VisAnywhere: Developing Multi-platform Scientific Visualization Applications

TL;DR

This work addresses the need for cross-device visualization of large-scale brain simulation data by delivering a responsive, web-based visualization built on a single codebase. It combines data preprocessing with Parquet-based per-timestep storage, a Babylon.js-driven 3D brain visualization, synchronized PlotlyJS 2D charts, and WebXR VR support to enable multi-device collaboration. The approach enables co-located and remote collaboration through synchronized views and collaboration sessions, validated by domain experts who found the tool intuitive and useful. The results demonstrate a practical, scalable pathway for deploying complex scientific visualizations across mobile devices, desktops, large display walls, and VR headsets, with a publicly accessible brain plasticity visualization.

Abstract

Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain, we present our work leveraging various web technologies to create a multi-platform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both co-located and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

Figures (11)

• Figure 1: Overview of the brain plasticity 3D visualization application. The application shows neurons (spheres) and connections (tubes), and it contains a user interface for controlling the application.
• Figure 2: Dynamic radius of neuron spheres. Radius is set based on distance from the camera. This enables users to see individual neurons in nearby clusters while still being able to view far away neuron clusters. Turquoise highlighted areas show more distant neuron clusters where spheres are larger to remain visible but overlap each other. Yellow highlighted areas show nearby neuron clusters where each neuron is easily distinguishable.
• Figure 3: Neuron displacement. Neuron spheres can be displaced towards the center of the brain based on their index in each cluster of ten neurons. This prevents sphere overlap and creates a line of easily distinguishable spheres.
• Figure 4: Table showing individual neuron properties. Users can select a cluster of neurons (ctrl + click) to pop up a table that contains raw values of all properties for the ten neurons in a cluster. Yellow highlight and mouse cursor show the neuron cluster that was selected.
• Figure 5: Tubes showing area-to-area connections. All tubes curve towards the center of the brain so as to ensure all connections remain on the interior. The radius of each tube corresponds to the number of connections from one area to another. The color indicates primary connection direction (red: left/right, green: back/front, blue: bottom/top), similar to DTI connectome imaging. The color gradient depicts connection direction (from white to colored), as highlighted by the yellow arrows.