Recommendation of: OntPreHer3D: Ontology for Preservation of Cultural Heritage 3D Models. Round#2

The exponential growth of digital data has fundamentally reshaped the framework within which scientific research is undertaken.The development and institutional adoption of the FAIR principles (Wilkinson et al. 2016) have further stimulated critical reflection on the preservation and reproducibility of archaeological research, an area intrinsically characterised by destructive methodologies.In response, archaeological practices have increasingly been questioned (Huvila & Huggett 2018), with best practices and workflows frequently proposed in academic conferences to facilitate more efficient data sharing and reuse.In this paper, Igor Piotr Bajena addresses the pressing issue of the longevity and reusability of 3D models, resources that are becoming ever more widespread, yet which present numerous challenges.These include the rapid obsolescence of digital formats and the considerable subjectivity involved in model creation, making it difficult to accurately reproduce models or even trace the workflows underpinning their development.Bajena illustrates these issues through a compelling case study, demonstrating how identical datasets and surveys can nonetheless result in significantly different 3D representations.To uphold the scientific integrity of such outputs, the author advocates for the adoption of standardised metadata grounded in a detailed ontology.This methodology embeds not only software and methodological information but also the interpretative reasoning and subjective decisions made by researchers, thereby recognising their active role in the modelling process.Following a comprehensive analysis of the current landscape, he proposes an extension of the CIDOC CRM ontology capable of documenting the entire lifecycle of 3D object creation.This initiative addresses a substantial gap in the production and documentation of digital heritage data.Moreover, by building upon Web 3.0 and Linked Open Data principles, the proposed standardisation ensures that metadata is both human-and machine-readable.This, in turn, enables greater automation, reproducibility, and reusability of models, and aligns closely with the core objectives of the FAIR principles.