When assessing a complex digital object for preservation, a framework is required to determine if the target is complete enough for preservation. Previous evaluations of emulation frameworks for obsolete digital art and computer games suggested checklists of items to be considered when preserving complex digital objects (Ciuffreda et al. 2012; Delve et al. 2010; Owens 2014; Van Doren / Michaan 2016). However, in order to further evaluate if emulation can be used as a component of the long-term preservation of archaeological virtual reconstructions, its practical rendering abilities, rather than a theoretical 100% accuracy, need to also be assessed. This needs to focus on both the final visualisation, e.g. video flythrough, and also whether information is lost in emulation of the final artefact’s development files, e.g. final virtual reconstruction model.
This poster redefines (Renderability, Original Form, 3D Object Relationships, Views of the 3D Model) and augments with additional categories (Chain of Custody, Preservation Metadata, Virtual Reconstruction Paradata) those evaluation checklists for archaeological virtual reconstructions based on their non-supported aspects. Thus, the recommended emulation framework provides a practical starting point for memory institutions to make decisions about the long-term preservation of the archaeological virtual reconstructions, submitted to their collections. Each archaeological virtual reconstruction does not have to address all the proposed criteria in order to be archived, but the framework provides an understanding of what will be preserved, and perhaps an opportunity at ingestion to check with the depositor whether any additional missing metadata or ancillary files are available.
It becomes clear that in redefining previous emulation frameworks, existing obsolete archaeological virtual reconstructions will not address many aspects of them. However, it is not suggested that existing metadata registries are erased, but that perhaps the recommended checklist can be used to highlight the deficiency in this data which may affect the usefulness and subsequent knowledge drawn from them.