Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

This project analyzes the 2020 World Happiness Report to draw conclusions about the general well being of Africa. It uses several CSV files consisting of survey responses formed from a Google Form survey, data from the 2020 World Happiness Report and data on countries only in Africa from the 2020 World Happiness Report. The main data set used includes over 150 countries and their happiness scores, freedom to make life choices, social support, healthy life expectancy, regional indicator, perceptions of corruption and generosity. This analysis was done to answer the following data-driven questions: 'Which African country ranked the happiest in 2020?' and 'Which variable predicts or explains Africa's happiness score?'

This project includes several programs created in R and Python.

Background

The Gallup World Poll (GWP) is conducted annually to measure and track public attitudes concerning political, social and economic issues, including controversial and sensitive subjects. Annually, this poll tracks attitudes toward law and order, institutions and infrastructure, jobs, well-being and other topics for approximately 150 countries worldwide. The data gathered from the GWP is used to create an annual World Happiness Report (WHR). The World Happiness Report is conducted to review the science of understanding and measuring the subjective well-being and to use survey measures of life satisfaction to track the quality of lives in over 150 countries.

At first glance, it seems that world happiness isn't important or maybe it's just an emotional thing. However, several governments have started to look at happiness as a metric to measure success. Happiness Scores or Subjective Well-being (SWB) are national average responses to questions of life evaluation. They are important because they remind policy makers and people in power that happiness is based on social capital, not just financial. Happiness is often considered an essential and useful way to guide public policies and measure their effectiveness. It is also important to note that happiness scores point out the importance of qualitative rather than quantitative. At times, quality is better than quantity.

Africa is the world's second largest and second most populous continent in the world. It consists of 54 countries meaning that Africa has the most countries. Africa has approximately 30% of the earth's mineral resources and has the largest reserves of precious metals. Africa reserves over 40% of the gold reserves, 60% on cobalt and 90% of platinum. However, Africa unfortunately has the most developmental challenges. It is the world's poorest and most underdeveloped continent. Africa is also almost 100% colonized with the exceptions of Ethiopia and Liberia. Given this information, one can wonder what the SWB or state of happiness is in Africa?

This site analyzes the 2020 World Happiness Report to draw conclusions to data-drive questions listed later on this page. The focus is specifically on countries in Africa. Even though there are 54 countries in Africa, only 43 participated in the 2020 WHR.

Content

The dataset used is generated from the 'World Happiness Report 2020'. This dataset contains the Happiness Score for over 150 countries for the year of 2020. The data gathered from the Gallup World Poll gives a national average of Happiness scores for countries all over the world. It is a annual landmark survey of the state of global happiness.

This dataset is from the data repository "Kaggle". On Kaggle's dataset page, I searched for Africa Happiness after filtering the search to CSV file type. I wasn't able to find any datasets that could answer my questions that didn't include other countries from different continents. I decided to use a Global Happiness Report to answer the questions I have. The dataset I am using was publish by Micheal Londeen and it was created on March 24, 2020. His main source is the World Happiness Report for 2020.

Variables

Happiness score or subjective well-being (variable name ladder ): The survey measure of SWB is from the Feb 28, 2020 release of the Gallup World Poll (GWP) covering years from 2005 to 2019. Unless stated otherwise, it is the national average response to the question of life evaluations. The English wording of the question is “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” This measure is also referred to as Cantril life ladder, or just life ladder in our analysis.

Healthy Life Expectancy (HLE). Healthy life expectancies at birth are based on the data extracted from the World Health Organization’s (WHO) Global Health Observatory dat...

