In conclusion, we have demonstrated how the tools of persistent homology, especially the persistence diagram and the concept of tracking births and deaths of individual features, can be useful to analyse temporally varying data. The use of temporal persistence diagrams analysing unique rings being born and dying (possibly repeatedly) over the course of a simulation gives us the ability to clearly identify which rings are long-lived and which are ephemeral. It also allows us to observe how medium-range structure (as represented by rings) changes over time, and which topological features are favoured when. The key difference from traditional persistence diagrams, in which time is not a factor, is the fact that given rings can be born, die, and be born again. This is critical for analysis of repair phenomena in biological networks which have, until now, been difficult to visualise and effectively quantify. Critically, the density of the temporal data does not appear to mask significant network changes from being identified. The method employed aids as a lens to analyse the networks, and this provides further insights into how a network changes over time, allowing us to assess the timescale of network behaviours. We anticipate that this analysis can be extended from 2D biological networks to other network formation and damage analysis tasks, such as glass ageing or zeolite formation.