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Citing GBIF Mediated Data

John Waller

2021-12-20

Source: vignettes/gbif_citations.Rmd
gbif_citations.Rmd

Data accessed through the GBIF network is free for all, but not free of obligations.

Under the terms of the GBIF data user agreement, users who download data agree to cite a DOI. Good citation also rewards data-publishing institutions and individuals by reinforcing the value of sharing open data and demonstrating its impact to their funders.

Please do read GBIF’s citation guidelines.

The Best Way to Cite

The best way to get data from GBIF is with rgbif::occ_download().

occ_download(pred("taxonKey",7412043))

The newest version of rgbif will give you the DOI you need to make a good citation.

<<gbif download>>
  Your download is being processed by GBIF:
  https://www.gbif.org/occurrence/download/0056004-210914110416597
  Most downloads finish within 15 min.
  Check status with
  occ_download_wait('0056004-210914110416597')
  After it finishes, use
  d <- occ_download_get('0056004-210914110416597') %>%
    occ_download_import()
  to retrieve your download.
Download Info:
  Username: jwaller
  E-mail: jwaller@gbif.org
  Format: DWCA
  Download key: 0056004-210914110416597
  Created: 2021-11-17T09:17:21.828+00:00
Citation Info:  
  Please always cite the download DOI when using this data.
  https://www.gbif.org/citation-guidelines
  DOI: 10.15468/dl.9hqqbn
  Citation:
  GBIF Occurrence Download https://doi.org/10.15468/dl.9hqqbn Accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2021-11-17

For this download, you would use this DOI-citation:

GBIF Occurrence Download https://doi.org/10.15468/dl.9hqqbn Accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2021-11-17

You could also get this citation by using rgbif::gbif_citation()

gbif_citation("0056004-210914110416597") # using the downloadkey

These would be the preferred and easiest ways to create a citation of GBIF mediated data. Below I will describe other special cases that you might want to consider.

Register a Derived Dataset

Derived datasets are a new citation feature on GBIF. Derived datasets are citable records of GBIF-mediated occurrence data. To register a derived dataset, you will need to create a simple text file with two columns:

  1. A GBIF datasetkey (uuid)
  2. A count of the number of occurrences from each dataset

This allows GBIF to give credit to each involved dataset. The file you register with GBIF should look like the table below.

datasetkey n
4fa7b334-ce0d-4e88-aaae-2e0c138d049e 213
906e6978-e292-4a8b-9c39-adf6bb0f3323 2
721a99a4-71f4-4466-b346-83c367889238 35

Remember that you should also upload your filtered GBIF dataset of occurrences to a public repository like Zenodo.

There are 3 main reasons to register a derived dataset:

  1. A GBIF download that has been filtered/reduced significantly (e.g. CoordinateCleaner).
  2. Data accessed through a cloud service.
  3. Occurrences obtained using occ_search() or similar.

Before using option 3, it is important to consider: could my occ_search() have been accomplished with an occ_download()? The answer is almost always YES!.

Here is a simple example of using rgbif::derived_dataset().

library(rgbif)
library(dplyr)
library(CoordinateCleaner)

gbif_download <- occ_download_get('0056004-210914110416597') %>%
    occ_download_import()
    
gbif_download_cleaned <- gbif_download %>%
    setNames(tolower(names(.))) %>% 
    filter(occurrencestatus  == "PRESENT") %>%
    filter(year >= 1900) %>% 
    cc_cen(buffer = 2000) %>% # remove country centroids within 2km 
    cc_inst(buffer = 2000) %>% # remove zoo and herbaria within 2km 
    cc_sea() # remove from ocean

readr::write_tsv(gbif_download_cleaned,"cleaned_data_for_zenodo.tsv")

At this point, you would have to stop and upload to public repository.

https://zenodo.org/

Once you are finished, you can run the following, with the source_url being the link to your publicly accessible modified data. You will need to setup your GBIF credentials for this to work.

# https://www.gbif.org/derived-dataset/about)

derived_data <- gbif_download_clean %>%
group_by(datasetkey) %>% 
count()

derived_dataset_prep(
citation_data = derived_data,
title = "Test Derived Dataset",
description = "This data was filtered using CoordinateCleaner.",
source_url = "https://zenodo.org/record/4246090#.YPGS2OgzZPY"
)

# If output looks ok, run derived_dataset to register the dataset on GBIF
# derived_dataset(
# citation_data = data,
# title = "Test Derived Dataset",
# description = "This data was filtered using CoordinateCleaner.",
# source_url = "https://zenodo.org/record/4246090#.YPGS2OgzZPY"
# )

Check your derived-dataset user page to see if it worked.

Further Reading

https://www.gbif.org/citation-guidelines
https://data-blog.gbif.org/post/derived-datasets/
https://www.gbif.org/derived-dataset/register
https://discourse.gbif.org/t/gbif-exports-as-public-datasets-in-cloud-environments/1835