  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.
