26 datasets found
  1. T

    GDP GROWTH RATE RATE by Country Dataset

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Oct 19, 2022
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    TRADING ECONOMICS (2022). GDP GROWTH RATE RATE by Country Dataset [Dataset]. https://tradingeconomics.com/country-list/gdp-growth-rate-rate?vm=r
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    Oct 19, 2022
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    2025
    Area covered
    World
    Description

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

  2. Chemical companies - highes R & D spending, by country

    • statista.com
    Updated Feb 15, 2011
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    Statista (2011). Chemical companies - highes R & D spending, by country [Dataset]. https://www.statista.com/statistics/273020/chemical-companies-with-the-highest-r-und-d-spending-by-country/
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    Dataset updated
    Feb 15, 2011
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2009
    Area covered
    Worldwide
    Description

    The statistic shows 130 chemical companies with the highest research and development spending in 2009, by country. In the U.S., 40 chemical companies spent a total of 7,252 million euros on research and development in 2009.

  3. Estimating the true (population) infection rate for COVID-19: A Backcasting...

    • zenodo.org
    zip
    Updated Nov 19, 2020
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    Steven John Phipps; Steven John Phipps; R. Quentin Grafton; R. Quentin Grafton; Tom Kompas; Tom Kompas (2020). Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods [Dataset]. http://doi.org/10.5281/zenodo.3821525
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    zipAvailable download formats
    Dataset updated
    Nov 19, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Steven John Phipps; Steven John Phipps; R. Quentin Grafton; R. Quentin Grafton; Tom Kompas; Tom Kompas
    License

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

    Description

    Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.

  4. f

    Table_1_The Determinants of the Low COVID-19 Transmission and Mortality...

    • frontiersin.figshare.com
    docx
    Updated May 30, 2023
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    Yagai Bouba; Emmanuel Kagning Tsinda; Maxime Descartes Mbogning Fonkou; Gideon Sadikiel Mmbando; Nicola Luigi Bragazzi; Jude Dzevela Kong (2023). Table_1_The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis.docx [Dataset]. http://doi.org/10.3389/fpubh.2021.751197.s001
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Yagai Bouba; Emmanuel Kagning Tsinda; Maxime Descartes Mbogning Fonkou; Gideon Sadikiel Mmbando; Nicola Luigi Bragazzi; Jude Dzevela Kong
    License

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

    Description

    Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

  5. Leading countries by R&D spending worldwide 2022

    • statista.com
    • ai-chatbox.pro
    Updated Jun 26, 2025
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    Statista (2025). Leading countries by R&D spending worldwide 2022 [Dataset]. https://www.statista.com/statistics/732247/worldwide-research-and-development-gross-expenditure-top-countries/
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    OECD, Worldwide
    Description

    The United States is the leading country worldwide in terms of spending on research and development (R&D), with R&D expenditure exceeding *** billion purchasing power parity (PPP) U.S. dollars. China is invested about *** billion U.S. dollars into R&D. Health and technology Overall, health and technology dominate R&D spending globally. In 2022, health constituted nearly ** percent of all R&D spending, while hardware producers accounted for over ***percent and software producers accounted for over ***percent. Tech companies such as Meta, Amazon, and Alphabet contribute massively to tech spending, while spending continues to grow in areas such as medical technology and pharmaceuticals. Other sources of R&D spending Other sources of R&D spending include the automotive industry, chemicals, and manufacturing. Notably, within the automotive industry, the EU leads in spending, contributing nearly ** billion euros to the *** billion euros spent on automotive R&D globally. By company, Volkswagen spent the most at **** billion U.S. dollars, while in the United States, Ford spent the most on R&D at *** billion U.S. dollars.

