A substrate for modular, extensible data-visualization.
A substrate for modular, extensible data-visualization.
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DOI：
10.1186/s41044-019-0043-6
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发表时间：
2020-01-01
影响因子：
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通讯作者：
Gray-Roncal, William
中科院分区：
文献类型：
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作者：
Matelsky, Jordan K;Downs, Joseph;Gray-Roncal, William
背景：随着科学问题范围的扩大和数据集的增大，相关信息的可视化相应地变得更加困难和复杂。在合作者和公众之间共享可视化可能特别繁重，因为在一个易于访问的包中协调软件依赖项、数据格式和特定用户需求是一项挑战。结果：我们提出了一种数据可视化框架，旨在简化不同研究团队之间的沟通和代码重用。我们的平台为科学家提供了一个简单、强大、基于浏览器的界面，以快速构建有效的三维场景和可视化。我们的目标是减少现有系统的局限性，这些系统通常规定有限的高级组件集，很少针对任意大的数据可视化或自定义数据类型进行优化。结论：为了进一步吸引更广泛的科学界并实现与现有科学工作流的无缝集成，我们还提供了Pytri，这是一个Python库，它将底层的使用与无处不在的科学计算平台Jupyter连接起来。我们的目的是降低探索性数据分析、数据可视化和出版质量交互场景之间转换所需的激活能量。
BACKGROUND: As 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.RESULTS: We 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.CONCLUSIONS: To 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.