21, 2021
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2011
    Area covered
    Latvia
    Description

    Abstract

    Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

    The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

    Geographic coverage

    National Coverage.

    Analysis unit

    Individual

    Universe

    The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

    Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

    Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

    The sample size in Latvia was 1,006 individuals.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

    Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

  5. Instagram: distribution of global audiences 2024, by gender

    • statista.com
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    Stacy Jo Dixon, Instagram: distribution of global audiences 2024, by gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

                  Instagram’s Global Audience
    
                  As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
                  As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.
    
                  Who is winning over the generations?
    
                  Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.
    
  6. Gapminder data

    • kaggle.com
    Updated Jun 26, 2023
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    Hsu Yee Mon (2023). Gapminder data [Dataset]. https://www.kaggle.com/datasets/hsuyeemon/gapminder-subset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 26, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Hsu Yee Mon
    Description

    This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

    GapMinder collects data from a handful of sources, including the Institute for Health
    Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

    Variable Name , Description of Indicator & Sources Unique Identifier: Country

    1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

    2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

    3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

    4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

    5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

    6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

    7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

    8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

    9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

    10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

    11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

    12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

    13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

    14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

    15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

  7. Leading causes of death, total population, by age group

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
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    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
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    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

  8. Income by Country

    • kaggle.com
    zip
    Updated Jul 27, 2020
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    Frank Mollard (2020). Income by Country [Dataset]. https://www.kaggle.com/datasets/frankmollard/income-by-country/data
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    zip(197208 bytes)Available download formats
    Dataset updated
    Jul 27, 2020
    Authors
    Frank Mollard
    Description

    Context

    This data set contains global economic income indicators per country. The data has been prepared for ease of use.

    The data is divided into: Male, female, dimestic credit, gross domestic product, gross national income, fixed capital formation, labour share. The individual files are briefly described below:

    Income index:

    Dimension: Income/composition of resources Definition: GNI per capita (2011 PPP International $, using natural logarithm) expressed as an index using a minimum value of $100 and a maximum value $75,000.

    Domestic credit provided by financial sector (% of GDP)

    Dimension: Income/composition of resources Definition: Credit to various sectors on a gross basis (except credit to the central government, which is net), expressed as a percentage of GDP.

    Estimated gross national income per capita, female (2011 PPP $)

    Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

    Estimated gross national income per capita, male (2011 PPP $)

    Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Derived from the ratio of female to male wages, female and male shares of economically active population and gross national income (in 2011 purchasing power parity terms).

    GDP per capita (2011 PPP $)

    Dimension: Income/composition of resources Definition: GDP in a particular period divided by the total population in the same period.

    Gross domestic product (GDP), total (2011 PPP $ billions)

    Dimension: Income/composition of resources Definition: Sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products, expressed in 2011 international dollars using purchasing power parity (PPP) rates.

    Gross fixed capital formation (% of GDP)

    Dimension: Income/composition of resources Definition: Value of acquisitions of new or existing fixed assets by the business sector, governments and households (excluding their unincorporated enterprises) less disposals of fixed assets, expressed as a percentage of GDP. No adjustment is made for depreciation of fixed assets.

    Gross national income (GNI) per capita (2011 PPP $)

    Full and productive employment and decent work for all women and men,including for young people and persons with disabilities, and equal pay for work of equal value Dimension: Income/composition of resources Definition: Aggregate income of an economy generated by its production and its ownership of factors of production, less the incomes paid for the use of factors of production owned by the rest of the world, converted to international dollars using PPP rates, divided by midyear population.

    Labour share of GDP, comprising wages and social protection transfers (%)

    Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality Dimension: Income/composition of resources Definition: Total compensation of employees given as a percent of GDP, which is a measure of total output. Total compensation refers to the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period.

    Additional Information

    For more information see : http://hdr.undp.org/sites/default/files/hdr2019_technical_notes.pdf

    The title picture is from https://searchengineland.com/international-ppc-deal-currency-fluctuations-245601

  9. Data from: lifetime risk of depression?

    • kaggle.com
    zip
    Updated Oct 31, 2024
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    willian oliveira (2024). lifetime risk of depression? [Dataset]. https://www.kaggle.com/datasets/willianoliveiragibin/lifetime-risk-of-depression
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    zip(1791 bytes)Available download formats
    Dataset updated
    Oct 31, 2024
    Authors
    willian oliveira
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Depression is one of the most common health conditions globally. It's estimated that between two to six percent of people in the world have experienced depression in the past year.1

    But what are the chances that people have depression at any time in their lives?

