This dataset provides a synthetic overview of the 1,000 wealthiest individuals in the world, offering insights into the distribution of wealth across industries and regions. It is designed to help analysts, researchers, and data enthusiasts explore global wealth trends, industry dominance, and regional wealth concentration.

Whether you're conducting market research, financial analysis, or data modeling, this dataset serves as a valuable resource for understanding the characteristics of the world's top billionaires.

📊 Key Features: Name 👤: The name of the billionaire. Country 🌍: Country of residence or primary business operation. Industry 🏭: Industry in which the individual has built their wealth. Net Worth (in billions) 💵: Estimated net worth in billions of USD. Company 🏢: The primary company or business associated with the billionaire. ⚠️ Important Note: This dataset is 100% synthetic and does not contain real financial or personal data. It is artificially generated for educational, analytical, and research purposes.

Content

This dataset consists of top most billionaires in the world and respective their names, whether it is a finance company or any software company, how much money they have ,these all the details which are in the dataset

Researchers have compiled a multi-decade database of the super-rich. Building off the Forbes World’s Billionaires lists from 1996-2014, scholars at Peterson Institute for International Economics have added a couple dozen more variables about each billionaire - including whether they were self-made or inherited their wealth. (Roughly half of European billionaires and one-third of U.S. billionaires got a significant financial boost from family, the authors estimate.)

Acknowledgements

Reference : https://corgis-edu.github.io/corgis/csv/billionaires/

Inspiration

Some of the datasets which I have seen in the kaggle or somewhere but it is limited to less number of columns . Kagglers are not able to get an insights from very low amount of data. so that I decided that to be more helpful to them or we can able to get an more insights from this dataset

As of March 2025, Elon Musk had a net worth valued at 328.5 billion U.S. dollars, making him the richest man in the world. Amazon founder Jeff Bezos followed in second, with Marc Zuckerberg, the founder of Facebook, in third. The list is dominated by Americans, and Alice Walton and Francoise Bettencourt Meyers are the only women among the 20 richest people worldwide.

Cleaned dataset from the Billionaires Statistic Dataset (2023) that can be found here.

The code I used to clean and re-structure the data is also here.

First things first: a big shout-out to Nidula Elgiriyewithana for providing the original data.

As with it, this dataset contains various information about the world's wealthiest persons in different columns that can be grouped into three different types:

• Business-related information. These columns contain data about the industry in which the billionaires' operate, their source of wealth, total wealth and position they occupy in the ranking.
• Personal information. Such as name, age, nationality, country and city of residence.
• Economic activity information. These columns are related to the country in which the billionaire resides and provide different economic indicators like GDP, education enrollment or Consumer Price Index (CPI).

Column names

• position. Ranking of the billionaire measured by their wealth.
• wealth. The wealth of the billionaire measured in $.
• industry. Industry in which the billionaire's operates their businesses.
• full_name. Complete name of the billionaire.
• age. The age of the billionaire.
• country_of_residence. Country in which the billionaire resides.
• city_of_residence. City in which the billionaire resides.
• source. The source of the billionaire's wealth.
• citizenship. The country of citizenship of the billionaire.
• gender. The gender of the billionaire.
• birth_date. The birth date of the billionaire.
• last_name. The last name of the billionaire.
• first_name. The first name of the billionaire.
• residence_state. State in which the billionaire resides (only for billionaires who reside in the U.S.).
• residence_region. Region in which the billionaire resides (only for billionaires who reside in the U.S.).
• birth_year. The birth year of the billionaire.
• birth_month. The birth month of the billionaire.
• birth_day. The birth data of the billionaire.
• cpi_country. Consumer Price Index (CPI) for the billionaire's country.
• cpi_change_country. CPI change for the billionaire's country.
• gdp_country. Gross Domestic Product (GDP) in $ for the billionaire's country.
• g_tertiary_ed_enroll. Enrollment in tertiary education in the billionaire's country.
• g_primary_ed_enroll. Enrollment in primary education in the billionaire's country.
• life_expectancy. Life expectancy in the billionaire's country.
• tax_revenue. Tax revenue in the billionaire's country.
• tax_rate. Total tax rate in the billionaire's country.
• country_pop. Population of the billionaire's country.
• country_lat. Latitude coordinate of the billionaire's country.
• country_long. Longitude coordinate of the billionaire's country.
• continent. Continent in which the country of the billionaire's residence is located.

Potential analyses

• Analyze which industries contain the biggest groups of billionaires overall and in different countries.
• Explore number of billionaires and total wealth across countries and continents and display the result in a map.
• Focus on personal information columns such as age or gender to explore the distribution of billionaires from this perspective.
• Discover if countries' economic indicators have any impact in the presence of billionaires.
• The U.S. is the country with most billionaires presented in the dataset and also the only one with attributes in the residence_state and residence_region columns. This makes the American billionaires a good focus for a specific analysis.

