Shodhyantri Engineering Services
Research · Analysis · Editing · Publication Support
Support for search strategy, Scopus and Web of Science data export, cleaning, performance analysis and science maps that show how a field has grown.
Tell us about your bibliometric analysis need. We reply within one working day.
Bibliometric analysis studies publications as data. It counts, links and maps articles, authors, sources, countries and keywords to show how a research area has developed. Bibliometric review papers have become a popular route for scholars to understand a field before their own study, and a common first paper during a PhD. The quality of such a paper rests on a clean dataset and a careful reading of the maps.
We support the whole technical chain. You define the research area and the questions. We help you build a reproducible search string, export records from Scopus or Web of Science, merge and clean the data, run performance and science mapping in VOSviewer, Biblioshiny or CiteSpace, and interpret clusters so your discussion goes beyond describing pictures. The research questions and the conclusions remain yours.
Bibliometric work has two broad parts. Performance analysis describes productivity and impact: annual publication growth, most productive authors, institutions, countries and sources, and most cited documents. Science mapping shows relationships: co-authorship networks, co-citation of references or sources, bibliographic coupling of documents, and keyword co-occurrence revealing themes.
Each tool has strengths. VOSviewer, from Leiden University's CWTS, produces clear network, overlay and density maps. Biblioshiny is the web interface of the bibliometrix R package and offers thematic maps, thematic evolution and Bradford's and Lotka's laws. CiteSpace, developed by Chaomei Chen, is known for citation bursts, timeline views and detecting emerging fronts.
We help you write and document a search string with fields, date range, document types and language filters that another researcher could repeat exactly.
Author name variants, duplicate records and synonym keywords are merged using thesaurus files, so maps reflect the field rather than spelling differences.
We match each research question to VOSviewer, Biblioshiny or CiteSpace, using the tool whose method actually answers it rather than running everything.
Clusters are labelled by reading representative papers, so your discussion explains what each theme means for the field and for future research.
A record-selection flow diagram shows identification, screening and inclusion clearly, which reviewers increasingly expect to see in bibliometric review papers.
When you combine Scopus and Web of Science records, we help with format conversion and deduplication so counts are not inflated.
Ten stages, each with a clear output. Nothing moves to the next stage until you have reviewed the last one.
— You review every stage first, then decide —
The string is tested against known key papers to confirm that core literature is actually retrieved.
A sample of included records is checked to confirm that off-topic papers have been removed.
Author, institution and keyword variants are reviewed so one entity is not split into many.
Minimum occurrence and citation thresholds for every map are recorded and justified in your methods.
Cluster labels are compared with the content of top papers to avoid misleading theme names.
Maps are checked for readable labels, sensible colours and resolution suitable for journal submission.
Every stage is shared with notes explaining what was changed and why. You and your supervisor check it, and the next stage starts only after your go-ahead.
| Common problem | How Shodhyantri helps |
|---|---|
| Using a short, untested search string that misses large parts of the literature. | Build the string from synonyms and related terms, test it against known key papers, and refine it before final export. |
| Mapping raw data where one author or keyword appears under several spellings. | Create thesaurus files to merge variants and synonyms, then rerun maps so nodes represent real entities and real themes. |
| Describing maps only by colour and size without explaining what clusters mean. | Read key papers in each cluster, give the theme a meaningful name and explain its significance and gaps for future research. |
| Combining Scopus and Web of Science files without removing duplicate records first. | Convert formats, merge carefully and deduplicate using DOI and title matching, then report the final record count. |
| Comparing citation counts of recent papers directly with papers published many years earlier. | Consider citations per year or field-normalised views, and note that recent papers have had less time to gather citations. |
| Leaving default thresholds that create crowded, unreadable maps with hundreds of tiny labels. | Adjust minimum occurrence thresholds, report the values chosen, and keep maps clear enough for readers to interpret. |
Restricting the search to title, abstract and keyword fields usually balances relevance and coverage better than all-field searches.
Co-citation and coupling need cited reference data. Always include full records and cited references when exporting from databases.
Databases update constantly. Note the exact search date so readers understand why later searches may show different numbers.
VOSviewer overlay maps coloured by average publication year quickly show which topics are newer and which are well established.
Software groups items mathematically. Only reading the key papers tells you what intellectual theme a cluster really represents.
A table listing top items in each cluster makes maps easier to understand and gives reviewers numbers they can check.
End with specific future research questions drawn from gaps in the maps. This turns a descriptive study into a useful contribution.
Each database covers different journals and languages. Mention this limitation honestly, especially for regional or Indian-language research.
Your work is handled by people who read your field's literature, not by a single generalist.
A PhD-qualified specialist from your field who checks the research logic, gap and contribution.
Handles statistics, models and qualitative coding, and documents every analytical decision.
Improves structure, flow, clarity and academic tone without changing your meaning.
Builds and validates MATLAB, Python, ANSYS or COMSOL work where your study needs it.
Checks grammar, consistency, numbering, citations and the small errors examiners notice.
Applies your university or journal template, reference style and front matter exactly.
Many studies use more than one. VOSviewer gives clean network and overlay maps. Biblioshiny adds descriptive indicators, thematic maps and thematic evolution. CiteSpace is strong for citation bursts and timelines. We choose based on your research questions rather than using every tool by default.
Usually yes, because full records with cited references come from these subscription databases. Most universities provide access through their library. Some studies use free sources such as Dimensions or OpenAlex, which we can discuss, keeping in mind that coverage differs.
No. A systematic review reads and appraises each included study in depth to answer a focused question. Bibliometric analysis works at scale with metadata to map structure and trends. Some papers combine both, using maps to select a smaller set for detailed review.
It depends on the field. Very small sets produce weak networks, and very large sets need tighter thresholds to stay readable. Several hundred to a few thousand records is common. The key is a well-defined scope and a reproducible search rather than a particular number.
No. Journals decide through peer review. Many journals now expect bibliometric papers to offer real interpretation and a research agenda, not only maps. We help you meet those expectations with clean data and careful analysis, but acceptance remains the journal's decision.
Yes. We offer training sessions using your own dataset, covering data import, thesaurus files, thresholds, map types and exporting figures. Learning the tool helps when reviewers ask for changes and when you plan follow-up studies.
Typically annual growth charts, top source and author tables, co-authorship and country networks, co-citation or coupling maps, keyword co-occurrence maps, overlay maps and, where relevant, thematic evolution or burst charts, all exported at high resolution for journal use.
We provide research guidance, statistical analysis, editing, formatting and training support. All research content remains the scholar's own work, in line with UGC and university academic integrity norms. We do not guarantee publication, acceptance, indexing or academic outcomes: journals decide through peer review and universities award degrees.
Share your topic, draft or data. We will tell you honestly what it needs, and what it does not, before you pay anything.