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55 results for “GDP”
Figure data and code used in Inconsistent definitions of GDP: Implications for estimates of decoupling
<p>Figure code in R and underlying data to reproduce all figures in the article "Inconsistent definitions of GDP: Implications for estimates of decoupling".</p>
Relationship between Australia suicide and Catalonia GDP
<p>Experiment that uses open data from 2000 to 2014.</p> <p><br> Datasets used:</p> <ul> <li>Suicide people in AUS:<br> https://data.oecd.org/healthstat/suicide-rates.htm</li> <li>Catalonia GDP by demand components (2000-2016):<br> https://www.kaggle.com/xavier14/catalonia-gdp-by-demand-components-20002016</li> </ul> <p>This Experiment compares these two datasets and make a chart for comparison.</p>
ADS usage versus region GDP per capita (data)
<p>These files contain the data used for constructing figure 2 in "The ADS in the Information Age - Impact on Discovery" (http://adsabs.harvard.edu/abs/2012opsa.book..253H, arXiv:1106.5644). This figure shows the fraction of ADS world usage (for specific regions) as a function of GDP per capita (all values normalized by their 1997 value). For specifics: see paper. The data have been uploaded as a JPG figure, plain text file and a TAR archive with files used by the graphing program DataGraph (http://www.visualdatatools.com/DataGraph/).</p>
Data for: Downscaled gridded global dataset for Gross Domestic Product (GDP) per capita at purchasing power parity (PPP) over 1990-2022
<p>This dataset provides a gridded dataset for GDP per capita at purchasing power parity (PPP) downscaled to an admin 2 level (43,501 admin units). The dataset is based on reported subnational admin data (from 89 countries and 2,708 subnational units) and spans three decades from 1990 to 2022. </p> <p>The dataset is presented in details in the following publication. <strong><em>Please cite this paper when using data. </em></strong></p> <p>Kummu, M., Kosonen, M. & Masoumzadeh Sayyar, S. 2025. Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022. Scientific Data 12: 178. <a href="https://doi.org/10.1038/s41597-025-04487-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04487-x</a></p> <p><strong>Code is available</strong> at: <a href="https://github.com/mattikummu/griddedGDPpc" target="_blank" rel="noopener">https://github.com/mattikummu/griddedGDPpc </a></p> <p> </p> <p><strong>The following data is given (formats in brackets)</strong></p> <ul> <li>GDP per capita (PPP) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 1 level (at the level of reporting, either admin 1 level or admin 0 level) (GeoTIFF, gpkg, csv)</li> <li>GDP per capita (PPP) at admin 2 level (downscaled from admin 1 level) (GeoTIFF, gpkg, csv)</li> <li>Total GDP (PPP), downscaled admin 2 level GDP per capita (PPP) multiplied by gridded population count, with three resolutions: 30 arc-sec, 5 arc-min, and 30 arc-min (GeoTIFF) </li> <li>Input data for the script that was used to generate the data above (code_input_data.zip). Code available at https://github.com/mattikummu/griddedGDPpc </li> </ul> <p><strong>Files are named as follows</strong><br><em>Format</em>: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. <br><em>Admin levels:</em> adm0 for admin 0 level, adm1 for admin 1 level, and adm2 for admin 2 level<br><em>Product type:</em> GDP per capita at purchasing power parity (PPP): _gdp_perCapita_; and total GDP at purchasing power parity (PPP): _gdp_tot_</p> <p> </p> <p><strong>Metadata </strong></p> <p><em>Grids for GDP per capita data:</em></p> <p>Resolution: 5 arc-min (0.083333333 degrees) (for admin 2 level also 30 arc-min, 0.5 degree, resolution is provided)</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 </p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Grids for total GDP:</em></p> <p>Resolution: 30 arc-sec, 5 arc-min or 30 arc-min</p> <p>Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) </p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: Multiband geotiff; each band for each year over 1990-2022 (5 arc-min, 30 arc-min) or for each five years 1990, 1995, ... 2015, 2020 (30 arc-sec)</p> <p>Unit: USD in 2017 international dollars</p> <p> </p> <p><em>Geospatial polygon (gpkg) files: </em></p> <p>Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) </p> <p>Temporal extent: annual over 1990-2022</p> <p>Coordinate ref system: EPSG:4326 - WGS 84 </p> <p>Format: gkpk </p> <p>Unit: USD in 2017 international dollars</p>
