Data for: Managing Retractions and their Afterlife: A Tripartite Framework for Research Datasets

This deposit includes supplementary data files for the paper "Managing Retractions and Their Afterlife: A Tripartite Framework for Research Datasets," which has been accepted for presentation at the International Digital Curation Conference (IDCC) 2025 in The Hague, Amsterdam (https://dcc.ac.uk/events/idcc25)

The dataset consists of retraction records crawled via Google Dataset Search queries, along with the selected sample and annotated data resulting from the assessment of these records. A README file is included to provide further details on the CSV files and their structure.

Abstract: Retractions serve as a critical, albeit last-resort, post-publication correction mechanism in scholarly publishing, playing an important role in upholding the integrity of the scientific record. By formally retracting flawed or misleading research, the scientific community mitigates the harm caused by errors or misconduct that may have escaped detection during peer review. While retractions of research articles have been extensively discussed across scientific disciplines and are well-integrated into most publishers' workflows, the retraction of research datasets remains underexplored and rarely implemented. This paper seeks to address this gap by reviewing recent developments in this area, analyzing a sample of publicly available retracted dataset records considering existing recommendations and guidelines, and putting forward a few points for discussions—particularly for cases where datasets have been published and correction is no longer feasible, or when all efforts to amend the dataset have been exhausted. These considerations are framed into three main categories: (1) preventive actions and timely response, (2) purposeful damage control, and (3) community engagement and shared standards. Although still preliminary, this framework aims to help entertain future debates and inform actionable strategies for addressing the unique challenges of managing retracted datasets where scientific rigor has been compromised. By contributing to the discussion on dataset retractions, this work seeks to better equip data curators, repository managers, and other stakeholders with tools to enhance accountability and transparency throughout the data preservation process while also helping to mitigate the error cascade effect in science.