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The digital transformation of the GLAM (galleries, libraries, archives, and museums) sector is a multifaceted process that must address a range of requirements and critical challenges, such as traceability, long-term preservation, and sustainability. The potential of disruptive technologies to revolutionize this sector is significant, offering new ways to overcome these challenges and align with the broader objectives of smart cities. While substantial research has been conducted on the digital transformation of the GLAM sector, many opportunities remain unexplored by various stakeholders. This paper contributes to the field by proposing a reference architecture rooted in a design-oriented approach for developing and implementing processes related to the digital transformation within the context of smart cities. To illustrate the practical application of the proposed architecture, a proof of concept was developed in the form of an immersive digital gallery, which features an AI-powered virtual storyteller. The gallery, built using the open, cross-platform, Unreal, incorporates digitized physical art and a virtual metahuman tutor who guides users through the gallery. This interactive 3D environment enables users to explore the space, engage with artworks, and interact with a virtual guide. The environment’s performance was evaluated using a keystroke-level model analysis. Key findings from a pilot study involving 10 users showed that, when directed by the AI storyteller, the average time to locate an artwork was 16.5 s, while the average time to ask a question was 15.8 s. By integrating such technologies, the GLAM sector can contribute to the cultural fabric of smart cities, fostering more sustainable, accessible, and interactive public spaces.
Uncovering AI bias in digital collections
Museums are using data science and NLP to detect and contextualize derogatory language in legacy catalog records. A case study from the Harvard University Herbaria shows how digital stewardship can promote ethical access and institutional transparency.
https://www.aam-us.org/2025/06/29/improving-the-search-uncovering-ai-bias-in-digital-collections/
#DigitalStewardship #MuseumTech #EthicalAI #GLAM #BiasDetection #AIethics #CollectionsManagement #DataScience #NaturalLanguageProcessing #DigitalHumanities #Museums
I wanted to share this thought with you.
What do you think – especially in the context of visualizing museum and collection data?
Does something like this already exist? What’s considered best practice? Who’s working on this?
Would love to hear your thoughts! 👇
#digitalarthistory #bigvisualdata #culturaldata #museumtech
The Dispersed Chinese Art Digitization Project bridges centuries and continents through digital reconstructions of major historical sites and artifacts – from the Zhihua Temple to the Six Horses of Zhaoling and Longmen Binyang Central Cave.
Led by the Center for the Art of East Asia at UChicago and Xi’an Jiaotong University, with major museum partners in China, Japan, and the US.
https://caea.lib.uchicago.edu/dcadp/en/
#DigitalHeritage #EastAsianArt #3DReconstruction #MuseumTech #DigitalHumanities
Interesting: Muse Software is a comprehensive platform for museum operations management — including CRM, ticketing, fundraising, POS, reporting and event management.
https://www.musesoftware.ai/
#MuseumTech #CRM #CulturalHeritage #DigitalTransformation #MuseumManagement
+ we want to show our organisations that we can be lean, agile, savvy and practical but it’s when what we make stays at the level of (good quality) proof of concept that it gets a bit… tense.
Another ArcGis story map used to help tell an important story but *not* supported/hosted by the originating organisation!!
storymaps.arcgis.com/stories/3f2f...
#museumTech Thread commencing…
Art experts prefer being able to look at the individual images they are working on in the course of their research. However, if one were to look at digitally accessible images in the field of visual art, one would be dealing with billions of images; no one can handle visually examining such huge numbers of images one at a time. Therefore, art experts need special tools to examine and describe artworks in the context of other artworks. We used our experience from previous projects and interviews with members of the target group (art historians, curators, art dealers, and artists) to identify the central issues these experts encounter when working with large image collections and to determine the functionality and properties a system must offer to support their work. The results led to the customized interface LadeCA.View, which is now used in several projects. LadeCA.View enables experts to describe an exhibition or a collection of visual art in such a way that a user can obtain an overview of the intention, content, and structures of the exhibition or collection within a short period of time without looking at each image individually. LadeCA.View can also be used as an interface to probe more deeply into a collection or exhibition. In this paper we show the functions and visualizations of the interface and explain the design decisions. Furthermore, we outline LadeCA.View’s scope of applicability using three case studies
Versitron
Harald Klinke
FINOkoye