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A collection of Aquamaps native layers in NetCDF format
Many research communities working in biology and related fields are deeply interested in having a wide collection of environmental and species distribution data. Obviously, for these communities to be able to carry out their studies in a fast and efficient manner, these data need to be well organize...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988293/ https://www.ncbi.nlm.nih.gov/pubmed/29876396 http://dx.doi.org/10.1016/j.dib.2018.01.026 |
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author | Scarponi, Paolo Coro, Gianpaolo Pagano, Pasquale |
author_facet | Scarponi, Paolo Coro, Gianpaolo Pagano, Pasquale |
author_sort | Scarponi, Paolo |
collection | PubMed |
description | Many research communities working in biology and related fields are deeply interested in having a wide collection of environmental and species distribution data. Obviously, for these communities to be able to carry out their studies in a fast and efficient manner, these data need to be well organized, meticulously described and possibly represented in a standard format that allows for a direct usage. In fact, being the final goal of these communities to extract information from the data and applying some kind of data processing workflows, cutting down the data preparation and preprocessing time is key. This is the main reason that triggered the activity presented in this paper that aimed at converting the whole collection of 10,385 species distribution models published by the Aquamaps consortium (Aquamaps Native Distributions) into NetCDF files, creating a re-usable, portable and self-describing collection of native habitat datasets. |
format | Online Article Text |
id | pubmed-5988293 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-59882932018-06-06 A collection of Aquamaps native layers in NetCDF format Scarponi, Paolo Coro, Gianpaolo Pagano, Pasquale Data Brief Environmental Science Many research communities working in biology and related fields are deeply interested in having a wide collection of environmental and species distribution data. Obviously, for these communities to be able to carry out their studies in a fast and efficient manner, these data need to be well organized, meticulously described and possibly represented in a standard format that allows for a direct usage. In fact, being the final goal of these communities to extract information from the data and applying some kind of data processing workflows, cutting down the data preparation and preprocessing time is key. This is the main reason that triggered the activity presented in this paper that aimed at converting the whole collection of 10,385 species distribution models published by the Aquamaps consortium (Aquamaps Native Distributions) into NetCDF files, creating a re-usable, portable and self-describing collection of native habitat datasets. Elsevier 2018-01-31 /pmc/articles/PMC5988293/ /pubmed/29876396 http://dx.doi.org/10.1016/j.dib.2018.01.026 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Environmental Science Scarponi, Paolo Coro, Gianpaolo Pagano, Pasquale A collection of Aquamaps native layers in NetCDF format |
title | A collection of Aquamaps native layers in NetCDF format |
title_full | A collection of Aquamaps native layers in NetCDF format |
title_fullStr | A collection of Aquamaps native layers in NetCDF format |
title_full_unstemmed | A collection of Aquamaps native layers in NetCDF format |
title_short | A collection of Aquamaps native layers in NetCDF format |
title_sort | collection of aquamaps native layers in netcdf format |
topic | Environmental Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988293/ https://www.ncbi.nlm.nih.gov/pubmed/29876396 http://dx.doi.org/10.1016/j.dib.2018.01.026 |
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