Data analysis – Jean Golding Institute News

AskJGI Example Queries from Faculty of Science and Engineering

Posted on 25 June 202525 June 2025 by kerry.turcsi

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.

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.

Aerosol particles

A researcher approached us with Python code they’d written for simulating radioactive aerosol particle dynamics in a laminar flow. For particles smaller than 10 nanometers, they observed unexplained error “spikes” when comparing numerical to analytical results, suggesting that numerical precision errors were accumulating due to certain forces being orders of magnitude smaller than others for the tiny particles.

We provided documentation and advice for implementing higher-precision arithmetic using Python’s ‘mpmath’ library so that the researcher could use their domain knowledge to increase precision in critical calculation areas, balancing computational cost with simulation accuracy. We also wrote code to normalise the magnitude of different forces to similar scales to prevent smaller values from being lost in the calculation.

This was a great query to work on. Although Ask-JGI didn’t have the same domain knowledge for understanding the physics of the simulation, the researcher worked closely with us to help find a solution. They provided clear and well documented code, understood the likely cause of their problem and identified the solutions that we explored. This work highlights how computational limitations can impact the simulation of physical systems, and demonstrates the value of collaborative problem-solving between domain specialists and data scientists.

Diagram A shows straight arrow lines and B shows curvy arrow lines — *Laminar flow (a) in a closed pipe, Turbulent flow (b) in a closed pipe. Image credit: SimScale*

Training/course development

The JGI offers training in programming, machine learning and software engineering. We have some standard training courses that we offer as well as upcoming courses being development and shorter “lunch and learn” sessions on various topics.

Queries have come in to both Ask-JGI and the JGI training mailbox (jgi-training@bristol.ac.uk) asking follow up questions from training courses which people have attended. Additionally, requests have come through for further training to be developed in specific areas (e.g. natural language processing, advanced data visualisation or LLM useage). The JGI training mailbox is the place to go, but Ask-JGI will happily redirect you!

People sitting at tables in a computer lab looking at a large computer screen at the end of the table — *Introduction to Python training session for Bristol Data Week 2025.*

Network visualization

Recently Ask-JGI received a query from a PhD researcher in the School of Geographical Sciences. The Ask JGI team offered support on exploring visualisation options for the data provided, and provided example network visualisations of the UK’s industries’ geographical distribution similarity. Documented code solution was also provided so that further customisation and extension of the graphs is possible. At the Ask JGI, we are happy to help researchers who are already equipped with substantive domain knowledge and coding skills to complete small modules of their research output pipeline.

Network made up with lines and dots. Each colour represents a different UK industry — *Network visualisation of similarity of UK industry geographical distribution.*

Spin Network Optimisation

The aim of this query was to accelerate the optimization of a spin network which is a network of nodes coupled together by a certain strength, to perform transfer of information (spin) from one node to another by implementing parallel processing. The workflow involved a genetic algorithm (written in Fortran and executed via a bash script) and a Python-based gradient ascent algorithm.

Initial efforts focused on parallelizing the gradient ascent step. However, significant challenges arose due to the interaction between the parallelized Python code and the sequential execution of the Fortran-based Spinnet script.

Code refactoring was undertaken to improve readability and introduce minor speed enhancements by splitting the Python script into multiple files and grouping similar function calls.

Given the complexity and time investment associated with these code modifications, it was strongly recommended to explore the use of High-Performance Computing (HPC) facilities. Running the current code on an HPC system went on to provide the desired speed improvements without requiring any code changes, as HPC is designed for computationally intensive tasks like this.

Grant development

The Ask-JGI helpdesk is the main place researchers get in contact with the JGI with regards to getting help with grant applications. The JGI can support with grant idea development, giving letters of support for applications and costing in JGI data scientists or research software engineers to support the workload for potential projects. You can read more about how the JGI team can support grant development on the JGI website!

Using ‘The Cloud’ to enhance UoB laboratory data security, storage, sharing, and management

Posted on 13 June 202518 June 2025 by kerry.turcsi

JGI Seed Corn Funding Project Blog 2023/24: Peter Martin, Chris Jones & Duncan Baldwin

Introduction

As a world-leading research-intensive institution, the University of Bristol houses a multi-million-pound array of cutting-edge analytical equipment of all types, ages, function, and sensitivity – distributed across its Schools, Faculties, Research Centres and Groups, as well as in dozens of individual labs. However, as more and more data are captured – how can it be appropriately managed to comply with the needs of both researchers and funders alike?

