Public Git Archive: a Big Code dataset for all
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Abstract (arXiv)
The number of open source software projects has been growing exponentially. The major online software repository host, GitHub, has accumulated tens of millions of publicly available Git version-controlled repositories. Although the research potential enabled by the available open source code is clearly substantial, no significant large-scale open source code datasets exist. In this paper, we present the Public Git Archive -- dataset of 182,014 top-bookmarked Git repositories from GitHub. We describe the novel data retrieval pipeline to reproduce it. We also elaborate on the strategy for performing dataset updates and legal issues. The Public Git Archive occupies 3.0 TB on disk and is an order of magnitude larger than the current source code datasets. The dataset is made available through HTTP and provides the source code of the projects, the related metadata, and development history. The data retrieval pipeline employs an optimized worker queue model and an optimized archive format to efficiently store forked Git repositories, reducing the amount of data to download and persist. Public Git Archive aims to open a myriad of new opportunities for ``Big Code`` research.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2023CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Modelscited by 34
- 2021CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computingcited by 28
- 2023MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformerscited by 5
- 2023A Machine Learning Approach Towards SKILL Code Autocompletioncited by 0
- 2024Multi-modal Learning for WebAssembly Reverse Engineeringcited by 0
- 2026PuzzleMark: Implicit Jigsaw Learning for Robust Code Dataset Watermarking in Neural Code Completion Modelscited by 0
- 2026PuzzleMark: Implicit Jigsaw Learning for Robust Code Dataset Watermarking in Neural Code Completion Models30 Apr 2026
- 2024Multi-modal Learning for WebAssembly Reverse Engineering4 Apr 2024
- 2023A Machine Learning Approach Towards SKILL Code Autocompletion4 Dec 2023
- 2023CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models28 Aug 2023
- 2023MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers16 May 2023
- 2021CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing6 Apr 2021