Building and exporting a reference set

A retrieval becomes useful once it leaves the package, as a reading list in a reference manager or as input to a science-mapping tool. This article covers that export end of the workflow, using the bundled example_records so every step runs without an API key.

Take stock first

Before exporting anything, it helps to see what the set contains. summary() reports the size, the span of years, the number of distinct sources, the citation spread and the most-cited record.

summary(example_records)
#> <scopus_records> summary
#> 6 records, from 2016 to 2021.
#> 5 sources, 6 with a DOI.
#> Cited 5505 times in total, median 299 per record.
#> Most frequent source: Nature.
#> Most cited: Observation of gravitational waves from a binary black hole merger.

A clean, deduplicated DOI list

Reference managers such as Zotero import most reliably from DOIs. scopus_extract_dois() pulls them out, normalises them and removes duplicates, so the same article imports once even when its DOI was stored with a resolver prefix or in a different case.

dois <- scopus_extract_dois(example_records)
dois
#> [1] "10.1038/s41586-019-0001-1"      "10.1038/s41586-020-0002-2"     
#> [3] "10.1038/s41558-018-0085-1"      "10.1002/adma.202100001"        
#> [5] "10.1016/S1470-2045(20)30013-9"  "10.1103/PhysRevLett.116.061102"

The list can be written to a single-column CSV at a path you choose. Nothing is written unless a path is given.

out <- file.path(tempdir(), "reference-set.csv")
scopus_extract_dois(example_records, file = out)
readLines(out)
#> [1] "\"doi\""                            "\"10.1038/s41586-019-0001-1\""     
#> [3] "\"10.1038/s41586-020-0002-2\""      "\"10.1038/s41558-018-0085-1\""     
#> [5] "\"10.1002/adma.202100001\""         "\"10.1016/S1470-2045(20)30013-9\"" 
#> [7] "\"10.1103/PhysRevLett.116.061102\""

Handing off to science mapping

as_bibliometrix() re-maps the records to the tagged column layout that the bibliometrix package and the wider ISI convention expect.

m <- as_bibliometrix(example_records)
m[, c("AU", "TI", "PY", "SO", "TC", "DB")]
#>          AU                                                                 TI
#> 1  ZHANG F.       GENOME EDITING WITH CRISPR-CAS9: PRINCIPLES AND APPLICATIONS
#> 2  KUMAR S.                 DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS: A REVIEW
#> 3 OKAFOR N.                    CLIMATE CHANGE ADAPTATION IN COASTAL MEGACITIES
#> 4 TANAKA H.             GRAPHENE ELECTRODES FOR NEXT-GENERATION ENERGY STORAGE
#> 5 GARCIA M.                      CHECKPOINT INHIBITORS IN CANCER IMMUNOTHERAPY
#> 6 ABBOTT B. OBSERVATION OF GRAVITATIONAL WAVES FROM A BINARY BLACK HOLE MERGER
#>     PY                      SO   TC     DB
#> 1 2019                  NATURE  540 SCOPUS
#> 2 2020                  NATURE  210 SCOPUS
#> 3 2018   NATURE CLIMATE CHANGE  122 SCOPUS
#> 4 2021      ADVANCED MATERIALS   45 SCOPUS
#> 5 2020     THE LANCET ONCOLOGY  388 SCOPUS
#> 6 2016 PHYSICAL REVIEW LETTERS 4200 SCOPUS

From there the usual bibliometrix entry points apply. This step needs that package, so it is shown but not run.

if (requireNamespace("bibliometrix", quietly = TRUE)) {
  results <- bibliometrix::biblioAnalysis(as_bibliometrix(records))
  summary(results, k = 10)
}

As noted on the as_bibliometrix() help page, this reconstructs the core descriptive fields only. Analyses that need full affiliations or cited references still call for a complete ‘Scopus’ CSV or BibTeX export read with bibliometrix::convert2df().

Saving the working set

To pick the work up in a later session, save the records and read them back. The .rds form preserves the types and class exactly, while .csv is portable plain text.

path <- file.path(tempdir(), "records.rds")
write_scopus_records(example_records, path)
identical(read_scopus_records(path), example_records)
#> [1] TRUE