WithdrarXiv: A Large-Scale Dataset for Retraction Study

TL;DR

This paper introduces WithdrarXiv, a large dataset of arXiv retracted papers, along with a taxonomy of retraction reasons and tools for automatic categorization, aiming to enhance scientific integrity and automated verification.

Contribution

The paper presents the first large-scale arXiv retraction dataset, a taxonomy of retraction reasons, and a zero-shot method for automatic categorization, plus an enriched dataset for scientific research.

Findings

Achieved a weighted F1-score of 0.96 in automatic retraction reason classification.

Created WithdrarXiv-SciFy, an enriched dataset with full-text PDFs for scientific studies.

Provided insights into retraction causes to improve scientific quality control.

Abstract

Retractions play a vital role in maintaining scientific integrity, yet systematic studies of retractions in computer science and other STEM fields remain scarce. We present WithdrarXiv, the first large-scale dataset of withdrawn papers from arXiv, containing over 14,000 papers and their associated retraction comments spanning the repository's entire history through September 2024. Through careful analysis of author comments, we develop a comprehensive taxonomy of retraction reasons, identifying 10 distinct categories ranging from critical errors to policy violations. We demonstrate a simple yet highly accurate zero-shot automatic categorization of retraction reasons, achieving a weighted average F1-score of 0.96. Additionally, we release WithdrarXiv-SciFy, an enriched version including scripts for parsed full-text PDFs, specifically designed to enable research in scientific feasibility studies, claim verification, and automated theorem proving. These findings provide valuable insights for improving scientific quality control and automated verification systems. Finally, and most importantly, we discuss ethical issues and take a number of steps to implement responsible data release while fostering open science in this area.