Department of Public Health

TY - JOUR

T1 - r-cubed

T2 - Guiding the overwhelmed scientist from random wrangling to Reproducible Research in R

AU - Johnston, Luke

AU - Juel, Helene Bæk

AU - Lengger, Betinna

AU - Witte, Daniel Rinse

AU - Chatwin, Hannah

AU - Christiansen, Malene Revsbech

AU - Isaksen, Anders Aasted

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - Reproducible research

KW - data management and documentation

KW - data management infrastructure

KW - Programming Education

U2 - 10.21105/jose.00122

DO - 10.21105/jose.00122

M3 - Journal article

VL - 4

JO - The Journal of Open Source Education

JF - The Journal of Open Source Education

SN - 2577-3569

IS - 44

M1 - 122

ER -