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Simple search and citation of occurrences

Hannah L. Owens

Cory Merow

Brian Maitner

Jamie M. Kass

Vijay Barve

Robert Guralnick

2024-10-28

Introduction

We have entered the age of data-intensive scientific discovery. As data sets increase in complexity and heterogeneity, we must preserve the cycle of data citation from primary data sources to aggregating databases to research products and back to primary data sources. The citation cycle keeps science transparent, but it is also key to supporting primary providers by documenting the use of their data. The Global Biodiversity Information Facility (GBIF), Botanical Information and Ecology Network (BIEN), and other data aggregators have made great strides in harvesting citation data from research products and linking them back to primary data providers. However, this only works if those that publish research products cite primary data sources in the first place. We developed occCite, a set of R-based tools for downloading, managing, and citing biodiversity data, to advance toward the goal of closing the data provenance cycle. These tools preserve links between occurrence data and primary providers once researchers download aggregated data, and facilitate the citation of primary data providers in research papers.

The occCite workflow follows a three-step process. First, the user inputs one or more taxonomic names (or a phylogeny). occCite then rectifies these names by checking them against one or more taxonomic databases, which can be specified by the user (see the Global Names List). The results of the taxonomic rectification are then kept in an occCiteData object in local memory. Next, occCite takes the occCiteData object and user-defined search parameters to query BIEN (through rbien) and/or GBIF(through rGBIF) for records. The results are appended to the occCiteData object, along with metadata on the search. Finally, the user can pass the occCiteData object to occCitation, which compiles citations for the primary providers, database aggregators, and R packages used to build the dataset.

Future iterations of occCite will track citation data through the data cleaning process and provide a series of visualizations on raw query results and final data sets. It will also provide data citations in a format congruent with best-practice recommendations for large biodiversity data sets. Based on these data citation tools, we will also propose a new set of standards for citing primary biodiversity data in published research articles that provides due credit to contributors and allows them to track the use of their work. Keep checking back!

Setup

If you plan to query GBIF, you will need to provide them with your user login information. We have provided a dummy login below to show you the format. You will need to provide actual account information. This is because you will actually be downloading all of the records available for the species using occ_download(), instead of getting results from occ_search(), which has a hard limit of 100,000 occurrences.

library(occCite);
#Creating a GBIF login
GBIFLogin <- GBIFLoginManager(user = "occCiteTester",
                              email = "****@yahoo.com",
                              pwd = "12345")

Performing a simple search

The basics

At its simplest, occCite allows you to search for occurrences for a single species. The taxonomy of the user-specified species will be verified using EOL and NCBI taxonomies by default.

# Simple search
mySimpleOccCiteObject <- occQuery(x = "Protea cynaroides",
                                  datasources = c("gbif", "bien"),
                                  GBIFLogin = GBIFLogin, 
                                  GBIFDownloadDirectory = 
                                    system.file('extdata/', package='occCite'),
                                  checkPreviousGBIFDownload = T)

Here is what the GBIF results look like:

# GBIF search results
head(mySimpleOccCiteObject@occResults$`Protea cynaroides`$GBIF$OccurrenceTable)

##                name longitude  latitude coordinateUncertaintyInMeters day month
## 1 Protea cynaroides  18.43928 -33.95440                             8  17     2
## 2 Protea cynaroides  22.12754 -33.91561                             4  11     2
## 3 Protea cynaroides  18.43927 -33.95429                             8  17     2
## 4 Protea cynaroides  18.43254 -34.29275                            31   6     2
## 5 Protea cynaroides  18.42429 -34.02934                          2167  10     2
## 6 Protea cynaroides  18.43529 -34.10545                             2   8     2
##   year                           datasetKey dataService
## 1 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
## 2 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
## 3 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
## 4 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
## 5 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
## 6 2022 50c9509d-22c7-4a22-a47d-8c48425ef4a7        GBIF
##                               datasetName
## 1 iNaturalist Research-grade Observations
## 2 iNaturalist Research-grade Observations
## 3 iNaturalist Research-grade Observations
## 4 iNaturalist Research-grade Observations
## 5 iNaturalist Research-grade Observations
## 6 iNaturalist Research-grade Observations

