University of York
Computational Biology, Bioinformatics, Data Science, Reproducibility, Data Analytics, Teaching and Learning
I’m a Lecturer (Teaching & Scholarship) in the Department of Biology at the University of York. I teach data analysis and reproducibility in analytic pipelines predominantly to those who do not see themselves as programmers. I teach students at all levels as well as research professionals and one thing they have in common is that they do not want to learn to code, they just want to be good researchers! Underlying all biological discoveries is data!
The ability to generate reliable measures of biological phenomena through experimental design, modelling or simulation, and then analyse and communicate the results are essential skills for a biologist. This has always been true but an explosion of large-scale, complex and noisy data has made the acquisition of data skills even more crucial. Such skills include being able to statistically analyse and visualise data generated by research from the ecological to the biomolecular.
The ability to reproduce, and critically evaluate, results arising from these analyses is dependent on research findings being published with their data and analysis code. Many undergraduate degree courses in the life sciences now provide some training in data analysis and programming which equip individuals to implement programming-based solutions to scientific problems. However, this training rarely extends to the skills needed to document and disseminate code. These are exactly the skills needed for a culture of reproducibility and sustainability to fully develop!
I am passionate about developing the bioscience community’s capacity in sustainable software to enable world-class research and deliver CPD courses in R and Python for both the Royal Society of Biology and the Biochemical Society. I gave a tutorial at the international useR conference in Toulouse in 2019 and I am a member of R Forwards, the R foundation’s taskforce on women and underrepresented groups.