Biological data is inherently complex, with intricate structures, patterns, transitions, and relationships. Many problems in biology and biomedicine also require fusing different data sets from various sources to gain a deeper understanding of the underlying phenomena. However, accurately representing this complexity is crucial for both biological/medical research and for communicating biological information to the public. Oversimplifying this data can limit insights for researchers and hinder public understanding.

Faithful data representation is key. While this is universally true for visual analysis, the field of biological data communication has its unique challenges and open questions. Researchers need the right context to interpret data accurately, especially when uncertainties arise from factors like sampling choices or analysis parameters. Simplifying data for wider audiences must still convey its complexity and uncertainty to support informed decisions.

This Dagstuhl Seminar will bring together experts in data visualisation and design, researchers, public educators, and journalists to explore methods for embracing and communicating biodata complexity and uncertainty. We aim to establish a well-grounded framework of guidelines that help researchers and developers working in biological visualisation to empower their diverse audience – ranging from fellow visualisation experts to biologists and the general public – with tailored analysis systems.

We will address faithful biodata representation on different fronts. First, hidden complexity and bias needs to be made apparent. This includes visualising analysis parameters and using technical solutions like dashboards. Second, we will discuss and establish rules for ethical data communication to help viewers understand the complexity behind the visualised results. This is especially important for communication to a wider public, where the aim can be to convey a certain message, but still enable viewers to draw their own conclusions. Third, we will explore FAIR ecosystems for visualisation, enabling shared and adaptable visualisations across communities. Fourth, we will explore how we can empower experts with decision support systems that enhance their judgement by integrating data-driven insights with expert knowledge. Finally, we will develop inclusive visualisation techniques to help the public understand global challenges like biodiversity and climate change. By using narrative visualisation, we will ensure data retains its complexity while reducing bias, allowing informed decisions and promoting the common good.

To support that the discussions between these different disciplines will continue after the seminar, the intended outcomes include a research agenda for the coming years, joint manuscripts, and new research networks between experts from the diverse fields taking part in the seminar.

Creative Commons BY 4.0

Jan Aerts, Jillian Aurisano, Helena Jambor, and Michael Krone