Model validation is the process of evaluating how well a computational model represents reality. That is to say, does the model make predictions that adequately agree with the experimental evidence? Both model validation and uncertainty quantification have gained tremendous attention from researchers in engineering, physics, chemistry, and biology. Uncertainty quantification methods have been successfully applied to assessing model predictions of unmeasured quantities of interest and assisting in the development of computationally efficient, yet predictive, reduced-order models. In both cases, experimental data are incorporated into the analysis to refine the uncertainty estimate. However, with the amount of experimental data published and being generated through ongoing scientific endeavors, it is crucial to organize and integrate experimental data with the uncertainty quantification methods.