PRIO is hosting a copy of this dataset with permission from Global Data Lab. Please see their webpage for more information about this data.

Health index

Subnational Gender Development Index

Life expectancy at birth men

People Data Labs is an aggregator of B2B person and company data. We source our globally compliant person dataset via our "Data Union".

The "Data Union" is our proprietary data sharing co-op. Customers opt-in to sharing their data and warrant that their data is fully compliant with global data privacy regulations. Some data sources are provided as a one time dump, others are refreshed every time we do a new data build. Our data sources come from a variety of verticals including HR Tech, Real Estate Tech, Identity/Anti-Fraud, Martech, and others. People Data Labs works with customers on compliance based topics. If a customer wishes to ensure anonymity, we work with them to anonymize the data.

Our company data has identifying information (name, website, social profiles), company attributes (industry, size, founded date), and tags + free text that is useful for segmentation.

The GDL Area Database (www.globaldatalab.org/areadata) presents socioeconomic, health, and demographic development indicators at the level of sub-national areas (provinces, states, prefectures, and the like) within low and middle income countries to the global community. The indicators are created by aggregating data from household survey datasets.

The GDL Area Database (www.globaldatalab.org/areadata) presents socioeconomic, health, and demographic development indicators at the level of sub-national areas (provinces, states, prefectures, and the like) within low and middle income countries to the global community. The indicators are created by aggregating data from household survey datasets.

Population size in thousands

The GDL Area Database (www.globaldatalab.org/areadata) presents socioeconomic, health, and demographic development indicators at the level of sub-national areas (provinces, states, prefectures, and the like) within low and middle income countries to the global community. The indicators are created by aggregating data from household survey datasets.

Subnational HDI

This dataset contains data from the World Development Indicators on Poverty and Shared Prosperity presenting indicators that measure progress toward the World Bank Group’s twin goals of ending extreme poverty by 2030 and promoting shared prosperity in every country in a sustainable manner.

This dataset contains data presented on the World Emissions Clock hosted by the World Data Lab.

The World Emissions Clock provides trajectories of future greenhouse gas emissions until 2050 for 180 countries, five main sectors and up to 24 subsectors, and three different scenarios. These hypothetical scenarios are:

• Business as usual (BAU), where technological advancement and policy-making roughly follows past trends without major shifts.
• Nationally determined contributions (NDC), where countries fully implement their unconditional climate pledges as submitted to the United Nations Framework Convention on Climate Change (UNFCCC).
• Achieving 1.5°C, where secotral emissions within countries follow a cost-efficient pathway towards limiting global warming to 1.5° Celsius by 2100.

For further information, see the Methodology section of the World Emissions Clock. Contact wec@worlddata.io for access information.

The World Emissions Clock was created in a cooperation of the World Data Lab with the International Institute for Applied Systems Analysis (IIASA), the Vienna University of Economics and Business (WU Vienna), and the University of Oxford and was supported from the German Federal Ministry for Economic Cooperation and Development (BMZ), the German Agency for International Cooperation (GIZ), and the Patrick J. McGovern Foundation.

The GDL Ethnic Group Database presents socio-economic, health, gender and demographic development indicators at the level of 367 ethnic groups within 71 low and middle income countries. The indicators are created by aggregating data from household surveys to the ethnic group level.

This dashboard is part of SDGs Today. Please see sdgstoday.orgExtreme poverty poses a major challenge to the livelihood of current and future generations everywhere and threatens Agenda 2030’s promise of leaving no one behind. The World Poverty Clock developed by the World Data Lab provides real-time poverty estimates through 2030 for nearly all countries. The World Poverty Clock uses publicly available data on income distributions, production factors, and household consumption provided by various international organizations, including the World Bank and the International Monetary Fund (IMF). These organizations compile data provided to them by the local governments, and when this information is not available, the World Poverty Clock uses specific models to estimate poverty in these countries. The models include how individual incomes might change over time using IMF growth forecasts for the medium-term complemented by long-term “shared socio-economic pathways” developed by the International Institute for Applied Systems Analysis (IIASA) and similar analysis developed by the OECD. The World Poverty Clock dataset was updated in February 2021, taking into consideration the COVID-19 pandemic effects on the economy.

Percentage of households with a computer in region

This contains the building damage data described in the manuscript 'A Bayesian Approach for Earthquake Impact Modelling' (available at: https://arxiv.org/abs/2412.15791).The code used to generate the R objects are contained in https://github.com/hamishwp/ODDRIN. It compiles data from several sources including:Global Data Lab: J. Smits and I. Permanyer. The Subnational Human Development Database. Scientific data, 6(1):1–15, 2019.Vs30: D. C. Heath, D. J. Wald, C. B. Worden, E. M. Thompson, and G. M. Smoczyk. A global hybrid VS 30 map with a topographic slope–based default and regional map insets. Earthquake Spectra, 36(3):1570–1584, 2020.Earthquake frequency: K. Johnson, M. Villani, K. Bayliss, C. Brooks, S. Chandrasekhar, T. Chartier, Y. Chen, J. Garcia-Pelaez, R. Gee, R. Styron, A. Rood, M. Simionato, and M. Pagani. Global Earthquake Model (GEM) seismic hazard map (version 2023.1 - June 2023). GEM https://doi.org/10.5281/zenodo.8409647, 2023.Income Inequality: F. Alvaredo, A. B. Atkinson, T. Piketty, and E. Saez. World Inequality Database, 2022. URL http://wid.world/data.Copernicus Building Damage Footprints: Copernicus Emergency Management Service. Copernicus emergency management service - mapping, 2012. URL https://emergency.copernicus.eu/mapping. The European Commission.UNITAR/UNOSAT Building Damage Footprints: UNITAR/UNOSAT. UNITAR’s Operational Satellite Applications Programme – UNOSAT, 2023. URL https://unosat.org/products/.WorldPop Population: A. J. Tatem. WorldPop, open data for spatial demography. Scientific Data, 4(1):1–4, 2017. doi: 10.1038/sdata.2017.4.Bing Building Footprints: Microsoft. Global ML Building Footprints, 2022. URL https://github.com/microsoft/GlobalMLBuildingFootprints. Accessed:2024-06-17.Shakemap: D. J. Wald, B. C. Worden, V. Quitoriano, and K. L. Pankow. ShakeMap manual: Technical manual, user’s guide, and software guide. Technical Report 12-A1, United States Geological Survey, 2005.

Percentage of households with a phone in region

Global trade data of Lab equipment under 84715010000, 84715010000 global trade data, trade data of Lab equipment from 80+ Countries.

This dataset contains the annual average quantity of oil spilled from tankers globally, by decade. Data retrieved from Global Change Data Lab. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Electrophysiology Lab Systems (Cardiovascular) – Global Market Analysis and Forecast Model (COVID-19 Market Impact) is built to visualize quantitative market trends within Cardiovascular Devices therapeutic area. Read More