Bonus

If you want a challenge, you can create a dashboard using tools such as Plotly to dynamically visualize the data using one or different attributes (such as industry, age or country). I did it, leave the link below in case you want to investigate:

Dashboard notebook here

If you find this dataset informative or inspirational, a vote is appreciated for others to easily discover value in it 💎💰

The Forbes published "The Definitive Ranking of the Wealthiest Americans In 2022". And here is the top 200 list including additional information on each of the billionaires.

About Dataset

This dataset contains the top 200 richest American based on their net worth.

Columns Attributes

| Column | Meaning | | -- | -- | | rank | their rank | | name | their name | | net worth | their net worth | | age | their age | | title | their title (e.g. CEO, Chairman etc.) | | source of wealth | the source of how they've managed to get this much money | | self made score | shows how far each of these billionaires has climbed to make it to the top. According to Forbes, The score ranges from 1 to 10, with 1 through 5 indicating someone who inherited some or all of his or her fortune; while 6 through 10 are for those who built their company or established a fortune on his or her own. | | philanthropy score | this score shows how much these billionaires donates on nonprofits foundations | | residence | their residence | | marital status | their marital status | | children | their children | | education | their education |

Methodology

This dataset was acquired using a web scraping tool called Beautiful soup and scraped Forbes website

Image by pch.vector on Freepik

1) Data Introduction • The Billionaires Statistics Dataset (2023) is a comprehensive set of personal and business information, including rankings of billionaires worldwide, net assets, industries, businesses, nationalities, birth and residence information, and asset sources.

2) Data Utilization (1) Billionaires Statistics Dataset (2023) has characteristics that: • The dataset consists of more than 35 columns, including the billionaire's rank, final Worth, industry, country, age, country of residence, source of assets, related industries, citizenship, organization, selfMade, birth information, data collection date, economic and social indicators (GDP, CPI, education enrollment, life expectancy, tax revenue, population, etc.). • In addition to individual asset information, economic indicators and demographic data by country are combined, allowing a three-dimensional analysis of billionaires and each country's economic and social environment. (2) Billionaires Statistics Dataset (2023) can be used to: • Wealth Distribution and Industry Analysis: Using billionaires' net worth, industry, and national data, we can analyze global wealth concentration and wealth distribution by industry and region. • A study linking demographics and economic indicators: Billionaire data can be combined with various economic and social indicators such as GDP, CPI, tax revenue, education, and life expectancy to be used for in-depth research on wealth formation, social background, ratio of self-made and inherited wealth, and regional characteristics.

About this dataset

Context

The dataset presents the median household income across different racial categories in Rich Square. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Rich Square population by race & ethnicity, the population is predominantly Black or African American. This particular racial category constitutes the majority, accounting for 63.98% of the total residents in Rich Square. Notably, the median household income for Black or African American households is $34,031. Interestingly, Black or African American is both the largest group and the one with the highest median household income, which stands at $34,031.

https://i.neilsberg.com/ch/rich-square-nc-median-household-income-by-race.jpeg" alt="Rich Square median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Rich Square.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Rich Square median household income by race. You can refer the same here

This comprehensive dataset encapsulates a detailed snapshot of the wealthiest individuals globally, as listed by Forbes in 2024. Compiled through meticulous web scraping and data aggregation, the dataset includes a wide range of attributes for each billionaire. Fields encompass basic personal information such as name, age, and gender, alongside financial details including net worth and sources of wealth. The dataset further delves into aspects like industry involvement, organizational affiliations, philanthropic endeavors, and educational backgrounds.

Key attributes in this dataset include:

Name: Full legal name of the billionaire. Age: Age of the individual. 2024 Net Worth: Estimated net worth in USD for the year 2024. Industry: Primary industry or sector of operation. Source of Wealth: Origin of the billionaire’s wealth. Title: Professional title or position. Organization: Name of the associated organization. Self-Made: Indicator if the wealth is self-made. Self-Made Score: A quantitative score assessing how self-made their wealth is. Philanthropy Score: A score reflecting the extent of their philanthropic activities. Residence: Main residence of the individual. Citizenship: Legal citizenship. Gender: Gender identity. Marital Status: Current marital status. Children: Number of children. Education: Highest level of education attained.

This dataset is ideal for analysis, offering insights into the distribution of wealth, the influence of education on wealth accumulation, and trends across different industries. It also provides a foundation for exploring the impact of socioeconomic factors on personal wealth. The data were collected and formatted with careful consideration to ensure accuracy, making it a valuable resource for researchers, economists, and anyone interested in the dynamics of wealth and success.