    This question is difficult to answer because depression is not a constant condition – people tend to transition in and out of depressive episodes. The chances of ever having an episode of depression are therefore much higher than the figure of two to six percent.

    Researchers estimate that around one in three women and one in five men in the United States have an episode of major depression by the time they are 65.2 Studies in other high-income countries suggest even higher figures. In the Netherlands and Australia, it's estimated that this affects 40% of women and 30% of men.3

    In this post, I will explain why measuring the lifetime risk of depression can be challenging, and how researchers are able to address the challenges and estimate the risk of major depression over a person’s lifetime. One way to estimate the lifetime risk of depression is to ask elderly people whether they have ever had depression in their lives. This sounds straightforward, but it leads to several problems.

    One is that it relies on self-reporting. Major depression is diagnosed based on the symptoms that people report to a professional. Since some are unwilling to share these symptoms, we would underestimate the risk of depression if we relied on this information alone.5

    This is particularly important for older generations, who lived much of their lives at a time when recognition and acceptance of mental illness was lower. That relates to a second problem: people from different generations might be less willing to report symptoms.6

    Another issue is that getting these estimates on a global level is difficult because this data is lacking across many countries. This is especially true for low-income countries.7 For example, the Global Burden of Disease study finds that only a quarter of countries and territories had direct data on the prevalence of major depression between 2005 and 2015.8

    This means our findings mostly come from a small number of high-income countries where these studies have been done.

    But even in countries where the data does exist, there is yet another major challenge. People often forget about previous episodes of depression – especially if they happened a long time ago. This is called ‘recall bias’, and it is one more problem that makes it hard to rely on people's self-report of symptoms of depression.

    You can see this in the chart. This comes from a large study of people who were interviewed several times, years apart, about symptoms of mental and physical illness they had in their lives.9

    Some people described having an episode of depression between one interview and the next. But some failed to recall episodes that they described in earlier interviews. This led to a more or less constant share who described lifetime depression at each interview.

    As we might expect, older people were much more likely to forget previous symptoms. People older than 60 were around seven times more likely to forget past episodes than those under 50.

  10. Violence Tweet Classification

    • kaggle.com
    zip
    Updated May 19, 2023
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    Gaurav Dutta (2023). Violence Tweet Classification [Dataset]. https://www.kaggle.com/datasets/gauravduttakiit/violence-tweet-classification
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    zip(16594188 bytes)Available download formats
    Dataset updated
    May 19, 2023
    Authors
    Gaurav Dutta
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Description Trigger warning: The data in this competition can contain graphic descriptions of or extensive discussion of abuse, especially sexual abuse or torture.

    Gender-based violence, or GBV, is an ongoing and ever-resent scourge around the world and is particularly prevalent in developing and least-developed countries. Gender-based violence also increased in many parts of the world during the COVID-19 pandemic.

    One of the greatest challenges in combating GBV is the ‘culture of silence’, where victims of violence are scared, ashamed, or intimidated to discuss their experiences with others and often do not report their experiences to authorities.

    Another challenge faced by victims is achieving justice for their abusers. Some may not be aware of support systems, or not know where and how to report the perpetrators.

    Victims may find safety sharing their experiences online (as evidenced by the #MeToo movement), allowing them to get more support in an anonymous and safe way.

    The objective of this challenge is to create a machine-learning algorithm that classifies tweets about GBV into one of five categories: sexual violence, emotional violence, harmful traditional practices, physical violence, and economic violence.

    Your solutions can be used to summarise tweets and present evidence to policymakers and law enforcement agencies. Along with the classification algorithm, statistics about when and who made the tweet can be used to find trends while preserving anonymity.

    About SDG5: Gender Equality

    Gender equality is a fundamental and inviolable human right and women’s and girls’ empowerment is essential to expand economic growth, promote social development and enhance business performance. The full incorporation of women’s capacities into labor forces would add percentage points to most national growth rates – double digits in many cases. Further, investing in women’s empowerment produces the double dividend of benefiting women and children, and is pivotal to the health and social development of families, communities, and nations.

