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.

翻译：评估学术思想的破坏性是研究评价的新领域，它通过考虑出版物或专利的更广泛引文语境，超越了基于标准引文指标的评估方法。为数学化地表征科学出版物或专利的破坏性，研究者提出了"$CD$指数"及相关指标。虽然该领域近年来备受关注，但学术界对破坏性指数的意义与可靠性尚未达成共识。尽管亟需更多实验与评估，但这些指标的计算成本高昂且耗时，尤其在大规模引文网络中操作时更为突出。本文提出一种新型破坏性指数计算方法，该方法利用Dimensions云基础研究基础设施，将此类指数的计算时间降低一个数量级，并能在无需任何部署配置的在线环境中提供相关功能。我们阐释了这一新型算法，并描述了其结果如何与现有破坏性指标实现保持一致。该方法将使研究者能够更快速、更精确地开发、验证和完善数学破坏性模型，从而推动这一新兴研究领域的发展。