  6. T

    SOCIAL SECURITY RATE FOR COMPANIES by Country in AUSTRALIA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 27, 2017
    + more versions
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    TRADING ECONOMICS (2017). SOCIAL SECURITY RATE FOR COMPANIES by Country in AUSTRALIA [Dataset]. https://tradingeconomics.com/country-list/social-security-rate-for-companies?continent=australia
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    May 27, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    2025
    Area covered
    Australia
    Description

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

  7. Coronavirus (covid-19) in Sierra Leone

    • kaggle.com
    Updated Jun 10, 2020
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    todowa2 (2020). Coronavirus (covid-19) in Sierra Leone [Dataset]. https://www.kaggle.com/todowa2/coronaviruscovid19sierraleone/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 10, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    todowa2
    Area covered
    Sierra Leone
    Description

    Coronavirus (covid-19) in Sierra Leone

    This repository contains datasets relating to coronavirus in Sierra Leone, as well as on demographic and other information from the 2015 Population and Household Census (PHC). It also includes mapping shapefiles by district, so that you can map the district-level coronavirus statistics.

    See here for a full description of how the data files have been created from the source data, including the R code.

    Last updated: 10 June 2020.


    Context

    The novel 2019 coronavirus (covid-19) arrived late to West Africa and Sierra Leone in particular. This dataset provides the number of reported cases on a district-by-district basis for Sierra Leone, as well as various additional statistics at the country level. In addition, I provide district-by-district data on demographics and households' main sources of information, both from the 2015 census. For convenience, I also provide shapefiles for mapping the 14 districts of Sierra Leone.

    Content

    The dataset consists of four main files, which are in the output folder. See the column descriptions below for further details.

    1. Coronavirus confirmed cases by district (sl_districts_coronavirus.csv). I found the original data by looking in the static/js/data folder in the source code for covid19.mic.gov.sl, last accessed 10 June 2020. The file contains the cumulative number of confirmed coronavirus cases in the 14 districts of Sierra Leone as a time series. I have used the R tidyverse to reshape the data and ensure naming is consistent with the other data files.

    2. Demographic statistics by district (sl_districts_demographics.csv). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset covers the 14 districts of Sierra Leone, which increased to 16 in 2017. Last accessed 10 June 2020.

    3. Main Sources of Information by district (sl_districts_info_sources.csv). Data from the 2015 Population and Housing Census (PHC), sourced from Open Data Sierra Leone. The dataset presents the main sources of information, such as television or radio, for households in the 14 districts of Sierra Leone. Last accessed 2 June 2020. I note that I have made one correction to the source data (see R code with correction here).

    4. Country-wide coronavirus statistics for Sierra Leone (sl_national_coronavirus.csv). The original data also comes from covid19.mic.gov.sl, last accessed 10 June 2020. The file contains numerous statistics as time series, listed in the Column Description section below. I note that there are various potential issues in the file which I leave the user to decide how to deal with (duplicate datetimes, inconsistent statistics).

    Additionally I include a set of five files with district-by-district mapping (shapefiles) and other data, unchanged from their original source. Each file is labelled in the following way: sl_districts_mapping.*. These files come from Direct Relief Open Data on ArcGIS Hub. The data also include district-level data on maternal child health attributes, which was the original context of the mapping data.

    Column Descriptions

    Coronavirus confirmed cases by district sl_districts_coronavirus.csv:

    1. date: Date of reporting
    2. district: District of Sierra Leone (based on pre-2017 administrative boundaries)
    3. confirmed_cases: Cumulative number of confirmed coronavirus cases; NA if no data reported
    4. decrease: Dummy variable indicating whether the number of reported cases has been revised down. NA if no reported cases on that date; 1 if there is a decrease from the last reported cases; 0 otherwise

    Demographic statistics by district sl_districts_demographics.csv:

    1. district: District of Sierra Leone (based on pre-2017 administrative boundaries)
    2. d_code: District code
    3. d_id: District id
    4. total_pop: Total population in district
    5. pop_share: District's share of total country population
    6. t_male: Total male population
    7. t_female: Total female population
    8. s_ratio: (*) Sex ratio at birth (number of males for every 100 females, under the age of 1)
    9. t_urban: Total urban population
    10. t_rural: Total rural population
    11. prop_urban: Proportion urban
    12. t_h_pop: Sum of h_male and h_female
    13. h_male: (?)
    14. h_female: (?)
    15. t_i_pop: Sum of i_male and i_female
    16. i_male: (?)
    17. i_female: (?)
    18. working_pop: Working population
    19. depend_pop: Dependent population

    ...

