r-cubed: Guiding the overwhelmed scientist from random wrangling to Reproducible Research in R

Luke Johnston*, Helene Bæk Juel, Betinna Lengger, Daniel Rinse Witte, Hannah Chatwin, Malene Revsbech Christiansen, Anders Aasted Isaksen

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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The amount of biological data created increases every year, driven largely by technologies such as high-throughput -omics, real-time monitoring, or high resolution imaging in addition to greater access to routine administrative data and larger study populations. This not only presents operational challenges, but also highlights considerable needs for the skills and knowledge to manage, process, and analyze this data.
Along with the open science movement on the rise, methods and analytic processes are also increasingly expected to be open and transparent and for scientific studies to be reproducible.
Unfortunately, training in modern computational skills has not kept pace, which is particularly evident in biomedical research, where training tends to focus on clinical, experimental, or wet-lab skills. The computational learning module we have developed and described below aims to introduce and improve skills in R, reproducibility, and open science for researchers in the biomedical field, with a focus on diabetes research.
The r-cubed (Reproducible Research in R or R3) learning module is structured as a three-day workshop, with five sub-modules. We have specifically designed the module as an open educational resource that: 1) instructors can make use of directly or modify for their own lessons; and, 2) learners can use independently or as a reference after participating in the workshop. All content is available for re-use under CC-BY License.
Original languageEnglish
Article number122
JournalThe Journal of Open Source Education
Number of pages5
Publication statusPublished - Oct 2021


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