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A substrate for modular, extensible data-visualization
Jordan K Matelsky1, Joseph Downs1, Hannah P Cowley1
1The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA.
Big Data Analytics
|April 21, 2021
Summary
Scientists can now easily create and share complex 3D data visualizations with Substrate, a new framework. It simplifies data visualization and integrates with Jupyter via pytri for seamless scientific workflows.
Area of Science:
- Scientific visualization
- Data analysis
- Computational science
Background:
- Increasingly large datasets and complex scientific questions necessitate advanced visualization techniques.
- Sharing scientific visualizations is challenging due to software dependencies, data formats, and user needs.
- Existing systems often lack optimization for large-scale data or custom data types.
Purpose of the Study:
- To develop a data-visualization framework simplifying communication and code reuse for research teams.
- To provide a browser-based interface for rapid creation of 3D scientific visualizations.
- To overcome limitations of existing systems in handling large data and custom types.
Main Methods:
- Development of the Substrate data-visualization framework.
- Implementation of a browser-based interface for scene and visualization construction.
- Creation of pytri, a Python library for integrating Substrate with Jupyter.
Main Results:
- Substrate offers a simple, powerful platform for creating effective 3D scientific visualizations.
- The framework supports rapid development and customization, addressing limitations of prior systems.
- pytri facilitates seamless integration between data analysis in Jupyter and interactive visualization.
Conclusions:
- Substrate and pytri lower the barrier for transitioning between data analysis, visualization, and publication.
- The tools enhance collaboration and code reuse across diverse scientific research teams.
- This approach promotes wider engagement with advanced scientific visualization techniques.
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