The development of deep learning methods to predict the single structure representations in the PDB have been a breakthrough for structural biology . However, the next challenge lies in predicting ensembles. This is important for two reasons; first, ensembles dictate function, second, the accuracy gap between prediction methods and experiments may result from an incomplete consideration of ensembles on both sides . A substantial upgrade in representing our experimental structural data is needed to meet this challenge. X-ray crystallography and single-particle cryoEM capture an ensemble of atomistic information, enabling the modeling of compositional and conformational states. Numerous methods exist to disentangle these states , but the current PDBx/mmCIF format inadequately captures these states. While it is possible to capture this data with ensemble data representation, there can be an explosion of the number of states due to the atomistic detail. Ensemble representations in NMR result from inferential determination of positions based on highly local observables, which face this issue and generally represent a fixed number without any Boltzmann weighting implied . Similarly, recent integrative modeling approaches that rely heavily on distance restraint and other non-atomistic data can be deposited in the PDB-DEV . However, these integrative and inferential modeling challenges are distinct from those faced by real and reciprocal space modeling in X-ray crystallography and cryoEM in an important way: integrative approaches are generally underdetermined and inferential, whereas X-ray/EM density maps are generally overdetermined for a single model.