Calculating Disruption Indices at scale with Dimensions - Supplementary Materials|学术评估数据集|颠覆性指数数据集

This repository contains the Jupyter notebooks and SQL queries for Dimensions on Google BigQueryrelated to the publication "Dimensions: Calculating Disruption Indices at Scale", M. Pasin & J. Sixt, 2023.ABSTRACT of the related publication:Evaluating the disruptive nature of academic ideas is a new area of research evaluation that moves beyond standard citation-based metrics by taking into account the broader citation context of publications or patents. The "CD index" and a number of related indicators have been proposed in order to characterise mathematically the disruptiveness of scientific publications or patents. This research area has generated a lot of attention in recent years, yet there is no general consensus on the significance and reliability of disruption indices. More experimentation and evaluation would be desirable, however is hampered by the fact that these indicators are expensive and time-consuming to calculate, especially if done at scale on large citation networks. We present a novel method to calculate disruption indices that leverages the Dimensions cloud-based research infrastructure and reduces the computational time taken to produce such indices by an order of magnitude, as well as making available such functionalities within an online environment that requires no set-up efforts. We explain the novel algorithm and describe how its results align with preexisting implementations of disruption indicators. This method will enable researchers to develop, validate and improve mathematical disruption models more quickly and with more precision, thus contributing to the development of this new research area.

本仓库包含了与出版物《Dimensions: Calculating Disruption Indices at Scale》，作者 M. Pasin 与 J. Sixt，2023 年相关联的 Jupyter 笔记本和 SQL 查询。相关出版物的摘要：评估学术思想的颠覆性是一个超越传统基于引用指标的研究评估新领域，它通过考虑出版物或专利的更广泛的引用背景来实现。为了表征科学出版物或专利的颠覆性，已提出了“CD 指数”以及一系列相关指标。这一研究领域近年来引起了广泛关注，但对于颠覆性指数的意义和可靠性尚无普遍共识。尽管进行更多实验和评估是可取的，但由于这些指标的计算成本高昂且耗时，尤其是在大规模的引用网络中进行时，这一目标受到了阻碍。我们提出了一种新颖的方法来计算颠覆性指数，该方法利用了 Dimensions 基于云的研究基础设施，将生成这些指数所需的时间缩短了一个数量级，同时使这些功能在无需设置努力的在线环境中可用。我们解释了这一新颖算法，并描述了其结果如何与现有的颠覆性指标实现相一致。此方法将使研究人员能够更快、更精确地开发、验证和改进数学颠覆性模型，从而促进这一新兴研究领域的发展。