paper.md

In jessicatytam/specieshindex: How (scientifically) popular is a given species?

title: 'specieshindex: How scientifically popular is a species?' tags: - R - h-index - species - ecology date: "8 December 2021" output: pdf_document: default html_document: df_print: paged authors: - name: Jessica Tam affiliation: 1 - name: Malgorzata Lagisz orcid: 0000-0002-3993-6127 affiliation: 1 - name: Shinichi Nakagawa orcid: 0000-0002-7765-5182 affiliation: 1 - name: Will Cornwell orcid: 0000-0003-4080-4073 affiliation: 1 bibliography: paper.bib affiliations: - name: Evolution & Ecology Research Centre and School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia index: 1

Summary

Conservation efforts rely heavily on the body of scientific research on the relevant species. Reintroduction projects, pest eradication, breeding programs and habitat restoration, all require sound scientific knowledge for their design and implementations. Unfortunately, there is an uneven spread of research among species, taxonomic bias [@bonnet_taxonomic_2002], such that certain species receive more research interest than the others [@donaldson_taxonomic_2017; @dos2020drivers; @seddon_taxonomic_2005; @titley_scientific_2017; @troudet_taxonomic_2017]. Over time, taxonomic bias in research has widened the gap between well- and poorly-studied species. Quantifying taxonomic bias is the first step towards allowing the scientific community to fill the gaps and to direct scientific efforts towards where they are most needed for conservation.

Over the last decade, the quantification of research biases have gained popularity, as shown by the growing number of studies on taxonomic bias among species of plants and animals [@adamo2021; @donaldson_taxonomic_2017; @dos2020drivers; @ducatez2019; @fleming_good_2016; @mckenzie_which_2015; @robertson_scientific_2015; @schiesari2007; @tensen2018; @trimble_aarde2010]. Most of these studies evaluated small groups of species, no more than a few hundred. Additionally, the majority of the studies used publication or citation counts to identify the bias. However, publication count can only capture the total research productivity. While citation count can capture research interest, it can be easily inflated by highly cited papers [@hirsch_index_2005], without equally considering each publication. Here, we propose and implement the use of Hirsch's h-index [@hirsch_index_2005] as a better alternative in quantifying research interest for taxonomic groups of species including genus and species.

Statement of need

specieshindex aims to make streamline the calculation of h-index in the context of measuring popularity of species in the scientific literature. The h-index was introduced by Hirsch [@hirsch_index_2005] to compare the influence of academics with a single number [@hirsch2014meaning]. The h-index is calculated as the total number of publications (n) that have at least been cited n times, after ranking the publications in a descending order by their number of citations. The h-index is now also being used to measure the research interest and influence of the publications of different academic disciplines [@banks_extension_2006; @harzing_google_2016], journals [@braun_hirsch-type_2006], countries [@csajbok_hirsch-index_2007], species of animals [@fleming_good_2016; @mckenzie_which_2015; @robertson_scientific_2015], plants [@adamo2021], and pathogens [@cox_comparison_2016]. specieshindex calculates the h-index of different species and genera.

The package specieshindex connects to the Scopus and Web of Science databases (interdisciplinary broad-range databases of academic literature) and extracts citation records of relevant publications, as identified via a search query. It returns blibliometric information including the publication title, number of citations, publication type, etc. The binomial or genus names of the species should be used in the search query, instead of their common names, since the latter are less specific and may refer to more than 1, and even unrelated species. An example of such is 'pig', which can refer to the domestic pig (Sus scrofa - an ungulate), or the guinea pig (Cavia porcellus - a rodent). Although this package also connects to Bielefeld Academic Search Engine (BASE), article extraction is not available, hence h-index calculation is unavailable with BASE.

There are two main functions that connect to the literature databases. The Count() function returns the total publication count without extracting any citation records. Whereas the Fetch() function extracts citation records for index calculations. Apart from the h-index, specieshindex can also compute other established influence indices, including the h5 index, the m-index, and the i10 index. The h5 index is the h-index of articles published in the most recent 5 years [@crotty2017other]. The m-index is the h-index divided by the number of years since the first publication [@hirsch_index_2005]. The i10 index is the total number of articles with 10 or more citations; it is currently used by Google Scholar [@jacso2021].

Implementation

The following packages are required for specieshindex to work.

# Installation from GitHub
install.packages("rscopus")
install.packages("wosr")
install.packages("rbace")
install.packages("taxize")
install.packages("XML")
install.packages("httr")
install.packages("dplyr")
install.packages("data.table")
install.packages("tidyr")
remotes::install_github("jessicatytam/specieshindex",
                         build_vignettes = TRUE,
                         dependencies = TRUE)

# Load the library
library(specieshindex)

# See the vignette
vignette("specieshindex")

Connecting to Scopus

Scopus is one of the most extensive inter-disciplinary literature database that includes peer-reviewed journal articles, books, conference proceedings, and more. Functions that extract data will only run if you or your institution are a paid subscriber. An API key from Scopus is required to extract citation records from their database legally. Here are the steps to obtain the key.

An API key from Scopus is required to extract citation records from their database legally. However, note that the API would only works if your institution or organisation has a subscription with Elsevier. Otherwise, functionality is limited to basic searches. Here are the steps to obtain the key.

1. Go to https://dev.elsevier.com/ and click on the button I want an API key.
2. Create an account and log in.
3. Go to the My API Key tab on top of the page and click Create API Key.
4. Read the legal documents and check the boxes.

