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.

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 🙂

This dataset offers a detailed comparison of key global players like USA, Russia, China, India, Canada, Australia, and others across various economic, social, and environmental metrics. By comparing countries on indicators such as GDP, population, healthcare access, education levels, internet penetration, military spending, and much more, this dataset provides valuable insights for researchers, policymakers, and analysts.

🔍 Key Comparisons:

Economic Indicators: GDP, inflation rates, unemployment rates, etc. Social Indicators: Literacy rates, healthcare quality, life expectancy, etc. Environmental Indicators: CO2 emissions, renewable energy usage, protected areas, etc. Technological Advancements: Internet users, mobile subscriptions, tech exports, etc. Military Spending: Defense budgets, military personnel numbers, etc. This dataset is perfect for those who want to compare countries in terms of development, growth, and global standing. It can be used for data analysis, policy planning, research, and even education.

✨ Key Features:

Comprehensive Coverage: Includes multiple countries with key metrics. Multiple Domains: Economic, social, environmental, technological, and military data. Up-to-date Information: Covers data from the last decade to provide recent insights. Research Ready: Suitable for academic research, visualizations, and analysis.

The average for 2023 based on 196 countries was 656095 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2023. Below is a chart for all countries where data are available.

This data package contains geography related datasets which include various codes, for example, airport codes, country and continent codes, IMO IMDG classification codes, dialing codes, currency codes, container codes, language codes, package codes, US states and territories codes. It also contains data on population figures and geolocation of cities and countries.

As of November 2025, there were a reported 4,165 data centers in the United States, the most of any country worldwide. A further 499 were located in the United Kingdom, while 487 were located in Germany. What is a data center? Data centers are facilities designed to store and compute vast amounts of data efficiently and securely. Growing in importance amid the rise of cloud computing and artificial intelligence, data centers form the core infrastructure powering global digital transformation. Modern data centers consist of critical computing hardware such as servers, storage systems, and networking equipment organized into racks, alongside specialized secondary infrastructure providing power, cooling, and security. AI data centers Data centers are vital for artificial intelligence, with the world’s leading technology companies investing vast sums in new facilities across the globe. Purpose-built AI data centers provide the immense computing power required to train the most advanced AI models, as well as to process user requests in real time, a task known as inference. Increasing attention has therefore turned to the location of these powerful facilities, as governments grow more concerned with AI sovereignty. At the same time, rapid data center expansion has sparked a global debate over resource use, including land, energy, and water, as modern facilities begin to strain local infrastructure.

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.

National Geospatial Data Asset

This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee.

Dataset Source Details

Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Cartographic Visualization

The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below.

Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html

Contact

Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip

Attribute Structure

The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension

These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE

The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB.

Core Attributes

The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields.

County Code and Country Name Fields

“CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard.

The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.

Descriptive Fields

The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes

Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line.

ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line

A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively.

The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps.

The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line.

Use of Core Attributes in Cartographic Visualization

Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between:

• International Boundaries (Rank 1);
• Other Lines of International Separation (Rank 2); and
• Special Lines (Rank 3).

Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction.

The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling.

Use of

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.

In 2021, the United States is the leading country in the big data and business analytics (BDA) market, with ** percent market share. The following four leading counties all hover around * percent market share. Global BDA spending is forecast to reach almost *** billion U.S. dollars in 2021, with the majority to be spent on IT services and software.

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

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

Saudi Arabia SA: Population in Largest City data was reported at 6,669,098.000 Person in 2017. This records an increase from the previous number of 6,439,767.000 Person for 2016. Saudi Arabia SA: Population in Largest City data is updated yearly, averaging 2,066,960.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 6,669,098.000 Person in 2017 and a record low of 156,699.000 Person in 1960. Saudi Arabia SA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

The Potential Impacts of Climate Change on World Food Supply: Datasets from a Major Crop Modeling Study contain projected country and regional changes in grain crop yields due to global climate change. Equilibrium and transient scenarios output from General Circulation Models (GCMs) with three levels of farmer adaptations to climate change were utilized to generate crop yield estimates of wheat, rice, coarse grains (barley and maize), and protein feed (soybean) at 125 agricultural sites representing major world agricultural regions. Projected yields at the agricultural sites were aggregated to major trading regions, and fed into the Basic Linked Systems (BLS) global trade model to produce country and regional estimates of potential price increases, food shortages, and risk of hunger. These datasets are produced by the Goddard Institute for Space Studies (GISS) and are distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Streetview by Country

Intro

Hi, I thought I would pull together a dataset comprising of streetview images from different countries. I saw there were a couple datasets on Kaggle with a similar intention, but I wanted to help solve 2 main problems.

1) Balanced classes across countries. 2) Images tagged with coordinates.