What were the aims of the seed corn project?

When an instrument is purchased, the associated computing, data storage/resilience, and post-capture analysis is seldom, if ever, considered beyond the standard Data Management Plans.

Before this project, there existed no centralised or officially endorsed mechanism at UoB supported by IT Services to manage long-term instrument data storage and internal/external access to this resource – with every group, lab, and facility individually managing their own data retention, access, archiving, and security policies. This is not just a UoB challenge, but one that is endemic of the entire research sector. As the value of data is now becoming universally realised, not just in academia, but across society – the challenge is more pressing than ever, with an institution-wide solution to the entire data challenge critically required which would be readily exportable to other universities and research organisations. At its core, this Seed Corn project sought to develop a ‘pipeline’ through which research data could be; (1) securely stored within a unified online environment/data centre into perpetuity, and (2) accessed via an intuitive, streamlined and equally secure online ‘front-end’ – such as Globus, akin to how OneDrive and Google Drive seamlessly facilitate document sharing.

What was achieved?

The Interface Analysis Centre (IAC), a University Research Centre in the School of Physics currently operates a large and ever-growing suite of surface and materials science equipment with considerable numbers of both internal (university-wide) and external (industry and commercial) users. Over the past 6-months, working with leading solution architects, network specialists, and security experts at Amazon Web Services (AWS), the IAC/IT Services team have successfully developed a scalable data warehousing system that has been deployed within an autonomous segment of the UoB’s network, such that single-copy data that is currently stored locally (at significant risk) and the need for it to be handled via portable HDD/emailed across the network can be eliminated. In addition to efficiently “getting the data out” from within the UoB network, using native credential management within Microsoft Azure/AWS, the team have developed a web-based front-end akin to Google Drive/OneDrive where specific experimental folders for specific users can be securely shared with these individuals – compliant with industry and InfoSec standards. The proof of the pudding has been the positive feedback received from external users visiting the IAC, all of whom have been able to access their experiment data immediately following the conclusion of their work without the need to copy GB’s or TB’s of data onto external hard-drives!

Future plans for the project

The success of the project has not only highlighted how researchers and various strands within UoB IT Services can together develop bespoke systems utilising both internal and external capabilities, but also how even a small amount of Seed Corn funding such as this can deliver the start of something powerful and exciting. Following the delivery of a robust ‘beta’ solution between the Interface Analysis Centre (IAC) labs and AWS servers, it is currently envisaged that the roll-out and expansion of this externally-facing research storage gateway facility will continue with the support of IT Services to other centres and instruments. Resulting from the large amount of commercial and external work performed across the UoB, such a platform will hopefully enable and underpin data management across the University going forwards – adopting a scalable and proven cloud-based approach.

Contact details and links

Dr Peter Martin & Dr Chris Jones (Physics) peter.martin@bristol.ac.uk and cj0810@bristol.ac.uk

Dr Duncan Baldwin (IT Services) d.j.baldwin@bristol.ac.uk

Ask-JGI Example Queries from Faculty of Health and Life Sciences

Posted on 13 June 202513 June 2025 by kerry.turcsi

AI prediction on video data

Example of AI video prediction using video data taken from the EPIC-KITCHENS-100 study. The image shows qualitative results of action detection. Predictions with confidence > 0.5 are shown with colour-coded class labels.

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

*Bemisia tabaci (MED) (silverleaf whitefly); two adults on a watermelon leaf. Image by Stephen Ausmus*.

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

*A visualisation of a multilevel model, original figure produced by JGI Data Scientist, Dr Leo Gorman.*

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.

