Possible new pkg idea: publication bias

[arw36]

A tedious task for many of the meta-analyses I do is to retrieve an article count for every data point, usually species names, from different publication databases like Google Scholar, Web of Science, or PubMed. I was hoping there could be a package that would allow me to input my search terms and it would automatically loop them through these systems generate the article count. I would imagine this would be very helpful for a wide range of uses, providing an easy way to assess publication counts for a large database or how publication counts change over time.

Disclosure - I am unfamiliar package building, so if you need clarification or this doesn’t make sense please let me know!

[dlebauer]

It’s not exactly clear what you mean by ‘article count for every species name’ or how this would help estimate publication bias but

If it is helpful here is a script that queries google scholar for records by species+trait

PecanProject/pecan/blob/master/modules/meta.analysis/inst/citation_search.py

useful on its own; could also be wrapped or reimplemented in a package

[sckott]

Thanks for your question @arw36

Thanks for sharing @dlebauer

One solution is rcrossref:

library(rcrossref)
library(data.table)

Define a function

species_cr_search <- function(x, ...) {
  data.frame(
    species = x, 
    matches = cr_works(query = x, limit = 0, ...)$meta$total_results,
    stringsAsFactors = FALSE
  )
}

A species list

spp <- c("Poa annua", "Helianthus annuus", "Abies magnifica")

Apply function across species

rbindlist(lapply(spp, species_cr_search))
#>              species matches
#> 1:         Poa annua    2425
#> 2: Helianthus annuus    3446
#> 3:   Abies magnifica    4752

A cool thing about using Crossref is you can set lots of different filters, etc. Here, constrain to publications that have “ecology” in their title

rbindlist(lapply(spp, species_cr_search, flq = c(`query.container-title` = 'ecology')))
#>              species matches
#> 1:         Poa annua      46
#> 2: Helianthus annuus      48
#> 3:   Abies magnifica     356

A caveat about Crossref is that they only search text that they provide in their web services, which is authors, title, and in some cases abstract (http://api.crossref.org/works?filter=has-abstract:true&rows=0 shows about 824K papers) - that is, they’re not searching full text of the papers

---

You could use Google Scholar but you have to jump through more hoops as they don’t want people to programmatically scrape their data.

You could also use Scopus - e.g., Wrapping Elsevier’s Sciencedirect/Scopus API? but i don’t know much about that.

---

One approach would be to create a pkg that can interface to many different sources and the user can choose.

[sckott]

Any thoughts @arw36 on the above comments ?

[arw36]

Thanks @sckott and @dlebauer ! Using the rcrossref package is easiest and quick and was a quick solution for this. I ran it for my 280 species, and a minute later had the matches. I am excited to see the modifications I can do with this, like adding search terms as suggested or extracting article counts at different time intervals (publications at 1980 vs 2010 for instance).

One question with using CrossRef -> are matches text matches or individual papers? For instance if “Artibeus fimbriatus” is in the title then multiple times in a papers abstract, would this be counted multiple times even though only one publication?

[arw36]

Also, I am a bit concerned with the specificity of cross ref. For instance a search for Poa annua and “Poa annua” output the same counts, and pick up matches for just Poa or annua, rather than items with Poa and annua/

[sckott]

@arw36

One question with using CrossRef -> are matches text matches or individual papers? For instance if “Artibeus fimbriatus” is in the title then multiple times in a papers abstract, would this be counted multiple times even though only one publication?

can you give an example?

@arw36

Also, I am a bit concerned with the specificity of cross ref. …

which of these do you really want?

