Magnetic Resonance Materials in Physics, Biology and Medicine - CALL FOR PAPERS

Special "MRI Together" Issue on Open Science

Since the publication of “Why Most Published Research Findings Are False” by John Ioannidis in 2005, the importance of “reproducible research” has gained increasing traction in various areas of science. At its core, “reproducible research” is all research performed in a way that allows scientific results to be independently verified and transparently interpreted.

Reproducibility is a multi-dimensional vehicle of scientific excellence, starting from rigorous study planning to ensure that sufficient statistical power is reached for hypothesis testing, preregistration of the research plans, ending with full transparency in the methodology and data used in the work.

Open Science, the aspect of reproducible research that concerns the use of open source methods and hardware, open data, and open access publication, has also become popular within our society with increasing coverage in publications and conferences, and with its dedicated annual MRI Together workshop.

Moreover, openness and reusability of methodology and data has started to become a core tenet of funding bodies, which now often mandate publishing publicly funded research in open access outlets, and depositing data in open and persistent repositories.

Within this context, we invite submissions of original research on all aspects of open science in the field of MR physics and methodology, including clinical application of MRI-based techniques with a methodological emphasis, specifically, but not limited to:

1. Open Source Software: Development and application of open-source software tools in MRI/MRS and open-source pulse sequences (based on non-vendor-specific frameworks) and imaging techniques.
2. Open AI methods: Methods concerning the use of AI in MRI/MRS, where the algorithms, models, model weights and/or training data are publicly shared.
3. Open Hardware: Innovations and advancements in open hardware for MRI/MRS.
4. Open Data: Publicly released datasets, including raw data, image data, and spectral data, or repositories for MR data.

All papers should uphold the highest scientific standards and best practices in Open Science: in particular, we encourage the preregistration of the research plans, the use of preprints, and the full dissemination of method details and data. We particularly encourage submitting executable preprints which include the code to reproduce the figures on Github/Zenodo (see more information here (this opens in a new tab)) or a dedicated executable preprint server such as Neurolibre (this opens in a new tab).

All supporting code, diagrams, and data, must be available at the time of submission for review.

We invite manuscripts on topics pertinent to the scope of the Special Issue. In order to meet the timeline, papers should be submitted not later than December 1st 2024 (and preferably sooner) using the normal submission procedure using the MAGMA portal (this opens in a new tab). Authors should indicate in their cover letter that the manuscript is submitted "For inclusion in the Special Issue on Open Science”.

In parallel to this, a separate Special Issue on Reproducibility and Quality Assurance is also open. If your study is focusing on these aspects rather than Open Science, please check the description (this opens in a new tab).

Issue editors: Francesco Santini, Sophie Schauman, Mo Shahdloo, Petra van Houdt, Yu-Feng Wang, Andrew Webb