Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Global Subnational River Borders

This dataset presents Global Subnational River-Borders (GSRB), a first global geospatial dataset of subnational, as well as national, political borders set by large rivers. GSRB incorporates three previous vector datasets (GAUL, GRWL + +, and WDBII) to map and quantify the use of large rivers as political borders at local (Level 2), state (Level 1), and national (Level 0) scales. When referencing this dataset, please use the full paper citation: Sarah J. Popelka, Laurence C. Smith; Rivers as political borders: a new subnational geospatial dataset. Water Policy 1 June 2020; 22 (3): 293–312. doi: <a href="https://doi.org/10.2166/wp.2020.041">https://doi.org/10.2166/wp.2020.041</a>

opencc-by-4.0Jul 2023View details →

The CORESIDENCE Database: National and Subnational Data on Household and Living Arrangements Around the World, 1964-2021

Households are the fundamental units of co-residence and play a crucial role in social and economic reproduction worldwide. They are also widely used as units of enumeration for data collection purposes, with substantive implications for research on poverty, living conditions, family structure, and gender dynamics. However, reliable comparative data on households and changes and living arrangements around the world is still under development. The CORESIDENCE database (CoDB) aims to bridge the existing data gap by offering valuable insights not only into the documented disparities between countries but also into the often-elusive regional differences within countries. By providing comprehensive data, it facilitates a deeper understanding of the complex dynamics of co-residence around the world. This database is a significant contribution to research, as it sheds light on both macro-level variations across nations and micro-level variations within specific regions, facilitating more nuanced analyses and evidence-based policymaking. The CoDB is composed of three datasets covering 155 countries (National Dataset), 3563 regions (Subnational Dataset), and 1511 harmonized regions (Subnational-Harmonized Dataset) for the period 1960 to 2021, and it provides 146 indicators on household composition and family arrangements across the world.  This repository is composed of the following elements: a RData file named CORESIDENDE_DATABASE containing the CoDB in the form of a List.  The CORESIDENDE_DB list object is composed of six elements: <ol> <li>NATIONAL: a data frame with the household composition and living arrangements indicators at the national level.</li> <li>SUBNATIONAL: a data frame with the household composition and living arrangements indicators at the subnational level computed over the original subnational division provided in each sample and data source.</li> <li>SUBNATIONAL_HARMONIZED: a data frame with the household composition and living arrangements indicators computed over the harmonized subnational regions.</li> <li>SUBNATIONAL_BOUNDARIES_CORESIDENCE: a spatial data frame (a sf object) with the boundary’s delimitation of the subnational harmonized regions created for this project.</li> <li>CODEBOOK: a data frame with the complete list of indicators, their code names and description.</li> <li>HARMONIZATION_TABLE: a data frame with the full list of individual country-year samples employed in this project and their state of inclusion in the 3 datasets composing the CoDB.</li> </ol> Elements 1, 2, 3, 5 and 6 of the R list are also provided as csv files under the same names. Element 4, the harmonized boundaries, is at disposal as gpkg (Geopackage) file. Articles published by the CORESIDENCE team <a href="https://www.nature.com/articles/s41597-024-02964-3" rel="nofollow">2024, Scientific Data, CORESIDENCE: National and subnational data on household size and composition around the world, 1964–2021</a> <a href="https://genus.springeropen.com/articles/10.1186/s41118-024-00211-6" rel="nofollow">2024, Genus, A global perspective on household size and composition, 1970–2020</a> <a href="https://onlinelibrary.wiley.com/doi/10.1111/padr.12603" rel="nofollow">2024, Population and Development Review, Trends in Living Arrangements Around the World</a>

opencc-by-4.0Nov 2024View details →

Global subnational landed cost of grains and oilseeds

Dataset of landed cost of traded grains and oilseeds for subnational administrative regions globally. This dataset and code allows reproducing the results and figures of the paper Verschuur, Vittis, Obersteiner and Hall (2024), "Heterogeneities in landed costs of traded grains and oilseeds contribute to unequal food access", Nature Food.  This repository includes the following folders and files: -All input data to reproduce the results and figures in the paper. -A Readme File explaining all the data variables in the input data.  -A Jupyter Notebook to load the data and reproduce the results and figures in the paper.

openeupl-1.2Dec 2023View details →

CY-Bench: A comprehensive benchmark dataset for subnational crop yield forecasting

