Are you a researcher looking for data scientist support?

Researchers across the University benefit from our JGI Seedcorn Funding. Funding is great when you have someone to do the work – but what if you don’t have the right data science expertise in house? For that, this summer we are trialling a new JGI Data Scientist Support service. This provides an alternative support mechanism for researchers who need expertise and time, but not funding. 

The Jean Golding Institute’s team of data scientists and research software engineers are here to support researchers across the University of Bristol fostering a collaborative research environment spanning multiple disciplines. Over the past seven years, our team has expanded thanks to various funding sources, reflecting the increasing importance of data science support in facilitating research outcomes and impact. 

Get in touch with our team to find out how they can help you with: 

  • Data analysis – recommendations or support with tools and methods for statistics, modelling, machine learning, natural language processing, computer vision, geospatial datasets and reproducible data analysis. 
  • Software development – technical support, coding (for example: Python, R, MATLAB, SQL, bash scripts), code review and best practices. 
  • Data communication – data visualisation, dashboards and websites. 
  • Research planning – experimental design, data management plans, data governance, data hazards and ethics. 

Our aim is to support researchers and groups that may not have in-house expertise but have project ideas that can be developed into applications for funding. We’re seeking projects that can take place over the summer until early autumn (July – October 2024). 

How to apply 

Please complete an online expression of interest form  

Deadline: 15 July 2024 

Selection process 

The JGI team will get back to you within one week, to discuss your request.  

If demand exceeds our current resource levels, we’ll meet with applicants to help prioritise projects. As with seedcorn funding, priority will go to applications that match JGI strategic goals and have clear pathways to benefit, such as an identified funding call or impact case. 

Examples of data science projects 

  • Social mobility analysis project – using local and national level data to investigate how different people in Bristol and other UK cities feel about life in their local environment. The JGI data scientist worked as part of a multidisciplinary team including University of Bristol researchers and external stakeholders, for around 2 days per week for 3 months. They analysed survey and geospatial data using Python, presented findings to the group. The output of the project was a grant application in which a data scientist was costed longer-term. 
  • Antimicrobial resistance project – examining patterns in observed levels of antimicrobial resistance during the COVID pandemic. The JGI data scientist worked with a University of Bristol researcher and collaborated with a public sector stakeholder, for around 4 days per week for 4 months. They performed statistical modelling using R, producing data visualisations of the trends found. The project has led to an Impact Acceleration Funding application to develop a tool used to support local health planning. 
  • Transport research-ready dataset grant – linking administrative datasets to support research into car and van use in the UK. The JGI data scientist developed data pipelines and provided methodological and data governance input into a successful ESRC funding application in a collaboration between researchers at the universities of Bristol and Leeds. The data scientist was a named researcher on the application and went on to perform data analysis as part of the project team. 

Turing Fellowships 2024 announcement

The Jean Golding Institute is pleased to announce the next University of Bristol-based researchers that have been awarded The Alan Turing Institute Fellowship.

Starting on the 1st of March, two new Bristol-based researchers will be joining the Turing network and becoming Turing fellows alongside forty-nine other fellows being announced today.

Turing fellows are researchers with proven research excellence in data science, artificial intelligence (AI) or a related field whose research will be significantly enhanced through active involvement with The Alan Turing network of partners and universities.

The aim of the Turing Fellowship scheme is to grow the data science and AI ecosystem in the UK through supporting, retaining and developing the careers of the next generation of world leading researchers, while also contributing to the Alan Turing Institute’s overarching goals.

Meet & hear from our new incoming Turing Fellows at the University of Bristol:

Dr Caitlin Robinson

“I am excited to meet other fellows working across a wide range of disciplines. I am especially interested to learn more from researchers at the Turing about issues of ethics and justice in data science and AI.” Dr Caitlin Robinson, Turing Fellow.

Dr Robinson is a quantitative human geographer and UKRI Future Leaders Fellow interested in understanding and mapping different forms of spatial inequality, especially related to energy.

Dr Genevieve Liveley

Dr Liveley is a Professor of Classics and Director of the Research Institute for Sociotechnical Cyber Security (RISCS). She is also the co-founder of FLiNT (Futures Literacy through Narrative) and a narratologist whose research interests focus upon narratives and narrative theories (both ancient and modern) and their impact on futures thinking.

Drs Robinson and Lively are both a part of new cohort of Turing Fellows that will tackle science and innovation challenges and support the Alan Turing Institute’s work in skills and public engagement. Read more about the announcement of the new cohort of fellows here.

Dr Dan Lawson appointed as interim Director of the Jean Golding Institute

Dr Dan Lawson has been appointed as the interim Director of the Jean Golding Institute for an initial period of six months. As well as the interim Director role, he will assume the role of Academic Liaison for The Alan Turing Institute, the UK’s national institute for Data Science and AI, on behalf of the University of Bristol.

“Dan Lawson is a visionary academic leader whose prominent work in data science has transgressed disciplinary boundaries. I am delighted that he is taking up the position of Director of the Jean Golding Institute, and greatly look forward to working with him.” said Pro Vice Chancellor for Research and Enterprise, Professor Phil Taylor.

Dr Lawson is Associate Professor in Data Science in the School of Mathematics, University of Bristol. He has been a longstanding friend of the Jean Golding Institute, becoming the Academic Lead for the JGI Data Science Seminar Series from 2018 and a JGI Steering Group Member since 2021, bringing his experience and knowledge in the field of Data Science advising the JGI of where to focus its activities and contributing to our five year plan.

