Download
Share
NOT PEER-REVIEWED
"PeerJ Preprints" is a venue for early communication or feedback before peer review. Data may be preliminary.

R Python, and Ruby clients for GBIF species occurrence data

,
1 rOpenSci, Museum of Paleontology, University of California, Berkeley, Berkeley, CA, United States
2 rOpenSci, Department of Environmental Science, Policy and Management, University of California, Berkeley, Berkeley, CA, United States
DOI
10.7287/peerj.preprints.3304v1
Published
Accepted
Subject Areas
Bioinformatics, Ecology, Computational Science
Keywords
ecology, bioinformatics, gbif, occurrences, biodiversity, reproducibility, python, ruby
Copyright
© 2017 Chamberlain et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Chamberlain SA, Boettiger C. 2017. R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5:e3304v1

Abstract

Background. The number of individuals of each species in a given location forms the basis for many sub-fields of ecology and evolution. Data on individuals, including which species, and where they're found can be used for a large number of research questions. Global Biodiversity Information Facility (hereafter, GBIF) is the largest of these. Programmatic clients for GBIF would make research dealing with GBIF data much easier and more reproducible.

Methods. We have developed clients to access GBIF data for each of the R, Python, and Ruby programming languages: rgbif, pygbif, gbifrb.

Results. For all clients we describe their design and utility, and demonstrate some use cases.

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.

Author Comment

This is a preprint submission to PeerJ Preprints. We may submit to a journal for peer-review as well.

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Scott A Chamberlain conceived and designed the experiments, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Carl Boettiger conceived and designed the experiments, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding data availability:

The source code for this paper is on GitHub at https://github.com/sckott/gbifms. The clients can be found at GitHub: R: https://github.com/ropensci/rgbif, Python: https://github.com/sckott/pygbif, Ruby: https://github.com/sckott/gbifrb

Funding

This project was supported in part by the Alfred P Sloan Foundation (Grant No. G-2014-13485), and in part by the Helmsley Foundation (Grant No. 2016PG-BRI004) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Add your feedback

Before adding feedback, consider if it can be asked as a question instead, and if so then use the Question tab. Pointing out typos is fine, but authors are encouraged to accept only substantially helpful feedback.

Some Markdown syntax is allowed: _italic_ **bold** ^superscript^ ~subscript~ %%blockquote%% [link text](link URL)
 
By posting this you agree to PeerJ's commenting policies
Download
Content
Alert
Just enter your email

Download article

R Python, and Ruby clients for GBIF species occurrence data
PDF Save to Mendeley RIS XML BibTeX
60 Citations   Views   Downloads