# SMAuC -- The Scientific Multi-Authorship Corpus

arXiv: https://arxiv.org/abs/2211.02477 · HTML: https://mlsanity.com/paper/2211.02477/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers](https://mlsanity.com/paper/2108.02922/) (2021)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [A Large Parallel Corpus of Full-Text Scientific Articles](https://mlsanity.com/paper/1905.01852/) (2019)
- [Patterns of Text Reuse in a Scientific Corpus](https://mlsanity.com/paper/1412.2716/) (2014)

## Built on by (most cited first, 2 of 2)

- [Generation, Evaluation, and Explanation of Novelists' Styles with Single-Token Prompts](https://mlsanity.com/paper/2511.20459/) (2025)
- [AuthBench: A Large-Scale Multilingual Benchmark for Authorship Representation across Genres and Lengths](https://mlsanity.com/paper/2609.06771/) (2026)

## 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.