Calculating Disruption Indices at scale with Dimensions - Supplementary Materials

This repository contains the Jupyter notebooks and SQL queries for Dimensions on Google BigQuery<br>related to the publication "Dimensions: Calculating Disruption Indices at Scale", M. Pasin &amp; J. Sixt, 2023.<b>ABSTRACT of the related publication:</b>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.

本仓库收录了用于Google BigQuery上Dimensions项目的Jupyter笔记本与SQL查询代码，相关联的研究论文为2023年M. Pasin与J. Sixt发表的《Dimensions: Calculating Disruption Indices at Scale》。相关论文摘要：评估学术创意的颠覆性是科研评价领域的新兴研究方向，其突破了传统基于引用的计量评价范式，转而考量出版物或专利的更广维度引用语境。目前已有研究者提出CD指数（CD index）及一系列相关指标，以数学方式刻画科学出版物或专利的颠覆性。近年来，该研究领域受到学界广泛关注，但针对颠覆性指数的重要性与可靠性，尚未形成普遍共识。尽管亟需开展更多实验与评估工作，但此类指标的计算成本高昂且耗时较长，尤其是在大型引用网络中开展大规模计算时，这一现状阻碍了相关研究的推进。本文提出一种全新的颠覆性指数计算方法，该方法依托Dimensions云基科研基础设施，将此类指数的生成计算耗时降低一个数量级，同时在无需任何前期搭建工作的在线环境中开放相关功能。本文详细阐释了这一全新算法，并说明其计算结果与现有颠覆性指标实现方案的一致性。该方法将助力研究人员更快速、精准地开发、验证与优化数学颠覆性模型，从而推动这一新兴研究领域的发展。