Call closes on 1st October

Would your research benefit from access to more advanced visualization techniques? Take advantage of the possibility to get support from the national research infrastructure InfraVis. The InfraVis visualization experts can assist in analyzing and enriching your data visualization to its fullest potential.

You can apply for up to 600 hours of visualization support and receive help from InfraVis with, for example, data curation, visualized material for user studies, visual analyses of experimental data, and interaction techniques.

In the infographic to the right, all important dates are marked. Even without a pre-proposal you are most welcome to send in your full application. The call closes 1st October 2024.

For all details and how to apply, please take a look at the website.

Logo with a deeper meaning

XANADU—"X-rays And Neutrons for ADvanced sUstainability research"—is a graduate school within Lund University’s Faculty of Science focusing on sustainability research by utilizing MAX IV and ESS. 

For XANADU's new logotype, they wanted something deeper than just a new font, and InfraVis was consulted to incorporate science in the creation. The letters were 3D-printed, rebuilt using tomography and finally coated in gold. The full process is described here.

Storytelling Through Visualization: Challenging Modern Ways of Viewing by Reading Text and Archaeology Together

PI: Michael Lindblom, IAE: Mattis Lindmark & Carl-Erik Enqvist, Humlab @ Umu

This project was designed to integrate the ancient narrative of Pausanias' Periegesis Hellados (Description of Greece) from the second century CE with the history of Swedish archaeological excavations since the 1920s. The goal was to create an exhibition that not only showcased Pausanias' perspective and the significance of the excavations but also offered personal insights into the archaeologists involved. The team at IAE employed virtual reality (VR) and game design techniques to craft an immersive environment, replicating a 1970s researcher's office. This virtual space contained a variety of materials linked to Pausanias and the archaeological site's history. These resources were presented as interactive, multilayered puzzles with hyperlinks and detailed documentation. The project invited users to explore history in an engaging and immersive way, connecting both ancient texts and modern archaeological efforts for a deeper understanding of the past.

Dynamically Visualizing high-dimensional datasets using parallel coordinates plot

PI: Dimitrios Kourakovounis (KTH), Jörg Conradt (KTH). IAE: Filip Berendt (KTH).

Associate Professor Jörg Conradt and PhD student Dimitrios Korakovounis from the Neuromorphic Computing Systems Lab are researching Spiking Neural Networks, focusing on image processing of objects in motion. Dimitrios' project trains an array of image convolution kernels, known as "Receptive Fields," to enhance image accuracy. These fields are defined by six parameters, and their activity is measured over time. The goal is to "silence" poorly performing fields while retaining the more effective ones.

A key challenge is understanding the influence of the parameters on the Receptive Fields' activity, as the data is highly complex, with eight dimensions, making visualization difficult. Traditional methods struggle to display all dimensions without losing detail. To address this, the research team applied to InfraVis in 2023, proposing the use of parallel coordinates for visualization. This technique could potentially benefit other fields by offering a way to visualize high-dimensional data without sacrificing clarity or relational details.

Maria Podkorytova, Umeå

Application Expert

Maria is a PhD student in Geography, specializing on globalization and global corporate networks.

As an application expert in InfraVis, Maria predominantly works with different types of geographical data in ArcGIS.

She also has experience in visualizing networks and structuring the complex datasets.

Maria is specifically interested in collecting data from open sources and processing it by open software.

Keywords: Geographical data, Visualizing networks, Open software, ArcGIS, QGIS, Gephi, MATLAB