Huawei Computing Systems LabSwitzerland
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arXiv 2026

GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement

Emilien Guandalino, Lorenz K. Müller, Beatrice Alessandra Motetti, Konstantin Berestizshevsky, Lukas Cavigelli

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Abstract summary

GitScholar connects AI papers, citations and timestamped GitHub activity to study early signals of research impact. The dataset links approximately 444,000 repositories with more than 558,000 AI arXiv papers. Experiments investigate whether repository engagement improves the identification of papers that later attract substantial citation attention, beyond academic information alone. Its temporal structure supports retrospective evaluation using information available at a given date.

Abstract on arXiv
arXivPDFDatasetWebsite

Cite this work

@article{guandalino2026gitscholar,
  title = {GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement},
  author = {Guandalino, Emilien and Müller, Lorenz K. and Motetti, Beatrice Alessandra and Berestizshevsky, Konstantin and Cavigelli, Lukas},
  journal = {arXiv preprint arXiv:2609.26361},
  year = {2026},
  url = {https://arxiv.org/abs/2609.26361}
}
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