  8. M

    Russia Death Rate (1950-2025)

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). Russia Death Rate (1950-2025) [Dataset]. https://www.macrotrends.net/global-metrics/countries/rus/russia/death-rate
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    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, 1950 - Dec 31, 2025
    Area covered
    Russia
    Description

    Historical chart and dataset showing Russia death rate by year from 1950 to 2025.

  9. M

    South Korea Literacy Rate

    • macrotrends.net
    csv
    Updated Jun 30, 2025
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    MACROTRENDS (2025). South Korea Literacy Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/kor/south-korea/literacy-rate
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    MACROTRENDS
    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, 2008 - Dec 31, 2018
    Area covered
    South Korea
    Description

    Historical chart and dataset showing South Korea literacy rate by year from 2008 to 2018.

  10. r

    Current LGA Population density & gaming expenditures statistics

    • researchdata.edu.au
    Updated Aug 1, 2014
    + more versions
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    data.vic.gov.au (2014). Current LGA Population density & gaming expenditures statistics [Dataset]. https://researchdata.edu.au/current-lga-population-expenditures-statistics/634186
    Explore at:
    Dataset updated
    Aug 1, 2014
    Dataset provided by
    data.vic.gov.au
    License

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

    Description

    This data set included population and expenditure breakdowns by LGA,\r demographic statistics, labor statistics and Socio Economis Indexes for Areas\r (SEIFA) LGA score and ranking per LGA.\r \r Detailed descriptions of this data set include: \r \- LGA name \r \- LGA code \r \- Region \r \- Total Net Expenditure \r \- SEIFA DIS RANK State \r \- SEIFA DIS RANK Country \r \- SEIFA DIS RANK Metro \r \- SEIFA ADV DIS Score \r \- SEIFA ADV DIS RANK State \r \- SEIFA ADV DIS RANK Country \r \- SEIFA ADV DIS RANK Metro \r \- Adult population \r \- Adult population per venue \r \- EGM numbers per 1000 adults \r \- Expenditure per adult \r \- Workforce \r \- Unemployment \r \- Unemployment rate\r \r

  11. Number of e-commerce users SEA 2024-2029, by country

    • ai-chatbox.pro
    • statista.com
    Updated Sep 17, 2024
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    R. Hirschmann (2024). Number of e-commerce users SEA 2024-2029, by country [Dataset]. https://www.ai-chatbox.pro/?_=%2Ftopics%2F9919%2Fe-commerce-in-singapore%2F%23XgboD02vawLbpWJjSPEePEUG%2FVFd%2Bik%3D
    Explore at:
    Dataset updated
    Sep 17, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    R. Hirschmann
    Description

    Over the last two observations, the number of users is forecast to significantly increase in all regions. From the selected regions, the ranking by number of users in the e-commerce market is forecast to be lead by Indonesia with 99.1 million users. In contrast, the ranking is trailed by Singapore with 4.9 million users, recording a difference of 94.2 million users to Indonesia. Find further statistics on other topics such as a comparison of countries or regions regarding the penetration rate. The Statista Market Insights cover a broad range of additional markets.

  12. The potential impact of international migration on prospective population...

    • zenodo.org
    bin, csv, txt
    Updated Dec 8, 2024
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    Markus Dörflinger; Markus Dörflinger; Michaela Potančoková; Michaela Potančoková; Guillaume Marois; Guillaume Marois (2024). The potential impact of international migration on prospective population ageing in Asian countries: Code and datasets [Dataset]. http://doi.org/10.5281/zenodo.12705066
    Explore at:
    bin, csv, txtAvailable download formats
    Dataset updated
    Dec 8, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Markus Dörflinger; Markus Dörflinger; Michaela Potančoková; Michaela Potančoková; Guillaume Marois; Guillaume Marois
    License

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

    Area covered
    Asia
    Description

    We assess the potential impact of international migration on population ageing in Asian countries by estimating replacement migration for the period 2022-2050.