<h1>CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting</h1> <h2> Overview</h2> CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the <a href="https://www.agml.org/">AgML Community</a>, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide. * Crops : Wheat & Maize * Spatial Coverage : Wheat (29 countries), Maize (38).   See <a href="https://github.com/WUR-AI/AgML-CY-Bench/blob/main/notebooks/cybench_summary.ipynb">CY-Bench Summary</a> for the list of countries. * Temporal Coverage : Varies. See <a href="https://github.com/WUR-AI/AgML-CY-Bench/blob/main/notebooks/cybench_summary.ipynb">CY-Bench Summary</a>. <h2>Data </h2> <h3>Data format</h3> The benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable. <h3>Data content</h3> All data files are provided as .csv. <table style="width: 100%;"> <tbody> <tr> <td style="width: 10.3122%;">Data</td> <td style="width: 21.9489%;">Description</td> <td style="width: 18.069%;">Variables (units)</td> <td style="width: 13.8137%;">Temporal Resolution</td> <td style="width: 35.8562%;">Data Source (Reference)</td> </tr> <tr> <td style="width: 10.3122%;">crop_calendar</td> <td style="width: 21.9489%;">start and end of growing season</td> <td style="width: 18.069%;">sos (day of the year), eos (day of the year)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">World Cereal (Franch et al, 2022)</td> </tr> <tr> <td style="width: 10.3122%;">crop_mask</td> <td style="width: 21.9489%;">crop area fraction</td> <td style="width: 18.069%;">crop_area (km2), crop_area_percentage (%)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">WorldCereal (Van Tricht et al., 2023; EC-JRC, 2024)</td> </tr> <tr> <td style="width: 10.3122%;">fpar</td> <td style="width: 21.9489%;">fraction of absorbed photosynthetically active radiation</td> <td style="width: 18.069%;">fpar (%)</td> <td style="width: 13.8137%;">Dekadal (3 times a month; 1-10, 11-20, 21-31)</td> <td style="width: 35.8562%;">European Commission's Joint Research Centre (EC-JRC, 2024)</td> </tr> <tr> <td style="width: 10.3122%;">ndvi</td> <td style="width: 21.9489%;">normalized difference vegetation index</td> <td style="width: 18.069%;">-</td> <td style="width: 13.8137%;">approximately weekly</td> <td style="width: 35.8562%;">MOD09CMG (Vermote, 2015)</td> </tr> <tr> <td style="width: 10.3122%;">meteo</td> <td style="width: 21.9489%;">temperature, precipitation (prec), radiation, potential evapotranspiration (et0), climatic water balance (= prec - et0)  </td> <td style="width: 18.069%;">tmin (C), tmax (C), tavg (C), prec (mm0, et0 (mm), cwb (mm), rad (J m-2 day-1)</td> <td style="width: 13.8137%;">daily</td> <td style="width: 35.8562%;">AgERA5 (Boogaard et al, 2022)</td> </tr> <tr> <td style="width: 10.3122%;">soil_moisture</td> <td style="width: 21.9489%;">surface soil moisture, rootzone soil moisture</td> <td style="width: 18.069%;">ssm (kg m-2), rsm (kg m-2)</td> <td style="width: 13.8137%;">daily</td> <td style="width: 35.8562%;">GLDAS (Rodell et al, 2004)</td> </tr> <tr> <td style="width: 10.3122%;">soil</td> <td style="width: 21.9489%;">available water capacity, bulk density, drainage class</td> <td style="width: 18.069%;">awc (c m-1), bulk_density (kg dm-3), drainage class (category)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;">WISE Soil database (Batjes, 2016)</td> </tr> <tr> <td style="width: 10.3122%;">location</td> <td style="width: 21.9489%;">centroid</td> <td style="width: 18.069%;">latitude, logitude, region_area (km2)</td> <td style="width: 13.8137%;">static</td> <td style="width: 35.8562%;"> </td> </tr> <tr> <td style="width: 10.3122%;">yield</td> <td style="width: 21.9489%;">end-of-season yield</td> <td style="width: 18.069%;">yield (t ha-1)</td> <td style="width: 13.8137%;">yearly</td> <td style="width: 35.8562%;">Various country or region specific sources (see crop_statistics_... in