bibliometrix-Assisted Scientometric Analysis
Also known as: bibliometrix scientometrics, R-based scientometric analysis, bibliometrix workflow, science-of-science analysis with bibliometrix
bibliometrix-assisted scientometric analysis is a reproducible, R-based workflow that applies the bibliometrix package to analyse the structure and dynamics of scientific fields using publication metadata. It integrates descriptive statistics, citation metrics, and network analysis — co-citation, bibliographic coupling, co-authorship, and co-word — into a single scriptable environment, enabling systematic, transparent mapping of research landscapes at scale.
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When to use it
Use bibliometrix-assisted scientometric analysis when you need to map the intellectual landscape of a research field systematically and reproducibly — for example, to identify foundational papers, productive authors, emerging themes, or temporal trends across hundreds or thousands of publications. It is particularly suited to literature reviews that aim to characterise a field rather than synthesise effect sizes (which would call for meta-analysis). The method requires access to a bibliographic database export and basic R skills. Do not use it as a substitute for a content-based systematic review: it analyses publication metadata and citation links, not the substance of findings. It is also not appropriate when the corpus is very small (fewer than ~100 records), as network analyses become unstable, or when the research question requires qualitative interpretation of text rather than quantitative mapping of structure.
Strengths & limitations
- Fully reproducible: the entire workflow — from data import to network maps — is scripted in R, making every analytical step transparent and auditable.
- Handles large corpora efficiently: capable of processing thousands of records from multiple databases in a single session.
- Integrates multiple scientometric techniques (descriptive indicators, co-citation, bibliographic coupling, co-authorship, co-word) within one consistent framework.
- The biblioshiny() Shiny app lowers the barrier for researchers without deep R expertise while still producing reproducible outputs.
- Actively maintained open-source package with documented methodology and a large user community.
- Results depend entirely on the quality and completeness of the source database; coverage gaps (missing journals, regional literature, grey literature) introduce bias.
- Analyses metadata and citation links — it does not read or synthesise the content of papers, so it cannot answer questions about what studies found.
- Requires at least basic R proficiency; researchers unfamiliar with R face a learning-curve barrier despite biblioshiny.
- Network analysis outputs are sensitive to the choice of minimum threshold parameters (minimum co-citations, edge weights) and community-detection algorithm, requiring methodological justification.
- Citation counts from different databases are not directly comparable; multi-database merging must be handled carefully to avoid double-counting.
Frequently asked
Do I need advanced R skills to use bibliometrix?
Basic R skills are sufficient for most analyses. The bibliometrix package provides wrapper functions that abstract complex computations, and the biblioshiny() Shiny interface offers a point-and-click dashboard requiring no code. However, customising outputs, merging multi-database exports, and adjusting network thresholds benefit from intermediate R knowledge.
Which database should I use — Web of Science or Scopus?
Both are widely used and bibliometrix supports both. Web of Science has deeper historical coverage and curated citation data; Scopus offers broader journal coverage, particularly in engineering and social sciences. When feasible, use both and merge the deduplicated exports to maximise coverage. Document the choice as part of your methods section.
Can bibliometrix replace a traditional systematic review?
No. bibliometrix analyses publication metadata and citation networks — it does not extract or synthesise the substantive findings of individual studies. It is best used to characterise a research landscape, identify key papers, or map thematic evolution. For synthesising evidence on an intervention effect or qualitative theme, a systematic review or meta-analysis is required.
How many records do I need for a meaningful analysis?
There is no universal minimum, but most network analyses become statistically meaningful above roughly 100 records, and many published scientometric studies use corpora of 500 to several thousand papers. With fewer than 100 records, descriptive indicators are still interpretable, but co-citation and bibliographic coupling networks may be too sparse to yield reliable clusters.
How do I cite the bibliometrix package in my paper?
Cite the primary methods paper: Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007. Also cite the R package itself with its version number using citation('bibliometrix') in R.
Sources
- Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI: 10.1016/j.joi.2017.08.007 ↗
- Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349. link ↗
How to cite this page
ScholarGate. (2026, June 3). bibliometrix-Assisted Scientometric Analysis. ScholarGate. https://scholargate.app/en/scientometrics/bibliometrix-assisted-scientometric-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bibliographic CouplingBibliometrics↔ compare
- Bibliometric AnalysisScientometrics↔ compare
- Co-Citation AnalysisBibliometrics↔ compare
- Co-word AnalysisScientometrics↔ compare
- Scientometric AnalysisScientometrics↔ compare