And here are the BIEN results:

#BIEN search results
head(mySimpleOccCiteObject@occResults$`Protea cynaroides`$BIEN$OccurrenceTable)

##                name longitude latitude coordinateUncertaintyInMeters day month
## 1 Protea cynaroides  19.14767 -33.7137                            NA  30     1
## 2 Protea cynaroides  18.62500 -32.6250                            NA  24    10
## 3 Protea cynaroides  19.12500 -34.3750                            NA  22     7
## 4 Protea cynaroides  19.62500 -34.6250                            NA  10     4
## 5 Protea cynaroides  19.37500 -33.1250                            NA  30     3
## 6 Protea cynaroides  19.37500 -34.1250                            NA  12     3
##   year datasetName datasetKey dataService
## 1 1828        MNHN       4620        BIEN
## 2 1954       SANBI       3318        BIEN
## 3 1967       SANBI       3318        BIEN
## 4 1979       SANBI       3318        BIEN
## 5 1978       SANBI       3318        BIEN
## 6 1962       SANBI       3318        BIEN

There is also a summary method for occCite objects with some basic information about your search.

summary(mySimpleOccCiteObject)

##  
##  OccCite query occurred on: 20 June, 2024
##  
##  User query type: User-supplied list of taxa.
##  
##  Sources for taxonomic rectification: GBIF Backbone Taxonomy
##      
##  
##  Taxonomic cleaning results:     
## 
##          Input Name                Best Match Taxonomic Databases w/ Matches
## 1 Protea cynaroides Protea cynaroides (L.) L.         GBIF Backbone Taxonomy
##  
##  Sources for occurrence data: gbif, bien
##      
##                     Species Occurrences Sources
## 1 Protea cynaroides (L.) L.        2334      17
##  
##  GBIF dataset DOIs:  
## 
##                     Species GBIF Access Date           GBIF DOI
## 1 Protea cynaroides (L.) L.       2022-03-02 10.15468/dl.ztbx8c

If you want to visualize the results of your search, you can use the plot method on occCite objects to generate several kinds of summary plots.

plot(mySimpleOccCiteObject)

Simple citations

After doing a search for occurrence points, you can use occCitation() to generate citations for primary biodiversity databases, as well as database aggregators. Note: Currently, GBIF and BIEN are the only aggregators for which citations are supported.

#Get citations
mySimpleOccCitations <- occCitation(mySimpleOccCiteObject)

Here is a simple way of generating a formatted citation document from the results of occCitation().

print(mySimpleOccCitations)

