presents the first analysis of the geometric properties of the simulated flow. Since the scale of sparseness is a *small scale* (e.g., in the case of an ensemble of filamentary ‘regions of intense vorticity’ (RIVs), it is comparable to the transversal scale, i.e., to the diameter of an RIV, and not to the longitudinal scale, i.e., to the length of an RIV), we expect it to exhibit a scaling trend consistent with being in the dissipation range. This was indeed confirmed by performing time series analysis on the data sampled from JHTDB. The results are presented in the Appendix.

Finally, as we believe this scale of sparseness could be potentially impactful in many other situations, it is meaningful to take a closer look at how to optimize the data analysis algorithms. In the absence of heuristics, all RIVs must be investigated and an exhaustive search for the maximum $r$ value conducted ($r$ denotes a scale comparable to the scale of sparseness). This brute force approach is computationally demanding. One field that seeks to systematize the search for heuristics is Computational Citizen science. [^2] Computational Citizen science has proven a useful technique for optimization in other fields such as protein folding 