https://github.com/WUR-AI/AgML-CY-Bench/tree/main/data_preparation)</td> </tr> </tbody> </table> <h3> </h3> <h3>Folder structure</h3> <ol> <li>cybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. **variable_croptype_country.csv**. ``` CY-Bench │ └─── maize │   │ │   └─── AO │   │   -- crop_calendar_maize_AO.csv │   │   -- crop_mask_maize_AO.csv │   │   -- fpar_maize_AO.cs │   │   -- location_maize_AO.csv │   │   -- meteo_maize_AO.csv │   │   -- ndvi_maize_AO.csv │   │   -- soil_maize_AO.csv │   │   -- soil_moisture_maize_AO.csv │   │   -- yield_maize_AO.csv │   │  │   └─── AR │       -- crop_calendar_maize_AR.csv │       -- crop_mask_maize_AR.csv │       -- fpar_maize_AR.csv │       -- ... │    └─── wheat │   │ │   └─── AR │   │   -- crop_calendar_wheat_AR.csv │   │   -- crop_mask_wheat_AR.csv │   │   -- fpar_wheat_AR.csv │   │   ... ``` <h3>Example : CSV data content for maize in country X</h3> ``` X └─── crop_calendar_maize_X.csv │   -- crop_name (name of the crop) │   -- adm_id (unique identifier for a subnational unit) │   -- sos (start of crop season) │   -- eos (end of crop season) │ └─── crop_mask_maize_X.csv │   -- crop_name │   -- adm_id  │   -- crop_area │   -- crop_area_percentage │    └─── fpar_maize_X.csv │   -- crop_name │   -- adm_id  │   -- date (in the format YYYYMMdd) │   -- fpar │ └─── location_maize_X.csv │   -- crop_name │   -- adm_id  │   -- latitude │   -- longitude │   -- region_area │ └─── meteo_maize_X.csv │   -- crop_name │   -- adm_id  │   -- date (in the format YYYYMMdd) │   -- tmin (minimum temperature) │   -- tmax (maximum temperature) │   -- prec (precipitation) │   -- rad (radiation) │   -- tavg (average temperature) │   -- et0 (evapotranspiration) │   -- vpd (vapor pressure deficit) │   -- cwb (crop water balance) │  └─── ndvi_maize_X.csv │   -- crop_name │   -- adm_id │   -- date (in the format YYYYMMdd) │   -- ndvi   │    └─── soil_maize_X.csv │   -- crop_name │   -- adm_id │   -- awc (available water capacity) │   -- bulk_density │   -- drainage_class │    └─── soil_moisture_maize_X.csv │   -- crop_name │   -- adm_id │   -- date (in the format YYYYMMdd) │   -- ssm (surface soil moisture) │   -- rsm () │    └─── yield_maize_X.csv │   -- crop_name │   -- country_code │   -- adm_id │   -- harvest_year │   -- yield │   -- harvest_area │   -- production </li> <li>centroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows: centroids │   └─── AO │   │   -- AO.cpg │   │   -- AO.dbf │   │   -- AO.prj │   │   -- AO.shp │   │   -- AO.shx │   └─── AR │   │   -- AR.cpg │   │   -- AR.dbf │   │   -- AR.prj │   │   -- AR.shp │   │   -- AR.shx ... polygons │   └─── AO │   │   -- AO.cpg │   │   -- AO.dbf │   │   -- AO.prj │   │   -- AO.shp │   │   -- AO.shx │   └─── AR │   │   -- AR.cpg │   │   -- AR.dbf │   │   -- AR.prj │   │   -- AR.shp │   │   -- AR.shx ...</li> </ol> <h3>Data access</h3> The full dataset can be downloaded directly from Zenodo or using the ```zenodo_get``` library <h2> License and citation</h2> We kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included.   <h2>Version Notes</h2> 1.0 is the dataset submitted to NeurIPS Datasets and Benchmarks Track. The paper and discussions are here: https://openreview.net/forum?id=jkJDNG468g#discussion 1.1 and 1.2 fix some issues with column names and mismatches in adm_id between yield data and input data. 1.3 includes location information in the form of centroids and polygons of admin regions. 1.4 updates the fpar data for 2023. fpar data was incomplete for 2023 in earlier versions (due to unavailability in the data source itself). 1.5 fixes an issue in crop calendar 1.6 fixes an issue in ndvi time series 1.7 updates storage precision to 3 decimal places to reduce data size 1.8 filter out invalid yield values 1.9 Add vpd. Add location. ET0 obtained from AgERA5 (was AQUASTAT-FAO ). Use AgERA5 2.0 (was AgERA5 1.1) 1.10 Add region_are to location*.csv. Add crop_mask_*.csv. Fix error in yield Australia.