## Writing 5 Bibtex entries ... OK
## Results written to file 'temp.bib'

## AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BONNET P (2023). Pl@ntNet observations. Version 1.8. Pl@ntNet. https://doi.org/10.15468/gtebaa. Accessed via GBIF on 2022-03-02.
## AFFOUARD A, JOLY A, LOMBARDO J, CHAMP J, GOEAU H, CHOUET M, GRESSE H, BOTELLA C, BONNET P (2023). Pl@ntNet automatically identified occurrences. Version 1.8. Pl@ntNet. https://doi.org/10.15468/mma2ec. Accessed via GBIF on 2022-03-02.
## Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L., Ram, K. (2024). rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.8.1. https://CRAN.R-project.org/package = rgbif.
## Chamberlain, S., Boettiger, C. (2017). R Python, and Ruby clients for GBIF species occurrence data. PeerJ PrePrints.
## Fatima Parker-Allie, Ranwashe F (2018). PRECIS. South African National Biodiversity Institute. https://doi.org/10.15468/rckmn2. Accessed via GBIF on 2022-03-02.
## MNHN, Chagnoux S (2024). The vascular plants collection (P) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris). Version 69.384. MNHN - Museum national d'Histoire naturelle. https://doi.org/10.15468/nc6rxy. Accessed via GBIF on 2022-03-02.
## MNHN. Accessed via BIEN on NA.
## Maitner, B. (2023). . R package version 1.2.6. https://CRAN.R-project.org/package = BIEN.
## Missouri Botanical Garden,Herbarium. Accessed via BIEN on NA.
## Observation.org (2024). Observation.org, Nature data from around the World. https://doi.org/10.15468/5nilie. Accessed via GBIF on 2022-03-02.
## Owens, H., Merow, C., Maitner, B., Kass, J., Barve, V., Guralnick, R. (2024). occCite: Querying and Managing Large Biodiversity Occurrence Datasets. R package version 0.5.9. https://CRAN.R-project.org/package = occCite.
## Ranwashe F (2024). Botanical Database of Southern Africa (BODATSA): Botanical Collections. Version 1.25. South African National Biodiversity Institute. https://doi.org/10.15468/2aki0q. Accessed via GBIF on 2022-03-02.
## Rob Cubey (2022). Royal Botanic Garden Edinburgh Living Plant Collections (E). Royal Botanic Garden Edinburgh. https://doi.org/10.15468/bkzv1l. Accessed via GBIF on 2022-03-02.
## SANBI. Accessed via BIEN on NA.
## Senckenberg (2020). African Plants - a photo guide. https://doi.org/10.15468/r9azth. Accessed via GBIF on 2022-03-02.
## Taylor S (2019). G. S. Torrey Herbarium at the University of Connecticut (CONN). University of Connecticut. https://doi.org/10.15468/w35jmd. Accessed via GBIF on 2022-03-02.
## Team}, {.C. (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
## Teisher J, Stimmel H (2024). Tropicos MO Specimen Data. Missouri Botanical Garden. https://doi.org/10.15468/hja69f. Accessed via GBIF on 2022-03-02.
## Tela Botanica. Carnet en Ligne. https://doi.org/10.15468/rydcn2. Accessed via GBIF on 2022-03-02.
## UConn. Accessed via BIEN on NA.
## iNaturalist contributors, iNaturalist (2024). iNaturalist Research-grade Observations. iNaturalist.org. https://doi.org/10.15468/ab3s5x. Accessed via GBIF on 2022-03-02.
## naturgucker.de. naturgucker. https://doi.org/10.15468/uc1apo. Accessed via GBIF on 2022-03-02.

Simple Taxonomic Rectification

Note:The taxize package, which occCite uses for taxonRectification(), has been archived. To prevent occCite from being archived, which would result in downstream problems, we have disabled external taxonomic rectification as an option. If taxize comes back, or we identify an alternative, we will reinstate this feature. The code still exists, it’s just been commented out. Contact Hannah Owens (hannah.owens@gmail.com) for tips on how to reactivate the feature using the gitHub version of taxize.

In the simplest of searches, such as the one above, the taxonomy of your input species name is automatically rectified through the occCite function studyTaxonList() using gnr_resolve() from the taxize R package. If you would like to change the source of the taxonomy being used to rectify your species names, you can specify as many taxonomic repositories as you like from the Global Names Index (GNI). The complete list of GNI repositories can be found here.

studyTaxonList() chooses the taxonomic names closest to those being input and documents which taxonomic repositories agreed with those names. studyTaxonList() instantiates an occCiteData object the same way occQuery() does. This object can be passed into occQuery() to perform your occurrence data search.

#Rectify taxonomy
myTROccCiteObject <- studyTaxonList(x = "Protea cynaroides", 
                                  datasources = c("National Center for Biotechnology Information",
                                                  "Encyclopedia of Life", 
                                                  "Integrated Taxonomic Information SystemITIS"))
myTROccCiteObject@cleanedTaxonomy

For advanced features, please refer to vignette("Advanced", package = "occCite").

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.