While the above graphical caricature demonstrates how improper labeling can lead to a disorganized dataset with a "hidden" parameter-space axis, some problems lack a clear physical space altogether. For instance, the trajectories of agent-based Stuart-Landau oscillators only form a smooth phase portrait when ordered in a specific way that happens to correspond to their intrinsic heterogeneity . Similarly, the dynamic spread of SARS can be reordered to appear coherent in terms of travel patterns . Other examples include differentiating and ordering heterogeneous groups of single-cell trajectories , data from neuronal recordings, where the spatial axis is better described by the connectivity between neurons rather than their physical locations , or data from complex flow systems, in which organizing by intrinsic process heterogeneities (e.g. shear rates or electrode surface states) is crucial for coherent analysis. In all these cases, we are left with "shuffled and disorganized" datasets.