All University of Bristol researchers (from PhD student and up) are entitled to a day of free data science support from the Ask-JGI helpdesk. Just email ask-jgi@bristol.ac.uk with your query and one of our team will get back to you to see how we can support you. You can see more about how the JGI can support data science projects for University of Bristol based researchers on our website (https://www.bristol.ac.uk/golding/supporting-your-research/data-science-support/).
We support queries from researchers across all faculties and in this blog we’ll tell you about some of the researchers we’ve supported from the Faculty of Health and Life Sciences here at the University of Bristol.
AI prediction on video data
One particularly interesting query came from a PhD researcher with no prior experience in programming or AI. She was exploring the idea of using AI to predict how long doctors at different skill levels would need to train on medical simulators to reach advanced proficiency. Drawing inspiration from aviation cockpit simulators, her project involved analysing simulation videos to make these predictions. We provided guidance on the feasibility of using AI for this task, suggesting approaches that would depend on the availability of annotated data and introducing her to relevant computer vision techniques. We also recommended Python as a starting point, along with resources to help her build foundational skills. It was exciting to help someone new to AI navigate the early stages of their project and explore how AI could contribute to improving medical training.
Species Classification with ML
Another engaging query came from a researcher in biological sciences aiming to classify different species of plant pest insects—Bemisia, tabaci and two others—based on flight data. Her goal was not only to build machine learning classifiers but also to understand how different features contributed to species differentiation across various methods.
She approached the Ask-JGI data science support for guidance on refining her code and ensuring the accuracy of her analysis. We helped restructure the code to make it more modular and reusable, while also addressing bugs and improving its reliability. Additionally, we worked with her to create visualizations that provided clearer insights into model performance and feature importance. This collaboration was a great example of how machine learning can be applied to advancing research in ecological data analysis.
Providing guidance for HPC, RDSF, and statistical software users
High performance computing (HPC) and the Research Data Storage Facility (RDSF) have been used by an increasing number of people at our university. We also recommend them to students and staff when these tools align with their projects’ needs. However, getting started can be challenging—each system has its own frameworks, rules, and workflows. Researchers often find themselves overwhelmed by extensive training materials or stuck on specific technical issues that aren’t easily addressed.
We provide tailored guidance to make these tools more accessible and practical for our clients, which includes troubleshooting, script modifications, and directing researchers to relevant university services.
Additionally, this year’s Ask-JGI Helpdesk has brought together experienced users of SPSS, Stata, R, and Python. For researchers transitioning to new statistical software or adapting their workflows, we’ve helped them navigate the subtle differences in syntax across platforms and achieve their analysis goals.
Handling Group-Level Variability in Quantitative Effects: A Multilevel Modelling Perspective
We had a client who was researching differences in fluorescence intensity. This may be potentially due to factors such as antibody lot variation, differences in handling between researchers, or biological heterogeneity. This raises the question: How should such data be represented to ensure meaningful interpretation without misrepresenting the underlying biological processes? One of the key solutions that we recommend is to introduce multilevel modelling.
Modelling fluorescence intensity at one or multiple levels (e.g., individual, batch, researcher) can help distinguish biological effects from biases. To be specific, for example, by applying mixed effects, we can account for between-individual variation in baseline fluorescence levels (random intercept), as well as differential responses to experimental conditions (random slope). Sometimes, the application of multilevel modelling also appears to be limited by the group-level sample size. If this is the case, as we discussed with the client, we don’t need to go as extreme as fitting multilevel models. To control for variations with such a small amount of changes, we can use alternative strategies, such as correcting standard errors and introducing dummy variables to achieve similar performance.