# Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study

arXiv: https://arxiv.org/abs/1804.05492 · HTML: https://mlsanity.com/paper/1804.05492/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [StickyLand: Breaking the Linear Presentation of Computational Notebooks](https://mlsanity.com/paper/2202.11086/) (2022)
- [Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference](https://mlsanity.com/paper/2404.03085/) (2024)
- [MEGAnno: Exploratory Labeling for NLP in Computational Notebooks](https://mlsanity.com/paper/2301.03095/) (2023)
- [Machine Learning practices and infrastructures](https://mlsanity.com/paper/2307.06518/) (2023)
- [AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups](https://mlsanity.com/paper/2508.05648/) (2025)
- [VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse](https://mlsanity.com/paper/2206.11887/) (2022)
- [A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring](https://mlsanity.com/paper/2111.05120/) (2021)
- [Global Warming In Ghana's Major Cities Based on Statistical Analysis of NASA's POWER Over 3-Decades](https://mlsanity.com/paper/2308.10909/) (2023)
- [Plainbook: Data Science, in Plain Language](https://mlsanity.com/paper/2607.05717/) (2026)

## Abstract (arXiv)

As scientific work becomes more computational and data intensive, research processes and results become more difficult to interpret and reproduce. In this poster, we show how the Jupyter notebook, a tool originally designed as a free version of Mathematica notebooks, has evolved to become a robust tool for scientists to share code, associated computation, and documentation.