title: "The Design Space of Computational Notebooks: An Analysis of 60 Systems in Academia and Industry"
authors: Sam Lau, Ian Drosos, Julia M. Markel, Philip J. Guo
venue: IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
year: 2020
links:
- <a href="https://talkpython.fm/episodes/show/268/analyzing-dozens-of-notebook-environments">Podcast</a>

tweet: Computational notebooks vary widely in data import, code editing, code execution, and output format

abstract: >
  Computational notebooks such as Jupyter are now used by millions of
  data scientists, machine learning engineers, and computational
  researchers to do exploratory and end-user programming. In recent
  years, dozens of different notebook systems have been developed across
  academia and industry. However, we still lack an understanding of how
  their individual designs relate to one another and what their
  tradeoffs are. To provide a holistic view of this rapidly-emerging
  landscape, we performed, to our knowledge, the first comprehensive
  design analysis of dozens of notebook systems. We analyzed 60
  notebooks (16 academic papers, 29 industry products, and 15
  experimental/R&D projects) and formulated a design space that
  succinctly captures variations in system features. Our design space
  covers 10 dimensions that include diverse ways of importing data,
  editing code and prose, running code, and publishing notebook outputs.
  We conclude by suggesting ways for researchers to push future projects
  beyond the current bounds of this space.

bibtex: >
  @inproceedings{LauVLHCC2020,
    author = {Lau, Sam and Drosos, Ian and Markel, Julia M. and Guo, Philip J.},
    title={The Design Space of Computational Notebooks: An Analysis of 60 Systems in Academia and Industry},
    booktitle = {Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
    series = {VL/HCC '20},
    year={2020},
    month={Aug}
  }