Please note that some data is missing in this dataset, primarily due to the unavailability of information from Forbes. This issue becomes more prevalent beyond the top 400 entries. Many individuals lack a self-made score, a philanthropy score, or specific details regarding their title or organization as per Forbes' listings. I am currently working to update the dataset with this missing information. However, this update process is quite tedious and time-consuming since it is mostly manual. I appreciate your patience and understanding as I work through these details.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Rich Square. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Rich Square, the median income for all workers aged 15 years and older, regardless of work hours, was $24,265 for males and $17,431 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Rich Square. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Rich Square.

- Full-time workers, aged 15 years and older: In Rich Square, among full-time, year-round workers aged 15 years and older, males earned a median income of $45,893, while females earned $36,089, leading to a 21% gender pay gap among full-time workers. This illustrates that women earn 79 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Rich Square, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Rich Square median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Rich County, UT, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Rich County did not include 3-person households. Across the different household sizes in Rich County the mean income is $76,326, and the standard deviation is $24,212. The coefficient of variation (CV) is 31.72%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,301. It then further increased to $96,382 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/rich-county-ut-median-household-income-by-household-size.jpeg" alt="Rich County, UT median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Rich County median household income. You can refer the same here

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

This dataset is about books. It has 1 row and is filtered where the book is Reinvent rich : how to make more money, more moments, and more meaning in life. It features 7 columns including author, publication date, language, and book publisher.

This GLA Intelligence Update takes a brief look at evidence around the wealth gap in London and examines how this has changed in recent years. Key Findings There is a significant gap between the rich and poor in London, both in terms of their wealth and their income. A higher proportion of the wealthiest households are in the South East of England than in London. Pension wealth accounts for more than half the wealth of the richest ten per cent of the population. In London, the tenth of the population with the highest income have weekly income after housing costs of over £1,000 while people in the lowest tenth have under £94 per week. The gap between rich and poor is growing, with the difference between the average income for the second highest tenth and second lowest tenth growing around 14 per cent more than inflation since 2003.

The All CMS Data Feeds dataset is an expansive resource offering access to 118 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis tools and platforms. This ensures that users can easily integrate the data into their existing wo...

The values of any financial assets held including both formal investments, such as bank or building society current or saving accounts, investment vehicles such as Individual Savings Accounts, endowments, stocks and shares, and informal savings.

Most datasets show either financial robustness or technographic signals, not both. Consultants and SaaS teams need to know who has the money but lacks digital maturity. This segment blends hard financials with a 5-dimension Digital Maturity Index (incl. AI), benchmarked against sector and country peers, and explained with traceable comments.

Dominican Republic number dataset helps in many ways to gain huge amounts from business. Besides, this Dominican Republic number dataset is a very valuable directory that you can buy from us at a minimal cost. In addition, it creates many business chances because this country is rich in multiple sectors. Additionally, this directory makes all businesses more famous, competitive, and useful. For instance, this Dominican Republic number dataset builds new opportunities to do business in your selected places. Yet, the vendors can give sales promotions and make huge money from this lead. This time, they can join with the selected group of clients quickly. Overall, it provides the long-term success of your company or business. Dominican Republic phone data is a powerful way to connect many clients. Our Dominican Republic phone data can assist in getting speedy feedback from the public. In other words, our expert unit supplies this cautiously according to your needs. However, the List To Data website is the perfect source to get upgraded sales leads. Thus, check out the packages to find the one that works best for you and watch your business succeed. Moreover, the Dominican Republic phone data is perfect for sending text messages or making phone calls to potential new clients to make deals. By getting this people easily can reach out to people in this area and get positive results from the marketing. Likewise, this library retains millions of phone numbers from different businesses and people. Dominican Republic phone number list transforms your business into a profitable venture. Finding real contacts is very important because the Dominican Republic phone number list helps you reach a genuine audience, saving you time. Even, this List To Data helps you attach with many people quickly and boosts your marketing efforts. In addition, the Dominican Republic phone number list is a great source of earning from B2B and B2C platforms. The Dominican Republic’s economy is strong and diverse, with important sectors like technology, finance, and tourism. Besides, the country’s economy is persisting to grow. In the end, everyone should buy our contact data to earn a massive amount of profit from your targeted locations.

Welcome to the English Chain of Thought prompt-response dataset, a meticulously curated collection containing 3000 comprehensive prompt and response pairs. This dataset is an invaluable resource for training Language Models (LMs) to generate well-reasoned answers and minimize inaccuracies. Its primary utility lies in enhancing LLMs' reasoning skills for solving arithmetic, common sense, symbolic reasoning, and complex problems.

Dataset Content

This COT dataset comprises a diverse set of instructions and questions paired with corresponding answers and rationales in the English language. These prompts and completions cover a broad range of topics and questions, including mathematical concepts, common sense reasoning, complex problem-solving, scientific inquiries, puzzles, and more.

