Data Analysis Study
Explore an international landscape analysis of 22 research projects that use artificial intelligence and citizen participation for cultural heritage preservation in emergency settings
The study is conducted within the framework of the AISTER project that addresses AI-enabled Citizen Participation in University-driven Ukrainian Cultural Heritage Safeguarding.
Interactive Data Visualisations
The study collected data categorising the selected projects across several analytical dimensions that are based on established typologies, models, and frameworks, including levels of participation, cooperation models and types of technology. The analysis explored patterns, correlations, and emerging directions, sharing insights in the form of open-access interactive web visualisations.
Classification Framework
A spreadsheet with 24 categorisation fields was created, incorporating established typologies (e.g., citizen participation by Shirk et al.) alongside project-specific classifications. Project partners identified and categorised related initiatives, out of which 22 were selected. The initial spreadsheet was processed through cleaning, transformation into machine-actionable format, pivoting, and enrichment with calculated fields. Three main quantitative data analysis approaches were applied: geographical mapping, aggregations, and cross-category filtering to derive insights. Data visualisation worksheets were developed in Tableau and published with interactive elements, including tooltips and filters.
Access the AISTER Classification Framework, Open Dataset, and Report on Zenodo
Cite as:
Ziku, M., & Zourou, K. (2026). The AISTER Classification Framework, Open Dataset, and Report for Participatory Research Projects Combining AI and Citizen Participation for Cultural Heritage Preservation in Emergency Settings. Zenodo. https://doi.org/10.5281/zenodo.17404668
Visualisations at a glance
Click on thumbnail or scroll to see in detail
Spatio-temporal Representations: Geographic Distribution of Actions
Overview of initiatives and key dimensions
Faceted Data View
Insights across key dimensions
Project-Level View
Insights across key dimensions related to artificial intelligence
Combined View
Cross-Category Analysis in Key Dimensions
Data Overview
Project links and short descriptions of the 22 cultural heritage preservation projects
Team
Katerina Zourou
Mariana Ziku
Stefania Oikonomou
Acknowledgement
With contributions from Tugce Karatas (University of Luxembourg), Sanita Reinsone (University of Latvia), Pavlo Shydlovskyi (Taras Shevchenko National University of Kyiv), and Alba Irollo (Europeana).