The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates
<p>This repository contains the socioeconomic and emissions projections generated by the Resources for the Future Socioeconomic Projections (RFF-SPs) model as discussed in Rennert et al., "<a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates</a>" , forthcoming at the <em>Brookings Papers on Economic Activity</em>. The data take the form of a Monte Carlo simulation with n = 10,000 draws.</p> <p>File structure and column metadata are described here: </p> <p>--- <br> emissions/ <br> --- </p> <p>- rffsp_co2_emissions.csv <br> - rffsp_ch4_emissions.csv <br> - rffsp_n2o_emissions.csv </p> <p>Each file in this folder contains 3 columns: sample, year, and value. For each row: </p> <p>- sample contains the number identifying which draw a prediction belongs to (from 1 to 10,000). <br> - year contains the calendar year of the prediction. <br> - value contains the projected annual global emissions of the gas specified in the filename. IMPORTANT: Units are as follows: "rffsp_co2_emissions.csv" is in gigatons C (not CO2), "rffsp_ch4_emissions.csv " is in megatons CH4, and "rffsp_n2o_emissions.csv " is in megatons N2 (not N2O). </p> <p>--- <br> pop_income/ <br> --- </p> <p>- rffsp_pop_income_run_1.feather <br> - rffsp_pop_income_run_2.feather <br> - rffsp_pop_income_run_3.feather <br> . <br> . <br> . <br> - rffsp_pop_income_run_9999.feather <br> - rffsp_pop_income_run_10000.feather </p> <p>This folder contains 10,000 files in the .feather file format (https://arrow.apache.org/docs/python/feather.html), which is optimized for I/O speed and compressed to minimize storage requirements. Each file corresponds to one draw of our socioeconomic data, and contains 4 columns: Country, Year, Pop, and GDP. The number in each filename corresponds to the "sample" column in the emissions data. For each row:</p> <p>- Country contains the ISO Alpha-3 code (https://www.iso.org/iso-3166-country-codes.html) of the country whose GDP and population are projected. <br> - Year contains the calendar year of the predictions. <br> - Pop contains the projected population for a given country and year, in units of thousands of people. <br> - GDP contains the projected GDP for a given country and year, in units of millions of 2011 USD. </p> <p>--- </p> <p>The probabilistic population projections were produced by Adrian E. Raftery and Hana Ševčíková (University of Washington), using the methods described by Raftery and Ševčíková (2021). Please cite this reference in any publications using these projections. Their research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under NIH grant number R01 HD-070936. </p> <p>The probabilistic economic and emissions projections are from Rennert et al. (forthcoming), based in part on those from Müller, Stock, and Watson (forthcoming). </p> <p>---</p> <p><strong>References </strong></p> <p>Müller, U.K, Stock, J.H., and Watson, M.W. (forthcoming). An Econometric Model of International Growth Dynamics for Long-Horizon Forecasting. The Review of Economics and Statistics, available online 30 October 2020. URL: <a href="https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth">https://direct.mit.edu/rest/article-abstract/doi/10.1162/rest_a_00997/97738/An-Econometric-Model-of-International-Growth</a> </p> <p>Raftery, A.E. and Ševčíková, H. (2021). Probabilistic population forecasting: Short to very long-term. International Journal of Forecasting, available online 7 October 2021. URL: <a href="https://www.sciencedirect.com/science/article/pii/S0169207021001394">https://www.sciencedirect.com/science/article/pii/S0169207021001394</a> </p> <p>Rennert, K., Prest, B.C., Pizer, W., Newell, R.G., Anthoff, D., Kingdon, C., Rennels, L., Cooke, R., Raftery, A.E., Ševčíková, H, and Errickson, F. (forthcoming). The Social Cost of Carbon: Advances in Long-Term Probabilistic Projections of Population, GDP, Emissions, and Discount Rates. Brookings Papers on Economic Activity. Available online 27 October 2021. URL: <a href="https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/">https://www.rff.org/publications/working-papers/the-social-cost-of-carbon-advances-in-long-term-probabilistic-projections-of-population-gdp-emissions-and-discount-rates/ </a></p>
Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"
<p>This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4). </p>
Global gridded GDP under the historical and future scenarios
<p>We have extended the time series of global GDP based on Version 5 at https://zenodo.org/record/5880037#.Yyx4lsi5fRQ, which makes the following changes:</p> <p>a) includes annual global GDP from 2000 - 2020, the unit is PPP 2005 international dollars. </p> <p>b) updates the GDP projections for the period 2025 - 2100 at five-year intervals under five SSPs, and the unit is PPP 2005 international dollars, which allows for comparsion against the historical values mention above.</p> <p>This dataset consists of a total of 101 tif images with spatial resolutions of 1 km (in 7 zip files) and 0.25-degree, respectively. The gridded GDP are distributed over land, with Antarctica, oceans, and some non-illuminated or depopulated areas marked as zero. The spatial extents are 90S - 90N and 180E - 180W in standard WGS84 coordinate system.</p> <p>For more details, please refer to the article: Global gridded GDP data set consistent with the shared socioeconomic pathways that is consistent with Version 5 (GDP unit is PPP 2005 U.S. dollars).</p>
Global Sectoral GDP map at 30'' resolution (SectGDP30) v2.0
<p>- This dataset provides global sector-specific GDP distribution maps (in GeoTIFF format) with a 30-second spatial resolution. It allocates GDP at the 30-arcsecond grid level for three sectors (services, industry, and agriculture) by the distribution of country-level GDP data using high-resolution land cover map.<br>- The source GDP data for allocation is based on nominal GDP for the years 2010, 2015, and 2020, obtained from the World Bank. As the high-resolution land cover map, it uses the built-up area and non-residential area data by the Global Human Settlement Layer (Pesaresi and Politis, 2022) for the service and industrial sectors and the Global cropland map by Potapov et al. (2022) for the agriculture sector. Detailed descriptions of the data creation methodology can be found in Shoji et al. (In Review).<br>- Each pixel represents the monetary value of added value generated by economic activity hypothetically occurring within that pixel. The unit of each pixel value is in millions of USD (current prices for 2010, 2015, and 2020).</p> <p>(Updated to v2.0 on July 1, 2025)</p> <p>This update includes a major change to the spatial allocation method for service and agricultural GDP. For details on the new GDP mapping methodology, please refer to Shoji et al. (In Review). There are no changes to the industrial GDP map.</p> <p> </p> <p>Reference:<br>- Pesaresi M, Politis P.: GHS-BUILT-S R2022A: GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975–2030). European Commission, Joint Research Centre (JRC), 2022.<br>- Potapov P, Svetlana T, Matthew CH, Alexandra T, Viviana Z, Ahmad K, Xiao-Peng S, Amy P, Quan S, Jocelyn C.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3: 19–28, 2022.<br>- Shoji T, Kajiyama K, Yamazaki D, Kita Y, Watanabe M.: Global spatially-distributed sectoral GDP map for disaster risk analysis. In Review.</p>
Covid 19 Dataset, Cases by Country, Vaccination, GDP and Average Temperature
<p>Dataset del covid-19 obtenido mediante técnicas de web Scrapping</p>
GDP Exposure in Low-Elevation Coastal Zones of China
<p>GDP Exposure in Low-Elevation Coastal Zones of China</p> <p>Feixiang Li<sup>1</sup>, Liwei Mao<sup>2</sup>, Qian Chen<sup>1</sup>, Xuchao Yang<sup>1*</sup></p> <p><sup>1</sup> Ocean College, Zhejiang University, Zhoushan 316021, China.</p> <p><sup>2</sup> Hangzhou City Planning and Design Academy, Hangzhou 310012, China</p> <p>Recommended citation</p> <p> Article citation will be added once the article is available</p> <p>Use of the dataset and full description</p> <p>Before using the dataset, please read this document and the article describing the methodology.</p> <p>The article will be referenced here as soon as it is published.</p> <p>Please notify us (yangxuchao@zju.edu.cn) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above).