bibliometrix-assisted Bibliometric Analysis

Basic R literacy is sufficient for the core package; the companion biblioshiny Shiny app provides a point-and-click interface that covers most analyses without writing code. For reproducibility and custom analyses, an R workflow is strongly preferred.

There is no hard minimum, but networks become too sparse for reliable clustering with fewer than around 100 documents. Most published bibliometrix studies work with 200–5000 records. Very large corpora (tens of thousands of records) run efficiently but may require filtering to a focused subset to produce interpretable maps.

Google Scholar exports (via Publish or Perish) lack complete reference lists, which makes co-citation and bibliographic coupling networks unreliable. Scopus and Web of Science exports are strongly preferred. For fields not well covered by these databases, PubMed exports can be used for performance descriptives but not for full network analysis.

No. A bibliometric analysis maps publication patterns, citation structures, and thematic clusters — it does not extract or synthesise research findings. A systematic review appraises study quality and synthesises results to answer a specific clinical or empirical question. Bibliometrix-assisted analysis is often used as a preparatory or complementary step to frame a field before conducting a more targeted review.

Report the database(s) searched, the exact search string, the date of search, the number of records retrieved and retained, and the bibliometrix version. Describe each analysis type (performance, network, thematic) and the parameters chosen (e.g., clustering algorithm, minimum citation threshold). Provide the R script or Shiny session settings as supplementary material to enable replication.