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S. Bonaretti, Mojtaba Barzegari, M. Bevers, S. Boyd, Andrew J Burghardt, D. Cameron, Francesco Chiumento, G. Crimi, G. Degenhart, Pholpat Durongbhan, Michelle Alejandra Espinosa Hernandez, G. Fraterrigo, A. Ghasem-Zadeh, L. Grassi, J. Hirvasniemi, S. Hosseinitabatabaei, G. Iori, J. Kok, Michael Kuczynski, Youngjun Lee, Cecilia Liberati, S. Manske, Matthew McCormick, Maria Monzon, M. Pani, Simone Poncioni, Jilmen Quintiens, Sabine Räuber, P. Ritsche, Alfonso Dario Santamaria, Francesco Santini, F. Sarto, E. Schileo, V. Stadelmann, Kathryn S. Stok, R. Surowiec, Fulvia Taddei, Jared Vicory, M. Walle, M. Wesseling, Danielle E. Whittier, Bettina M. Willie, Andy Kin On Wong, Dženan Zukić
0 20. 2. 2026.

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

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.

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