    Empowering women and girls and achieving gender equality requires the concerted efforts of all stakeholders, including businesses. All companies have baseline responsibilities to respect human rights, including the rights of women and girls. Beyond these baseline responsibilities, companies also have the opportunity to support the empowerment of women and girls through core business, social investment, public policy engagement, and partnerships. As the engine for 90 percent of jobs in developing countries, technological innovation, capital creation, and investment, responsible business is critical to the advancement of women’s and girls’ empowerment around the world. With a growing business case, private sector leaders are increasingly developing and adapting policies and practices, and implementing cutting-edge initiatives, to advance women’s empowerment within their workplaces, marketplaces, and communities. The launch of the SDGs in September provides a tremendous opportunity for companies to further align their strategies and operations with global priorities by mainstreaming gender equality into all areas of corporate sustainability and systematically and strategically scaling up actions that support the development and livelihoods of women and girls.

    About Trigger warning: The data in this competition can contain graphic descriptions of or extensive discussion of abuse, especially sexual abuse or torture.

    The data was collected from Twitter using a Python library (twint) by Ambassador Lawrence Moruye for the AFD Gender-Based Violence Dataset Collection Challenge.

    The objective of this challenge is to create a machine-learning algorithm that classifies tweets about GBV into one of five categories: sexual violence, emotional violence, harmful traditional practices, physical violence, and economic violence.

  11. Instagram: most popular posts as of 2024

    • statista.com
    • de.statista.com
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    Stacy Jo Dixon, Instagram: most popular posts as of 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    Instagram’s most popular post

                  As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
                  After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.
    
                  Instagram’s most popular accounts
    
                  As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.
    
                  Instagram influencers
    
                  In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.
    
                  Instagram around the globe
    
                  Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.
    
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Nidula Elgiriyewithana ⚡ (2023). Global Country Information Dataset 2023 [Dataset]. https://www.kaggle.com/datasets/nelgiriyewithana/countries-of-the-world-2023
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Global Country Information Dataset 2023

A Comprehensive Dataset Empowering In-Depth Analysis and Cross-Country Insights

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8 scholarly articles cite this dataset (View in Google Scholar)
zip(24063 bytes)Available download formats
Dataset updated
Jul 8, 2023
Authors
Nidula Elgiriyewithana ⚡
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

DOI

Key Features

  • Country: Name of the country.
  • Density (P/Km2): Population density measured in persons per square kilometer.
  • Abbreviation: Abbreviation or code representing the country.
  • Agricultural Land (%): Percentage of land area used for agricultural purposes.
  • Land Area (Km2): Total land area of the country in square kilometers.
  • Armed Forces Size: Size of the armed forces in the country.
  • Birth Rate: Number of births per 1,000 population per year.
  • Calling Code: International calling code for the country.
  • Capital/Major City: Name of the capital or major city.
  • CO2 Emissions: Carbon dioxide emissions in tons.
  • CPI: Consumer Price Index, a measure of inflation and purchasing power.
  • CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
  • Currency_Code: Currency code used in the country.
  • Fertility Rate: Average number of children born to a woman during her lifetime.
  • Forested Area (%): Percentage of land area covered by forests.
  • Gasoline_Price: Price of gasoline per liter in local currency.
  • GDP: Gross Domestic Product, the total value of goods and services produced in the country.
  • Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
  • Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
  • Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
  • Largest City: Name of the country's largest city.
  • Life Expectancy: Average number of years a newborn is expected to live.
  • Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
  • Minimum Wage: Minimum wage level in local currency.
  • Official Language: Official language(s) spoken in the country.
  • Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
  • Physicians per Thousand: Number of physicians per thousand people.
  • Population: Total population of the country.
  • Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
  • Tax Revenue (%): Tax revenue as a percentage of GDP.
  • Total Tax Rate: Overall tax burden as a percentage of commercial profits.
  • Unemployment Rate: Percentage of the labor force that is unemployed.
  • Urban Population: Percentage of the population living in urban areas.
  • Latitude: Latitude coordinate of the country's location.
  • Longitude: Longitude coordinate of the country's location.

Potential Use Cases

  • Analyze population density and land area to study spatial distribution patterns.
  • Investigate the relationship between agricultural land and food security.
  • Examine carbon dioxide emissions and their impact on climate change.
  • Explore correlations between economic indicators such as GDP and various socio-economic factors.
  • Investigate educational enrollment rates and their implications for human capital development.
  • Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
  • Study labor market dynamics through indicators such as labor force participation and unemployment rates.
  • Investigate the role of taxation and its impact on economic development.
  • Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

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