The Software Heritage Graph Dataset
Bibliographic record
Abstract
Software Heritage is the largest existing public archive of software source<br> code and accompanying development history: it currently spans more than five<br> billion unique source code files and one billion unique commits, coming from<br> more than 80 million software projects. This is the Software Heritage graph dataset: a fully-deduplicated<br> Merkle DAG representation of the Software Heritage archive. The dataset links<br> together file content identifiers, source code directories, Version Control<br> System (VCS) commits tracking evolution over time, up to the full states of VCS<br> repositories as observed by Software Heritage during periodic crawls. The<br> dataset’s contents come from major development forges (including GitHub and<br> GitLab), FOSS distributions (e.g., Debian), and language-specific package<br> managers (e.g., PyPI). Crawling information is also included, providing<br> timestamps about when and where all archived source code artifacts have been<br> observed in the wild. The Software Heritage graph dataset is available in multiple formats, including<br> downloadable CSV dumps and Apache Parquet files for local use, as well as a<br> public instance on Amazon Athena interactive query service for ready-to-use<br> powerful analytical processing. By accessing the dataset, you agree with the Software Heritage Ethical Charter<br> for using the archive data, and the terms of use for bulk access. If you use this dataset for research purposes, please cite the following paper: Antoine Pietri, Diomidis Spinellis, Stefano Zacchiroli. <br> <em>The Software Heritage Graph Dataset: Public software development under one roof</em>. <br> In proceedings of MSR 2019: The 16th International Conference on Mining Software Repositories, May 2019, Montreal, Canada. Co-located with ICSE 2019. <br> preprint, bibtex You can also refer to the above paper for more information the dataset and sample queries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.011 | 0.000 |
| Open science | 0.011 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.132 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".