Evaluating Open Data Sharing in Scientific Publications: Manual vs. Automated Assessment in Biosciences and Circadian Mental Health Research at the University of Edinburgh.
Haya Deeb
(University of Edinburgh)
Andrew Millar
(University of Edinburgh)
Diego Lucini de Ugarte
(University of Edinburgh)
Emma Wilson
(University of Edinburgh)
Megan A. M. Kutzer
(University of Edinburgh)
Hwee Yun Wong
Tomasz Zieliński
Paper Short Abstract
Open data-sharing is increasingly prioritised by publishers, funders, and institutions. We assess 555 bioscience and 114 circadian mental health papers using manual and automated methods. Our findings reveal trends, challenges, and key factors driving improvements in FAIRness and reproducibility.
Paper Abstract
As scientific research increasingly values outputs beyond traditional publications—such as datasets, software, and code—the need to assess the openness and FAIRness of shared data has become more urgent. Funders, publishers, and institutions are prioritising data-sharing, yet its implementation varies widely across disciplines.
This talk examines data-sharing practices in biosciences at the University of Edinburgh from 2014 to 2023, analysing 555 research papers across biotechnology, regenerative medicine, infectious diseases, and non-communicable diseases. We extend our analysis to 114 publications in UK MRC Circadian Mental Health Network to identify domain-specific trends.
Using a manual scoring system, we assessed data completeness, reusability, accessibility, and licensing. Our findings show a significant improvement in bioscience data-sharing: by 2023, 45% of studies shared all relevant data, compared to just 7% in 2014. These figures stand in contrast to Hamilton et al. (2019), which reported 19% data-sharing in cancer research by 2019. However, circadian mental health research lags, with only 8% of studies sharing data in 2023. Genomic datasets were more frequently shared than image or human subject data, and data availability statements (DAS) and preprint sharing strongly correlated with higher data-sharing practices.
We also evaluated the automated tool ODDPub (Open Data Detection in Publications) (Riede et al. 2020), which demonstrated high specificity in identifying studies without shared data and improved sensitivity when better documentation was present.
These findings underscore both progress and persistent gaps in open data-sharing. They highlight the need for clearer policies, improved infrastructure, and automated tools to support reproducibility and FAIRness across disciplines.
Accepted Poster
Paper Short Abstract
Paper Abstract
As scientific research increasingly values outputs beyond traditional publications—such as datasets, software, and code—the need to assess the openness and FAIRness of shared data has become more urgent. Funders, publishers, and institutions are prioritising data-sharing, yet its implementation varies widely across disciplines.
This talk examines data-sharing practices in biosciences at the University of Edinburgh from 2014 to 2023, analysing 555 research papers across biotechnology, regenerative medicine, infectious diseases, and non-communicable diseases. We extend our analysis to 114 publications in UK MRC Circadian Mental Health Network to identify domain-specific trends.
Using a manual scoring system, we assessed data completeness, reusability, accessibility, and licensing. Our findings show a significant improvement in bioscience data-sharing: by 2023, 45% of studies shared all relevant data, compared to just 7% in 2014. These figures stand in contrast to Hamilton et al. (2019), which reported 19% data-sharing in cancer research by 2019. However, circadian mental health research lags, with only 8% of studies sharing data in 2023. Genomic datasets were more frequently shared than image or human subject data, and data availability statements (DAS) and preprint sharing strongly correlated with higher data-sharing practices.
We also evaluated the automated tool ODDPub (Open Data Detection in Publications) (Riede et al. 2020), which demonstrated high specificity in identifying studies without shared data and improved sensitivity when better documentation was present.
These findings underscore both progress and persistent gaps in open data-sharing. They highlight the need for clearer policies, improved infrastructure, and automated tools to support reproducibility and FAIRness across disciplines.
Poster session
Session 1 Tuesday 1 July, 2025, -