Biochemistry, bioinformatics and life sciences

Research generates significant amounts of data that are used to communicate the results of a particular investigation. However, currently, these data are usually unstructured and highly scattered. As a result, this data can not be used to verify, replicate or reanalyze the findings. Moreover, different standards, annotation practices and data formats for data and metadata (if available) might have been used by other researchers, introducing additional heterogeneity and difficulties in later data integration and interpretation.

The traditional approach to research programmes is to assume that students will find a way to analyse and visualise their data. This assumption brings problems for the students, their supervisors and a significant waste of time. Many students are scared by the data rather than curious and usually skip exploratory data analysis and go straight to advanced statistical models that they cannot explain later because they do not understand their data in depth.