Service overview
Twenty review formats, from systematic and scoping reviews to meta-analyses and landscape reviews. This service is part of that division and is scoped individually around what you need to learn, decide or publish.
Bibliometric reviews use quantitative analysis of publication data to describe a research field: how output has grown, which countries, institutions and journals contribute, which authors and papers are most cited and which topics cluster together. They are popular because they can handle thousands of records that no human could read, and the maps they produce, such as keyword networks and co-citation clusters, are easy to communicate.
The weakness is that bibliometric results depend heavily on the database, the query and the cleaning steps, and they describe publication patterns, not the quality or truth of the science. A bibliometric review that does not document these steps cannot be reproduced and may be misleading. Manuscript support concentrates on documentation, figure design and cautious interpretation.
What this service covers
Problems this service can address
- Network figures that are unreadable or unexplained
- Database and query details missing
- Descriptive results without insight
Methods and analytical approaches
- Query and database documentation
- Network and trend figure design
- Interpretation of clusters and trends
- Limitations of bibliometric data
Applications
- Field-evolution papers
- Research-trend reviews
- Collaboration-pattern studies
Relevant engineering disciplines
Scope, inputs and deliverables
Scope of work
- Assessment of your material: data, methods, draft and target venue
- Structuring and drafting or editing strictly from your supplied results and methods
- Consistency and source checks on text, tables, figures and references
- Journal-formatted files and a submission checklist
- Feasibility check on: exported bibliographic data or analysis outputs
Typical inputs required from you
- Exported bibliographic data or analysis outputs
- Query details
- Draft
Deliverables
- Bibliometric manuscript
- Figure and table design
- Methods and limitations text
Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.
Project workflow and method
- 01Enquiry and requirement assessment
- 02Technical scoping and proposal
- 03Execution
- 04Quality review and delivery, with an agreed round of revisions
Read the full project workflow.
In depth: how the work is done
Choosing the data source and query
Scopus, Web of Science, Dimensions and OpenAlex differ in coverage, metadata quality and update practices. The choice influences results, and the manuscript should justify it and report the date of retrieval. The query should be provided in full, including fields searched, wildcards, filters on document type, language and years, and any iterative refinement. We help authors write this section so that the dataset can be re-created.
Data cleaning and standardisation
Raw exports contain duplicate records, variant spellings of authors and institutions, inconsistent keywords and missing fields. Cleaning steps, such as merging synonyms with a thesaurus file or disambiguating author names, shape the results and must be documented. We encourage authors to report how many records were removed and how keywords were harmonised.
Descriptive indicators
Common indicators include annual output, citation counts, h-index-type measures, productive authors, journals and countries. They should be presented with definitions and caveats: citation counts depend on age and field, and authorship counting methods affect country rankings. We help authors choose indicators that answer the research question rather than listing every available statistic.
Network and cluster analysis
Co-authorship, co-citation, bibliographic coupling and keyword co-occurrence networks reveal structure. Settings such as the minimum occurrence threshold, normalisation method and clustering algorithm influence the maps and are reported. Clusters are labelled by examining their content, not only by automated labels. Sensitivity checks with alternative thresholds increase credibility.
Designing readable figures
Network maps can be cluttered. Choosing thresholds, legible labels, consistent colour schemes and explanatory captions makes figures informative. Time overlays show how topics evolve. We advise on design and on providing high-resolution or vector figures, and we ensure that the text interprets what is shown.
Interpreting patterns responsibly
Bibliometrics shows what has been published and cited, not what is correct or important. Rising keyword frequency may reflect fashion; low citation may reflect recency or small community size. The discussion should connect patterns to the field's context and avoid causal claims unsupported by the data.
Limitations of bibliometric data
Limitations include database coverage bias towards English-language and certain disciplines, indexing delays, name ambiguity and the exclusion of non-journal outputs. Stating them and, where possible, checking key findings with a second database improves reliability.
Open data and software for bibliometric reproducibility
Bibliometric exports can often be shared as identifier lists even when full records are restricted by licence. Sharing the list of document identifiers, the cleaning thesaurus and the script or software settings lets others reproduce the dataset and maps. We help authors prepare a short data statement describing what can be shared and where, which meets growing journal expectations.
Positioning the review among existing bibliometric studies
Many fields already have bibliometric overviews, so the introduction should state what the new one adds: a wider time window, a different database, a narrower focus or a new analytical angle. A short comparison with earlier bibliometric studies helps reviewers see the contribution and avoids the objection that the work repeats a map that already exists.
Quality control and good practice
Quality-control approach
- Every statement, number and reference is checked against your supplied data and the original sources.
- No data, results, citations or author contributions are invented; gaps are flagged to you.
- Authorship and contribution decisions stay with you and your co-authors, and the use of AI tools is disclosed where journals require it.
- Journal guidelines and reporting checklists are followed; acceptance is never promised.
Common pitfalls this service helps avoid
- Undocumented query or cleaning. Results cannot be reproduced.
- Overinterpreting citation counts. Citations depend on age, field and visibility.
- Cluttered or unlabelled maps. Readers cannot extract the message.
- Treating bibliometrics as quality assessment. Publication patterns do not measure scientific quality.
What good reporting looks like
Support for a bibliometric review is documented with a query and retrieval record, a cleaning-steps log, a parameter table for network analyses and a figure-quality checklist. The authors perform the data retrieval and analysis; we do not generate data or fabricate counts, and no acceptance is promised.
An illustrative scenario
Hypothetical example written for explanation. It is not a client case, a result or a testimonial.
Suppose authors map research on hydrogen storage using a Scopus export of 6,000 records. The draft shows a keyword map with no thresholds stated. Revision documents the query, the date, the exclusion of conference reviews and the minimum keyword occurrence, includes a sensitivity check with a different threshold and removes a claim that a rising keyword shows scientific progress, replacing it with the observation that publication volume grew.
Questions to settle before you begin
- Which database and why?
- Can someone reproduce my query and cleaning?
- What thresholds produced my maps, and are they stable?
- What do my indicators actually measure?
- What are the coverage limitations?
Key terms
- Co-citation
- Two documents cited together by a third.
- Bibliographic coupling
- Two documents sharing references.
- Keyword co-occurrence
- Keywords appearing together in documents.
- h-index
- A measure combining productivity and citation counts.
- Thesaurus file
- A list merging equivalent terms.
Working together on Review Article Writing projects
Review article engagements begin by choosing the review type that fits the question, because systematic, scoping, narrative and bibliometric reviews have different methods and reporting expectations. We ask for the topic, the target journal and any searches or papers you already have. The method is documented so that the review can be audited, and every source cited is checked against the original. We do not invent references or studies, and we state limitations such as database coverage. Acceptance depends on editors and reviewers and cannot be promised.
Frequently asked questions
Do you run the bibliometric analysis?
That is the Bibliometric & Scientometric Analysis service; this one writes the article from the analysis.
Which software is used for bibliometric analysis?
Common choices include VOSviewer, the bibliometrix R package and CiteSpace; the manuscript names the software and version.
Can you run the analysis for me?
Analysis can be supported through research services; manuscript support concentrates on documentation and writing.
Is one database enough?
It may be, but justification and a discussion of coverage are expected.
Do bibliometric reviews need ethics approval?
Generally not for published data, but check institutional rules.
Does ENGRexpert guarantee acceptance in a journal?
No. Editors and reviewers decide. We support the clarity, rigour and fit of your manuscript and never promise an outcome.
How is this service scoped and priced?
After your enquiry we review what you need, ask for missing information and send a written scope and quotation. Price depends on complexity, deliverables and timeline. See the project workflow.