    This open data deposit contains the code (R-scripts) and the datasets (csv-files) for the replacement migration scenarios and a zero-migration scenario:

    • Constant chronological old-age dependency ratio (Constant OADR scenario)
    • Constant prospective old-age dependency ratio (Constant POADR scenario)
    • Constant chronological working-age population (Constant WA scenario)
    • Constant prospective working-age population (Constant PWA scenario)
    • Zero-migration (ZM scenario)

    Countries included in the analysis: Armenia, China, Georgia, Hong Kong, Japan, Macao, North Korea, Singapore, South Korea, Taiwan, Thailand.

    Please note that for Armenia and Hong Kong (2023) and Georgia (2024) later baseline years are applied due to the UN country-specific assumptions on post-Covid-19 mortality.

    For detailed information about the scenarios and parameters:

    Dörflinger, M., Potancokova, M., Marois, G. (2024): The potential impact of international migration on prospective population ageing in Asian countries. Asian Population Studies. https://doi.org/10.1080/17441730.2024.2436201

    All underlying data (UN World Population Prospects 2022) are openly available at:

    https://population.un.org/wpp/Download/Archive

    Code

    1_Data.R:

    • Load and merge data from UN World Population Prospects 2022
    • Define sample
    • Prepare data (prospective old-age thresholds, model sex and age pattern of migrants)

    2_Scenarios.R:

    • Replacement migration scenarios:
      • Constant chronological old-age dependency
      • Constant prospective old-age dependency
      • Constant chronological working-age population
      • Constant prospective working-age population
    • Zero-migration scenario

    3_Robustness_checks.R:

    • Run replacement migrations scenarios with different model sex and age patterns for net migration

    Program version used: RStudio "Chocolate Cosmos" (e4392fc9, 2024-06-05). Files may not be compatible with other versions.

    Datasets

    The datasets contain the key information on population size, the relevant indicators (OADR, POADR, WA, PWA) and replacement migration volumes and rates by country and year. Please see readme_datasets.txt for detailed information.

    Acknowledgements

    Part of the research was developed in the Young Scientists Summer Program at the International Institute for Applied Systems Analysis, Laxenburg (Austria) with financial support from the German National Member Organization.

  13. COVID-19 cases by Continent

    • kaggle.com
    Updated Aug 27, 2020
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    OJ (2020). COVID-19 cases by Continent [Dataset]. http://doi.org/10.34740/kaggle/dsv/1445192
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 27, 2020
    Dataset provided by
    Kaggle
    Authors
    OJ
    Description

    Context

    Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

    Content

    The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a regular basis with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also scraped from worldometer.

    The datasets

    COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

    Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

    COVID-19 tests covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

    Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

    2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

    Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

    Acknowledgements

    1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
    2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
    3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
    4. Worldometer: https://www.worldometers.info/
    5. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
    6. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers
  14. r

    International Liabilities by Country of the Australian-located Operations of...

