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Using Compute Canada’s resources and technical expert help, you can easily convert the results of your numerical simulations or your experimental data into engaging images or movies to share with colleagues, to put online, or into a publication. Our technical staff have extensive experience in scientific visualization and visual data analysis, primarily using open-source tools such as ParaView, VisIt, VTK, Blender, VMD, and various Python libraries to work with a wide variety of data types. Large multi-dimensional datasets can be visualized directly on Compute Canada clusters without having to move them to your desktop. We can help you with all stages of visualization, from preparing data in the right format to interactive analysis. For more information, please contact us at vis-support@computecanada.ca
Compute Canada Visualization Working Group
Alex Razoumov, WestGrid (Lead)
Chris Want, U of Alberta
Dmitri Rozmanov, U of Calgary
Jarno van der Kolk, U of Ottawa
Marcelo Ponce, U of Toronto
Maxime Boissonneault, U Laval
Michael Hanlan, Queen’s
Pier-Luc St-Onge, McGill
Tyson Whitehead, Western
Weiguang Guan, McMaster

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Visualize This! is a Canada-wide competition that aims to celebrate the innovative ways visualization can help researchers to explore datasets and answer important scientific questions.

2018 Visualize This! Challenge was hosted by WestGrid, a regional partner of Compute Canada. This year 31 people and research groups expressed interest in participating in the competition, and we had some strong submissions this year. After thoughtful deliberation, the selection committee has picked three winners.

The first place was taken by Philippe Nazair, a Data Visualization Developer at Université du Québec à Rimouski and part of the MERIDIAN consortium. Philippe’s visualization is implemented as a static website with two layers: a graph viewer using the 3d-force-graph web component, and a map viewer implemented with Leaflet to display markers on the map and written biographies from Cambridge’s Orlando author’s page found in the dataset. Below you can see a screen-capture video showing different interactions on this site. All JSON files for the visualization were created with a Python backend. We especially appreciated sample graphs for radical and liberal ideologies and other groups, as well as the ability to build more complex graphs by selecting a predicate and setting it to a certain value, e.g. hasActiveInvolvementIn = socialism, or multiple values; each graph is limited to one predicate. Individual graph nodes can be displayed as either text or draggable spheres.

The second prize went to Usman Alim and Roberta Cabral Ramos Mota from the Department of Computer Science at the University of Calgary. Below you can watch one of their visualizations (rendered with OSPRay in ParaView) showing the protein cavity and two “highly-interacting” PO4 beads with the colour representing time step. This work was selected for their skillful use of Python scripting for the bulk of the analysis: using MDAnalysis library for identifying closely interacting membrane beads, and writing bead positions in VTK to be rendered in ParaView.

The third place was taken by Catherine Winters from the Digital Humanities Innovation Lab at Simon Fraser University. This visualization is powered by D3.js and Three.js, with all JSON data files produced with Python scripting. Not all features of this visualization are complete, so we are not showing it here, but it is likely to remain a side project in the lab over the next few months, with the promise to show clustering based on ideology, religion, or geographic proximity.

The top prize for this competition — a 43″ 4K Multi-Client Monitor — was generously donated by Dell EMC. We would like to acknowledge Dell EMC for their continued support of Advanced Research Computing in Canada!

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