Quantifying the evolving topical structure of science across journals, countries, regions, and research domains

Abstract

Timely and comparable indicators of the evolving structure of science are increasingly needed for research policy and strategic planning. We present a reproducible and scalable framework for quantifying the topical prevalence and recent dynamics of scientific activity using open scholarly metadata from OpenAlex. The approach combines a unified topic ontology with simple trend estimators derived from short time series, enabling consistent comparisons across journals, countries, regions, and domain-focused corpora. We illustrate the methodology through representative case studies spanning generalist journals, national output, metropolitan research ecosystems, and structural biology. Across these examples, the framework captures both system-level normalization effects and fine-grained specialization patterns. Because the pipeline is fully general and based on open data, it can be readily extended to continuous, multi-scale monitoring of the scientific landscape. The proposed methodology provides a compact and interpretable quantitative layer that can complement expert assessment in science policy, research evaluation, and strategic decision-making.