Year: 2015
Type: article
Abstract: Background Many biological disciplines use data visualization alongside computational methods to explore large-scale biomedical data. Visualization often provides insight into patterns in the data that are not available in the numerical data and statistics [1]. The development of new visualization tools requires the use of sophisticated software and programming skills. Commercial standalone software like Tableau [2] create multiple types of common visualizations and have the ability to customize certain features. There are also freely available software libraries like D3.js [3] that can be used to make interactive web applications based on static or dynamic data. Nevertheless, modern data visualization is highly sophisticated, and creating customized visualizations to interact with a specific dataset can be challenging for a variety of reasons. Specifically with D3.js, which builds a scalable vector graphic (SVG) programmatically, generating the visualization is a process of trial and error. The programmer generates SVG markup manually and then views it with a browser and does this iteratively until the final graphic is realized. We present an iterative workflow shown in Figure 1 that simplifies the creation of SVG images using freely available software. We demonstrate this workflow in constructing an interactive dashboard to track clinical trial enrollment.
Source: BMC Bioinformatics
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W2130901067