See https://github.com/CrossRef/rest-api-doc/blob/master/rest_api.md#parameters which for the query parameter lists

limited DisMax query terms

Which links to DisMax - Solr - Apache Software Foundation and therein to The DisMax Query Parser | Apache Solr Reference Guide 6.6

I don’t know what specifically limited Dismax means, but i’d just go with whats documented on The DisMax Query Parser | Apache Solr Reference Guide 6.6

see also https://github.com/CrossRef/rest-api-doc/blob/master/rest_api.md#queries

[arw36]

Ideally I want the intersection of both the genus AND the species to get publication count. I think it is an issue with how CrossRef does queries, using that limited DisMax method. To solve, I modified the function you provided to run separately for “Genus species”, “Genus”, “species”

hostname <- as.vector(host$hHostNameIUCN)
genus <- as.vector(host$hGenus)
species <- as.vector(host$hSpecies)
species_cr_search <- function(x,y,z, ...) {
  data.frame(
    hostname = x, 
    hmatches = cr_works(query = x, limit = 0, ...)$meta$total_results,
    genus = y, 
    gmatches = cr_works(query = y, limit = 0, ...)$meta$total_results,
    species = z, 
    smatches = cr_works(query = z, limit = 0, ...)$meta$total_results,
    stringsAsFactors = FALSE
  )
}

Then, from the outputed matrix modified to remove duplicates or publications for only one search term, rather than both.

researcheffort <- mapply(species_cr_search, x=hostname, y=genus, z=species)
researcheffort <- t(researcheffort) #flip matrix
rownames(researcheffort) <- c() # get rid of species rownames
researcheffort <- as.data.frame(researcheffort)
researcheffort$publications <- as.numeric(researcheffort$gmatches) + as.numeric(researcheffort$smatches) - as.numeric(researcheffort$hmatches)

What I end up with is a dataframe with separate search terms, then what I believ is the actual publications

Maybe this is a bit round about, but I couldnot query multiple times within the cr_works function.

[sckott]

You can also try fulltext - there are some defaults set for crossref in particular (that you can change) - so keep that in mind

install from dev version

devtools::install_github("ropensci/fulltext")

Taxa list

library(fulltext)
taxa <- c("Acerodon celebensis", "Anoura caudifer", 
          "Anoura geoffroyi", "Antrozous pallidus")

With Entrez

(taxa1_entrez <- ft_search(query = taxa[1], from = 'entrez', limit = 100))
#> Query:
#>   [Acerodon celebensis] 
#> Found:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 7; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 0; Microsoft: 0] 
#> Returned:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 7; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 0; Microsoft: 0] 

(taxa3_entrez <- ft_search(query = taxa[3], from = 'entrez', limit = 100))
#> Query:
#>   [Anoura geoffroyi] 
#> Found:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 43; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 0; Microsoft: 0] 
#> Returned:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 43; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 0; Microsoft: 0] 

With Scopus

(taxa1_scopus <- ft_search(query = taxa[1], from = 'scopus', limit = 100,
    scopusopts = list(key = Sys.getenv('ELSEVIER_SCOPUS_KEY'))))
#> Query:
#>   [Acerodon celebensis] 
#> Found:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 0; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 10; Microsoft: 0] 
#> Returned:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 0; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 10; Microsoft: 0] 

(taxa3_scopus <- ft_search(query = taxa[3], from = 'scopus', limit = 100,
    scopusopts = list(key = Sys.getenv('ELSEVIER_SCOPUS_KEY'))))
#> Query:
#>   [Anoura geoffroyi] 
#> Found:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 0; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 143; Microsoft: 0] 
#> Returned:
#>   [PLoS: 0; BMC: 0; Crossref: 0; Entrez: 0; arxiv: 0; biorxiv: 0; Europe PMC: 0; Scopus: 100; Microsoft: 0]

[arw36]

Thanks @sckott . This is definitely the package I was looking for, especially because of it utilizes rentrez to get pubmed results.

library(rentrez)
taxa <- c("Acerodon celebensis", "Anoura caudifer", 
          "Anoura geoffroyi", "Antrozous pallidus")
species_pubmed_search <- function(x,...) {
  entrez_search(db= "pubmed", term = x)
}
taxa_ft <- lapply(taxa, species_pubmed_search)
ft_effort <- stringi::stri_list2matrix(taxa_ft, byrow=TRUE) %>% as.data.frame()
ft_effort <- select(ft_effort, V2)
taxa_df <- as.data.frame(taxa)
taxa_df <- bind_cols(taxa_df, ft_effort)
taxa_df$V2 <- as.numeric(levels(taxa_df$V2))[taxa_df$V2]

V2 = pubmed hits

[sckott]

@arw36 Great! glad it works for you.

Love any feedback on that package