Emma Rand

Teaching and scholarship

Underlying all biological discoveries are 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 is 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. To critically evaluate inferences arising from these analyses and the advancing the methodology is dependent on research findings being published with their data and analysis code. This is a characteristic known as “reproducibility” and it requires at least some coding. Coding makes everything you do with your raw data explicitly described, totally transparent and completely reproducible. However, learning to code can be a daunting prospect for many biologists!

My philosophy is that anyone can learn to analyse and present their data reproducibly and that learning to do so can be fun. I believe learning for learning's sake is of value and try not to let assessment interfere with students’ ability to enjoy learning. I aim to create learning environments characterised by collaboration, fun, inquisitivity and the freedom to make mistakes. Connection and relationship are important elements of my teaching style so I use workshops, where we can communicate, more frequently than lectures. Where I do lecture I like to use audience response systems to encourage interaction.

My tutorials are usually about how data analytical methods have informed our knowledge and understanding of biological systems. We often also work together to solve data problems and develop a higher level understanding of underlying concepts.

Many of the projects I offer arise from my interactions with research-active colleagues and these may analyse recently generated or publically available data or may develop pipelines and tools to apply methods reproducibly. The biological fields can be diverse and have recently included: wind farm collision-risk modelling, anaerobic digestion metabolomics, stem cell proteomics and transcriptomics, immune cell flow cytometrics, neuroblastoma transcriptomics, image analysis, psychiatric morbidity and child development. In addition, I’m interested in text analysis in biologically relevant areas such feature detection in publications or successful grant applications.