Analyzing The Lens.org Data with VOSviewer and Biblioshiny || Bibliometric Analysis || Hindi

Lens.org export data can be fed into VOSviewer to generate author, keyword, and citation-network visualizations—and the same workflow can be extended into Biblioshiny for a fuller bibliometric dashboard. The practical takeaway is that Lens-based bibliographic exports (including large sets like ~10,000 records) are usable for bibliometric mapping, letting researchers move from raw publication lists to interpretable networks of relationships such as co-authorship, co-occurrence, and citation coupling.

After noting that Lens exports can be used as an input source for bibliometric analysis, the walkthrough emphasizes how to start in VOSviewer: open the “Create” flow, choose the Lens dataset as the source, and then set analysis parameters. For author-focused work, the configuration includes selecting the unit of analysis (authors), choosing counting rules (full counting), and setting thresholds such as the minimum number of documents per author. The example dataset is described as an author analysis built from Lens-exported records, with the resulting network visualizations showing how authors connect through shared publication relationships.

The interface then supports multiple visualization modes and interpretation aids. Network visualization is presented as the core view, with color coding used to distinguish publication recency or grouping patterns (older versus more recent publications appear in different color bands). The cursor-driven interaction is highlighted: moving across nodes reveals strength values, helping users interpret which links are strongest. Additional layers like overlay visualization and density visualization are offered as ways to refine what the network communicates, while scaling and layout controls let users zoom in/out and adjust the view for clearer reading.

Beyond authors, the workflow shifts to keyword and source-based mapping. The walkthrough demonstrates switching the analysis focus to keywords (e.g., author keywords), adjusting thresholds to control which terms appear, and using the “document sources” option to reorganize results by where the work is published. It also notes practical export and sharing options—saving images via screenshot tools and exporting data for reuse.

To extend the analysis, the same Lens-derived dataset is exported into a format suitable for Biblioshiny (CSV is used). The process includes selecting the number of records to export (for example, exporting 169 entries from a larger set), choosing fields to include, and downloading the file for upload into Biblioshiny. Once loaded, Biblioshiny provides an overview of annual scientific production, author-focused summaries, and network views such as citation/source networks. The walkthrough acknowledges occasional data-loading errors during repeated attempts, but reports that the dataset ultimately loads and the plots populate.

Overall, the workflow links three steps into a single pipeline: export bibliographic data from Lens.org, map relationships in VOSviewer (authors/keywords/sources with network overlays), and then run a Biblioshiny dashboard for higher-level bibliometric indicators and clustering/coupling views. The result is a repeatable method for turning Lens-based publication records into actionable bibliometric insights.