Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
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SuperNOVA is an interactive tool to help researchers explore existing notebook VA tools and search for design inspiration and implementation references. Anyone can easily add new notebook VA tools to this open-source explorer.
supernova-preview.mp4
Computational notebooks such as Jupyter Notebook have become data scientists' de facto programming environments. Many visualization researchers and practitioners have developed interactive visualization tools that support notebooks. However, little is known about the appropriate design of visual analytics (VA) tools in notebooks. To bridge this critical research gap, we investigate the design strategies in this space by analyzing 159 notebook VA tools and their users' feedback. SuperNOVA is an interactive browsers to help researchers explore the landscape of notebook VA tools and search for related work.
For a live web demo of SuperNOVA, visit: https://poloclub.github.io/supernova/.
To add a new notebook VA tool to the SuperNOVA collection, please submit a pull request. You can add your VA tool to the YAML file with the following steps:
Make sure you have the necessary information for the entry you want to add, including the tool's title, authors, publication or release year, DOI or URL, and a brief description of the paper's content.
- Open the YAML file where the existing entries are stored.
- Use the following YAML template, and paste it below the last entry. For the definition details of each entry, please check out the SuperNOVA paper.
- # {string} The bibtex of this entry
bibtex: ''
# {string} The bibtex key
bibtexKey: ''
# {'no' | 'one-way' | 'two-way'} VA-notebook communication style
communication: ''
# {string} A description of the tool, can be the abstract of its paper
description: ''
# {string?} GitHub repository URL
githubURL: ''
# {'ipywidget'| 'extension' | 'html' | 'nova' | 'other-package' | 'custom'} Implementation strategy
implementation: ''
# {'on-demand' | 'always-on'} The display style of the VA tool
layouts: ''
# {['runtime' | 'code' | 'external']} Types of data this VA tool uses
materials: ['']
# {'monolithic' | 'modular'} The modularity of this VA tool
modularity: ''
# {string} Name of this tool in lowercase. It has to match the thumbnail file name.
name: ''
# {string} The name of this tool
nameDisplay: ''
# {string[]?} Other URLs of this tool
otherURLs: []
# {string?} Paper URL
paperURL: ''
# {number} The release/publication year
releaseYear: 2023
# {'paper' | 'package'} If this tool has a related paper, use 'paper'.
sourceType: ''
# {['jupyter' | 'lab' | 'colab' | 'vscode']} List of supported notebook platforms
supportedNotebooks: ['']
# {string} The thumbnail file name
thumbnail: ''
# {'data scientist' | 'scientist' | 'educator'} The main targeted users
user: ''- Save a thumbnail image in the
./public/images/thumbnails/directory. Please resize the image so that both width and height are smaller than 300px and compress the image to be smaller than 20kb. - Submit a pull request to this repository.
Code for data collection and cleaned GitHub issues are at ./metadata.
SuperNOVA is created by Jay Wang, David Munechika, Seongmin Lee, and Polo Chau.
To learn more about SuperNOVA, please read our research paper (published at CHI 2024).
@article{wangSuperNOVADesignStrategies2023,
title = {{{SuperNOVA}}: {{Design Strategies}} and {{Opportunities}} for {{Interactive Visualization}} in {{Computational Notebooks}}},
shorttitle = {{{SuperNOVA}}},
author = {Wang, Zijie J. and Munechika, David and Lee, Seongmin and Chau, Duen Horng},
year = {2023},
url = {http://arxiv.org/abs/2305.03039},
urldate = {2023-05-05},
archiveprefix = {arxiv},
journal = {arXiv 2305.03039}
}The code is available under the MIT License.
If you have any questions, feel free to open an issue or contact Jay Wang.