    • researchdata.edu.au
    • data.gov.au
    • +2more
    Updated May 12, 2013
    + more versions
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    Reserve Bank of Australia (2013). International Liabilities by Country of the Australian-located Operations of Banks and RFCs [Dataset]. https://researchdata.edu.au/international-liabilities-country-banks-rfcs/3000355
    Explore at:
    Dataset updated
    May 12, 2013
    Dataset provided by
    data.gov.au
    Authors
    Reserve Bank of Australia
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    In March 2003, banks and selected Registered Financial Corporations (RFCs) began reporting their international assets, liabilities and country exposures to APRA in ARF/RRF 231 International Exposures. This return is the basis of the data provided by Australia to the Bank for International Settlements (BIS) for its International Banking Statistics (IBS) data collection. APRA ceased the RFC data collection after September 2010.\r \r The IBS data are based on the methodology described in the BIS Guide on International Financial Statistics (see http://www.bis.org/statistics/intfinstatsguide.pdf; Part II International banking statistics). Data reported for Australia, and other countries, on the BIS website are expressed in United States dollars.\r \r Data are recorded on an end-quarter basis.\r \r There are two sets of IBS data: locational data, which are used to gauge the role of banks and financial centres in the intermediation of international capital flows; and consolidated data, which can be used to monitor the country risk exposure of national banking systems. Only locational data are reported in this statistical table. \r \r Data are shown for a range of countries and regions. Similar data for a selected group of countries are also available in B12.2.\r \r Country and regional groupings are based on the classification used in the IBS.\r \r Some liabilities are reported at market value, but contractual or nominal values are used where market values are not appropriate.\r \r This statistical table contains seven data worksheets. Six present data for countries within each specified region, while the 'Summary' worksheet shows total international liabilities of Australian-located banks (and RFCs between March 2003 and September 2010) for each region, and Australia. In each of these worksheets, the data in the last column measures total international liabilities for the region. Total international liabilities for each country add to total international liabilities for the region. However, in some quarters, this cannot be directly verified because data for individual countries and regions have blank entries in order to avoid disclosing confidential bank exposures.\r \r In the 'Summary' worksheet, the positions by region are summed to produce a ‘Total non-residents’ figure that represents reporting entities’ total positions with offshore counterparties in all currencies. The positions shown for 'Australia' are positions with residents in foreign currency.\r

  15. J

    National natural rates of interest and the single monetary policy in the...

    • journaldata.zbw.eu
    txt, zip
    Updated Dec 7, 2022
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    Sebastien Fries; Jean-Stéphane Mésonnier; Sarah Mouabbi; Jean-Paul Renne; Sebastien Fries; Jean-Stéphane Mésonnier; Sarah Mouabbi; Jean-Paul Renne (2022). National natural rates of interest and the single monetary policy in the euro area (replication data) [Dataset]. http://doi.org/10.15456/jae.2022327.0706691948
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    txt(3614), zip(10440933)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Sebastien Fries; Jean-Stéphane Mésonnier; Sarah Mouabbi; Jean-Paul Renne; Sebastien Fries; Jean-Stéphane Mésonnier; Sarah Mouabbi; Jean-Paul Renne
    License

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

    Description

    We estimate time-varying national natural real rates of interest (r?) for the four largest economies of the euro area over 1999-2016. We further derive the associated national real interest rate gaps, which gauge the perceived monetary policy stance in each country. We find that the average r? have been lower after 2008. Furthermore, national r? were significantly negative in southern countries during the sovereign crisis. As their effective real rates soared, national rate gaps across the euro area diverged. However, a common policy stance has been restored since 2014 as the European Central Bank's unconventional programs gathered pace.

  16. P

    Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury...

    • ceicdata.com
    Updated Jul 21, 2018
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    CEICdata.com (2018). Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate [Dataset]. https://www.ceicdata.com/en/papua-new-guinea/interest-rates/pg-risk-premium-on-lending-lending-rate-minus-treasury-bill-rate
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    Dataset updated
    Jul 21, 2018
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Dec 1, 1999 - Dec 1, 2016
    Area covered
    Papua New Guinea
    Description

    Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data was reported at 4.040 % pa in 2016. This records an increase from the previous number of 3.730 % pa for 2015. Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data is updated yearly, averaging 3.100 % pa from Dec 1995 (Median) to 2016, with 16 observations. The data reached an all-time high of 8.660 % pa in 2012 and a record low of -6.090 % pa in 1995. Papua New Guinea PG: Risk Premium on Lending: Lending Rate Minus Treasury Bill Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank: Interest Rates. Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the 'risk free' treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics database.; ;

