Service overview
Research planning, literature synthesis, methodology, proposals and feasibility work. This service is part of that division and is scoped individually around what you need to learn, decide or publish.
Bibliometric analysis describes research activity quantitatively. Starting from records exported from databases such as Scopus or Web of Science, it counts and connects publications, authors, institutions, journals, keywords and references to show how a field has grown, who is working together and which themes cluster. It is a map of the literature, not a measure of the truth or quality of the work it contains.
Used carefully, a bibliometric study is a strong foundation for a thesis chapter, a research strategy or a review article. Used carelessly it produces attractive network diagrams built on inconsistent author names, duplicated records and keywords that mean different things in different communities. The value of the service lies largely in the cleaning, the choice of what to count and the honesty of the interpretation.
If you plan to publish the outcome, see manuscript & publication support, for example computational & simulation manuscripts or original research article writing.
What is a bibliometric analysis?
Bibliometric analysis uses publication metadata (authors, venues, keywords, references, citations) to describe the structure and evolution of a research field quantitatively. It shows patterns such as growth, collaboration and thematic clusters, but it does not by itself measure the quality of individual studies.
What this service covers
Problems this service can address
- No clear picture of how a field has evolved or where activity is concentrated
- Keyword and co-citation networks that are produced without checking data cleaning
- Over-interpretation of citation counts as a proxy for quality
Methods and analytical approaches
- Bibliographic data export, cleaning and field-tag normalisation
- Co-authorship, co-citation and bibliographic-coupling analysis
- Keyword co-occurrence and thematic clustering
- Temporal trend and burst analysis
Applications
- Field-mapping chapters in theses
- Research-strategy and funding-landscape input
- Basis for bibliometric review articles
Relevant engineering disciplines
Scope, inputs and deliverables
Scope of work
- Review of your objectives, constraints and the evidence already available
- Selection of methods suited to the question and the data
- Analysis or synthesis with documented, repeatable steps
- Written outputs you can reuse in a thesis, proposal or report
- Feasibility check on: a topic definition and search query (or the topic, so we can draft it)
Typical inputs required from you
- A topic definition and search query (or the topic, so we can draft it)
- Access to your preferred bibliographic database exports, if available
- Time window of interest
Deliverables
- Cleaned dataset and documented query
- Network maps and trend charts with interpretation
- Methods note describing databases, filters and limitations
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
Defining the field and the query
Everything depends on the search query. Overly narrow queries miss relevant work; overly broad queries drown the field in noise. We build the query from concept groups, test it on known papers, check what the database actually indexes and decide on document types, languages and the time window. The query and the date it was run are documented, because database content changes constantly and results must be reproducible.
Cleaning and normalising the data
Raw exports contain duplicate records, variant spellings of author and institution names, inconsistent keyword forms and missing fields. Cleaning uses thesauri to merge synonyms and plural forms, resolves obvious name variants and flags records with insufficient metadata. Each cleaning rule is logged. When multiple databases are combined, records are matched and deduplicated with an explicit priority rule. This stage often takes longer than the analysis, and shortcuts here propagate into every figure.
Analysing structure: performance, collaboration and themes
Performance analysis summarises output and citations over time and by source. Co-authorship networks reveal collaboration patterns; co-citation and bibliographic coupling reveal intellectual structure; keyword co-occurrence and clustering reveal themes and their evolution. Each technique answers a different question and has known biases, for instance towards older highly cited works in co-citation. Parameters such as thresholds and clustering resolution are reported, and robustness is checked by varying them.
Interpretation with appropriate caution
Clusters are statistical groupings and need expert reading to be named and understood. Citation counts reflect age, field size and visibility as much as merit. We interpret patterns as descriptions of the literature in the chosen database, state coverage limits, avoid ranking individuals or institutions as a measure of quality and relate findings back to the research question rather than presenting maps without meaning.
