Scaling Data Sharing Expertise With AI: A Case Study from DataSeer and Taylor & Francis

Abstract

This paper outlines the collaborative development of an AI data curation tool to support data sharing in the journal publishing workflow. As scrutiny of research increases, concerns have grown regarding reproducibility, as well as fraud and bad actors in the research lifecycle. Transparent, reproducible, and well-curated data is foundational to restoring confidence. In this paper we describe the current data sharing policy landscape at academic publishers and outline key challenges which might limit further data policy implementation and enforcement on journals. We provide insights into a new approach to data sharing compliance checks, the DataSeer SnapShot tool, and how this tool was developed with the collaboration of the Open Science-, Implementation-, and Editorial Operations teams at academic publisher Taylor & Francis. The planned future iterations of the tool and the implications of its wider implementation are also discussed.