构建生物医学期刊影响力分析数据集，融合合作与AI研究特征。
BioMedJImpact: A Comprehensive Dataset and LLM Pipeline for AI Engagement and Scientific Impact Analysis of Biomedical Journals
- 基于174万篇论文构建多维度期刊数据集
- 合作强度与AI参与度共同提升期刊影响力
- 提出可复现的LLM三阶段分析框架
评估期刊影响力是学术传播的核心，但现有公开资源很少捕捉协作结构与人工智能（AI）研究如何共同影响生物医学领域期刊声望。我们提出BioMedJImpact，一个大规模、面向生物医学的数据集，用于推进期刊层面的科学影响力与AI参与度分析。该数据集涵盖来自2,744种期刊的174万篇PubMed Central文章，整合了引文指标、合作特征及通过可复现的三阶段大语言模型（LLM）管道提取的语义性AI参与度指标。利用该数据集，我们分析了疫情前后两个时期（2016–2019年，2020–2023年）中合作强度与AI参与度对科学影响力的联合影响。结果显示：合作强度更高，尤其是作者团队规模更大、多样性更强的期刊，其引用影响力更显著；而AI参与度已成为期刊声望的重要预测因子，尤其在四分位排名中表现突出。为验证所提三阶段LLM管道的有效性，我们进行了人工评估，确认了在AI相关性识别上具有较高一致性，并实现了稳定的子领域分类。综上，BioMedJImpact不仅是一个捕捉生物医学与AI交叉领域的综合性数据集，更提供了一个可扩展、内容感知的科学计量分析方法框架。代码已开源：https://github.com/JonathanWry/BioMedJImpact。
原文摘要 · Abstract (English)
Assessing journal impact is central to scholarly communication, yet existing open resources rarely capture how collaboration structures and artificial intelligence (AI) research jointly shape venue prestige in biomedicine. We present BioMedJImpact, a large-scale, biomedical-oriented dataset designed to advance journal-level analysis of scientific impact and AI engagement. Built from 1.74 million PubMed Central articles across 2,744 journals, BioMedJImpact integrates bibliometric indicators, collaboration features, and LLM-derived semantic indicators for AI engagement. Specifically, the AI engagement feature is extracted through a reproducible three-stage LLM pipeline that we propose. Using this dataset, we analyze how collaboration intensity and AI engagement jointly influence scientific impact across pre- and post-pandemic periods (2016-2019, 2020-2023). Two consistent trends emerge: journals with higher collaboration intensity, particularly those with larger and more diverse author teams, tend to achieve greater citation impact, and AI engagement has become an increasingly strong correlate of journal prestige, especially in quartile rankings. To further validate the three-stage LLM pipeline we proposed for deriving the AI engagement feature, we conduct human evaluation, confirming substantial agreement in AI relevance detection and consistent subfield classification. Together, these contributions demonstrate that BioMedJImpact serves as both a comprehensive dataset capturing the intersection of biomedicine and AI, and a validated methodological framework enabling scalable, content-aware scientometric analysis of scientific impact and innovation dynamics. Code is available at https://github.com/JonathanWry/BioMedJImpact.
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