Svarah: An Indic Accented English Speech Dataset

  Overview

India is the second largest English-speaking country in the world, with a speaker base of roughly 130 million. Unfortunately, Indian speakers are underrepresented in many existing English ASR benchmarks such as LibriSpeech, Switchboard, and the Speech Accent Archive. To address this gap, we introduce Svarah—a benchmark that comprises 9.6 hours of transcribed English audio from 117 speakers across 65… See the full description on the dataset page: https://huggingface.co/datasets/ai4bharat/Svarah.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The majority of the Canadian population, about 60% is concentrated within a thin belt of land representing 2.2% of the land between Windsor, Ontario and Quebec City. Even though Canada is the second largest country in the world in terms of land area, it only ranks 33rd in terms of population. The agricultural areas in the Prairies and eastern Canada have higher population densities than the sparsely populated North, but not as high as southern Ontario or southern Quebec.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2022, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

Business-critical Data Types We offer access to robust datasets sourced from over 13M job ads daily. Track companies’ growth, market focus, technological shifts, planned geographic expansion, and more: - Identify new business opportunities - Identify and forecast industry & technological trends - Help identify the jobs, teams, and business units that have the highest impact on corporate goals - Identify most in-demand skills and qualifications for key positions.

Fresh Datasets We regularly update our datasets, assuring you access to the latest data and allowing for timely analysis of rapidly evolving markets & dynamic businesses.

Historical Datasets We maintain at your disposal historical datasets, allowing for comprehensive, reliable, and statistically sound historical analysis, trend identification, and forecasting.

Easy Access and Retrieval Our job listing datasets are available in industry-standard, convenient JSON and CSV formats. These structured formats make our datasets compatible with machine learning, artificial intelligence training, and similar applications. The historical data retrieval process is quick and reliable thanks to our robust, easy-to-implement API integration.

Datasets for investors Investment firms and hedge funds use our datasets to better inform their investment decisions by gaining up-to-date, reliable insights into workforce growth, geographic expansion, market focus, technology shifts, and other factors of start-ups and established companies.

Datasets for businesses Our datasets are used by retailers, manufacturers, real estate agents, and many other types of B2B & B2C businesses to stay ahead of the curve. They can gain insights into the competitive landscape, technology, and product adoption trends as well as power their lead generation processes with data-driven decision-making.

Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

Context

This is a dataset comprising of the 380 municipalities in the Netherlands, which is a country in Western Europe with a population of approximately 17 million people. While Holland's population size is comparable to a country like Chile, the area that the Dutchmen have to share only comprises of 41,543 square kilometers, of which more than 18% consists of water (Chile: 756,096 square kilometers). Nonetheless, the Netherlands is the world's second-largest exporter of food and agricultural products.

'The Netherlands' literally translates to: 'lower countries', strongly influenced by the flat geography that characterises its lands. It is widely known as a peaceful and tolerant place to live and work, consequently ranking high in international indexes. According to the UN, the Netherlands is the sixth-happiest country in the world (United Nations World Happiness Report, 2017).

https://images.unsplash.com/photo-1468436385273-8abca6dfd8d3?ixlib=rb-0.3.5&ixid=eyJhcHBfaWQiOjEyMDd9&s=e71160983b3af78d30b19751a9574ce4&auto=format&fit=crop&w=1294&q=80" alt="enter image description here">

The municipality of Amsterdam.

Content

Rows (380):

• The 380 different municipalities in the Netherlands.

Columns (30):

