SMAuC -- The Scientific Multi-Authorship Corpus
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Abstract (arXiv)
The rapidly growing volume of scientific publications offers an interesting challenge for research on methods for analyzing the authorship of documents with one or more authors. However, most existing datasets lack scientific documents or the necessary metadata for constructing new experiments and test cases. We introduce SMAuC, a comprehensive, metadata-rich corpus tailored to scientific authorship analysis. Comprising over 3 million publications across various disciplines from over 5 million authors, SMAuC is the largest openly accessible corpus for this purpose. It encompasses scientific texts from humanities and natural sciences, accompanied by extensive, curated metadata, including unambiguous author IDs. SMAuC aims to significantly advance the domain of authorship analysis in scientific texts.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 4 Nov 2022.
Builds on
10 of 27 references.
References · 10
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
- 2018Datasheets for Datasetscited by 1,371
- 2016Bag of Tricks for Efficient Text Classificationcited by 869
- 2020Heterogeneous Graph Transformercited by 581
- 2016FastText.zip: Compressing text classification modelscited by 331
- 2018Construction of the Literature Graph in Semantic Scholarcited by 118
- 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
- 2020The Importance of Suppressing Domain Style in Authorship Analysiscited by 18
- 2019A Large Parallel Corpus of Full-Text Scientific Articlescited by 12
- 2014Patterns of Text Reuse in a Scientific Corpuscited by 5
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.