Modelling and Visualizing Flows of Objects from Museums

Significant numbers of UK museums have closed in the past 25 years, usually leading to the dispersal of their collections. However, there has been no systematic data synthesis or analysis about what happens to museums’ collections when they close. Thus, there has been no research into how closure may vary across different types of museums or the patterns of object dispersals from closed museums. This uncertainty about how collections might be being preserved, or lost, makes these questions significant to museum researchers and professionals. “Museum Closures in the UK 2000–2025” is an interdisciplinary project aiming to address these questions, working within the challenges of heterogeneous data that is hard to source and that exhibits a high degree of uncertainty. This article presents work we have conducted to overcome these challenges, presenting novel contributions including (1) an interdisciplinary methodology and light-weight technical architecture encompassing data collection, modelling, visualization and analysis; (2) a re-usable conceptual model, implemented within a graph database, allowing integration of diverse data from a wide range of sources; (3) a suite of data visualizations providing domain experts with an intuitive interface supporting them in their investigation of patterns of object dispersal. This work is of significance for researchers and practitioners in museum studies and the digital humanities. The methodology and techniques described here are applicable to analysing object flows in museum sectors beyond the UK, and to broader cultural heritage sectors such as galleries, libraries and archives.

• Publication year

  2026

• Venue

  Journal on Computing and Cultural Heritage

• Publication date

  2026-02-28

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1145/3798044
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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