R Python, and Ruby clients for GBIF species occurrence data

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Authors Scott Chamberlain, Carl Boettiger
Journal/Conference Name PeerJ PrePrints
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Paper Abstract 5 Corresponding Author: 6 Scott Chamberlain 7 rOpenSci, Museum of Paleontology, University of California, Berkeley, CA, USA 8 Email address: scott@ropensci.org 9 ∗Corresponding author Email addresses: scott(at)ropensci.org (Scott Chamberlain), carl(at)ropensci.org (Carl Boettiger) September 26, 2017 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3304v1 | CC BY 4.0 Open Access | rec: 29 Sep 2017, publ: 29 Sep 2017 Background. The number of individuals of each species in a given location forms the basis for many 10 sub-fields of ecology and evolution. Data on individuals, including which species, and where they’re 11 found can be used for a large number of research questions. Global Biodiversity Information Facility 12 (hereafter, GBIF) is the largest of these. Programmatic clients for GBIF would make research dealing 13 with GBIF data much easier and more reproducible. 14 Methods. We have developed clients to access GBIF data for each of the R, Python, and Ruby 15 programming languages: rgbif, pygbif, gbifrb. 16 Results. For all clients we describe their design and utility, and demonstrate some use cases. 17 Discussion. Programmatic access to GBIF will facilitate more open and reproducible science the three GBIF clients described herein are a significant contribution towards this goal. 2 PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3304v1 | CC BY 4.0 Open Access | rec: 29 Sep 2017, publ: 29 Sep 2017
Date of publication 2017
Code Programming Language R
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