opencc-by-4.0Jan 2020View details →

Subnational phosphorus budgets for EU28 agriculture in 2013

Output data from v1.0.1 of the software https://zenodo.org/record/3610989

opencc-by-4.0Sep 2024View details →

Data and code for the publication "Observed carbon decoupling of subnational production insufficient for net-zero goal by 2050"

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

Trade agreements and deforestation in Colombia: a subnational approach

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →

Global subnational Gini coefficient (income inequality) and gross national income (GNI) per capita PPP datasets for 1990-2023

This dataset provides a gridded subnational datasets for <ul> <li>Income inequality (Gini coefficient) at admin 1 level</li> <li>Gross national income (GNI) per capita PPP at admin 1 level</li> </ul> The datasets are based on reported subnational admin data and spans three decades from 1990 to 2023  The datasets are presented in details in the following publication. Please cite this paper when using data.  Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: <a href="https://doi.org/10.21203/rs.3.rs-5548291/v1" target="_blank" rel="noopener">https://doi.org/10.21203/rs.3.rs-5548291/v1</a> Code is available at following repositories: <ul> <li>Gini coefficient data creation: <a href="https://github.com/mattikummu/subnatGini" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGini</a> </li> <li>GNI per capita data creation: <a href="https://github.com/mattikummu/subnatGNI" target="_blank" rel="noopener">https://github.com/mattikummu/subnatGNI</a> </li> <li>analyses for the article: <a href="https://github.com/mattikummu/gini_gni_analyses">https://github.com/mattikummu/gini_gni_analyses</a> </li> </ul> The following data is given (formats in brackets) Gini coefficient: Please note, two distinct datasets for Gini cofficient are given. One based on SWIID national dataset, and another for WID national dataset. These are separated in file names as follows:  _disp_ for SWIID (disposable income); _WID_ for WID.   <ul> <li>Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Uncertainty (standard deviation for each year and admin area; uncertainty for slope) for Gini (based on SWIID) at admin 1 level (gpkg)</li> <li>Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)</li> </ul> Gross national income (GNI) pear capita  PPP (in 2021 USD): <ul> <li>Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv)</li> <li>Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv)</li> <li>Slope for GNI per capita (log10) at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)</li> <li>Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip)</li> </ul> Files are named as follows Format: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*.  Admin levels: adm0 for admin 0 level, adm1 for admin 1 level Product type:  <ul> <li>_gini_disp_  for gini coefficient based on SWIID national dataset (disposable income)</li> <li>_gini_WID_  for gini coefficient based on WID national dataset</li> <li>_uncertainty_slope_gini_disp_ for slope uncertainty of SWIID based Gini</li> <li>_uncertainty_SD_gini_disp_ for standard deviation of SWIID based Gini</li> <li>_gni_perCapita_ for GNI per capita PPP</li> </ul>   Metadata  Grids  Resolution: 5 arc-min (0.083333333 degrees)  Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)  Coordinate ref system: EPSG:4326 - WGS 84  Format: Multiband geotiff; one band for each year over 1990-2021 Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita   Geospatial polygon (gpkg) files:  Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)  Temporal extent: annual over 1990-2021 Coordinate ref system: EPSG:4326 - WGS 84  Format: gkpk  Unit:  no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita   Version 3 changes (24.07.2025) <ul> <li>both datasets updated to cover 1990-2023 (previously 1990-2021)</li> <li>extrapolation method updated</li> <li>Gini data now produced also using WID national data as a base (previously only one based on SWIID was provided)</li> <li>uncertainty analysis done for Gini (SWIID)</li> </ul>

Poverty Mapping Project: Global Subnational Infant Mortality Rates

The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational Units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 Units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Gridded Population of the World, Version 3 (GPWv3): Subnational Administrative Boundaries

The Gridded Population of the World, Version 3 (GPWv3): Subnational Administrative Boundaries are the basis of the population data products in GPWv3. Due to copyright restrictions, only maps of the subnational administrative boundaries are available, the underlying data cannot be released. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).

Global Subnational Infant Mortality Rates, Version 2.01

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition

The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).