SciDisruptor-PubMed

Research hypothesis and data interpretation This project is based on the hypothesis that patterns of disruption in scientific publications can be identified in a fully automated and reproducible way through citation networks and the Disruption Index. To test this, a series of Python scripts was developed to extract, organize, and analyze data directly from the PubMed database. What the code demonstrates The scripts automate the entire process: (1) querying articles by keyword using the PubMed E-utilities API; (2) extracting complete article data in XML format; (3) cleaning and organizing the extracted data; (4) building the citation network among the selected articles; and (5) calculating the Disruption Index using the formula proposed by Wu, Wang, and Evans, via the PySciSci package. How to interpret and use the code Each script is modular and designed to run sequentially, with comments and instructions for execution in a Jupyter Notebook environment. Input data is retrieved directly from PubMed, and the outputs include: PMIDs of all articles, structured citation networks, tables with the count of citation types (A, B, C), and the final Disruption Index score. The code can be used by other researchers to replicate this study or to apply the pipeline to other scientific domains.研究假设与数据解读 本项目基于如下研究假设：可通过引用网络与破坏指数（Disruption Index），以完全自动化且可复现的方式识别科学出版物中的颠覆性模式。为验证该假设，本项目开发了一系列Python脚本，可直接从PubMed数据库中提取、整理并分析数据。 代码实现的功能 本脚本可实现全流程自动化：(1) 借助PubMed E-utilities应用程序编程接口（PubMed E-utilities API）按关键词检索文献；(2) 提取XML格式的完整文献数据；(3) 对提取的数据进行清洗与整理；(4) 构建筛选后文献间的引用网络；(5) 通过PySciSci包，采用Wu、Wang与Evans提出的公式计算破坏指数。 代码的解读与使用方法 各脚本均采用模块化设计，支持顺序执行，并附带Jupyter Notebook环境下的运行注释与操作指引。输入数据直接从PubMed数据库获取，输出结果包括：所有文献的PubMed标识符（PMID）、结构化引用网络、含引用类型（A、B、C）计数的统计表，以及最终的破坏指数得分。其他研究人员可使用本代码复现本研究，或将该分析流程应用于其他科学领域。