# What Makes a Popular Academic AI Repository?

arXiv: https://arxiv.org/abs/2010.02472 · HTML: https://mlsanity.com/paper/2010.02472/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [What's documented in AI? Systematic Analysis of 32K AI Model Cards](https://mlsanity.com/paper/2402.05160/) (2024)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets](https://mlsanity.com/paper/2111.02374/) (2021)
- [Towards a Change Taxonomy for Machine Learning Systems](https://mlsanity.com/paper/2203.11365/) (2022)
- [PeaTMOSS: Mining Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2310.03620/) (2023)
- [A Two-Dimensional Study of the Model Context Protocol: Publication and Adoption](https://mlsanity.com/paper/2609.14721/) (2026)

## Abstract (arXiv)

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features---indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.