Standardized ontologies and formats for representing conformational collections, thermodynamic ensembles, and multiple-state models are crucial for effective communication and integration of structural data across the scientific community. Of course, the format of data deposition is inextricably related to the viewing capabilities of the software tools used to display such structures. Recent advances in experimental and computational methods, including machine learning (ML), provide exciting new opportunities for modeling and characterizing multiple conformations. Experimental distance restraint data can also be used as input for training ML-based structure prediction methods and will certainly impact ML-based methods for modeling multiple conformational states. As combined experimental and computational methods develop, models of the multiple conformational states of proteins and nucleic acids will enable biochemical, biophysical, and biological studies. The ability to consistently represent and archive information about conformations in dynamic equilibrium will facilitate research and enhance our understanding of biomolecular function. entries for NMR structures typically consist of a collection of ~20 conformers obtained through restrained modeling with NMR data. "Well-defined" parts of the structure can be determined using conventions encoded in programs like CYRANGE , FindCore2 , and dihedral angle order parameter DAOP software, and colored to indicate well-defined (blue) and not-well-defined (red) residues (left side, pdb 2kcd). The PDBStat program provides tools to create an image of the protein annotated with this information about model convergence. The medoid structure, determined by aligning models using well-defined heavy (or backbone N, Ca, C') atoms, is the first entry in a new file containing coordinates for the conformer collection in mmCIF (or conventional) PDB format. This file includes perresidue tags (q=1 for well-defined residues, q=0 for others). In addition, atom-specific coordinate variances are determined from the average atomic root mean-squared fluctuation (RMSF) across the conformer collection , and reported as effective "NMR B-factors", representing positional uncertainties across the collection. These mmCIF (or conventional) PDB format files can then be used to visualize well-defined regions (left) or atomic variances (shown schematically in three ways, top and right side) using programs like PyMOL. Representations showing atomic variances by coloring or by scaling the size of the ribbon are shown. For multiple domain structures, variance matrix analysis is used to parse the coordinates into well-defined units, which are then analyzed separately .