We employ the Questionnaire algorithm on the oscillator dataset to obtain a smooth embedding for each previously unusable axis. We begin by analyzing the axis of the agents, now with each time series-experiment matrix representing a data point (512 in total). The data appears disorganized when ordered by the imaginary part of their initial condition. By applying the Questionnaire algorithm, we recover two independent diffusion map eigenvectors, ζ 1 and ζ 2 (see Figure ). Despite the 2D appearance of the embedding, it is locally 1D. By applying diffusion maps to the data again (this time, with each data point represented by ζ 1 and ζ 2 ), we extract the arc length ζ along the curve. This becomes our emergent space embedding. For a given experiment, when we reorder the time-trajectories of each agent by ζ, we observe that the trajectories appear organized and the phase space smooth (see Figure ). Another way to perceive our emergent space embedding is that the recovered order corresponds directly to the order in which the agents appear in an apparent arc after synchronization. If we visualize a time slice captured after the agents synchronize, resulting in an arc-like arrangement, we can label this order from 1 to 512 and color ζ accordingly. Similar to our emergent space embedding, the recovered time embedding appears as a spiral in two dimensions, although it is locally 1D as anticipated. We observe that the oscillators start from an initial condition and gradually move towards a limit cycle; if we were ) at different coupling strengths, denoted as K, along with their disorganized space-time counterparts. While we have provided labels for the coupling strengths for illustrative purposes, we do not assume any a priori knowledge of them when building the model. The long term dynamics of the system are oscillatory, which becomes clear in the third depiction (K = 1.79).