Overview
Bibliometric studies have become a common thesis chapter and journal article type, especially in business, management, education, engineering and health. Journals that publish them increasingly reject papers that simply describe the most cited authors and a colorful keyword map. They want a clear research question, a transparent data retrieval process, justified parameter choices and an interpretation that links clusters back to the literature.
Our support covers the whole pipeline: defining the search and database, exporting complete records, cleaning and harmonizing author names, keywords and sources, running performance analysis and science mapping, and explaining what each cluster represents so you can write an informed interpretation. Where a review question needs content-level synthesis, we suggest combining bibliometrics with a systematic review or scoping review.
Who it’s for and the problems it solves
This is for you if you are…
- Master's and doctoral students writing a bibliometric chapter or stand-alone bibliometric paper
- Early-career researchers who want to map a field before choosing a thesis or grant topic
- Faculty and research offices analyzing a department's or journal's publication footprint
- Authors revising a bibliometric manuscript after reviewers questioned the data or parameters
Common problems we help with
- You merged Scopus and Web of Science exports and now have duplicates and mismatched fields
- The same author or keyword appears under several spellings, splitting your networks
- Your VOSviewer map has 400 unreadable nodes and you do not know which thresholds to set
- You cannot explain the difference between co-citation and bibliographic coupling to your supervisor
- Reviewers said your paper is 'descriptive' and lacks interpretation
What’s included — and what isn’t
Included
- Defining the research questions a bibliometric design can answer, and whether performance analysis, science mapping or both are needed
- Search design in Web of Science Core Collection, Scopus or other sources (for example Dimensions, OpenAlex or Lens), with field tags, document-type and date limits documented
- Export of full records with cited references in the formats each tool expects (plain text or tab-delimited for Web of Science, CSV or BibTeX for Scopus)
- Merging and deduplicating multi-database datasets and harmonizing author names, affiliations, source titles and keywords with a thesaurus file
- Performance analysis: annual output, citation counts, productive and cited sources, authors, institutions and countries, and Lotka's and Bradford's law where useful
- Co-citation analysis of references, authors or sources to reveal a field's intellectual base
- Bibliographic coupling of documents or sources to identify current research fronts
- Co-word (keyword co-occurrence) analysis, thematic maps and thematic evolution across time slices
- Co-authorship and collaboration networks across authors, institutions and countries
- Burst detection, timeline views and cluster labeling in CiteSpace where emerging topics matter
- Publication-ready figures with parameter settings recorded for your methods section
Not included
- Writing your interpretation, discussion or literature synthesis for submission as your own work; we explain what clusters contain so you can interpret them
- Inflating datasets or selectively trimming records to produce a particular map
- Presenting bibliometric indicators as measures of research quality for individual people
- Access to subscription databases your institution does not license
What you receive
- Data retrieval protocol
- Database, query string, field tags, filters, export date and record counts at each step, so the retrieval can be reproduced and reported in a flow diagram.
- Cleaned dataset
- The merged, deduplicated records plus the thesaurus files used to harmonize names and keywords.
- Performance tables
- Formatted tables of output, citations and leading sources, authors, institutions and countries, with notes on normalization choices.
- Science maps
- High-resolution co-citation, coupling, co-word and collaboration maps with the thresholds, normalization method and clustering resolution documented.
- Cluster briefing
- For each major cluster, the most central documents and terms, with a short neutral description to guide your own interpretation.
- Reproducible files
- VOSviewer map and network files, CiteSpace project settings or an R script using bibliometrix, depending on the tools used.
How it works
Question and scope
We agree what the analysis should reveal, the time window, the document types and which database or databases to use.
Retrieval and export
We test and refine the query, then export complete records with cited references and log every step.
Cleaning
Records are merged, deduplicated and harmonized. This step often takes longer than the mapping itself and has the most influence on results.
Analysis and mapping
We run performance analysis and the agreed science-mapping techniques, testing sensitivity to thresholds before fixing parameters.
Interpretation support
We walk you through each cluster and figure so you can connect them to the literature and write the discussion yourself.
Techniques and software
- Co-citation analysis
- Two works are linked when later papers cite them together. Useful for mapping the foundations and schools of thought in a field.
- Bibliographic coupling
- Two papers are linked when they share references. Useful for grouping recent publications into current research fronts, because it does not need the papers to have been cited yet.
- Co-word analysis
- Keywords or terms from titles and abstracts are linked when they occur together, revealing conceptual structure and, over time slices, thematic evolution.
- VOSviewer
- Free software from Leiden University's CWTS for building and visualizing networks, with network, overlay and density views. Version 1.6.x is current at the time of writing.
