Projecting your dataset onto an order parameter or free energy landscape is a powerful way to visualize the underlying energy minima and transition states. Reducing the dimensionality of high-dimensional protein structural space (e.g., tICA, PCA, UMAP) can be incredibly useful in identifying causative collective variables that drive the system functional behaviors. Additionally, when constructing models from parallel simulation datasets, it is important to ensure your equilibrium system is sampling reversible events and mixing. Applying ergodic constraints through trimming or prior counts ensures that all states have been visited. Finally, consider quantifying the uncertainty and error bars for any feature being extracted from your dataset .