• municipality: the name of the municipality.
• province: the name of the province that the municipality lies in.
• latitude: the latitude of the municipality.
• longitude: the longitude of the municipality.
• surface_km2: the surface (total area) of the municipality (land & water) in surface kilometers.
• surface_land_km2: the surface (total area) of the municipality (land only) in surface kilometers.
• surface_water_km2: the surface (total area) of the municipality (water only) in surface kilometers.
• population: the total population of the municipality.
• avg_household_income_2012: the average household income within the municipality in 2012 (€).
• avg_woz_2014: the average house value within the municipality in 2014 (€).
• ww_ratio_2014: the ratio of people that received unemployment benefits within the municipality in 2014.
• murders_2014: the amount of murders that took place within the municipality in 2014.
• university: a binary variable that indicates whether a Government Supported University lies within the borders of the municipality.
• asylum_migrants_1999: the amount of asylum migrants that settled in the municipality in 1999.
• asylum_migrants_2000: the amount of asylum migrants that settled in the municipality in 2000.
• asylum_migrants_2001: the amount of asylum migrants that settled in the municipality in 2001.
• asylum_migrants_2002: the amount of asylum migrants that settled in the municipality in 2002.
• asylum_migrants_2003: the amount of asylum migrants that settled in the municipality in 2003.
• asylum_migrants_2004: the amount of asylum migrants that settled in the municipality in 2004.
• asylum_migrants_2005: the amount of asylum migrants that settled in the municipality in 2005.
• asylum_migrants_2006: the amount of asylum migrants that settled in the municipality in 2006.
• asylum_migrants_2007: the amount of asylum migrants that settled in the municipality in 2007.
• asylum_migrants_2008: the amount of asylum migrants that settled in the municipality in 2008.
• asylum_migrants_2009: the amount of asylum migrants that settled in the municipality in 2009.
• asylum_migrants_2010: the amount of asylum migrants that settled in the municipality in 2010.
• asylum_migrants_2011: the amount of asylum migrants that settled in the municipality in 2011.
• asylum_migrants_2012: the amount of asylum migrants that settled in the municipality in 2012.
• asylum_migrants_2013: the amount of asylum migrants that settled in the municipality in 2013.
• asylum_migrants_2014: the amount of asylum migrants that settled in the municipality in 2014.
• asylum_migrants_2015: the amount of asylum migrants that settled in the municipality in 2015.

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The municipality of Rotterdam.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The majority of the Canadian population, about 60% is concentrated within a thin belt of land representing 2.2% of the land between Windsor, Ontario and Quebec City. Even though Canada is the second largest country in the world in terms of land area, it only ranks 33rd in terms of population. The agricultural areas in the Prairies and eastern Canada have higher population densities than the sparsely populated North, but not as high as southern Ontario or southern Quebec.

Previous studies on the topic of the 'Olympic Effect' and its impact on tourism reveal both positive and negative returns for host countries as well as unsuccessful bid host countries. The returns experienced, as explained by subject scholars, are greatly dependent on a variety of factors, many of which are uncontrollable. Therefore, it is an undeniable risk for a country to host a sporting mega-event; however, the potential successful outcome generally overshadows any concern. Building off of previous research conducted, this paper explores the effects host countries and unsuccessful bid host countries of sporting mega-events experience in regards to tourism after an event takes place. It not only examines the Olympics, but also observes the FIFA Men's World Cup, since it is considered the second largest sporting mega-event after the Olympics. In this analysis, hosts and unsuccessful bid hosts were observed under a variety of controls to fully understand factors that affect outcomes on tourism. The majority of results show a positive increase in tourism for hosts and unsuccessful bid hosts. However, it is apparent that many of these results are byproducts of a naturally occurring time trend that causes sectors such as tourism to increase over time.

What is the Counter-Trafficking Data Collaborative?

The Counter-Trafficking Data Collaborative is the first global data hub on human trafficking, publishing harmonized data from counter-trafficking organizations around the world. Launched in November 2017, the goal of CTDC is to break down information-sharing barriers and equip the counter-trafficking community with up to date, reliable data on human trafficking.

The global victim of trafficking dataset

The CTDC global victim of trafficking dataset is the largest of its kind in the world, and currently exists in two forms. The data are based on case management data, gathered from identified cases of human trafficking, disaggregated at the level of the individual. The cases are recorded in a case management system during the provision of protection and assistance services, or are logged when individuals contact a counter-trafficking hotline. The number of observations in the dataset increases as new records are added by the contributing organizations. The global victim of trafficking dataset that is available to download from the website in csv format has been mathematically anonymized, and the complete, non k-anonymized version of the dataset is displayed throughout the website through visualizations and charts showing detailed analysis.

Where do the data come from?

The data come from a variety of sources. The data featured in the global victim of trafficking dataset come from the assistance activities of the contributing organizations, including from case management services and from counter-trafficking hotline logs.

