Creating and maintaining high-quality ontologies is crucial as they encapsulate the logic for rule-based artificial intelligence. One way to objectively assess ontologies is ontology metrics, and the research community has already proposed measurement frameworks for assessing ontology quality. Arguably, the most holistic framework available is OQuaRE. Not only does it suggest metrics, but it also links these metrics to quality characteristics and interprets them using a school-like grading system. However, in an effort to implement the framework for the metric calculation software NEOntometrics, serious drawbacks came to light: (1) Important parts of the framework are only available at online resources (thus not part of the scientific record and without ensured permanent availability). (2) The proposing authors have been involved in all applications of the framework and corresponding research, an outside perspective is missing. (3) At times, the meaning of metrics changed throughout the papers without notice, leading to conflicting definitions. (4) A thorough evaluation of the quality grades is missing. This research aims to fill this gap: We first scrutinize the various metric proposals with their conflicting versions and resolve these conflicts. Afterward, we present OQuaRE to its full extent to create a permanent, lasting record. At last, we use a large body of evaluated ontologies to assess whether OQuaREs quality ratings sufficiently cover the ontology models used in the wild.