Open and reproducible research in musculoskeletal imaging: Why it matters and how to implement it with the guidelines of the ORMIR community

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Authors: Serena Bonaretti, Mojtaba Barzegari, Melissa S. A. M. Bevers, Steven K. Boyd, A. Burghardt, Donnie Cameron, Francesco Chiumento, Gianluigi Crimi, Gerard Degenhart, Pholpat Durongbhan, Michelle Alejandra Espinosa-Hernandez, Giulia Fraterrigo
+32 more Ali Ghasem-Zadeh, Lorenzo Grassi, Jukka Hirvasniemi, Seyedmahdi Hosseinitabatabaei, Gianluca Iori, Joeri Kok, Michael T. Kuczynski, YoungJun Lee, Cecilia Liberati, Sarah L. Manske, Matthew M. McCormick, Maria Monzon, Martino Pani, Simone Poncioni, Jilmen Quintiens, Sabine Räuber, Paul Ritsche, Alfonso Dario Santamaria, Francesco Santini, Fabio Sarto, Enrico Schileo, Vincent Stadelmann, Kathryn S. Stok, Rachel Surowiec, Fulvia Taddei, Jared Vicory, Matthias Walle, Mariska Wesseling, Danielle E. Whittier, Bettina M. Willie, Andy Kin On Wong, Dženan Zukić
Cited by: 1

Abstract

Abstract The Open and Reproducible Musculoskeletal Imaging Research (ORMIR) community is a scientific community dedicated to promoting openness and reproducibility in musculoskeletal imaging, image processing, and computational modelling. In this perspective paper, we outline the motivations for conducting transparent research and provide practical guidelines to implement it. We start with defining open and reproducible research and describing the benefits and challenges of working transparently. Next, we redefine the outputs of a computational research study as—ideally—a combination of data, code, and a publication, recommend a folder and file structure that reflects these three study outcomes, and describe how to maintain and update such a structure during the study and at study publication. Finally, we emphasize that working in an open and reproducible manner is a learning process and the best way to acquire the necessary competencies is simply to start. Lay summary: The ORMIR community promotes openness and reproducibility in musculoskeletal imaging research. In this perspective paper, we explain why transparency matters and recommend how to conduct a computational study in an open and reproducible manner focusing on its three outputs: data, code, and publication. Finally, we highlight that the best way to learn these practices is simply to start.

Fields of Study & Subjects

Scientific Computing and Data Management Meta-analysis and systematic reviews Cell Image Analysis Techniques

Publication details

Journal / source
JBMR Plus
Publisher
Oxford University Press (OUP)
Volume
10
Issue
4
Pages
ziag025–ziag025
Publication date
17 February 2026
Language
EN