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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.
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
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.
From November 2024 – April 2025, we (the Turing Liaison Team at Bristol) ran a fruitful Turing Seminar Series. This series boasted academics connected to the Turing Institute, speaking about their cutting-edge research in data science and AI.
From Machine Learning, Large Language Models and Digital Twins, to early prediction of dementia, disambiguation in historical texts and evolutionary biology, the range of speaker specialisms reflected the breadth of research at Bristol in this space, reaching academics and early career researchers across the institution.
Audience from Robert Blackwell’s Turing SeminerChristopher Burr’s Turing Seminar
Below are a list of the talks and speakers:
Wednesday 6 November:
Title: Machine Learning and Dynamical Systems meet in Reproducing Kernel Hilbert Spaces
Speaker: Boumediene Hamzi, Marie Curie Fellow, Imperial College London.
Wednesday 20 November:
Title: Trustworthy Digital Twins: designing, developing, and deploying open and reproducible pipelines
Speaker: Chris Burr, Head of the Innovation and Impact Hub, Turing Research and Innovation Cluster for Digital Twins, Alan Turing Institute
Wednesday 4 December:
Title: What can your shopping basket say about your health?
Speaker: Anya Skatova, Senior Research Fellow, Bristol Medical School (PHS)
Wednesday 15 January:
Title: AI-guided tools for early prediction of brain and mental health disorders
Speaker: Zoe Kourtzi, Professor of Computational Cognitive Neuroscience, University of Cambridge
Wednesday 12 February:
Title: Temporal models for Word Sense Disambiguation in historical texts
Speaker: Barbara McGillivray, Lecturer in Digital Humanities and Cultural Computation, Kings College London
Wednesday 26 February:
Title: “If you can’t tell, does it matter?” What should the law say about humanlike AI?
Speaker: Colin Gavaghan, Professor of Digital Futures, Bristol Digital Futures Institute, University of Bristol
Wednesday 12 March:
Title: “Cognition-first evolution”
Speaker: Richard Watson, Professor, (evolutionary biology and computer science), University of Southampton
Wednesday 26 March:
Title: “Big data as propeller for dynamic and time-sensitive service industries: a tourism sector perspective.”
Speaker: Nikolaos Stylos, Associate Professor in Marketing and Digital Innovation, Business School, University of Bristol
Wednesday 9 April:
Title: Can large language models reason about qualitative spatial information?
Speaker: Robert Blackwell, Senior Research Associate, Alan Turing Institute
These seminars have connected external researchers with relevant academics and departments at Bristol, and we have already seen these connections turn into longer-term collaborations. After the talk by Chris Burr, Alan Turing Institute, we organised a workshop between the Alan Turing Institute and the Bristol Digital Futures Institute (BDFI). This workshop provided an insight into digital twin projects run by both institutes, as well as facilitating connections. We took visitors on a tour of BDFI to show the incredible facilities, namely the Reality Emulator – the world’s first large-scale digital twin facility. Staff then went into roundtable discussions delving into shared areas of interest and what a longer-term collaboration could look like.
Over the series, we had 184 internal and external attendees, with 80% feeding back that they found the information / content provided during the event helpful. We are planning on running another series in the 2025-2026 academic year, building on our momentum and further increasing our external and internal networks.
If you have any suggestions of who you would like to see speak as part of next year’s series, please contact Isabelle Halton, Turing Liaison Manager – uob-turing@bristol.ac.uk
You can find out more about Turing events and opportunities at Bristol, including the previous Turing Seminar talks and slides on the Turing web pages.
Dr Leon Danon has been appointed as the new Director of the Jean Golding Institute. Leon is an Associate Professor in Infectious Disease Modelling and Data Analytics in the School of Engineering Mathematics and Technology. He has a PhD in Statistical Physics of Complex Networks but has been working on epidemiology of infectious diseases since 2004. His work combines mathematics, data science and AI with an understanding of behavioural and biological drivers of disease spread to solve pressing problems in public health.
He is Director of Modelling and Data at the Bristol Vaccine centre working with clinicians, immunologists, and statisticians on the epidemiology of vaccine preventable infections.
During COVID-19 he served on SPI-M-O, the modelling subgroup of SAGE, contributing to scientific advice to government on mitigation policies. He was part of the group awarded the Weldon Memorial Prize for this work, as well as the SPI-M-O Award for Modelling and Data Support (SAMDS). He has secured research funding totalling over £25M and continues to work at the science policy interface.