- CiteSpace
- Chaomei Chen's tool for time-sliced co-citation networks, citation burst detection, timeline views and cluster labeling. It is offered in a free Basic edition and paid editions, so we confirm which features you can access.
- Bibliometrix and biblioshiny
- An R package, with the point-and-click biblioshiny interface, for data import, performance analysis, thematic maps, thematic evolution and three-field plots. Recent releases add metadata completion from OpenAlex and Crossref.
- Supporting tools
- R or Python for custom cleaning, Excel for tables, and Gephi when a network needs custom layout or metrics.
Which data source, and why it matters
Web of Science Core Collection and Scopus index different journals, export different fields and handle cited references differently, so results are not interchangeable. Many studies use one database for consistency; others merge both to widen coverage, which requires careful deduplication and field mapping. Open sources such as OpenAlex offer broader coverage but patchier metadata in some fields. We explain the trade-off for your topic and document the choice.
Parameter choices reviewers will ask about
Every map depends on decisions: counting method (full or fractional), minimum occurrences or citations, normalization method, clustering resolution and time slicing. Small changes can merge or split clusters. We test sensitivity to these choices and record the final settings so your methods section can state them precisely.
- Report the minimum thresholds and how many items met them
- State the normalization method (for example association strength in VOSviewer)
- Explain how keywords were merged, including the thesaurus file
- Pair network maps with tables; readers cannot read labels on crowded maps
From clusters to insight
A cluster is a group of related items; it is not yet a finding. Interpretation means reading the most central papers in each cluster, naming what connects them and relating clusters to each other and to time. We give you a structured briefing on each cluster, and you build the narrative. Combining mapping with a close reading of key papers is what distinguishes a publishable bibliometric study from a descriptive one.
What the work can look like
Illustrative example — fictional data
Illustrative example: green human resource management research
A fictional doctoral student wants to map research on green human resource management to position her thesis. All figures are invented for illustration.
Retrieval and cleaning
A Scopus title-abstract-keyword search limited to articles and reviews in English returned 1,284 records. After harmonizing 212 keyword variants (for example 'green HRM' and 'green human resource management'), the co-word analysis used 96 keywords with at least 10 occurrences.
What the maps showed
Co-citation analysis identified three foundational clusters (environmental management, strategic HRM and pro-environmental behavior). Bibliographic coupling of papers from the last five years revealed a newer front on employee green creativity, with few studies from Africa: the gap the student then examined in her literature review.
Illustrative example with invented numbers; it does not describe a real client or dataset.
What you’ll need to provide
- Your research questions or the purpose of the analysis
- Your institution's database access, or agreement to use open sources such as OpenAlex
- Any existing export files or search strings
- Target journal or chapter guidelines, including figure requirements
- Preferred software, if your supervisor expects a specific tool
You don’t need everything ready to ask for a quote — tell us what you have and we’ll say what’s missing.
What affects price and turnaround
Price depends on
- Number of records and whether one or several databases are merged
- Amount of name and keyword harmonization needed
- Number of analyses and maps (performance, co-citation, coupling, co-word, collaboration, thematic evolution)
- Whether custom scripts or combined tools are required
- Depth of interpretation support and number of walkthrough sessions
Turnaround depends on
- Dataset size and cleaning complexity
- Availability of your institutional database exports
- Rounds of parameter adjustment requested after the first maps
Every project is quoted individually. How we price our work.
Questions people ask before starting
Scopus or Web of Science: which should I use?
Use the one your field and target journal expect, and that your institution licenses. Scopus often has broader journal coverage; Web of Science is often preferred for long citation histories. Merging both is possible but adds cleaning work and must be reported. We recommend an option based on your question.
Which software is best?
They do different things well. VOSviewer produces clear network maps, CiteSpace specializes in time-based analysis and citation bursts, and Bibliometrix is strong for performance analysis, thematic maps and reproducible R workflows. Many studies combine two tools, and we explain each choice.
Is a bibliometric analysis enough for a literature review chapter?
Usually not on its own. It shows structure and trends, but a literature review also needs critical engagement with content. Bibliometrics works well as a complement to a narrative, scoping or systematic review.
Can you use my existing export files?
Yes, if they contain full records and cited references. We check them first. Exports without cited references cannot support co-citation or coupling analysis, and we will tell you if a re-export is needed.
Do I need programming skills?
No. VOSviewer, CiteSpace and biblioshiny all have graphical interfaces. If we use R scripts, we provide annotated code and can walk you through running it.