OneCLick: Streamlined metadata enrichment using machine-inferred keywords from OpenAlex

OneCLick is a lightweight, web-based tool that enhances bibliographic metadata by retrieving machine-inferred keywords from the pre-assigned keyword field of the OpenAlex API. It addresses the common limitations of incomplete or inconsistent author-supplied keywords, thereby improving the reliability of downstream bibliometric analyses such as co-word mapping, thematic clustering, and trend detection. The application provides an intuitive browser-based interface requiring no programming expertise: users upload a DOI list in Excel format, and the system returns a structured dataset enriched with OpenAlex-provided terms. Performance evaluations on datasets of up to 1,097 records demonstrated high enrichment success rates (≥81%), negligible retrieval errors due to retry-and-backoff handling, stable latency (∼0.30 seconds/DOI), and reproducibility across repeated runs. Although enrichment is constrained by the coverage of OpenAlex’s keyword assignments, missing cases are clearly flagged and reported. The initial public release has generated over twenty user sessions, which we view as early indicators of feasibility and visibility rather than sustained adoption. Built on a modular architecture, OneCLick can be readily extended to incorporate additional metadata providers and enrichment services, including hierarchical topic levels and confidence scoring. By lowering technical barriers and embedding transparency, OneCLick provides a scalable, reproducible enrichment layer that complements existing bibliometric tools and strengthens scholarly metadata workflows.