Using your API key securely

After acquiring your key, make sure to store it safely. The following steps will enable you to save it as an environment variable, without saving it in the console or script.

file.edit("~/.Renviron")

This will bring up an empty file, which is where you will save your key into.

scopus_api_key = "a_long_string"

Restart your session for this to work. To retrieve your key, use Sys.getenv():

Sys.getenv("scopus_api_key")
#> [1] "a_long_string"

You can then load it to your environment as follows:

apikey <- Sys.getenv("scopus_api_key")

Connecting to Web of Science

An alternative to Scopus for literature search is Web of Science. No key is needed to extract data from Web of Science if you are using this package from your institution's location. You do not need a key to extract data from Web of Science if you are using this package from your institution's location. To gain access to the Web of Science database, run the following line of code:

# Setup session ID
sid <- auth(username = NULL, password = NULL)

Connecting to BASE

Bielefeld Academic Search Engine (BASE) is a literature database that contains both scholarly and grey literature. Since it does not provide citation information, data extraction is not available. Having a whitelisted IP address is essential when gaining access to the BASE database, which you can get on https://www.base-search.net/about/en/contact.php. A token or API key, however, is not required. Only the Count() function is available as BASE does not return citation counts. Hence, index calculations will also be unavailable using this database.

Example

The following example will demonstrate the calculation of various indices with citation records from Scopus. The species' binomial name (e.g. Sarcophilus harrisii) or genus name (e.g. Sarcophilus) is required for the extraction:

# Extract citation data
Woylie <- Fetch(db = "scopus',
                search = "tak",
                genus = "Bettongia", species = "penicillata")
Quokka <- Fetch(db = "scopus',
                search = "tak",
                genus = "Setonix", species = "brachyurus")
Platypus <- Fetch(db = "scopus',
                  search = "tak",
                  genus = "Ornithorhynchus", species = "anatinus")
Koala <- Fetch(db = "scopus',
               search = "tak",
               genus = "Phascolarctos", species = "cinereus")

These four datasets are readily available within the package. An efficient way to calculate all of the indices at once is to use the function Allindices().

# Calculate indices
W <- Allindices(data = Woylie,
                genus = "Bettongia", species = "penicillata")
Q <- Allindices(data = Quokka,
                genus = "Setonix", species = "brachyurus")
P <- Allindices(data = Platypus,
                genus = "Ornithorhynchus", species = "anatinus")
K <- Allindices(data = Koala,
                genus = "Phascolarctos", species = "cinereus")

# Combine citation records into a single dataframe
CombineSp <- rbind(W, Q, P, K)
CombineSp

##              genus_species     species           genus publications citations
## 1    Bettongia_penicillata penicillata       Bettongia          113      1903
## 2       Setonix_brachyurus  brachyurus         Setonix          242      3427
## 3 Ornithorhynchus_anatinus    anatinus Ornithorhynchus          321      6365
## 4   Phascolarctos_cinereus    cinereus   Phascolarctos          773     14291
##   journals articles reviews years_publishing  h     m i10 h5
## 1       55      110       3               43 26 0.605  54  7
## 2      107      237       5               66 29 0.439 121  4
## 3      153      308      13               67 41 0.612 177  7
## 4      227      744      29              139 53 0.381 427 14

Plots

This package has 2 built-in plotting functions. They are plotAllindices() and plotPub(). plotAllindices() plots the h-index, m-index, i10 index, and h5 index in the same plot.

# Plot the indices
plotAllindices(CombineSp)

Figure 1. The h-index, m-index, i10 index, and h5 index of the Woylie (Bettongia penicillata), Platypus (Ornithorhynchus anatinus), Koala (Phascolarctos cinereus), and Quokka (Setonix brachyurus).

plotPub() plots the total publications per year for species, after extracting data on years and number of publications per year with the getYear() function.

# Extract year and frequency
extract_year_W <- getYear(data = Woylie,
                          genus = "Bettongia", species = "penicillata")
extract_year_Q <- getYear(data = Quokka,
                          genus = "Setonix", species = "brachyurus")
extract_year_P <- getYear(data = Platypus,
                          genus = "Ornithorhynchus", species = "anatinus")
extract_year_K <- getYear(data = Koala,
                          genus = "Phascolarctos", species = "cinereus")

# Combine year and frequency into a single dataframe
Combine_pub <- rbind(extract_year_W,
                     extract_year_Q,
                     extract_year_P,
                     extract_year_K)

# Plot the number of publications by year
plotPub(Combine_pub)

Figure 2. The total number of publications per year of the Woylie (Bettongia penicillata), Platypus (Ornithorhynchus anatinus), Koala (Phascolarctos cinereus), and Quokka (Setonix brachyurus).

Concrete example

To see a concrete example, @tam_lagisz_cornwell_nakagawa_2021 has applied this package to study taxonomic bias among mammals by quantifying the scientific interest of 7,521 species of mammals.

Figure 3. Species h-index of mammals with a species h-index of h = 100 and larger (adapted from @tam_lagisz_cornwell_nakagawa_2021).

Acknowledgements

I acknowledge the contributions of the authors of the dependence packages: rscopus, wosr, rbace, taxize, XML, httr, dplyr, data.table, tidyr. specieshindex is enabled by Scopus, Web of Science, and BASE [@pieper_summann2006].

References

jessicatytam/specieshindex documentation built on June 24, 2022, 3:31 a.m.