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XPolaris: an R-package to retrieve United States soil data at 30-meter resolution
OBJECTIVES: This data article aims to introduce the “XPolaris” R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data fro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390218/ https://www.ncbi.nlm.nih.gov/pubmed/34446061 http://dx.doi.org/10.1186/s13104-021-05729-y |
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author | Moro Rosso, Luiz H. de Borja Reis, Andre F. Correndo, Adrian A. Ciampitti, Ignacio A. |
author_facet | Moro Rosso, Luiz H. de Borja Reis, Andre F. Correndo, Adrian A. Ciampitti, Ignacio A. |
author_sort | Moro Rosso, Luiz H. |
collection | PubMed |
description | OBJECTIVES: This data article aims to introduce the “XPolaris” R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data from the POLARIS database into traditional spreadsheet format [i.e., Comma-Separated Values (CSV)] for further data analyses. DATA DESCRIPTION: The core of this publication is a code-tutorial envisioned to assist users in retrieving soil raster data within the CONUS. All data is sourced from the POLARIS database, a 30-m probabilistic map of soil series and different soil properties [Chaney et al. Geoderma 274:54, 2016, Chaney et al. Water Resour Res 55:2916, 2019]. POLARIS represents an optimization of the Soil Survey Geographic (SSURGO) database, circumventing issues of spatial disaggregation, harmonizing, and filling spatial gaps. POLARIS was constructed using a machine learning algorithm, the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART-HPC) [Odgers et al. Geoderma 214:91, 2014]. Although the data is easily accessible in a raster format, retrieving large amounts of data can be time-consuming or require advanced programming skills. |
format | Online Article Text |
id | pubmed-8390218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-83902182021-08-27 XPolaris: an R-package to retrieve United States soil data at 30-meter resolution Moro Rosso, Luiz H. de Borja Reis, Andre F. Correndo, Adrian A. Ciampitti, Ignacio A. BMC Res Notes Data Note OBJECTIVES: This data article aims to introduce the “XPolaris” R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data from the POLARIS database into traditional spreadsheet format [i.e., Comma-Separated Values (CSV)] for further data analyses. DATA DESCRIPTION: The core of this publication is a code-tutorial envisioned to assist users in retrieving soil raster data within the CONUS. All data is sourced from the POLARIS database, a 30-m probabilistic map of soil series and different soil properties [Chaney et al. Geoderma 274:54, 2016, Chaney et al. Water Resour Res 55:2916, 2019]. POLARIS represents an optimization of the Soil Survey Geographic (SSURGO) database, circumventing issues of spatial disaggregation, harmonizing, and filling spatial gaps. POLARIS was constructed using a machine learning algorithm, the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART-HPC) [Odgers et al. Geoderma 214:91, 2014]. Although the data is easily accessible in a raster format, retrieving large amounts of data can be time-consuming or require advanced programming skills. BioMed Central 2021-08-26 /pmc/articles/PMC8390218/ /pubmed/34446061 http://dx.doi.org/10.1186/s13104-021-05729-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Data Note Moro Rosso, Luiz H. de Borja Reis, Andre F. Correndo, Adrian A. Ciampitti, Ignacio A. XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title | XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title_full | XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title_fullStr | XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title_full_unstemmed | XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title_short | XPolaris: an R-package to retrieve United States soil data at 30-meter resolution |
title_sort | xpolaris: an r-package to retrieve united states soil data at 30-meter resolution |
topic | Data Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8390218/ https://www.ncbi.nlm.nih.gov/pubmed/34446061 http://dx.doi.org/10.1186/s13104-021-05729-y |
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