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Resources","sub_category":"Soil and Land","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# SoilGrids250m\nGlobal spatial predictions of soil properties and classes at 250 m resolution.\n\nThis repository hosts the source code used to create the SoilGrids version published in 2017 (Hengl et al. 2017). The maps generated in 2017 can be downloaded from [here](https://files.isric.org/soilgrids/former/2017-03-10/data/). For information about the most recent version of SoilGrids, please visit the [project page](https://www.isric.org/explore/soilgrids). The new maps are displayed in the new portal [soilgrids.org](https://www.soilgrids.org).\n\nCitation:\n* Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) [SoilGrids250m: Global gridded soil information based on machine learning](http://dx.doi.org/10.1371/journal.pone.0169748). PLoS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748\n\nWhat can you find on this github repository:\n* [R scripts documenting processing steps](https://github.com/ISRICWorldSoil/SoilGrids250m/wiki/SoilGrids-overview),\n* [Sample code explaining the modelling framework](https://github.com/ISRICWorldSoil/GSIF_tutorials/blob/master/eberg/soilmaps_MLA.R),\n* [Functions for Cross-validation of ensemble models with examples](https://github.com/ISRICWorldSoil/SoilGrids250m/blob/master/grids/cv/),\n* [Examples of predictions, outputs and visualizations](https://github.com/ISRICWorldSoil/SoilGrids250m/wiki/Examples-of-outputs),\n* [Technical specifications of the system](https://github.com/ISRICWorldSoil/SoilGrids250m/wiki/Hardware-specifications),\n\n","funding_links":[],"readme_doi_urls":["http://dx.doi.org/10.1371/journal.pone.0169748"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["soils"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/1016","html_url":"https://ost.ecosyste.ms/projects/1016"}