Visualify

The magical data portal generator

Best mate for Docsify to be deployed on GitHub Pages

What it is

Visualify takes the complexity out of generating data portal websites, which is inspired by docsify. Forget about manually writing React components or generating static HTML files. Visualify smartly loads and parses your configuration and data API, allowing you to provide the information directly in a JavaScript file. The result? Your data is beautifully displayed and visualized as a website, all with minimal effort on your part.

To get started, simply create an index.html file and deploy it on GitHub Pages or your personal server.

Here is the Quick Start guide, which providing detailed instructions to help you begin.

Pages Mode Vs. Reacharts Mode

Visualify supports two modes: pages and reacharts. The pages mode is designed for creating a data portal website as the front router, while the reacharts mode is designed for creating a single page with multiple plots. The reacharts mode is the best mate for Docsify to show the plots of your data.

Pages mode will be the default mode if you don't specify the mode in the configuration. You can specify the mode in the configuration file by setting the mode field to pages or reacharts.

{
    "mode": "pages"
}

Features

• No Manual React Components: Automatically handles the creation of React components without the need for manual coding.
• Smart Configuration and Data Parsing: Loads and parses your configuration and data API, even if provided directly in a JavaScript file.
• Dynamic Website Visualization: Transforms your data into a visually appealing website on the fly.
• Easy Deployment: Just create an index.html file with *.json configuration and deploy it on GitHub Pages or your personal server.
• Best Mate for Docsify: Visualify is the best mate for Docsify to show the plots of your data.

Showcases

• MmTrBC: Zhou, Yizhuo, Ying Yang, Lihao Guo, Jun Qian, Jian Ge, Debora Sinner, Hongxu Ding, Andrea Califano, and Wellington V. Cardoso. "Airway basal cells show regionally distinct potential to undergo metaplastic differentiation." Elife 11 (2022): e80083.

• EsoDev: Yang, Ying, Carmel Grace McCullough, Lucas Seninge, Lihao Guo, Woo-Joo Kwon, Yongchun Z. Zhang, Nancy Yanzhe Li et al. "A Spatiotemporal and Machine-Learning Platform Accelerates the Manufacturing of hPSC-derived Esophageal Mucosa." bioRxiv (2023): 2023-10.