Meet the Ask-JGI team – Adrianna, Fahd, Yujie & Huw

Posted on 17 February 202517 February 2025 by kerry.turcsi

The new Ask-JGI helpdesk cohort started in September 2024 and have been busy answering queries from researchers across the university! We introduced half of the team in our January blog. Meet the other half of the team below:

Adrianna Jezierska (she/her) – Ask-JGI PhD Student

Adrianna Jezierska, PhD candidate in in the School of Business

I’m a PhD student at the University of Bristol Business School. My project focuses on social media influencers and their vegan content on YouTube. Using language derived from video transcripts, I analyse to what extent they legitimise veganism so that it becomes popular and desirable in society. Whilst most organisation and management scholars have developed theories based on qualitative data, resulting in small datasets and case study approaches, in my work, I highlight the role of computational social sciences and big data in helping social scientists answer their research questions.

Coming from a social science background, I was initially hesitant about joining the Ask-JGI team. However, this decision has turned out to be the most rewarding and challenging experience. Being part of the team is a continuous learning journey. The questions we receive span various disciplines, often pushing us out of our comfort zones. The most exciting part of the job is the opportunity to communicate with other researchers and receive their positive feedback. On the other hand, we constantly collaborate with other team members and learn from each other, which makes it a very supportive environment. I’m pleased to see more queries from social scientists and humanities researchers. The growing popularity of computational approaches and the shift towards interdisciplinary research is a trend that I find inspiring and exciting

Fahd Abdelazim (he/him) – Ask-JGI PhD Student

Fahd Abdelazim, PhD student on the Interactive AI CDT in the School of Computer Science

I am a PhD student in the Interactive Artificial Intelligence CDT, specializing in model understanding for Vision-Language models. My research focuses on introducing improvements to Vision-Language models that allow for better linking of specific ideas or attributes to physical items, in order to help models recognize and understand the properties of objects in images.

I first heard of the Ask-JGI team through fellow PhD students, and it was recommended to me as a way to apply data science skills to real-world applications. Joining the Ask-JGI helpdesk has been a unique experience where I’ve been able to delve into various domains and learn about topics that I would otherwise not have had the chance to learn about. The team truly values cross-functional collaboration and encourages tackling new challenges and learning on the job.

Working at Ask JGI is incredibly rewarding. I enjoy the diversity of challenges presented by each query which gives me the chance to improve as a data scientist and gain a better understanding of how data science can help improve academic research. I really enjoy the collaborative spirit within the team. The Ask-JGI team are from many different disciplines and interacting with them allows for interesting exchanges of ideas and problem-solving approaches. This allows me to grow not just as a data scientist but as a researcher as well.

Yujie Dai (she/her) – Ask-JGI PhD Student

Yujie Dai, PhD student in the Digital Health and Care CDT

I am a PhD student in the Digital Health and Care CDT, specializing in population health data science. My research focuses on leveraging large-scale real-world health data to address critical challenges in infectious diseases. Specifically, I utilize explainable AI (XAI) techniques to characterize and diagnose diseases, aiming to bridge the gap between data science and public health.

My journey with Ask-JGI began with a recommendation from a friend who was previously part of the team. They spoke highly of the collaborative and dynamic environment, and I was intrigued by the opportunity to apply my skills in real-world research settings. Joining Ask-JGI is an extension of my academic and research pursuits. I was drawn to the idea of supporting researchers across diverse disciplines, helping them navigate technical challenges in their projects, and learning from their different perspectives. The chance to engage with cutting-edge problems and contribute to solutions beyond the scope of my own research was exciting.

There’s so much to love about being part of Ask JGI. I love the variety of work. Each question I encounter presents a new challenge, whether it’s developing a data analysis pipeline, troubleshooting code, or brainstorming creative solutions for a computational problem. The variety keeps me constantly learning and growing as a data scientist. I also love the collaborative atmosphere. Working closely with researchers from different fields gives me diverse ways of thinking and problem-solving. It’s an opportunity to not only apply my skills but also to know more about the scientific community.

Huw Day (he/him) – Ask-JGI Lead

Huw Day, JGI Data Scientist

I am a JGI Data Scientist with a background in mathematics, working on a variety of data science projects with researchers across the university using a variety of data science methodologies and techniques. I also help run the Data Ethics Club.

As Ask-JGI Lead, I am responsible for recruiting, training and the general managing of the Ask-JGI team. They’re a fantastic group and I consider myself really lucky to be able to work with them. I support some of the general queries and I’m also responsible for talking with researchers interested in costing out data science support in grant applications.

