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GOODD, a global dataset of more than 38,000 georeferenced dams
By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972789/ https://www.ncbi.nlm.nih.gov/pubmed/31964896 http://dx.doi.org/10.1038/s41597-020-0362-5 |
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author | Mulligan, Mark van Soesbergen, Arnout Sáenz, Leonardo |
author_facet | Mulligan, Mark van Soesbergen, Arnout Sáenz, Leonardo |
author_sort | Mulligan, Mark |
collection | PubMed |
description | By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam. |
format | Online Article Text |
id | pubmed-6972789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69727892020-01-28 GOODD, a global dataset of more than 38,000 georeferenced dams Mulligan, Mark van Soesbergen, Arnout Sáenz, Leonardo Sci Data Data Descriptor By presenting the most comprehensive GlObal geOreferenced Database of Dams to date containing more than 38,000 dams as well as their associated catchments, we enable new and improved global analyses of the impact of dams on society and environment and the impact of environmental change (for example land use and climate change) on the catchments of dams. This paper presents the development of the global database through systematic digitisation of satellite imagery globally by a small team and highlights the various approaches to bias estimation and to validation of the data. The following datasets are provided (a) raw digitised coordinates for the location of dam walls (that may be useful for example in machine learning approaches to dam identification from imagery), (b) a global vector file of the watershed for each dam. Nature Publishing Group UK 2020-01-21 /pmc/articles/PMC6972789/ /pubmed/31964896 http://dx.doi.org/10.1038/s41597-020-0362-5 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Mulligan, Mark van Soesbergen, Arnout Sáenz, Leonardo GOODD, a global dataset of more than 38,000 georeferenced dams |
title | GOODD, a global dataset of more than 38,000 georeferenced dams |
title_full | GOODD, a global dataset of more than 38,000 georeferenced dams |
title_fullStr | GOODD, a global dataset of more than 38,000 georeferenced dams |
title_full_unstemmed | GOODD, a global dataset of more than 38,000 georeferenced dams |
title_short | GOODD, a global dataset of more than 38,000 georeferenced dams |
title_sort | goodd, a global dataset of more than 38,000 georeferenced dams |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972789/ https://www.ncbi.nlm.nih.gov/pubmed/31964896 http://dx.doi.org/10.1038/s41597-020-0362-5 |
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