How are the global datasets created?

Each dataset has been created through a process of comparing and harmonizing existing data models of contributing partners and data classification systems. Initial areas of compatibility were identified to create a unified system for organizing and mapping data to a single standard. Each contributing organization transforms its data to this shared standard and any identifying information is removed before the datasets are made available.

How is the individual-level data protected?

Step 1

Counter-trafficking case data contains highly sensitive information, and maintaining privacy and confidentiality is of paramount importance for CTDC. For example, all explicit identifiers, such as names, were removed from the global victim dataset and some data such as age has been transformed into age ranges. No personally identifying information is transferred to or hosted by CTDC, and organizations that want to contribute are asked to anonymize in accordance to the standards set by CTDC.

Step 2

In addition to the safeguard measures outlined in step 1 the global victim dataset has been anonymized to a higher level, through a mathematical approach called k-anonymization. For a full description of k-anonymization, please refer to the definitions page.

IOM collects and processes data in accordance to its own Data Protection Policy. The other contributors adhere to relevant national and international standards through their policies for collecting and processing personal data.

How to interpret the data?

These data reflect the victims assisted/identified/referred/reported to the contributing organizations, which may not represent all victims identified within a country. Nevertheless, the larger the sample size for a given country (or, the more victims displayed on the map for a given country), the more representative the data are likely to be of the identified victim of trafficking population.

A larger number of identified victims of trafficking does not imply that there is a larger number of undetected victims of trafficking (i.e. a higher prevalence of trafficking).

In addition, samples of identified victims of trafficking cannot be considered random samples of the wider population of victims of trafficking (which includes unidentified victims), since counter-trafficking agencies may be more likely to identify some trafficking cases rather than others. However, with this caveat in mind, the profile of identified victims of trafficking tends to be considered as indicative of the profile of the wider population, given that the availability of other data sources is close to zero.

How does human trafficking case data relate to prevalence data?

There are currently no global or regional estimates of the prevalence of human trafficking. National estimates have been conducted in a few countries but they are also based on modelling of existing administrative data from identified cases and should therefore only be considered as basic baseline estimates. Historically, producing estimates of the prevalence of trafficking based on the collection of new primary data through surveys, for example, has been difficult. This is due to trafficking’s complicated legal definition and the challenges of a...

Canada had a relatively small area when created in 1867, but it then expanded greatly to become, by area, the second largest country in the world. This map is a composite of 18 Atlas maps which show territorial changes at specific times during the period 1867 to 1999. Not only did Canada as a whole expand over time, but also most of the provinces expanded their areas: only two provinces (New Brunswick and Nova Scotia) had their present boundaries as of Confederation (1867). The boundaries and names of the territories also changed over time; one of the three existing territories, Nunavut, was created as recently as 1999.

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering the most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries worldwide. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa have the largest gender gap Looking at the different world regions, the Middle East and North Africa have the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

India is one of the major players in the agriculture sector worldwide and it is the primary source of livelihood for ~55% of India’s population. India has the world's largest cattle herd (buffaloes), largest area planted to wheat, rice, and cotton, and is the largest producer of milk, pulses, and spices in the world. It is the second-largest producer of fruit, vegetables, tea, farmed fish, cotton, sugarcane, wheat, rice, cotton, and sugar. Agriculture sector in India holds the record for second-largest agricultural land in the world generating employment for about half of the country’s population. Thus, farmers become an integral part of the sector to provide us with means of sustenance.

Consumer spending in India will return to growth in 2021 post the pandemic-led contraction, expanding by as much as 6.6%. The Indian food industry is poised for huge growth, increasing its contribution to world food trade every year due to its immense potential for value addition, particularly within the food processing industry. The Indian food processing industry accounts for 32% of the country’s total food market, one of the largest industries in India and is ranked fifth in terms of production, consumption, export and expected growth.

This data contains the production and area grown for each crop at ditrict level from 1997 to 2015.