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

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

Content

The United States Census Bureau’s International Dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the data set includes midyear population figures broken down by age and gender assignment at birth. Additionally, they provide time-series data for attributes including fertility rates, birth rates, death rates, and migration rates.

The full documentation is available here. For basic field details, please see the data dictionary.

Note: The U.S. Census Bureau provides estimates and projections for countries and areas that are recognized by the U.S. Department of State that have a population of at least 5,000.

Acknowledgements

This dataset was created by the United States Census Bureau.

Inspiration

Which countries have made the largest improvements in life expectancy? Based on current trends, how long will it take each country to catch up to today’s best performers?

Use this dataset with BigQuery

You can use Kernels to analyze, share, and discuss this data on Kaggle, but if you’re looking for real-time updates and bigger data, check out the data on BigQuery, too: https://cloud.google.com/bigquery/public-data/international-census.

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

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.

The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.

For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

Central African Republic CF: SPI: Pillar 3 Data Products Score: Scale 0-100 data was reported at 68.750 NA in 2022. This stayed constant from the previous number of 68.750 NA for 2021. Central African Republic CF: SPI: Pillar 3 Data Products Score: Scale 0-100 data is updated yearly, averaging 47.266 NA from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 68.750 NA in 2022 and a record low of 37.675 NA in 2005. Central African Republic CF: SPI: Pillar 3 Data Products Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Governance: Policy and Institutions. The data products overall score is a composite score measureing whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system’s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

The British Geological Survey has one of the largest databases in the world on the production and trade of minerals. The dataset contains annual production statistics by mass for more than 70 mineral commodities covering the majority of economically important and internationally-traded minerals, metals and mineral-based materials. For each commodity the annual production statistics are recorded for individual countries, grouped by continent. Import and export statistics are also available for years up to 2002. Maintenance of the database is funded by the Science Budget and output is used by government, private industry and others in support of policy, economic analysis and commercial strategy. As far as possible the production data are compiled from primary, official sources. Quality assurance is maintained by participation in such groups as the International Consultative Group on Non-ferrous Metal Statistics. Individual commodity and country tables are available for sale on request.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Private markets drive economic growth, tapping initiative and investment to create productive jobs and raise incomes. Trade is also a driver of economic growth as it integrates developing countries into the world economy and generates benefits for their people. Data on the private sector and trade are from the World Bank Group's Private Participation in Infrastructure Project Database, Enterprise Surveys, and Doing Business Indicators, as well as from the International Monetary Fund's Balance of Payments database and International Financial Statistics, the UN Commission on Trade and Development, the World Trade Organization, and various other sources.

This statistical dataset contains estimates on the number of active online Facebook users living outside of their country of origin within the European Union. The dataset includes information on Facebook users' age, gender, country of residence, and country of previous residence. The data is divided in the number of Monthly Active Users and Daily Active Users. The data was collected through standard CSV format via an advertising API platform by using an R Studio code, and the data collection was conducted twice a month from January to November 2021.

The dataset was originally published in DiVA and moved to SND in 2024.

The Trends in International Mathematics and Science Study, 2015 (TIMSS 2015) is a data collection that is part of the Trends in International Mathematics and Science Study (TIMSS) program; program data are available since 1999 at . TIMSS 2015 (https://nces.ed.gov/timss/) is a cross-sectional study that provides international comparative information of the mathematics and science literacy of fourth-, eighth-, and twelfth-grade students and examines factors that may be associated with the acquisition of math and science literacy in students. The study was conducted using direct assessments of students and questionnaires for students, teachers, and school administrators. Fourth-, eighth-, and twelfth-graders in the 2014-15 school year were sampled. Key statistics produced from TIMSS 2015 provide reliable and timely data on the mathematics and science achievement of U.S. students compared to that of students in other countries. Data are expected to be released in 2018.

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Private markets drive economic growth, tapping initiative and investment to create productive jobs and raise incomes. Trade is also a driver of economic growth as it integrates developing countries into the world economy and generates benefits for their people. Data on the private sector and trade are from the World Bank Group's Private Participation in Infrastructure Project Database, Enterprise Surveys, and Doing Business Indicators, as well as from the International Monetary Fund's Balance of Payments database and International Financial Statistics, the UN Commission on Trade and Development, the World Trade Organization, and various other sources.

The resilience of health care development in countries along the Belt and Road reflects the level of resilience of health care development in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of health care development in the countries along the Belt and Road. The World Bank statistical database was used for the preparation of the health resilience data. Based on the year-on-year data of these four indicators, and taking into account the year-on-year changes of each indicator, the product of resilience in the development of healthcare conditions was prepared through comprehensive diagnosis based on sensitivity and adaptability analysis. "The Resilience in Health Care Development dataset for countries along the Belt and Road is an important reference for analysing and comparing the current resilience in health care development in each country.

This dataset provides values for TRADE BALANCE EXTRA EA18 INTERMEDIATE GOODS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

Worldwide Bureaucracy Indicators

Worldwide Bureaucracy Indicators (WWBI) dataset from the World Bank.

The Worldwide Bureaucracy Indicators (WWBI) database is a unique cross-national dataset on public sector employment and wages that aims to fill an information gap, thereby helping researchers, development practitioners, and policymakers gain a better understanding of the personnel dimensions of state capability, the footprint of the public sector within the overall labor market, and the fiscal implications of the public sector wage bill. The dataset is derived from administrative data and household surveys, thereby complementing existing, expert perception-based approaches.

The World Bank introduced the dataset with a series of four blogs:

• blog1
• blog2
• blog3
• blog4

Can you replicate the figures in the blogs? Can you display any of the data more clearly than in the blogs?

Data Dictionary

wwbi_data.csv

wwbi_series.csv

wwbi_country.csv

This table shows the international developments in the capital stock and the investments. Beside the picture of the total economy, a category has been made for ICT (information and communication technology). The table is related both to the physical capital stock and its renewal or extension by means of (foreign) capital investments, and to the money that is necessary to finance the investments, in particular the venture capital. The scope of the capital and the investments in a country are mainly defined by the propensity of entrepreneurs to invest. Investment behaviour is partly defined by the investment climate.

Note: Comparable definitions are used to compare the figures presented internationally. The definitions sometimes differ from definitions used by Statistics Netherlands. The figures in this table could differ from Dutch figures presented elsewhere on the website of Statistics Netherlands.

Data available from 1990 up to 2012.

Status of the figures: The external source of these data frequently supplies adjusted figures on preceding periods. For example, it often happens that countries still provide figures on older years. The reverse, older figures being withdrawn, also happens now and then. These adjusted data are not mentioned as such in the table.

Changes as of 22 December 2017: No, table is stopped.

When will new figures be published? Not.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Mode of data collection

Face-to-face [f2f]

TRA210 - Extent to which respondents trust people and institutions across OECD participant countries. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Extent to which respondents trust people and institutions across OECD participant countries...

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains terrain elevation above sea level (ELE) in [m a.s.l.] covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km).

The data is hyperlinked under 'resources' with the following characeristics:
ELE - GISdata (GeoTIFF)
Data format: GEOTIFF
File size : 826.8 MB

There are two temporal representation of solar resource and PVOUT data available:
• Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals)
• Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals)

Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations:
• LTAy_YearlyTotals = LTAy_DailyTotals * 365.25
• LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month

*For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest)
*For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world.

For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).