We’re delighted to announce that the project Art History Visualization won the 2025 Digital Humanities Award in the “Best Tool / Suite of Tools” category!

The Digital Humanities Awards are annual, publicly driven awards in which anyone can nominate and vote for digital humanities resources, highlighting talent and expertise within the community. The awards aim to raise awareness of impactful Digital Humanities work and engage both the Digital Humanities community and the wider public, without restrictions based on geography, language, or institution. They also actively encourage diverse representation, including contributions from minority languages and underrepresented areas of digital humanities.

ArtVis’s research team includes Silvia Miksch, Velitchko Filipov, and Michaela Tuscher, as well as Victor Schetinger (until 2023), and Raphael Rosenberg Teresa Kamencek from the University of Vienna.

In order to better understand the history of art, a major challenge for Digital Art History (DAH) is to examine how the components of the art system (persons, objects, places, institutions, and events) interact with one another and how these interactions vary over time. The aim of the Art History Visualization project is to model such complex relations using Network Visualization. Networks have a wide range of applications in many domains, including social sciences, software engineering, and economics. Network Visualization has proven to be a successful tool for modeling data and phenomena in these domains, yet most modern solutions do not account for the complex aspects of the data and their dynamics. Real-world data is rarely static, and in most application domains and problems, it is essential to model and visualize the evolution and change of attributes and characteristics within the network, its actors, their relationships, and their movement over time.

The project is funded by the Austrian Science Fund (FWF) and is set to run until September 2027.

Congratulations to the ArtVis team on this excellent achievement!

About Silvia Miksch

Silvia Miksch is a Professor and Head of the Research Unit Visual Analytics at TU Wien Informatics. She studied Information Systems with a focus on Applied Statistics and Artificial Intelligence at the University of Vienna and TU Wien, earning her master’s degree in Social and Economic Sciences in 1987 and her PhD in 1990. Following her doctorate, she joined the Austrian Research Institute for Artificial Intelligence (OFAI) and later pursued postdoctoral research at Stanford University. In 1996, she began her tenure at TU Wien’s Institute of Software Technology and Interactive Systems and served as an Associate University Professor from 1999 to 2006. She was appointed as Professor and head of the Department of Information and Knowledge Engineering at Danube University Krems from 2006 to 2010. In 2010, she returned to TU Wien to establish the Center for Visual Analytics Science and Technology (CVAST) and was appointed as a full professor in 2015. As former chair of the Austrian Society for Artificial Intelligence and a board member of the European Coordinating Committee for Artificial Intelligence, she has significantly contributed to the field. Her research excellence has been acknowledged through various awards and honors.

About Velitchko Filipov

Velitchko Filipov’s research centers on advancing the field of Information Visualization and Visual Analytics, with a strong focus on Dynamic Network Analysis. He specializes in creating innovative visual representations and interactive techniques that enable researchers to intuitively explore complex, time-varying networks. His work has significant applications in fields such as Art History, where understanding the evolution of artistic movements and influence is crucial, and Information Diffusion, where it aids in visualizing the spread of ideas and knowledge. These approaches can also benefit other disciplines, such as computational social sciences, digital humanities, and communication studies, by providing tools to uncover patterns and insights within evolving relational data.

About Michaela Tuscher

Michaela Tuscher’s research focuses on Visual Analytics, with a particular emphasis on the analysis and visualization of relationships between objects and their attributes in the fields of Art History and Cultural Heritage. Her primary interests lie in network visualizations and other visual approaches that reveal underlying relationships and patterns within complex datasets. This work encompasses both the spatial and temporal dimensions of data, enabling a deeper understanding of how connections evolve across time and space.

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