Scaling Data Sharing Expertise with AI: a Case Study from DataSeer and Taylor & Francis
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| Format: | Recurso digital |
| Langue: | anglais |
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2026
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| _version_ | 1866901900923764736 |
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| author | Taylor-Grant, Rebecca Stanley, Adrian Vines, Timothy Cranston, Imogen Molly |
| author_facet | Taylor-Grant, Rebecca Stanley, Adrian Vines, Timothy Cranston, Imogen Molly |
| contents | <div> <div> <div> <p>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 potential future iterations of the tool and the implications of its wider implementation are also discussed.</p> </div> </div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18804246 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Scaling Data Sharing Expertise with AI: a Case Study from DataSeer and Taylor & Francis Taylor-Grant, Rebecca Stanley, Adrian Vines, Timothy Cranston, Imogen Molly Curation challenges and opportunities from Artificial Intelligence and Machine Learning Developing new curation tools and services Innovation in curation methods Large-scale curation service delivery Data policies Academic publishing AI tools Paper <div> <div> <div> <p>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 potential future iterations of the tool and the implications of its wider implementation are also discussed.</p> </div> </div> </div> |
| title | Scaling Data Sharing Expertise with AI: a Case Study from DataSeer and Taylor & Francis |
| topic | Curation challenges and opportunities from Artificial Intelligence and Machine Learning Developing new curation tools and services Innovation in curation methods Large-scale curation service delivery Data policies Academic publishing AI tools Paper |
| url | https://doi.org/10.5281/zenodo.18804246 |