EPIC: an iterative model for metadata improvement. (28th September 2022) - Tarver, Hannah; Phillips, Mark Edward; Krahmer, Ana
This paper provides a case study of iterative metadata correction and enhancement at the University of North Texas (UNT), within a model that we have developed to describe this process: Evaluate, Prioritise, Identify, Correct (EPIC). These steps are illustrated within the paper to show how they function at UNT and why it may serve as a useful tool for other organisations. We suggest that the EPIC model works for ongoing assessment, but is particularly useful for large remediation and enhancement projects to plan timelines and to allocate the people and resources needed to determine what issues should be addressed (evaluate), to rate their level of severity, importance, or difficulty (prioritise), to define subsets or records that are affected (identify) and to make changes based on prioritisation (correct).