</p> <p>Support</p> <p>If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact <a href="mailto:12034034@zju.edu.cn">yangxuchao@zju.edu.cn</a>.</p> <p> Abstract</p> <p>This dataset provides the GDP exposure of China's coastal areas and Low-elevation coastal zones (LECZs) in 2010 and 2019 based on the random forest algorithm and the integration of multi-source remote sensing data and geospatial big data, with a spatial resolution of 100m.</p> <p>Files included in the dataset</p> <p>The repository comprises several datasets. Each dataset coms in a TIIF file. The file name is constructed from dataset properties as follows: <Country><Source of the GDP><Region><Year></p> <p><Country></p> <p>The “Country” flag indicates the country represented by the data. In this data set, it is China</p> <p><Source of the GDP></p> <p>The “Source of the GDP” flag indicates the sector from which GDP comes.</p> <p>GDP1: The primary sector.</p> <p>GDP2: The secondary sector.</p> <p>GDP3: The tertiary sector.</p> <p>GDPall: The total GDP of the region usually includes primary, secondary and tertiary sector.</p> <p><Region></p> <p>The “Region” flag indicates the exact area of the country.</p> <p>Coastal: Coastal areas, in this data are the coastal provinces of China including Liaoning, Hebei, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, Guangxi and Hainan. Hong Kong, Macau and Taiwan are excluded because of their different political and economic status.</p> <p>LECZ: Low-elevation coastal zones (LECZ), which are areas below 10m above sea level and connected to the ocean</p> <p><Year></p> <p>The “Year” flag indicates the Year of data.</p> <p>Data format description</p> <p>Data format: GeoTIFF.</p> <p>Spatial resolution: 100m.</p> <p>Units: million CNY/ha.</p>
GDP Worldwide 1960-2017
<p>GDP per country in US$ from 1960 to 2017 </p>
Finding The Impact of GDP on Education Attainment in Austria - Datasets
<p>Finding The Impact of GDP on Education Attainment in Austria - Datasets</p>
Code and Data for "Green Growth in the Mirror of History: Long-Term Evidence on Decoupling Emissions from GDP"
<p><span>The code and files contained in this repository allow for the reproduction of the results and figures from the paper <em>“Green Growth in the Mirror of History: Long-Term Evidence on Decoupling Emissions from GDP”</em> (submitted to <em>Nature Communications</em>). In particular, it generates the historical database of gross domestic product (GDP), population, and greenhouse gas emissions (GHGe) at the national, regional, and global levels from 1820 to 2022. It also includes the code required for data processing and allows reproduction of all figures and analyses in the manuscript.</span></p> <p><span>The repository is organized into a set of folders functioning as an R project. </span><span>These folders are as follows:</span></p> <p><span><span>·<span> </span></span></span><strong><span><span>data_input:</span></span></strong><span><span> Contains the databases and files necessary to run our code that have been preprocessed by ourselves. The databases which we have not preprocessed are downloaded directly from their original sources using the links provided in the R scripts. These databases were created by other researchers. Please cite the original databases if you use them directly.</span></span></p> <p><span><span><span>·<span> </span></span></span></span><strong><span><span>code:</span></span></strong><span><span> Contains R scripts used to process the datasets from data_input and to produce the figures. These scripts are:</span></span></p> <p><span><span>o<span> </span></span></span><strong><span><span>common.</span></span><span><strong>R</strong> </span></strong><span>contains code that is sourced in the other scripts and does not need to be run by the user.</span></p> <p><span><span>o<span> </span></span></span><strong><span>input_processing.R</span></strong><span> processes the data input to generate the main dataset saved in the folder data_output and used to make the figures.</span></p> <p><span><span>o<span> </span></span></span><strong><span>figures.R</span></strong><span> contains the code that processes the main dataset to produce each figure.</span></p> <p><span><span>·<span> </span></span></span><strong><span><span>data_output:</span></span></strong><span><span> Contains datasets generated by the scripts contained in<strong> </strong>input_processing.R that are used to produce the figures.</span></span></p> <ul> <li><strong><span>figures:</span></strong><span> This folder contains all the figures produced by figures.R. Note that some figures also underwent purely aesthetic edits in external software (Adobe Illustrator) for clarity purposes.</span></li> </ul>
