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.