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

Abstract

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 (Brownson et al., 2015). 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 (Watson, 2015).