Data to estimate energy efficiency and environmental friendliness of Ukrainian GDP from indicators of structural, economic, and social development in Ukraine
<p><span>Data to estimate regression dependences of energy efficiency and environmental friendliness of GDP from indicators of structural, economic, and social development in Ukraine </span></p>
Dataset CO2 Emission per GDP Forecast 2020-2100
<p>The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100.</p> <p>The CO2 emission forecast is from the publication “<em>Dataset Global Warming Forecast using Acceleration Factors</em>” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase.</p> <p>The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase.</p> <p>CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year.</p> <p>The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.</p>
Macroeconomic Impact of Ebola outbreaks in SSA and potential mitigation of GDP loss with prophylactic vaccination programs
<p>Introduction: Decisions about prevention of and response to Ebola outbreaks require an understanding of the macroeconomic implications of these interventions. Prophylactic vaccines hold promise to mitigate the negative economic impacts of infectious disease outbreaks. The objective of this study was to evaluate the relationship between outbreak size and economic impact among countries with recorded Ebola outbreaks and to quantify the hypothetical benefits of prophylactic Ebola vaccination interventions in these outbreaks.</p> <p>Methods: The synthetic control method was used to estimate the causal impacts of Ebola outbreaks on per capita gross domestic product (GDP) of five countries in sub-Saharan Africa that have previously experienced Ebola outbreaks between 2000 and 2016, where no vaccines were deployed. Using illustrative assumptions about vaccine coverage, efficacy, and protective immunity, the potential economic benefits of prophylactic Ebola vaccination were estimated using the number of cases in an outbreak as a key indicator.</p> <p>Results: The impact of Ebola outbreaks on the macroeconomy of the selected countries led to a decline in GDP of up to 36%, which was greatest in the third year after the onset of each outbreak and increased exponentially with the size of outbreak (i.e., number of reported cases). Over three years, the aggregate loss estimated for Sierra Leone from its 2014-2016 outbreak is estimated at 16.1 billion International$. Prophylactic vaccination could have prevented up to 89% of an outbreak’s negative impact on GDP, reducing the outbreak’s impact to as little as 1.6% of GDP lost. </p> <p>Conclusion: This study supports the case that macroeconomic returns are associated with prophylactic Ebola vaccination. Our findings support recommendations for prophylactic Ebola vaccination as a core component of prevention and response measures for global health security. </p>
Global primary energy and GDP data
<p>Data used for article "<strong>Energy</strong> <strong>Limits to the Gross Domestic Product on Earth</strong>" https://arxiv.org/abs/2005.05244</p> <p>Aggregation from various data sets:</p> <p>https://ourworldindata.org/energy</p> <p>GDP data from World Bank</p>
Gha_per capita, FDI, GDP_per capita, KOF for EAEU 1992-2023
<p>This dataset contains a dataset on the EAEU economy in the interval 1992-2023: </p> <ul> <li><span>Gha_per capita</span></li> <li><span>FDI</span></li> <li><span>GDP_per capita</span></li> <li><span>KOF</span></li> </ul>
comparison of sites listed in ASAPbio (Preprint services) and Institute for Sustainable Infrastructure and their accessibility as a function of countries GDP
Open the record for dataset details and reuse information.
The power of nighttime lights: Exploring their suitability as a proxy for GDP, population, and other socioeconomic activity
<p>Final datasets that were used to arrive at my results in my bachelor's thesis.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.