

Digital preservation infrastructure remains unevenly accessible, with participation often constrained by institutional size, funding, and technical capacity. This paper presents a case study of the Academic Preservation Trust (APTrust), a community-governed digital preservation consortium, and examines how its Associate Member model functions as an incremental mechanism for broadening participation. Through sponsorship-based membership and shared governance, APTrust enables a wider range of organizations to engage in preservation activities without requiring full institutional equivalence. Drawing on early implementation experience, member census data, and the APTrust Associate Member Toolkit, this paper analyzes both the benefits and limitations of this approach, with particular attention to governance design, organizational readiness, and the distribution of risk and responsibility across member institutions.

This case study outlines the meta-data curation strategies and workflows employed towards a digital reproduction of an Indigenous print book, Cherokee Earth Dwellers. The data corpus includes multimedia assets in the form of unstructured file formats such as multilingual text, images, audio, and video documents. Five stages guide the digitization process that assist the development of an interactive e-publication from the print book. First, a programmatic approach is utilized to retrieve names of biodiversity species from the pre-print. Second, meta-data for audio, video and image files associated with the print book are weaved together to form an intermediate meta-data package (IMDP). Third, independent meta-data reviewers programmatically validate the resulting IMDP outputs. Fourth, Reproducible Python Files (RPF) are generated to reformat IMDP into Simple Archive Format (SAF) for ingestion into DSpace. Fifth, RPF is published open source to streamline data curation workflows for unstructured multimedia assets. The study concludes with a discussion on the emerging interplay between Generative AI and human curation practices, and its implications for digital publishers, and archival repositories.

Digitized special collections archives are discoverable largely through full-text search via indexing and categorical methods of librarianship expressed in finding aid arrangement, which privileges established terms and inherited description. By contrast, this work is a computational arrangement method that compares clustered sentence embedding structures to promote discovery when lexicon and semantic terms differ. We explore modeling texts as a graph of directed semantic displacements in a vector embedding space and conceptualize sections of these graphs as an extrinsically comparable morphism. We evaluate these graph structures as a semiotic medium by comparing alignments across graphs from various types of OCR texts, and language-like noise datasets. Findings suggest potential interoperative value in morphism aligned text. Work sees potential applications for interdisciplinary discovery and unsupervised archival arrangement.

Large-scale public participation, using everyday tools like smartphone cameras alongside metadata captured at the moment of documentation, may be one of the most practical ways to preserve vast amounts of undigitized cultural heritage. We describe how contemporary mobile devices can support selected cultural heritage digitization tasks within a practical quality framework consistent with ISO 19264 and FADGI. We then outline how digital collections, particularly for small-to-medium-sized institutions, can complement mobile-device capture with automated metadata generation and cataloging.

This study examines energy consumption in cultural heritage imaging systems and workflows, addressing a gap in sustainability research that has to date focused primarily on data storage infrastructure estimations. Using Home Assistant edge computing and Z-Wave smart plugs, seven distinct imaging systems were monitored over 203 hours, capturing 55,211 images, and rendering 2,448 objects. Results show an average energy requirement of 11.1 Wh per object, with an annual total of 747 kWh for digitization activities. Findings highlight opportunities to reduce energy demand and improve efficiency, such as automating continuous light shutoff and optimizing postprocessing routines that support institutional sustainability goals and provide a transparent, reproducible model for other labs and studios.

The Harvard Plate Stacks (HPS) Astronomical Photographic Glass Plate Collection comprises over 600,000 glass plate negatives. To support collection preservation and improve accessibility, HPS integrated an advanced imaging system to digitize astronomical features in the emulsion layer, as well as annotations made by pioneering Women Astronomical Computers in the late 19th and early 20th centuries. HPS installed a multispectral imaging system to capture high-quality images of astronomical features dating back to the 1880s, along with their historical annotation, to advance both astronomical and cultural heritage research. This digitization of glass plate negatives builds upon 20 years of advanced imaging methodologies and technologies, using the latest equipment and workflows. This includes new post-processing techniques, data management, and workflows across discipline boundaries by a collaborative team.

FamilySearch International, formerly the Genealogical Society of Utah, established in 1894, has been actively capturing images of records and books of genealogical value for nearly 90 years in microfilm or digital format worldwide. As new technologies have emerged in AI and image processing, several FamilySearch digitization projects have begun successfully implementing mobile phone solutions where practical camera work has become difficult to expand or initiate. This paper highlights a few mobile digitization projects in South Africa, Japan, and a home-grown solution.

Authentication of high-value cultural heritage objects relies primarily on legacy empirical frameworks developed prior to modern statistical decision theory, digital identity management, and multi-factor authentication (MFA). While conservation science provides diverse analytical techniques to evaluate material, structural, and chemical properties, these investigations typically operate as independent silos rather than components of a unified identity framework. Consequently, combining provenance, connoisseurship, and scientific analysis into a coherent protocol with quantifiable confidence levels remains an unaddressed challenge. This paper reformulates cultural heritage authentication as a statistical identity problem. Building on standards-based hyperspectral authentication and physical multi-factor frameworks, we formalize a new class of authentication factors called physicometric factors, defined as the measurement and statistical characterization of invariant physical properties of inanimate objects. These stable descriptors arise from an object's material composition, structural organization, fabrication history, and physical response to controlled interrogation. Examples include spectral reflectance, micro-topographic surface structure, chemical signatures, refractive index transitions, and fabrication-derived entropy patterns. To implement this framework, we introduce OMASIE (Optical Multiscale Aperture Synthesizing Invariant Extractor), an architecture employing structured illumination, coded apertures, and computational reconstruction to translate object responses into high-dimensional identity vectors suitable for statistical comparison. This methodology resolves measurement degeneracy, including metamerism and related ambiguities, through dimensional expansion, covariance-aware evidence fusion, and identity vector comparison. Beyond immediate verification, aggregating these physical identity vectors establishes a structured knowledge base to support retrospective scholarship, workshop attribution, and conservation monitoring.

This proposal presents an innovative framework for cultural heritage documentation aiming to overcome the multifaceted threats that face cultural heritage sites. The complex interdependencies between heritage sites (e.g., archaeological, religious, and cultural) are modeled in the form of a network model. This model includes nodes (sites) and edges (shared history or geography). Then, Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) are used to simulate degradation, prioritize conservation efforts, and generate high-fidelity 3D reconstructions from fragmented data. Preliminary results show that this networked approach identifies critical "keystone" sites whose preservation is essential for maintaining the integrity of the entire heritage ecosystem. The accuracy of GNN is 87% in predicting secondary damage patterns. The GAN successfully generates 3D reconstructions (20% of original scan data).