100+ datasets found
  1. Largest countries in the world by area

    • statista.com
    • ai-chatbox.pro
    Updated Aug 7, 2024
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    Statista (2024). Largest countries in the world by area [Dataset]. https://www.statista.com/statistics/262955/largest-countries-in-the-world/
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    Dataset updated
    Aug 7, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    World
    Description

    The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

    Population of Russia

    Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

  2. Big data and business analytics market share worldwide 2021, by country

    • statista.com
    Updated Jul 1, 2025
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    Statista (2025). Big data and business analytics market share worldwide 2021, by country [Dataset]. https://www.statista.com/statistics/1258046/worldwide-big-data-business-analytics-market-share-by-country/
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    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    Worldwide
    Description

    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.

  3. o

    Big Country Cross Street Data in Shepherdsville, KY

    • ownerly.com
    Updated Dec 8, 2021
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    Ownerly (2021). Big Country Cross Street Data in Shepherdsville, KY [Dataset]. https://www.ownerly.com/ky/shepherdsville/big-country-home-details
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    Dataset updated
    Dec 8, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Shepherdsville, Kentucky
    Description

    This dataset provides information about the number of properties, residents, and average property values for Big Country cross streets in Shepherdsville, KY.

  4. F

    OECD based Recession Indicators for Four Big European Countries from the...

    • fred.stlouisfed.org
    json
    Updated Dec 9, 2022
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    (2022). OECD based Recession Indicators for Four Big European Countries from the Peak through the Period preceding the Trough (DISCONTINUED) [Dataset]. https://fred.stlouisfed.org/series/4BIGEURORECP
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    jsonAvailable download formats
    Dataset updated
    Dec 9, 2022
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Description

    Graph and download economic data for OECD based Recession Indicators for Four Big European Countries from the Peak through the Period preceding the Trough (DISCONTINUED) (4BIGEURORECP) from Feb 1960 to Aug 2022 about 4 Big European Countries, peak, trough, and recession indicators.

  5. p

    Trends in Total Students (1991-2023): Big Country Elementary School

    • publicschoolreview.com
    Updated Jun 5, 2014
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    Public School Review (2014). Trends in Total Students (1991-2023): Big Country Elementary School [Dataset]. https://www.publicschoolreview.com/big-country-elementary-school-profile
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    Dataset updated
    Jun 5, 2014
    Dataset authored and provided by
    Public School Review
    License

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

    Description

    This dataset tracks annual total students amount from 1991 to 2023 for Big Country Elementary School

  6. p

    Big Country Elementary School

    • publicschoolreview.com
    json, xml
    Updated Jun 5, 2014
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    Public School Review (2014). Big Country Elementary School [Dataset]. https://www.publicschoolreview.com/big-country-elementary-school-profile
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    json, xmlAvailable download formats
    Dataset updated
    Jun 5, 2014
    Dataset authored and provided by
    Public School Review
    License

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

    Time period covered
    Jan 1, 1989 - Dec 31, 2025
    Description

    Historical Dataset of Big Country Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1991-2023),Total Classroom Teachers Trends Over Years (1993-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (1993-2023),Asian Student Percentage Comparison Over Years (1989-2013),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (1993-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2001-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022)

  7. Large Scale International Boundaries

    • catalog.data.gov
    • geodata.state.gov
    • +1more
    Updated Jul 4, 2025
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    U.S. Department of State (Point of Contact) (2025). Large Scale International Boundaries [Dataset]. https://catalog.data.gov/dataset/large-scale-international-boundaries
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    Dataset updated
    Jul 4, 2025
    Dataset provided by
    United States Department of Statehttp://state.gov/
    Description

    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 the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

  8. Population by country of birth, age (large age groups) and sex

    • ine.es
    csv, html, json +4
    Updated Jan 17, 2022
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    INE - Instituto Nacional de Estadística (2022). Population by country of birth, age (large age groups) and sex [Dataset]. https://www.ine.es/jaxiT3/Tabla.htm?t=36967&L=1
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    xls, csv, text/pc-axis, txt, xlsx, html, jsonAvailable download formats
    Dataset updated
    Jan 17, 2022
    Dataset provided by
    National Statistics Institutehttp://www.ine.es/
    Authors
    INE - Instituto Nacional de Estadística
    License

    https://www.ine.es/aviso_legalhttps://www.ine.es/aviso_legal

    Time period covered
    Jan 1, 2003 - Jan 1, 2022
    Variables measured
    Sex, Data type, Country of birth, Age (large age groups), Communities and provinces
    Description

    Continuous Register Statistics: Population by country of birth, age (large age groups) and sex. Annual. Provinces.

  9. Leading countries by number of data centers 2025

    • statista.com
    • ai-chatbox.pro
    Updated Mar 21, 2025
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    Statista (2025). Leading countries by number of data centers 2025 [Dataset]. https://www.statista.com/statistics/1228433/data-centers-worldwide-by-country/
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    Dataset updated
    Mar 21, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Worldwide
    Description

    As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

  10. p

    Distribution of Students Across Grade Levels in Big Country Elementary...