What is included

What I have here are roughly 1000 images (and each image is 640x640 pixels) per country/region (many island nations get separate groupings to their nominal sovereign state, e.g., Bermuda, Faroe etc.) that have sufficient official google streetview coverage. There are some countries that do have streetview coverage, but I have not included, because the coverage is extremely limited. For example, Belarus only has limited trekker coverage in the centre of Minsk, so I don't feel it is representative of the region at large in a geography-prediction dataset. More info on which countries are in the dataset and which aren't is in Country-picking.md.

There are 111 regions in total. Each is indexed by its 2-letter shortening as you can find here https://www.iban.com/country-codes, they are also in country-picking.md, for reference.

Methodology

To collect each set I first used an incredible tool to generate valid coordinates within a specified country, you can find this at https://github.com/slashP/Vali. Big thanks to the creator I could not have created this otherwise. The coordinates generated per country are intended to be spread apart, within respective sub-regions, which I hope will help create a representative dataset of that country overall. Downloaded images were then assorted to their respective country's folder and titled with the coordinates that they correspond to. For example

"42d436079_1d473612_h213.jpg"

means that the image was taken at a latitude of 42.436079, a longitude of 1.473612 and a heading (between 0 and 360) of 213.

Another note! Some regions are just that small that I couldn't curate 1000 locations within them, or I had to scale down the minimum distance between points. Notes explaining on which countries this is relevant are in Country-picking.md.

This is my first dataset so please if you have any suggestions please let me know I'd be happy to help make this a better dataset. In the future I'd love to expand this dataset into specific subregions of countries.

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.

As of October 2025, China has the world’s largest online population, with approximately 1.3 billion internet users. India, currently the most populous nation, ranks second with about 1.03 billion users. The United States follows in third place. Worldwide internet usage As of October 2025, there are more than six billion internet users worldwide. However, user distribution varies significantly by region. In 2024, Eastern Asia alone accounted for 1.34 billion internet users, while Africa and the Middle East reported considerably lower figures. As expected, urban areas also exhibited higher rates of internet access compared to rural regions. Internet use in China It is no surprise that China ranks first among countries with the most internet users. Driven by rapid economic development and a strong cultural embrace of technology, 91.6 percent of China’s estimated 1.4 billion residents are online. As of the third quarter of 2024, about 91.8 percent of Chinese internet users were active on WeChat, the country’s most popular social platform. During the same period, Chinese internet users spent an average of five hours and 33 minutes online each day.

Before the Second World War, the Soviet Union was the largest individual world power in terms of territory, at over 21 million square kilometers. When the territories of the United Kingdom it's colonies and dominions are combined, then the expanse of the British Empire totaled at almost 35 million square kilometers, making it larger than the USSR. The Axis Powers, led by Germany, Italy, and Japan, controlled a much smaller share of the globe than the Allied Powers in 1938 - however, the majority of Europe was under Axis control by 1941, while Japan had taken much of the Western territory in Asia and pushed further into China by this time.

This index compiles empirical data on AI and big data surveillance use for 179 countries around the world between 2012 and 2022— although the bulk of the sources stem from between 2017 and 2022. The index does not distinguish between legitimate and illegitimate uses of AI and big data surveillance. Rather, the purpose of the research is to show how new surveillance capabilities are transforming governments’ ability to monitor and track individuals or groups. Last updated February 2022.

This index addresses three primary questions: Which countries have documented AI and big data public surveillance capabilities? What types of AI and big data public surveillance technologies are governments deploying? And which companies are involved in supplying this technology?

The index measures AI and big data public surveillance systems deployed by state authorities, such as safe cities, social media monitoring, or facial recognition cameras. It does not assess the use of surveillance in private spaces (such as privately-owned businesses in malls or hospitals), nor does it evaluate private uses of this technology (e.g., facial recognition integrated in personal devices). It also does not include AI and big data surveillance used in Automated Border Control systems that are commonly found in airport entry/exit terminals. Finally, the index includes a list of frequently mentioned companies – by country – which source material indicates provide AI and big data surveillance tools and services.

All reference source material used to build the index has been compiled into an open Zotero library, available at https://www.zotero.org/groups/2347403/global_ai_surveillance/items. The index includes detailed information for seventy-seven countries where open source analysis indicates that governments have acquired AI and big data public surveillance capabilities. The index breaks down AI and big data public surveillance tools into the following categories: smart city/safe city, public facial recognition systems, smart policing, and social media surveillance.

The findings indicate that at least seventy-seven out of 179 countries are actively using AI and big data technology for public surveillance purposes:

• Smart city/safe city platforms: fifty-five countries • Public facial recognition systems: sixty-eight countries • Smart policing: sixty-one countries • Social media surveillance: thirty-six countries

Jamaica JM: Population in Largest City: as % of Urban Population data was reported at 36.767 % in 2017. This records a decrease from the previous number of 37.031 % for 2016. Jamaica JM: Population in Largest City: as % of Urban Population data is updated yearly, averaging 47.049 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 76.495 % in 1960 and a record low of 36.767 % in 2017. Jamaica JM: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jamaica – Table JM.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

Follow data.kapsarc.org for timely data to advance energy economics research.