A substrate for modular, extensible data-visualization

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 reconci...

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Bibliographic Details
Published in:Big data analytics Vol. 5; no. 1; pp. 1 - 15
Main Authors: Matelsky, Jordan K., Downs, Joseph, Cowley, Hannah P., Wester, Brock, Gray-Roncal, William
Format: Journal Article
Language:English
Published: London BioMed Central 01.01.2020
BMC
Subjects:
Algorithm Analysis and Problem Complexity
Big Data
Browser
Code reuse
Computational Biology/Bioinformatics
Computer Science
Data analysis
Data Mining and Knowledge Discovery
Data science
Data Structures
Data Structures and Information Theory
Data visualization
Graph visualization
Scientific visualization
Software
Substrates
Visualization
WebGL
WebGL
Visualization
JavaScript
Data science
Big data
Graph visualization
Jupyter
Browser
Python
ISSN:2058-6345, 2058-6345
Online Access:Get full text
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Description
Summary: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.
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Authors’ contributions
JM conceived the initial architecture for substrate and pytri and wrote the manuscript, with inputs from all contributors. HC and JD developed the use cases, contributed layers and testing to the software codebases, and aided in manuscript development. BW provided project supervision and reviewed and edited the manuscript. WGR conceptualized the project, supervised the research effort, and reviewed and edited the manuscript. All authors read and approved the final manuscript.
ISSN:2058-6345
2058-6345
DOI:10.1186/s41044-019-0043-6