    • publicschoolreview.com
    Updated Jun 5, 2014
    + more versions
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    Public School Review (2014). Distribution of Students Across Grade Levels in Big Country Elementary School [Dataset]. https://www.publicschoolreview.com/big-country-elementary-school-profile
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    Dataset updated
    Jun 5, 2014
    Dataset authored and provided by
    Public School Review
    License

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

    Description

    This dataset tracks annual distribution of students across grade levels in Big Country Elementary School

  11. Big Mac index worldwide 2025

    • statista.com
    • tiktok-play.menuridamusic.com
    • +1more
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    Statista, Big Mac index worldwide 2025 [Dataset]. https://www.statista.com/statistics/274326/big-mac-index-global-prices-for-a-big-mac/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    Worldwide
    Description

    At **** U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was **** dollars in the U.S., and **** U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

  12. h

    GeoGuessr-countries-large

    • huggingface.co
    Updated Jan 22, 2025
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    Pepijn (2025). GeoGuessr-countries-large [Dataset]. https://huggingface.co/datasets/deboradum/GeoGuessr-countries-large
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 22, 2025
    Authors
    Pepijn
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    deboradum/GeoGuessr-countries-large dataset hosted on Hugging Face and contributed by the HF Datasets community

  13. o

    Big Country Drive Cross Street Data in Boomer, NC

    • ownerly.com
    Updated Dec 15, 2021
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    Ownerly (2021). Big Country Drive Cross Street Data in Boomer, NC [Dataset]. https://www.ownerly.com/nc/boomer/big-country-dr-home-details
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    Dataset updated
    Dec 15, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Boomer, North Carolina, Big Country Drive
    Description

    This dataset provides information about the number of properties, residents, and average property values for Big Country Drive cross streets in Boomer, NC.

  14. o

    Big Country Court Cross Street Data in Moorpark, CA

    • ownerly.com
    Updated Dec 8, 2021
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    Ownerly (2021). Big Country Court Cross Street Data in Moorpark, CA [Dataset]. https://www.ownerly.com/ca/moorpark/big-country-ct-home-details
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    Dataset updated
    Dec 8, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Moorpark, Big Country Court, California
    Description

    This dataset provides information about the number of properties, residents, and average property values for Big Country Court cross streets in Moorpark, CA.

  15. o

    Big Bend Place Cross Street Data in Canyon Country, CA

    • ownerly.com
    Updated Dec 20, 2021
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    Ownerly (2021). Big Bend Place Cross Street Data in Canyon Country, CA [Dataset]. https://www.ownerly.com/ca/canyon-country/big-bend-pl-home-details
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    Dataset updated
    Dec 20, 2021
    Dataset authored and provided by
    Ownerly
    Area covered
    Big Bend Place, Canyon Country, California
    Description

    This dataset provides information about the number of properties, residents, and average property values for Big Bend Place cross streets in Canyon Country, CA.

  16. Big tech and select countries' electricity consumption comparison 2022-2023

    • statista.com
    Updated Jun 27, 2025
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    Statista (2025). Big tech and select countries' electricity consumption comparison 2022-2023 [Dataset]. https://www.statista.com/statistics/1488822/company-and-country-electricity-consumption/
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    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Electricity use in data centers run by Google and Microsoft accounted for ** terawatt hours in 2023, greater than that of the country of Jordan. The training of AI models has heavily contributed to an increase in energy requirements, leading a number of big tech companies to consume more energy than countries.

  17. Share of companies using data analytics in CEE 2023, by country

    • statista.com
    Updated Oct 30, 2024
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    Statista (2024). Share of companies using data analytics in CEE 2023, by country [Dataset]. https://www.statista.com/statistics/1385447/cee-big-data-use-in-enterprises-by-country/
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    Dataset updated
    Oct 30, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    CEE
    Description

    Hungary had the largest share of enterprises using data analytics among Central and Eastern European (CEE) countries, at over 53 percent in 2023. To compare, in Romania, around 22 percent of businesses used data analytics tools.

  18. d

    Replication Data and Code for: Export Conditions in Small Countries and...

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Alfaro, Martin; Warzynski, Frederic (2023). Replication Data and Code for: Export Conditions in Small Countries and their Effects on Domestic Markets [Dataset]. http://doi.org/10.5683/SP3/37PFAV
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Alfaro, Martin; Warzynski, Frederic
    Description

    The data and programs replicate tables and figures from "Export Conditions in Small Countries and their Effects on Domestic Markets", by Alfaro and Warzynski. Please see the ReadMe file for additional details.

  19. Big infrastructure construction projects worldwide 2022, by selected...

    • statista.com
    • ai-chatbox.pro
    Updated Aug 8, 2023
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    Statista (2023). Big infrastructure construction projects worldwide 2022, by selected countries [Dataset]. https://www.statista.com/statistics/1307599/big-infrastructure-construction-projects-worldwide-by-selected-countries/
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    Dataset updated
    Aug 8, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    World
    Description

    India was the country with the most infrastructure projects in development or execution valued at over 25 million U.S. dollars as of May 2022. In contrast to India's 1,944 construction projects, the United States had 1,866 such projects, and China with 1,175. Power facilities made up most of the new private infrastructure construction in the United States.

  20. Countries with the largest population 2025

    • statista.com
    • ai-chatbox.pro
    Updated Feb 21, 2025
    + more versions
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    Statista (2025). Countries with the largest population 2025 [Dataset]. https://www.statista.com/statistics/262879/countries-with-the-largest-population/
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    Dataset updated
    Feb 21, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    World
    Description

    In 2022, 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

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Statista (2024). Largest countries in the world by area [Dataset]. https://www.statista.com/statistics/262955/largest-countries-in-the-world/
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Largest countries in the world by area

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27 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Aug 7, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2021
Area covered
World
Description

The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.

Population of Russia

Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.

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