Leon Danon said: “I’m delighted to be joining the Jean Golding Institute as Director. Having been at Bristol since 2021, I’ve had the opportunity to contribute to the University’s vibrant research environment, particularly in infectious disease epidemiology. I’m now very excited to lead the JGI, building on its existing strengths to drive interdisciplinary data science and AI initiatives across a broad range of activity within the University. The JGI is the ideal setting for deepening existing collaborations across faculties and external partners, as well as building new ones, and I’m eager to get started.“
He continued: “I look forward to working with you all to support the continued success and growth of the Institute, and to support the University’s ambitions in high-impact research and innovation, sustainability, industry and policy partnerships, and local engagement, raising our global leadership and reputation.“
Dr Leon Danonwill commence in his role of Director of the Jean Golding Institute on the 1 May 2025.
The JGI team had a great time at the AI UK national showcase of data science and AI hosted by The Alan Turing Institute. The team got to experience community talking points on stage, exhibitions that demonstrate innovation from UK academic and commercial organisations, and a range of workshops. You can read our previous blog post about the University of Bristol demonstrations at AI UK.
As a collective, the team had a fantastic time at AI UK and for some team members, AI UK 2025 was their first AI UK showcase. Find out more below about how each team member experienced AI UK and their own personal reflections on the sessions.
JGI Team left to right: Richard Lane, Patty Holley, Huw Day, Isabelle Halton, Conor Houghton, Emmanouil Tranos, Leo Gorman.
Charting the Future of Research in the UK panel discussion and Celebration of the Turing AI Fellowships, Patty Holley
I especially enjoyed a panel discussion on the UK’s research ecosystem, titled Charting the Future of Research in the UK. The panel included Sana Khareghani (Former Head of the UK Government Office for AI and KCL, panel host), Samuel Kaski (Manchester Centre for AI Fundamentals), Anna Scaife (Jodrell Bank Centre for Astrophysics), and Neil Lawrence (University of Cambridge).
The panellists agreed that UK universities continue to lead global AI research. However, challenges remain in translating research into practical applications. Neil Lawrence was particularly critical of the funding and support directed toward Artificial General Intelligence (AGI). He argued that efforts should instead focus on working with communities to identify real-world problems and develop human-centric AI systems. He pointed to the Global South as a model, where mobile AI systems have been successfully deployed to support farmers.
I also attended the celebration of the Turing AI Fellowships, marking the culmination of the first five years of this funding program. The first cohort of Fellows included Tim Dodwell (University of Exeter), Yarin Gal (University of Oxford), Neil Lawrence (University of Cambridge), Anna Scaife (University of Manchester), and Maria Liakata (Queen Mary University). This funding has provided a strong foundation for AI research in the UK. It has supported initiatives ranging from the creation of a company exploring AI for fusion—now employing over 50 people—to the development of tools for safe AI. Beyond supporting individual researchers, the fellowships have also helped build capacity by funding early-career researchers.
Panel for the ‘Turing Fellowships Phase 1 Showcase’ session.
Overall, the event was successful in highlighting the UK’s strong position in AI research and the importance of continued investment, collaboration, and ethical responsibility in shaping the future of AI.
AI Openness in the Age of Deep Seek’s R1, Emmanouil Tranos
The most interesting session I attended was the ‘AI Openness in the Age of Deep Seek’s R1’. I was particularly intrigued by Laura Gilbert’s point about governmental organisations opening up the code they develop for public scrutiny. I do appreciate that this is not an easy nor a widely supported choice to make. But, it certainly adds greater value to the work of these organisations. Of equal interest was the fallacy that a company used their code to re-sell a packaged software product back to the government.
Debates at AI UK, Leo Gorman
I really enjoyed AI UK. Not being an academic conference, it was a chance to hear decision-makers talk about the big picture. Here’s my take on some of the major themes. Almost all speakers said they thought the UK punched above its weight academically, but there were debates about how the UK should position itself in relation to AI research. Debates such as: Is BlueSky research critical for the next AI breakthroughs, or should UK research be working on challenges that are not yet sufficiently profitable for industry, and bring them to sufficient scale? Should the UK focus on researching the impact/use of AI, rather than trying to compete with the US on AI development? Don’t expect to emerge with a consensus, but if you’re interested in these broader types of debates, I definitely recommend going!
