Exporting data from SCOPUS and WOS for Bibliometric Analysis

Bibliometric analysis depends on getting the right dataset first—especially when working with large, journal-based citation databases like Scopus and Web of Science. The core workflow starts with defining a clear research question and then translating it into reliable keywords. Keyword selection is treated as the most difficult and most consequential step: without targeted, dependable terms, the resulting manuscript and analysis can’t be trusted.

Once keywords are finalized, the next step is running structured searches inside Scopus (and then repeating the same logic in Web of Science). The search process begins by entering the keywords exactly as intended, then using Boolean logic to narrow results. When multiple terms are involved, the transcript emphasizes using operators like AND and OR to control subject scope, and using double quotation marks to force the database to match terms in specific fields such as the title, abstract, or author keywords. Without quotation marks, searches can return an unmanageably large volume of irrelevant records.

A practical example is given for narrowing a broad topic into a specific subfield: “urban flooding” is treated as an intersection where “urbanization” is the main theme, but the search must also require flooding-related terminology. The same idea applies to other domains (e.g., supply chain), where the database may treat related phrases differently (such as “supply chain” versus “supply-chain”), so the search strategy should account for variations. The transcript also mentions using wildcard or string approaches (e.g., star strings) and grouping terms in brackets to capture similar concepts while still keeping the query focused.

After the initial search returns thousands of documents, filtering becomes essential. The transcript describes narrowing by time window (such as limiting to the last 10 years), restricting document types (e.g., focusing on journal articles rather than conference proceedings, books, or book series), and limiting language to English when the research context requires it. These constraints reduce noise and make downstream bibliometric analysis more coherent.

Finally, exporting the dataset is presented as a key step in the data acquisition pipeline. In Scopus, export options include formats such as CSV (recommended because it aligns with Excel-style workflows), plain text, and other text-based formats. The export selection should include the bibliographic and citation fields needed for analysis—such as citation information, bibliography details, abstracts, and author keywords—while optionally excluding funding details. The same general approach is then applied to Web of Science, using equivalent selection and export choices (including plain text options). The overall message is straightforward: careful keyword engineering plus disciplined filtering and structured export is what turns massive database results into usable bibliometric data.