From maps to a research question
Maps are most useful when they lead somewhere. We link the patterns found to concrete next steps: which subtopics deserve a full systematic review, which collaborations or venues are central, which regions of the map are sparsely populated and may reveal a gap, and which are crowded and may be hard to enter. Each recommendation is tied to evidence in the data and accompanied by the caveat that the indexed literature is only a partial picture of research activity.
Quality control and good practice
Quality-control approach
- Search, selection or analysis steps are documented so another researcher can repeat them.
- Assumptions, exclusions and limitations are stated rather than implied.
- Numbers in text, tables and figures are cross-checked for consistency.
- Conclusions are limited to what the evidence supports.
Common pitfalls this service helps avoid
- Treating citations as quality. Counts depend on field, age and discoverability, and they cannot substitute for reading the work.
- Ignoring database coverage. No database indexes everything, and coverage varies by region, language and publication type, which shapes every result.
- Using default software settings. Network thresholds, normalisation and clustering parameters change maps substantially and should be chosen and reported deliberately.
- Failing to clean keywords. Unmerged synonyms split a theme into several apparent clusters and distort the picture of the field.
What good reporting looks like
A bibliometric report states the database(s), query, date, filters and number of records at each stage, describes cleaning rules, specifies the software and parameters for each analysis, presents tables and maps with clear labelling and interprets them in relation to the research question. Limitations such as coverage, language bias and the descriptive nature of the method are stated explicitly. Data and scripts can be provided so the analysis is reproducible. Where the result is to become a review article, the bibliometric review writing service covers manuscript preparation.
An illustrative scenario
Hypothetical example written for explanation. It is not a client case, a result or a testimonial.
Imagine a postgraduate team mapping research on additive manufacturing of lattice structures. After cleaning, the keyword network shows clusters on design methods, mechanical testing and process simulation, with a recent rise in topics linking machine learning to design. Co-authorship analysis shows that several research groups collaborate closely while others work in isolation. The team treats these as descriptions of the indexed literature, uses them to decide which subtopics to review in depth and notes that conference papers are under-represented in the database they used.
Questions to settle before you begin
- What exactly defines the boundaries of my field?
- Which database exports can I access, and for which years?
- How will I merge variant keywords and author names?
- What question should the maps answer, and what would be over-interpretation?
- Which document types might be missing from my data?
Key terms
- Co-citation
- Two documents being cited together by a later paper.
- Bibliographic coupling
- Two documents sharing references.
- Keyword co-occurrence
- Frequency of keywords appearing together in records.
- Thesaurus
- A mapping that merges synonyms and variants into one term.
- Clustering
- Grouping items by similarity in a network or dataset.
Working together on Research & Academic Consulting projects
Research and academic consulting engagements usually begin with a short conversation and a written summary of the question, the existing evidence and the intended outcome, such as a thesis chapter, a proposal or a journal article. We ask for what you already have, however rough: notes, drafts, data tables and the instructions of the committee or funder. Review is iterative and focuses on reasoning rather than wording, so you should expect questions that challenge assumptions. Authorship, scientific claims and institutional compliance remain yours. Deliverables are written so that you can reuse them directly in your own documents and defend them when asked how a decision was reached.
Frequently asked questions
Which databases do you work with?
That depends on what you have access to and what the topic requires. We state the database coverage and its biases in the methods note rather than treating any single database as complete.
Which database should I use?
It depends on your field and what you need. Scopus and Web of Science are common; their coverage differs. Using more than one, with documented deduplication, improves completeness at the cost of complexity.
Can bibliometric analysis identify the best papers?
It can identify highly cited or central papers, but that is not the same as quality. It should guide reading, not replace it.
What tools are used?
Standard open and commercial tools for science mapping and statistical environments are used. The choice is described in the methods so that the analysis can be repeated.
How is this different from a systematic review?
Bibliometrics analyses publication metadata quantitatively; a systematic review reads and synthesises the content of selected studies. They can complement each other.
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.