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The purpose of this study is to determine which metadata schemas are used in the museums and university collections of the main universities in Spain and other European countries. Although libraries and archives are also university memory institutions (according to a Galleries, Libraries, Archives and Museums perspective), their collections are not included in this study because their metadata systems are highly standardized and their inclusion would, therefore, skew our understanding of the diverse realities that the study aims to capture.

The analysis has three components. The first is a bibliographic review based on Web of Science. The second is a direct survey of the individuals responsible for university collections to understand their internal work and documentation systems. Finally, the results obtained are complemented by an analysis of collective university heritage portals in Europe.

The results of this study confirmed the hypothesis that isolation and a lack of resources are still major issues in many cases. Increasing digitalization and the desire to participate in content aggregation systems are forcing change, although the responsibility for that change at universities is still vague.

Universities, particularly those with a long history, have an important heritage whose parts are often scattered or hidden. Although many contemporary academic publications have focused on the dissemination of university collections, this study focuses on the representation of information based on the conviction that good metadata are essential for dissemination.

This article examines curatorial practices, both traditional and digital, in the Guizhou Provincial Museum’s ethnic exhibition to assess their effectiveness in representing ethnic minority cultures, fostering learning and inspiring curiosity about ethnic textiles and costumes and associated cultures. It also explores audience expectations concerning digital technology use in future exhibitions.

A mixed-methods approach was employed, where visitor data were collected through questionnaires, together with interviews with expert, museum professionals and ethnic minority textile practitioners. Their expertise proved instrumental in shaping the design of the study and enhancing the overall visitor experience, and thus fostering a deeper appreciation and understanding of ethnic minority cultures.

Visitors were generally satisfied with the exhibition, valuing their educational experience on ethnic textiles and cultures. There is a notable demand for more immersive digital technologies in museum exhibitions. The study underscores the importance of participatory design with stakeholders, especially ethnic minority groups, for genuine and compelling cultural representation.

This study delves into the potentials of digital technologies in the curation of ethnic minority textiles, particularly for enhancing education and cultural communication. Ethnic textiles and costumes provide rich sensory experience, and they carry deep cultural significance, especially during festive occasions. Our findings bridge this gap; they offer insights for museums aiming to deepen the visitor experiences and understanding of ethnic cultures through the use of digital technologies.

This article sets out to show how principles and questions about method that underlie a way of interpreting the discipline of conservation and restoration can find results in research and studies, aiming at achieving even conscious reuse process. The occasion is the very recent research performed on the former Church of Saints Gerolamo and Francesco Saverio in Genoa, Italy, the Jesuit church annexed to the 17th-century College of the order. It is a small Baroque jewel in the heart of the ancient city, former University Library and actually abandoned, forgotten for years, inaccessible and awaiting a new use.

The two-year work carried out on the monumental building was conducted according to a study and research methodology developed and refined over the years within the activities of the School of Specialisation in Architectural Heritage and Landscape of the University of Genoa. It is a multidisciplinary and rigorous approach, which aims to train high-level professionals, up-to-date and aware of the multiple problems that interventions on existing buildings, especially of a monumental nature, involve.

The biennal study has been carried out within the activities of the Post-Graduate Programme in Architectural Heritage and Landscape of the University of Genoa. The work methodology faces the challenges of the contemporary complexity, raised by the progressive broadening of the concept of cultural “heritage” and by the problems of its conservation, its active safeguard and its reuse: safety in respect of seismic risk, fire and hydro geological instability, universal accessibility – cognitive, physical and alternative – resource efficiency, comfort and savings in energy consumption, sustainability, communication and involvement of local communities and stakeholders.

The goals of the work were the following: understanding of the architectural heritage, through the correlated study of its geometries, elements and construction materials, surfaces, structures, spaces and functions; understanding of the transformations that the building has undergone over time, relating the results of historical reconstructions from indirect sources and those of direct archaeological analysis; assessment of the state of conservation of the building recognising phenomena of deterioration, damage, faults and deficits that affect materials, construction elements, systems and structures; identification of the causes and extent of damage, faults and deficits, assessing the vulnerability and level of exposure of the asset to the aggression of environmental factors and related risks; evaluation of the compatibility between the characteristics of the available spaces, the primary needs of conservation, the instance of regeneration and possible new uses; the definition of criteria and guidelines for establishing the planning of conservation, restoration and redevelopment interventions.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

This research delves into the realm of Business Model Innovation (BMI), integrating it with the human-centric, sustainable, and resilient principles of Industry 5.0, proposing a new theoretical framework.

An abductive approach has been chosen to expand existing knowledge developing new ideas based on emerging phenomena. Data were gathered via semi-structured interviews with directors, managers and curators of public institutions in Italy, Switzerland, Germany and Spain encompassing Galleries, Libraries, Archives, and Museums (GLAM). These data were subsequently subjected to thematic analysis.

The findings indicate that the main enablers for Business Model Innovation (BMI) in combination with Industry 5.0 encompassed stakeholder, customer and organizational engagement, collaborative environment, knowledge and innovation management, and sustainability. These drivers were effectively leveraged through three pivotal facilitators-inhibitors: technology, resources, and leadership.