  17. Leading countries by R&D spending as share of GDP globally 2022

    • statista.com
    • ai-chatbox.pro
    Updated Jun 26, 2025
    + more versions
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    Statista (2025). Leading countries by R&D spending as share of GDP globally 2022 [Dataset]. https://www.statista.com/statistics/732269/worldwide-research-and-development-share-of-gdp-top-countries/
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide, OECD
    Description

    In 2022, Israel invested *** percent of the country's gross domestic product (GDP) into research and development, the highest worldwide. In South Korea, the expenditure on R&D reached over **** percent of its GDP.

  18. Results of Mixed Model Analysis with the country specific data nested within...

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Wenpeng You; Maciej Henneberg (2023). Results of Mixed Model Analysis with the country specific data nested within WHO regions. [Dataset]. http://doi.org/10.1371/journal.pone.0199594.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Wenpeng You; Maciej Henneberg
    License

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

    Description

    Means of prevalence (%) of obesity (>30kg/m2) for males and females in countries with Ibs values above and below median are shown.

  19. o

    Data from: Recent adverse mortality trends in Scotland: comparison with...

    • explore.openaire.eu
    • data.niaid.nih.gov
    • +1more
    Updated Oct 1, 2019
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    Lynda Fenton; Jon Minton; Julie Ramsay; Maria Kaye-Bardgett; Colin Fischbacher; Grant Wyper; Gerry McCartney (2019). Data from: Recent adverse mortality trends in Scotland: comparison with other high-income countries. [Dataset]. http://doi.org/10.5061/dryad.hc627cj
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    Dataset updated
    Oct 1, 2019
    Authors
    Lynda Fenton; Jon Minton; Julie Ramsay; Maria Kaye-Bardgett; Colin Fischbacher; Grant Wyper; Gerry McCartney
    Area covered
    Scotland
    Description

    Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance. Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071. Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/. Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067. Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322. Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013. Please see README.txt for further information HMD international_updated Jan 2019.xlsx Comprises 20 worksheets, of which 14 contain data. These data are arranged by country and by year. Missing data codes: "" The tab 'contents and sources' provides descriptions of the data source and contents of each sheet. HMD Scotland time trend analysis.xlsx Comprises 5 worksheets, including a combination of data and charts. The sheet 'contents' describes the data source and contents of other sheets. The variables include year, life expectancy, and various measures of change in life expectancy Missing data codes: "" Segmented regression chart.xlsx Comprises 2 worksheets, 'Data' and 'Chart'. Variables within the 'data' worksheet include: Year 4 quarter rolling period ending Female observed mortality rate Female predicted by one-break model Female predicted by two-break model Male observed mortality rate Male predicted by one-break model Male predicted by two-break model Chart breakpoint indicator Missing data codes: (blank space) Summary findings from segmented regression.xlsx Excel workbook containing table 1 of paper 'summary of results of segmented regression by population group and model/test'

  20. f

    Estimating the completeness of death registration: An empirical method

    • plos.figshare.com
    pdf
    Updated Jun 1, 2023
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    Tim Adair; Alan D. Lopez (2023). Estimating the completeness of death registration: An empirical method [Dataset]. http://doi.org/10.1371/journal.pone.0197047
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Tim Adair; Alan D. Lopez
    License

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

    Description

    IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.

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TRADING ECONOMICS (2022). GDP GROWTH RATE RATE by Country Dataset [Dataset]. https://tradingeconomics.com/country-list/gdp-growth-rate-rate?vm=r

GDP GROWTH RATE RATE by Country Dataset

GDP GROWTH RATE RATE by Country Dataset (2025)

Explore at:
csv, excel, xml, jsonAvailable download formats
Dataset updated
Oct 19, 2022
Dataset authored and provided by
TRADING ECONOMICS
License

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

Time period covered
2025
Area covered
World
Description

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

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