The rise of open data practices among bioscientists at the University of Edinburgh

• Published in: PloS one Vol. 20; no. 7; p. e0328065
• Main Authors: Deeb, Haya, Creasey, Suzanna, de Ugarte, Diego Lucini, Strevens, George, Usman, Trisha, Wong, Hwee Yun, Kutzer, Megan A. M., Wilson, Emma, Zieliński, Tomasz, Millar, Andrew J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.07.2025; Public Library of Science (PLoS)
• Subjects: Accessibility; Biological research; Biology, Experimental; Biomedical Research; Biotechnology; Completeness; Computer and Information Sciences; Documentation; Drug resistance; Engineering and Technology; Funding; Humans; Infectious diseases; Information Dissemination - methods; Information sharing; Medicine and Health Sciences; Metadata; Open access; Open data; Regenerative medicine; Reproducibility; Research and Analysis Methods; Research data management; Science Policy; Scientific research; Scientists; Scotland; Technology application; Universities; Web portals; Scotland; United Kingdom; United Kingdom > UK
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0328065

Open science promotes the accessibility of scientific research and data, emphasising transparency, reproducibility, and collaboration. This study assesses the Openness and FAIR (Findable, Accessible, Interoperable, and Reusable) aspects of data-sharing practices within the biosciences at the University of Edinburgh from 2014 to 2023. We analysed 555 research papers across biotechnology, regenerative medicine, infectious diseases, and non-communicable diseases. Our scoring system evaluated data completeness, reusability, accessibility, and licensing, finding a progressive shift towards better data-sharing practices. The fraction of publications that share all relevant data increased significantly, from 7% in 2014 to 45% in 2023. Data involving genomic sequences were shared more frequently than image data or data on human subjects or samples. The presence of data availability statement (DAS) or preprint sharing correlated with more and better data sharing, particularly in terms of completeness. We discuss local and systemic factors underlying the current and future Open data sharing. Evaluating the automated ODDPub (Open Data Detection in Publications) tool on this manually-scored dataset demonstrated high specificity in identifying cases where no data was shared. ODDPub sensitivity improved with better documentation in the DAS. This positive trend highlights improvements in data-sharing, advocating for continued advances and addressing challenges with data types and documentation.