To me, the Ask-JGI helpdesk is based on the idea that any researcher who wants to do data science should be empowered to do so. Whilst we often do the data science for people, I think the most rewarding outputs from our helpdesk is when we empower researchers to do data science themselves, guiding and validating their work. It’s also a wonderful opportunity for myself and the rest of the helpdesk to learn about research across the university.

All University of Bristol researchers (including PhDs) 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.

If you’re a PhD student interested in joining the Ask-JGI team, we will do recruiting for the next academic year in summer of 2025 so keep an eye on the JGI mailing list for when we have our recruiting call. We recruit a new cohort every year but do not accept speculative applications outside of the recruiting call.

Successful Seedcorn Awardees 2024-2025

Posted on 6 November 202420 May 2025 by kerry.turcsi

The Jean Golding Institute Seedcorn Funding is a fantastic opportunity to develop multi and interdisciplinary ideas while promoting collaboration in data science and AI.  We are delighted that a new cohort of multidisciplinary researchers has been supported through this funding.

Leighan Renaud – Building a Folk Map of St Lucia

Dr. Leighan Renaud is a lecturer in Caribbean Literatures and Cultures in the Department of English. Her research interests include twenty-first century Caribbean fiction, mothering and motherhood in the Caribbean, folk and oral traditions in the Anglophone Caribbean, and creative practices of neo-archiving.

Louise AC Millard – Using digital health data for tracking menstrual cycles

Dr. Louise Millard is a Senior Lecturer in Health Data Science in the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol. Following an undergraduate Computer Science degree and MSc in Machine Learning and Data Mining, they completed an interdisciplinary PhD at the interface of Computer Science and Epidemiology. Their research interests lie in the development and application of computational methods for population health research, including using digital health and phenotypic data, and statistical and machine learning approaches.

Laura Fryer – Visualisation tool for Enhancing Public Engagement Using Supermarket Loyalty Card Data

Laura is a senior research associate in the Digital Footprints Lab based within the Bristol Medical School. Their aim is to use novel data to unlock insights into behavioural science for the purposes of public good. Laura is particularly passionate about broadening the public’s understanding of digital footprint data (e.g. from loyalty cards, bank transactions or wearable technology such as a smart watch) and demonstrating how vital it can be in developing our understanding of population health within the UK and beyond. Laura’s project is focused on developing a data-visualisation tool that will support public engagement activities and provide a tangible representation of the types of data that we use – building further trust between the public and scientific researchers.

Nicola A Wiseman – Cellular to Global Assessment of Phytoplankton Stoichiometry (C-GAPS)

Dr. Nicola Wiseman is a Research Associate in the School of Geographical Sciences. They received their PhD in Earth System Science from the University of California, Irvine, where they specialized in using ocean biogeochemical models to investigate the impacts of phytoplankton nutrient uptake flexibility on ocean carbon uptake. They also are interested in using statistical methods and machine learning to better understand the interactions between marine nutrient and carbon cycles, and the role of these interactions in regulating global climate.

Georgia Sains – Collecting & Analysing Multilingual EEG Data

Georgia Sains is a Doctoral Teaching Associate in the Neural Computation research group at the School of Computer Science. Her research is focused on the overlap between Computer Science, Neuroscience, and Linguistics. Georgia has worked on developing models to help understand how linguistic traits have evolved. More recently, she has been using Bayesian modelling to find patterns between grammar and neurological response and are now focused on using Electroencephalography experimentation to explore the relationship between linguistic upbringing and how the brain processes language.

Alex Tasker – Building a Strategic Critical Rapid Integrated Biothreat Evaluation (SCRIBE) data tool for research, policy, and practice

Dr. Tasker is a Senior Lecturer at the University of Bristol, a Research Associate at the KCL Conflict Health Research Group and Oxford Climate Change & (In)Security (CCI) project, and a recent ESRC Policy Fellow in National Security and International Relations. Dr. Tasker is an interdisciplinary researcher working across social and natural sciences to understand human-animal-environmental health in situations of conflict, criminality, and displacement using One Health approaches. Alongside this core focus, Dr. Tasker’s work also explores emerging areas of relevance to biosecurity and biothreat including engineering biology, antimicrobial resistance, subterranean spaces, and the use of new forms of evidence and expertise in a rapidly changing world for climate, security, and defense.