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.

翻译：随着科研政策与战略规划需求的日益增长，及时且可比的科学结构演变指标变得愈发重要。我们提出了一种可复现且可扩展的框架，利用OpenAlex的开放学术元数据来量化科学活动的主题分布及其近期动态。该方法通过统一主题本体与基于短时间序列的简易趋势估计器相结合，实现了对期刊、国家、地区及特定领域语料库的一致性比较。我们通过涵盖综合期刊、国家产出、都市科研生态系统及结构生物学等代表性案例研究来阐释该方法。这些案例表明，该框架既捕捉了系统层面的归一化效应，也揭示了精细化的专业分工模式。由于该流程具有完全通用性且基于开放数据，因此可轻松扩展至对科学图景进行连续、多尺度的监测。本方法提供了一个简洁且可解释的量化层，可补充科学政策、研究评估及战略决策中的专家判断。