Sessions on defence and critical national infrastructure, Richard Lane
AI UK covered a wide range of topics, from the role of research institutions to the environmental impact of emerging technologies. Many discussions focused on how these technologies are being applied across different sectors, and what steps are needed to ensure they deliver practical value. There was plenty of debate around infrastructure, energy use and how we balance innovation with long-term sustainability. Several sessions also touched on the challenges of ensuring wider access to tools and benefits- particularly in areas like education, healthcare, and local services- rather than focusing solely on commercial applications.
The sessions on defence and critical national infrastructure brought some of the more grounded and practical conversations. In defence, there was a clear emphasis on improving back-office systems, accelerating decision-making, and thinking through how automation might reshape responsibilities while also raising questions around oversight and accountability. The importance of regulation and clear ethical standards came up throughout. On critical infrastructure, the focus was on system resilience: not just preventing attacks, but designing systems that can detect, adapt, and recover. There was also discussion around the limitations of purely technical solutions, and the need to consider human factors, long-term planning, and better feedback mechanisms.
Panel for the ‘Creating an immune system for critical national infrastructure’ session.
Overall, the week highlighted both the opportunities and the complications that come with deploying new technologies at scale; especially in public systems where reliability, transparency, and sustainability matter just as much as capability.
The future they want: lessons from the Children’s AI summit, Huw Day
The session involved listening to a series of talks from children and young people on learnings from the Children’s AI Summit in February, which brought children from across the UK together to share their messages with world leaders at the Paris AI Action Summit. The Children’s Manifesto for the Future of AI was produced as part of the Children’s AI Summit.
The speakers who ranged from ages 10 to 17 (and who were some of the best at the entire conference!) touched on topics such as the role of AI in education, the sustainability issues associated with AI and what young people expect from world leaders. A key point was a desire to have their voices listened to and respected, particularly given that the world we make now is the one that young people will be living in the longest.
A wide perspective of AI UK, Conor Houghton
There was a lot to enjoy at AI-UK. For a start there were people from all sorts of backgrounds, the academy, enterprise and government which made for lots of interesting conversations; the stands were good too, particularly the whale language one from Northeastern University on the research side and the Prolific stand from the corporate side. The keynote session by Tania Duarte, Andrew Fitzgibbon, Lauren Beukes pretending to be a set of AI-UK talks from the future, seemed like a goofy idea but made for some provoking and contrasting talks; Andrew’s talk from the near future was optimistic and interesting, the other two, pretending to come from many decades hence from were wilder, but, strangely, didn’t seem to take account of the wild implications AI has for our sense of our human-ness. A few people, people who didn’t know I was involved with the Bristol stands, commented on how impressive they were and seemed excited by how big a splash Bristol had made, so I was super happy about that.
Implications of AI from the future of research to bridging the skill gap, Isabelle Halton
This was my first time at the AI UK conference and overall, I thought it was well organised, and had a wide breadth of exhibition stands, talks and workshops which made the conference more inclusive. The standout talks I attended were ‘Charting the future of research in the UK’, ‘Bridging the skills gap: regional approaches to AI upskilling’ and the ‘Environmental implications of AI’.
Turing Liaison Managers from Universities across England. From left to right: Emily Cruz (London School of Economics and Political Science), Catherine Healy (KCL), Joshua Panteli (University of Birmingham), Isabelle Halton (UoB), Moi Hoon Yap (Manchester Metropolitan University), Moses Chinta (University of Liverpool)
Across the three talks, a common theme seemed to be the incredible possibilities of AI to solve the most local and national pressing issues, but there are many difficulties to scaling up AI solutions – whether this is logistically, financially, legally, environmentally – there seem to be so many barriers to AI solutions becoming an accessible resolution.
Panel for ‘The environmental implications of AI’ session.
The skills AI gap seems to also be having a huge impact onto the future of the workforce. As more businesses require AI solutions and a staggering 52% of the population do not have the necessary skills for the workforce (according to one of the speakers; Liz Williams from FutureDotNow), this clash of circumstances will undoubtedly create gaps across sectors. This raises questions around the role of businesses, the government and local communities, and the broader challenge of digital poverty and inclusion.
There was a lot of food for thought and I came away feeling more knowledgeable about the wider AI landscape. It was great to connect with organisations across the UK, as well as catch up with the other Turing Liaison Managers from other UK universities.
