Evidence map›Paper›PMID 40700356›Full record

ArticlePloS one2025

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

Haya Deeb, Suzanna Creasey, Diego Lucini de Ugarte, George Strevens, Trisha Usman, Hwee Yun Wong, Megan A M Kutzer, Emma Wilson, Tomasz Zieliński, Andrew J Millar

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Haya DeebCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0002-7044-7321
Suzanna CreaseyCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Diego Lucini de UgarteCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0009-0002-7978-5084
George StrevensCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.
Trisha UsmanCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0009-0004-5044-0618
Hwee Yun WongCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0009-0004-9503-9705
Megan A M KutzerCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0002-8696-6978
Emma WilsonCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0002-8100-7508
Tomasz ZielińskiCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0002-0194-5706
Andrew J MillarCentre for Engineering Biology and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.ORCID https://orcid.org/0000-0003-1756-3654

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Biomedical ResearchInformation DisseminationHumansScotlandUniversities

Identifiers

PMID40700356
PMCPMC12286402

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.