On the open‐source landscape of Magnetic Resonance in Medicine

Abstract

The tides of the open-source movement reached the coast of our journal just prior to the coronavirus disease 2019 (COVID-19) pandemic with an editorial 1 on reproducibility and the future of MRI research. In it the authors argued that, for improved reproducibility, the concept of a "paper" should be extended to encapsulate the entirety of the scholarly work done authors. These extensions should include data and analysis code, which may be as essential to papers as figures and tables. In the spring of 2020, the journal therefore added an optional (but strongly encouraged) Data Availability Statement section to articles, making it easier to access supplementary data and code from an author's linked repository. Also, the journal's outreach initiative, MRM Highlights,* transitioned from highlighting Editor's Picks, to focusing on articles that demonstrate exemplary reproducible research practices, such as sharing all the data/code needed to reproduce figures, the use of Jupyter/R Markdown notebooks, container technology (eg., Docker, Singularity), interactive figures, etc. In addition to the Highlights interviews that showcased the authors and their publication, each feature was accompanied by an additional written interview where those authors explicitly discussed their reproducible research practices.In this editorial, we explore the reproducible research practices of the community in the wake of these changes.It would be interesting to observe what effect these changes had on the reproducible research practices of Magn Reson Med authors. Fortunately, we have an earlier snapshot of the open-source landscape of Magn Reson Med before the changes, captured in a blog post from 2019. † For this blog post, all the articles published in Magn Reson Med in 2019 were analyzed with an automated script to find which of them contained at least one of a list of keywords related to data/code sharing or reproducible research practices. These were all entered in a spreadsheet, and then manually checked to see if a reproducible research object was really shared, or if it was a false positive. The identified articles were then evaluated based on what was shared (e.g., code, data, etc.

Reproducible Research Practices in Magnetic Resonance Neuroimaging: A Review Informed by Advanced Language Models

“…The shift toward comprehensive sharing is motivated both by a scientific ethic of transparency and the practical need for rigorous validation of complex methodologies. 49 , 50 …”

The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community

“…Though currently in beta testing, with a view to open to public submissions in December 2023, NeuroLibre has already been featured in a few journal editorials [33][34][35][36]. Following the standards defined by NeuroLibre, executable notebooks for some of these journals were built by a small team of collaborators and have been featured in the recently announced MRPub [37], as well as in a survey on code sharing conducted by PLoS [38].…”