Data visualization is the science and practice of creating interactive graphical representations from data to support the data’s exploration and presentation. In data analysis, visualization is the method of choice when the analysis objectives cannot be crisply and formally specified – i.e., when looking for regularities (patterns, trends,...) or irregularities (outliers, anomalies,...) in the data in an "I-know-it-when-I-see-it" manner. If accompanied by advanced computational analysis methods like machine learning, data mining, or AI techniques, visualization becomes visual analytics – a highly active research field at the intersection of visualization, data science, and human-data interaction. 

Here at Aarhus University, we specifically pursue research on immersive visual analytics (i.e., visual analytics in combination with VR/AR approaches), situated visualization (i.e., data visualization that shows data where it is relevant to users), progressive visual analytics (i.e., visual analytics for very large data or computationally intensive analyses), visual literacy, visual guidance and visualization onboarding (i.e., aiding in the use of visualization and in the visual-interactive exploration of large datasets). 

Research in data visualization and visual analytics is making contributions to a variety of fields by its very nature. After all, the data to be visualized and analyzed needs to come from some domain. Currently, we are working with academic collaborators from the life sciences, food science, and engineering to develop novel visual analytics methods that accompany and extend their data-driven research methods. Our industrial collaborators include many companies headquartered in Aarhus, such as Systematic, Arla, or Scalgo, for which we provide knowledge transfer from academia into practice and pursue joint research projects.