Abstract
BackgroundAs the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data formats, and specific user needs in an easily accessible package.ResultsWe present substrate, a data-visualization framework designed to simplify communication and code reuse across diverse research teams. Our platform provides a simple, powerful, browser-based interface for scientists to rapidly build effective three-dimensional scenes and visualizations. We aim to reduce the limitations of existing systems, which commonly prescribe a limited set of high-level components, that are rarely optimized for arbitrarily large data visualization or for custom data types.ConclusionsTo further engage the broader scientific community and enable seamless integration with existing scientific workflows, we also present pytri, a Python library that bridges the use of substrate with the ubiquitous scientific computing platform, Jupyter. Our intention is to lower the activation energy required to transition between exploratory data analysis, data visualization, and publication-quality interactive scenes.
Highlights
As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex
By lowering the overhead associated with task-switching between visualization and analysis, substrate provides an opportunity for a team to more intimately explore their data and iterate on analyses in realtime
Similar to how web frameworks such as Angular [31], React [9], and Vue [10] popularized the reusable component model for maintainable web interface composition, our work emphasizes the reusability of visualization components by exposing an interface for discrete entities in a 3D scene
Summary
As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Using modern web-based visualization frameworks [1, 2] makes it easy to generate beautiful, interactive, and informative visualizations of scientific data. These renderings simplify the processes of exploring data and sharing insights with the community. This has become a key step in the research and discovery pipeline [3] One challenge with these technologies is the difficulty of adapting prior visualization work to a new use-case. These tools are often built to be single-purpose rather than interoperable It can be difficult or even impossible to combine aspects of disparate visualization scenes, even when the visualizations use the same technologies or frameworks. The developers of modern visualization systems have chosen to either enjoy wide adoption at the expense of domain-
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