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1. Microsoft Planetary Computer Data Catalog [[link](https://planetarycomputer.microsoft.com/catalog)]
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1. Registry of Open Data on AWS [[link](https://registry.opendata.aws/)]
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1. World Terrestrial Ecosystems(WTE) 2015 and 2050 [[link](https://www.arcgis.com/home/item.html?id=e247d15898804e42b9a8f2aa7128b2b6)][[blog](https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/announcements/2050-projections-of-world-terrestrial-ecosystems-are-now-available/)]
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1. Public Data on Google Cloud [[link](https://cloud.google.com/storage/docs/public-datasets)]
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1. Umbra Open Data Program [[link](https://umbra.space/open-data)]
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1. Maxar Open Data Program [[link](https://www.maxar.com/open-data/)]
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1. Planet Education and Research Data [[link](https://www.planet.com/markets/education-and-research/)]
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1. Google Earth Engine Data Catalog [[link](https://developers.google.com/earth-engine/datasets)]
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1. Global River Widths from Landsat (GRWL) Database [[link](https://zenodo.org/record/1297434)]
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1. Open Topography [[link](https://opentopography.org/)]
1. A multi-year crop field boundary labels for Africa [[link](https://zenodo.org/records/11060871)]
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## Projects
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1. World Bank’s Open Night Lights ([link](https://worldbank.github.io/OpenNightLights/welcome.html))
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1. A GUIDEBOOK ON MAPPING POVERTY THROUGH DATA INTEGRATION AND ARTIFICIAL INTELLIGENCE ([link](https://www.adb.org/sites/default/files/publication/698091/guidebook-mapping-poverty-data-integration-ai.pdf))
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1. Cookiecutter Data Science ([link](https://drivendata.github.io/cookiecutter-data-science/))
AWS Open Data Registry: https://registry.opendata.aws/africa-field-boundary-labels/
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1. Understanding at risk crops in 2050 [[link](https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/analytics/climate-change-threatens-the-worlds-olive-legacy-how-gis-can-help-understand-crops-at-risk-by-2050/)]
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1. World Bank’s Open Night Lights [[link](https://worldbank.github.io/OpenNightLights/welcome.html)]
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1. A GUIDEBOOK ON MAPPING POVERTY THROUGH DATA INTEGRATION AND ARTIFICIAL INTELLIGENCE [[link](https://www.adb.org/sites/default/files/publication/698091/guidebook-mapping-poverty-data-integration-ai.pdf)]
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1. Cookiecutter Data Science [[link](https://drivendata.github.io/cookiecutter-data-science/)]
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1. Analyze NLCD annual time series [[link](https://www.usgs.gov/centers/eros/science/annual-national-land-cover-database)]
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