The principal constraints are rooted in the narrow contextual focus and the limited participants number. However, upcoming research efforts may broaden the horizons of this multifaceted and extensive investigation.

This study is groundbreaking as it fills a significant gap in the existing literature by integrating Business Model Innovation (BMI) with the Industry 5.0 paradigm, a novel approach that has not been explored previously. Additionally, the inclusion of GLAM institutions in this research adds a unique dimension, as they have been largely overlooked in both research domains.

The evolving visitors' expectations and the unfolding digital transformation compel rethinking on the service offering of museums and cultural institutions. Although digitalization and people-centeredness are widely exploited to enhance the visiting experience, there is limited evidence of their implications on organizational attractiveness. The article investigates this issue, examining the service attributes that entice visitors.

The study collected secondary data from the latest census study by the Italian Institute of Statistics on museums and cultural institutions. Two hierarchical regression models have been run on a sample of large publicly owned organizations (n = 312) to identify the service factors that were most effective in attracting Italian and foreign visitors.

Museums and cultural institutions undergoing a digital transformation were more effective in attracting visitors. The delivery of virtual tours and online events captivated the Italian audience. Foreigners appreciated the opportunity to use applications augmenting the on-site visit.

Digitalization and people-centeredness improve the attractiveness of museums and cultural institutions. Using digital channels to engage visitors fosters their desire to interact with cultural heritage. Furthermore, digitalization enriches the on-site visit, expanding conventional services with virtuality. However, the adverse effects on cultural heritage should be carefully handled.

This study highlights the service attributes that add to the attractiveness of museums and cultural institutions, enabling them to engage visitors and improve the visiting experience.

Although universities have extensive research and initiatives in place that align with the United Nations’ Sustainable Development Goals (SDGs), there is still a significant gap in documenting and assessing these efforts. This paper aims to discuss how academic libraries can apply their information management skills and open-access platforms, to facilitate the discoverability and retrieval of evidence on SDGs.

Introduced by a brief literature review on the role of libraries in contributing to the SDGs in general, the authors draw on their personal experiences as metadata specialists, participating in a project aimed at linking their university’s research output to the SDGs. A case study, from the University of Pretoria’s Veterinary Science Library, is used as an example to demonstrate the benefits of resourceful metadata in organising, communicating and raising awareness about the SDGs in the field of veterinary science.

Through practical examples and recommended workflows, this paper illustrates that metadata specialists are perfectly positioned to apply their information management skills and library platforms to facilitate the discoverability and retrieval of evidence on SDGs.

Although there are increasing reports on the contributions of libraries to support the successful implementation of the SDGs, limited information exists on the role of metadata specialists, as well as those with a practical focus.

This research explores the community's role in feeding a culturally sustainable development project over time and the practices which operationally allow the bridging of cultural heritage management and sustainable development according to the approach of “culture as sustainability”.

The primary and secondary sources relate to nearly 20 years of life of the Tuscan Mining Geopark case belonging to the United Nations Educational, Scientific and Cultural Organization (UNESCO) European and Global Geopark Networks. Textual analysis was applied to the dataset. The interpretative approach was aligned with other investigations within this research field.

The results highlight how a bold project in an uncertain context harnessed bottom-up mobilisation and accountability to stimulate a sustainable community empowerment. The ability to experiment and learn from experience depicts an organisational logic far from the top-down and predefined design practice widely contested in the literature.

Despite a single case study was analysed, it enables researchers to craft a conceptual model for culturally sustainable development projects, and it fills the literature gap on how to operationalise culture as sustainability under the managerial perspective.

The model assembles an organisational process view and practices that can be tailored to a cultural context with insights for developing culturally sustainable projects.

The research increases the observations of community empowerment within culturally sustainable development projects. It demonstrates how the “incompleteness of the design” was not a weakness but rather a trigger of effective organisational practices.

Amazon Explore is a new online experience product from Amazon, which offers live stream sightseeing of destinations around the world. This paper aims to provide a first insight into how exactly Amazon Explore enters the virtual experience (VE) industry, and discuss the possible implications of its business model for the future of virtual and offline travel experiences.

Qualitative and quantitative methods of content analysis methods were employed in this study. The sample of this research consisted of 175 Amazon Explore products. Firstly, the authors analysed the content of each product, and then quantitative method were used to present the data. With this analysis, the authors presented how Amazon Explore is testing the foundations for new business models, and discuss the possible implications for the future of tourism. One year later, those same Amazon Explore products were reviewed again to check development progress. The following were the main questions regarding VE: How interactive are Amazon Explore products? How does Amazon Explore aim to ensure the monetisation of these products in an economically sustainable way in order to go beyond the “freemium” business model, often associated with VE as just another destination marketing tool?

This article presents the discussions of VE and virtual reality (VR). The authors determined the level of interactivity of the diverse VE offered themes with a model of four levels of interactivity. The results show that the analysed products achieve mostly only low levels of interaction. Regarding monetisation, Amazon Explore shows three possible future directions in the development of VE: as a marketing tool, as an extension of the offline experience and as potentially someday replacing real travel as part of the future monetisation scenario.

The article offers the first insights into Amazon's extension into the realm of VE in two time periods in 2020 and 2021, and, based on these preliminary results, discusses four possible scenarios for the future development of VE monetisation.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.