Introduction to Bibliometrix || Manual of Biblioshiny R Studio || User Guide to Bibliometrix

Bibliometrix (used through Biblioshiny in RStudio) turns Scopus-style bibliographic exports into an interactive, filterable analysis dashboard—then expands that dashboard into author, journal, affiliation, keyword, and citation-network insights. The practical payoff is speed: once the raw file is loaded and processed, the system outputs structured tables and multiple visualizations that summarize publication volume over time, top contributors, and the intellectual structure of a research field.

After installing and opening Biblioshiny in a browser from RStudio, the workflow starts with loading “raw data” (the transcript references Scopus data). Processing reads the dataset and reports key counts (for example, the number of documents), then renders results in tabular form. From there, users can apply filters and use an Overview panel to quickly scan headline metrics such as annual scientific production, average citations per year, and author-related summaries. The transcript emphasizes that this kind of bibliometric analysis requires at least some familiarity with the field’s dimensions—especially when moving beyond basic counts into deeper mapping.

The interface then branches into several analysis layers. A “three-field plot” links top authors, keywords, and journals, making it easier to see which authors publish in which outlets and what topics dominate. The system also surfaces “most relevant sources” (including highly cited documents and journals), plus author production over time—where bubble size reflects publication proportion and line trends reflect changes across the selected time span. Additional breakdowns connect output to affiliations and countries, including options to isolate or “disambiguate” specific institutions so users can focus on a single university or organization’s contribution.

Beyond descriptive analytics, the transcript highlights knowledge synthesis and mapping. It references conceptual, intellectual, and social structure analysis, including network-factorial approaches such as Multiple Correspondence Analysis (MCA) and clustering. Visual outputs include factor maps and dendrograms that group similar terms or concepts; the height/distance in dendrograms helps decide how to cut the tree into partitions. Thematic mapping is described as a way to classify themes into categories like emerging, declining, transversal, motor, and highly developed/isolated—using axes tied to development and centrality. Time-sliced views can show how themes shift across periods.

Finally, citation-based knowledge synthesis is presented as a way to trace research pathways. A citation network links documents and authors through directed “cites” relationships (past vs present), producing one-way paths that show how earlier work feeds into later publications. The transcript also notes that users can switch between different data views—authors, sources, and affiliations—so the same underlying dataset can be explored from multiple angles.

Overall, the core message is that Bibliometrix/Biblioshiny provides a structured pipeline: import → process → filter → visualize (descriptive and relational) → map intellectual structure (clusters, themes, networks). That pipeline is positioned as a foundation for broader research workflows, including related methods such as SLR, meta-analysis, and diplomatic analysis—where bibliometric mapping helps identify key authors, institutions, journals, and evolving topics.