Each prompt is meticulously accompanied by a response and rationale, providing essential information and insights to enhance the language model training process. These prompts, completions, and rationales were manually curated by native English people, drawing references from various sources, including open-source datasets, news articles, websites, and other reliable references.

Our chain-of-thought prompt-completion dataset includes various prompt types, such as instructional prompts, continuations, and in-context learning (zero-shot, few-shot) prompts. Additionally, the dataset contains prompts and completions enriched with various forms of rich text, such as lists, tables, code snippets, JSON, and more, with proper markdown format.

Prompt Diversity

To ensure a wide-ranging dataset, we have included prompts from a plethora of topics related to mathematics, common sense reasoning, and symbolic reasoning. These topics encompass arithmetic, percentages, ratios, geometry, analogies, spatial reasoning, temporal reasoning, logic puzzles, patterns, and sequences, among others.

These prompts vary in complexity, spanning easy, medium, and hard levels. Various question types are included, such as multiple-choice, direct queries, and true/false assessments.

Response Formats

To accommodate diverse learning experiences, our dataset incorporates different types of answers depending on the prompt and provides step-by-step rationales. The detailed rationale aids the language model in building reasoning process for complex questions.

These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled English Chain of Thought Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt complexity, prompt category, domain, response, rationale, response type, and rich text presence.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses and rationales are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The English version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom chain of thought prompt completion data tailored to specific needs, providing flexibility and customization options.

License

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy English Chain of Thought Prompt Completion Dataset to enhance the rationale and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

Abstract

Dataset used in World Bank Policy Research Working Paper #2876, published in World Bank Economic Review, No. 1, 2005, pp. 21-44.

The effects of globalization on income distribution in rich and poor countries are a matter of controversy. While international trade theory in its most abstract formulation implies that increased trade and foreign investment should make income distribution more equal in poor countries and less equal in rich countries, finding these effects has proved elusive. The author presents another attempt to discern the effects of globalization by using data from household budget surveys and looking at the impact of openness and foreign direct investment on relative income shares of low and high deciles. The author finds some evidence that at very low average income levels, it is the rich who benefit from openness. As income levels rise to those of countries such as Chile, Colombia, or Czech Republic, for example, the situation changes, and it is the relative income of the poor and the middle class that rises compared with the rich. It seems that openness makes income distribution worse before making it better-or differently in that the effect of openness on a country's income distribution depends on the country's initial income level.

Kind of data

Aggregate data [agg]

Welcome to the Spanish Chain of Thought prompt-response dataset, a meticulously curated collection containing 3000 comprehensive prompt and response pairs. This dataset is an invaluable resource for training Language Models (LMs) to generate well-reasoned answers and minimize inaccuracies. Its primary utility lies in enhancing LLMs' reasoning skills for solving arithmetic, common sense, symbolic reasoning, and complex problems.

Dataset Content

This COT dataset comprises a diverse set of instructions and questions paired with corresponding answers and rationales in the Spanish language. These prompts and completions cover a broad range of topics and questions, including mathematical concepts, common sense reasoning, complex problem-solving, scientific inquiries, puzzles, and more.

Each prompt is meticulously accompanied by a response and rationale, providing essential information and insights to enhance the language model training process. These prompts, completions, and rationales were manually curated by native Spanish people, drawing references from various sources, including open-source datasets, news articles, websites, and other reliable references.

Our chain-of-thought prompt-completion dataset includes various prompt types, such as instructional prompts, continuations, and in-context learning (zero-shot, few-shot) prompts. Additionally, the dataset contains prompts and completions enriched with various forms of rich text, such as lists, tables, code snippets, JSON, and more, with proper markdown format.

Prompt Diversity

To ensure a wide-ranging dataset, we have included prompts from a plethora of topics related to mathematics, common sense reasoning, and symbolic reasoning. These topics encompass arithmetic, percentages, ratios, geometry, analogies, spatial reasoning, temporal reasoning, logic puzzles, patterns, and sequences, among others.

These prompts vary in complexity, spanning easy, medium, and hard levels. Various question types are included, such as multiple-choice, direct queries, and true/false assessments.

Response Formats

To accommodate diverse learning experiences, our dataset incorporates different types of answers depending on the prompt and provides step-by-step rationales. The detailed rationale aids the language model in building reasoning process for complex questions.

These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled Spanish Chain of Thought Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt complexity, prompt category, domain, response, rationale, response type, and rich text presence.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses and rationales are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Spanish version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom chain of thought prompt completion data tailored to specific needs, providing flexibility and customization options.

License

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Spanish Chain of Thought Prompt Completion Dataset to enhance the rationale and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.