mlsanityData to 7 Oct 2026

StickyLand: Breaking the Linear Presentation of Computational Notebooks

arXiv 2202.11086 ↗First posted 22 Feb 2022cs.HC cs.LGcited by 6

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Abstract (arXiv)

How can we better organize code in computational notebooks? Notebooks have become a popular tool among data scientists, as they seamlessly weave text and code together, supporting users to rapidly iterate and document code experiments. However, it is often challenging to organize code in notebooks, partially because there is a mismatch between the linear presentation of code and the non-linear process of exploratory data analysis. We present StickyLand, a notebook extension for empowering users to freely organize their code in non-linear ways. With sticky cells that are always shown on the screen, users can quickly access their notes, instantly observe experiment results, and easily build interactive dashboards that support complex visual analytics. Case studies highlight how our tool can enhance notebook users's productivity and identify opportunities for future notebook designs. StickyLand is available at https://github.com/xiaohk/stickyland.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 22 Feb 2022.

Builds on

7 of 26 references.

References · 7

  1. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
  2. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  3. 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
  4. 2021GAM Changer: Editing Generalized Additive Models with Interactive Visualizationcited by 18
  5. 2020mage: Fluid Moves Between Code and Graphical Work in Computational Notebookscited by 13
  6. 2018Using the Jupyter Notebook as a Tool for Open Science: An Empirical Studycited by 9
  7. 2020Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browserscited by 3

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2.

6 papers.