论文标题
Novelpy：python包装，用于测量书记和专利数据的新颖性和破坏性
Novelpy: A Python package to measure novelty and disruptiveness of bibliometric and patent data
论文作者
论文摘要
Novelpy（V1.2）是一种开源Python包装，旨在计算文献分布学指标。该计划旨在为科学计量学界提供一种工具，以集中不同的新颖性和破坏性度量，使其比较并促进了可重复性。本文通过正式描述这些措施（无论是数学上还是图形），对Novelpy中可用的不同指标进行了全面审查，并提出了它们的好处和限制。然后，我们比较了从PubMed知识图中绘制的150万篇文章的随机样本中的不同度量，以证明模块的功能。我们鼓励任何有兴趣的人参加未来版本的开发。
Novelpy (v1.2) is an open-source Python package designed to compute bibliometrics indicators. The package aims to provide a tool to the scientometrics community that centralizes different measures of novelty and disruptiveness, enables their comparison and fosters reproducibility. This paper offers a comprehensive review of the different indicators available in Novelpy by formally describing these measures (both mathematically and graphically) and presenting their benefits and limitations. We then compare the different measures on a random sample of 1.5M articles drawn from Pubmed Knowledge Graph to demonstrate the module's capabilities. We encourage anyone interested to participate in the development of future versions.
