Web-based Visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats
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Abstract (arXiv)
Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We also optimize network utilization while streaming from petascale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any internet connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA's use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Aug 2024.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scriptingcited by 1
- 2026Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Explorationcited by 0
- 2026Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasetscited by 0
- 2026Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets22 Aug 2026
- 2026Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration1 May 2026
- 2026Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting7 Mar 2026