BioMedJImpact：用于生物医学期刊AI参与度及科学影响力分析的综合数据集与大语言模型（LLM）流程
BioMedJImpact: A Comprehensive Dataset and LLM Pipeline for AI Engagement and Scientific Impact Analysis of Biomedical Journals
AI总结：
本研究构建了生物医学领域的BioMedJImpact数据集，通过三阶段LLM流程提取AI参与度，分析发现作者规模与期刊影响力正相关、AI参与度仅在2019年子集与影响因子正相关，验证了LLM流程的有效性。
AI中文摘要：
评估期刊影响力是学术交流的核心环节，但现有资源很少能捕捉到合作与人工智能（AI）研究如何共同塑造生物医学领域的期刊声望。本文提出BioMedJImpact，这是一个基于2744种期刊的174万篇PubMed Central文章构建的面向生物医学的大规模数据集。BioMedJImpact整合了文献计量指标、合作特征以及通过大语言模型（LLM）衍生的AI参与度，该指标定义为每种期刊-年份中AI相关文章的占比。具体而言，AI参与度是通过一个可复现的三阶段LLM流程提取的。我们在两个时间子集（2016-2019年、2020-2023年）中分析了合作强度与AI参与度如何共同影响科学影响力。研究发现两个主要模式：作者团队规模更大的期刊往往具有更高的引用影响力，而AI参与度仅在2019年子集中与影响因子呈正相关。为验证用于推导AI参与度的LLM流程，我们开展了人工评估，确认其在AI相关性检测中具有高度一致性，且子领域分类结果稳定。综上，BioMedJImpact既提供了生物医学与AI交叉领域的综合数据集，也提供了可扩展、内容感知的文献计量分析的验证框架。代码和数据集可在指定网址获取。
英文摘要：
Assessing journal impact is central to scholarly communication, yet existing resources rarely capture how collaboration and artificial intelligence (AI) research jointly shape venue prestige in biomedicine. We present BioMedJImpact, a large-scale, biomedical-oriented dataset built from 1.74 million PubMed Central articles across 2,744 journals. BioMedJImpact integrates bibliometric indicators, collaboration features, and an LLM-derived AI engagement rate, defined as the proportion of AI-related articles within each journal-year. Specifically, AI engagement rate is extracted through a reproducible three-stage LLM pipeline. We analyze how collaboration intensity and AI engagement rate jointly influence scientific impact across two temporal subsets (2016-2019, 2020-2023). Two main patterns emerge: journals with larger author teams tend to have higher citation impact, while AI engagement rate is positively associated with Impact Factor only in the 2019 subset. To validate the LLM pipeline for deriving the AI engagement rate, we conduct human evaluation, confirming substantial agreement in AI relevance detection and consistent subfield classification. Together, BioMedJImpact provides both a comprehensive dataset at the interface of biomedicine and AI and a validated framework for scalable, content-aware scientometric analysis. Code and dataset are available at https://github.com/JonathanWry/BioMedJImpact.
