Training ML models on these disorganized datasets would likely fail to accurately learn and generalize the underlying dynamics of the processes-these models can only be as good as the dataset they have been trained with. Therefore we ask: is it possible to salvage these disorganized datasets for use in (differentiable, predictive) modeling? It is important to note that the collected data is not truly random. While the sensors are shuffled, all measurements across sensors are taken at the same time instances (though the temporal instances can be shuffled) or at the same locations in parameter space (though experiments can be disordered). Recent research has exploited this "partial structure" and has proposed using unsupervised learning techniques for data reorganization before using the data in modeling. Prior work in this field includes using the manifold learning technique diffusion maps to construct entirely data-driven realizations of the dynamics ; learning a single emergent dimension through diffusion maps and then utilizing the embedding in residual neural networks (NNs) ; and reorganizing data through Questionnaires to build parameter-free models using deep neural networks (DNNs) .