College expertise on the subjects of reproducibility, transparency and reduction of bias and waste in research. What is reproducibility? Reproducibility in research means that when the research is repeated, other researchers are able to produce the same findings as the original researchers. Making data, code and research methods open to all facilitates reproducibility and improves reliability, transparency and credibility of results. Researchers across the College are involved in several reproducibility communities, including the UK Reproducibility Network (UKRN). This section highlights the contributions and publications of CMVM researchers made about the Network. Publications about the UK Reproducibility Network The UKRN is a peer-led consortium which aims to ensure the UK retains its place as a centre for world-leading research by promoting a research culture that prioritises rigour and transparency. It does this by investigating factors that contribute to robust research, promoting training activites, sharing best practice and collaborating with external stakeholders.The UKRN is led by a Supervisory Board comprising Marcus Munafò (Bristol), Emily Farran (Surrey), Eike Mark Rinke (Leeds), Etienne Roesch (Reading), Harvinder Virk (Leicester) and Malcolm MacLeod (Edinburgh).The UKRN local network lead for the University of Edinburgh is Sarah Stanton and the institutional leads are Crispin Jordan and William Cawthorn.Professor Malcolm MacLeod has contributed to publications about the UKRN:The UK Reproducibility Network: A progress report – article in Journal of Neuroscience Methods (2023)From grassroots to global: A blueprint for building a reproducibility network – article in PLoS Biology (2021)Research Culture and Reproducibility – article in Trends in Cognitive Sciences (2020)Malcolm MacLeod’s profile on Edinburgh Research Explore UK Reproducibility Network website (external link) Open Research initiatives at the University and beyond CMVM publications on the subject of reproducibility Names in bold denote CMVM authors 2021- 2025Constantinos Eleftheriou and others: Better statistical reporting does not lead to statistical rigour: lessons from two decades of pseudoreplication in mouse-model studies of neurological disorders - article in Molecular AutismAminah Jatoi and Barry Laird: In support of rigor and reproducibility in cancer cachexia research - editorial in Current Opinion in Supportive and Palliative Care2024Bruce Whitelaw, co-author with Lydia Teboul and others: Improving laboratory animal genetic reporting: LAG-R guidelines - perspective article in Nature Communications2023Michael J. Thrippleton, co-author with Ben R. Dickie and others: A community-endorsed open-source lexicon for contrast agent–based perfusion MRI: A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) - article in Magnetic Resonance in MedicineEmma Wilson, Fiona J. Ramage, Kimberley E. Wever, Emily S. Sena, Malcolm R. Macleod and Gillian L. Currie: Designing, conducting, and reporting reproducible animal experiments – article in Journal of Endocrinology2021Cyril Pernet, co-author with Elise Bannier and others: The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data - editorial in Human Brain MappingCyril R. Pernet, Ramon Martinez-Cancino, Dung Truong, Scott Makeig and Arnaud Delorme: From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG – article in Frontiers in Neuroscience 2010-2020 2020Gaia Brezzo, Gillian Currie, Jill Fowler, Karen Horsburgh, Malcolm Macleod, Emily Sena and Stefan Szymkowiak, co-authors with Aisling McFall and others: UK consensus on pre-clinical vascular cognitive impairment functional outcomes assessment: questionnaire and workshop proceedings - article in Journal of Cerebral Blood Flow & Metabolism Cyril Pernet and others: Issues and recommendations from the OHBM COBIDAS MEEG committee for reproducible EEG and MEG research – perspective article in Nature NeuroscienceMalcolm Macleod, co-author with Walter J. Koroshetz and others: Research Culture: Framework for advancing rigorous research – feature article in eLife magazine2019Stewart J. Wiseman, Rozanna Meijboom, Maria del C. Valdés Hernández, Cyril Pernet, Eleni Sakka, Dominic Job, Adam D. Waldman & Joanna M. Wardlaw: Longitudinal multi-centre brain imaging studies: guidelines and practical tips for accurate and reproducible imaging endpoints and data sharing - article in Trials2018Michael J Thrippleton, Yulu Shi, Gordon Blair, Iona Hamilton, Gordon Waiter, Christian Schwarzbauer, Cyril Pernet, Peter JD Andrews, Ian Marshall, Fergus Doubal, and Joanna M Wardlaw: Cerebrovascular reactivity measurement in cerebral small vessel disease: rationale and reproducibility of a protocol for MRI acquisition and image processing - article in International Journal of StrokeBernhard Voelkl, Lucile Vogt, Emily S. Sena, Hanno Würbel: Reproducibility of preclinical animal research improves with heterogeneity of study samples – article in PLOS Biology 2017Peter-Paul Zwetsloot, Mira Van Der Naald, Emily S Sena, David W Howells, Joanna IntHout, Joris AH De Groot, Steven AJ Chamuleau, Malcolm R MacLeod and Kimberley E Wever: Standardized mean differences cause funnel plot distortion in publication bias assessments – article in eLifeMalcolm MacLeod and Emily Sena, co-authors with Zsanett Bahor and others: Risk of bias reporting in the recent animal focal cerebral ischaemia literature - article in Clinical ScienceRustam Salman and Malcolm MacLeod, co-authors with Eivind Berge and others: Increasing value and reducing waste in stroke research - article in The Lancet Neurology2016Gillian L Currie and Emily S Sena, co-authors with Nick A Andrews and others: Ensuring transparency and minimization of methodologic bias in preclinical pain research: PPRECISE considerations - article in PAIN2015Cyril Pernet, Member of Open Science Collaboration: Estimating the reproducibility of psychological science - article in ScienceCyril Pernet and Jean-Baptiste Poline: Improving functional magnetic resonance imaging reproducibility - article in GigaScience If you are a CMVM-affiliated author and would like your publication on reproducibility featured on this page, please get in touch at CMVMopenaccess@ed.ac.uk. This article was published on 2024-09-09