Dr Lawson is a member of the Royal Statistical Society, a Fellow of the Higher Education Academy, and a Turing Fellow in Data Science, with The Alan Turing Institute and co-directs Compass, the EPSRC centre for Computational Statistics and Data science.

“It is a great honour to be guiding the Data Science community at Bristol as interim Director of the Jean Golding Institute. This is a great time to celebrate and build upon the monumental impact that the JGI has already had on Data Intensive Research, both within the University and beyond.” said Associate Professor Lawson on his appointment.

“I am looking forward to speaking to people across the University about their ideas for finding more ways to interact with, learn from, and understand the world with data. With so much exciting research being done, this is a great time to be a data scientist, and change is a good opportunity to start new discussions.”

“The JGI is open for business as ever. From the Ask JGI service for getting advice, to providing data expertise on grants, we are here to serve – and to inspire – our community.” he added.

Associate Professor Lawson began his career at Imperial College London, receiving his PhD in Mathematics and Computer Science in 2007.

In 2014, he joined the University of Bristol as a Sir Henry Dale Wellcome Trust Research Fellow before progressing to Lecturer, Senior Lecturer to Associate Professor in Data Science.

Associate Professor Lawson’s dedication to diversity and outreach initiatives is commendable. Through pioneering initiatives like “Access to Data Science,” he increased the gender, ethnic, and social diversity in academia, thereby contributing to a more inclusive research environment. His engagement in data science outreach efforts, spanning from Bayesian interpretation of Ghost Stories, to COVID modelling, and “What to know before studying Data Science” showcases his commitment to making complex concepts accessible to broader audiences. Additionally, his research, which ranges from landscape management policy documents to industry applications like the finestructure software, underscores his impact across diverse domains, cementing his status as a trailblazer in the field of data science and beyond.

His membership in advisory boards such as the Transdisciplinary Centre of Excellence Estonian Roots (CoEER), showcases his commitment to fostering interdisciplinary collaborations and advancing scientific endeavours.

The Jean Golding Institute is the central hub for data science and data-intensive research at the University of Bristol. We connect a multidisciplinary community of experts across the University and beyond. We offer free 1 day of support from our Ask-JGI “ask a data scientist” service for all staff and doctoral students at the University of Bristol, as well as a calendar of events and training throughout the year, such as the annual Bristol Data Week held in early June packed with interactive talks, training, and workshops, open to all and completely free of charge. Save the date for this year’s Bristol Data Week which will be held 3rd – 7th June 2024.

Associate Professor Lawson will commence in his role of Director of the Jean Golding Institute on the 19th February 2024.

Hear the JGI’s first monthly podcast: Data Hazards and Digital Phenotyping 

The JGI is delighted to launch the JGI Podcast, where the team at the Jean Golding Institute talk to different members of the University of Bristol’s data science research community. Each episode aims to highlight both the variety of backgrounds and paths that our guests come from as well as the diversity in methods, approaches and applications in data science research at the university.

Nina Di Cara shown on the left, Huw day shown in the middle & Léo Gorman on the right.

This month, Huw Day and Léo Gorman (Data Scientists at the JGI) talk to Nina Di Cara about Data Hazards and Digital Phenotyping.

Visit our podcast website, find it in your usual podcast catalogue, or use the player below:

Assessing the pathogenic potential of novel bacterial lineages: Towards an early warning system for problem pathogens

JGI Seed Corn Funding Project Blog 2022-2023: Sion Bayliss and Daniel Lawson

The future of disease control relies heavily on understanding the evolution and emergence of bacterial strains in real time, a daunting yet crucial task. This project marked a significant step towards this goal, aiming to rapidly unmask potentially problematic ‘hybrids’ caused by the crossing of already established disease lineages and demystify the evolution and adaptation of harmful bacteria. We aim to provide the first steps in an early warning system against potential public health threats posed by new bacterial strains.

Understanding Bacterial Evolution: The Key to Future Disease Outbreaks

Genomic sequencing has become a key part of research programmes worldwide, notably pathogens that affect human and animal health and related strains that exist in the wild. This has led to the deposition of vast numbers of disease genomes in public repositories, a rich resource for the study of evolutionary processes underpinning the emergence of new bacterial lineages. During this project we developed a tool which could be used to search within these large collections and identify potentially problematic strains of disease-causing bacteria.

The focus of the work was on differentiating between whether emerging lineages had predominantly evolved due to hybridisation between of existing lineages or were a product of transfer from novel sources, such as animals or the environment. This differentiation was achieved by scrutinizing the bacterium’s genome sequences, enabling us to identify hybridized DNA sections, a critical step towards comprehending bacterial evolution.

Outcomes: A Rapid Detection System for Dangerous Hybrid Lineages

To classify lineages, we clustered sample based upon their ‘family-tree’. By examining these ‘phylogenetic trees’ we are able to identify candidate hybrid lineages present on distinctive long-branches (Figure 1). These ‘long-branch lineages’ could have evolved in various ways – having an increased evolutionary rate, being imported from a previously unknown source, or having recently emerged via hybridisation between two or more parent lineages.

Our groundbreaking achievement lies in the development of a software pipeline capable of rapidly differentiating between these various scenarios. Tested on both simulated and real-world genomic datasets, our software can identify otherwise cryptic ‘hybrid’ lineages (Figure 2). This development lays the foundation for a software tool to ‘flag’ potentially alarming strains being routinely added to large genomic databases which could pose a significant public health threat.

Future Developments

Our ultimate goal is to equip researchers and healthcare professionals with a tool that can provide early warnings for new and potentially dangerous bacterial strains. To this end, our project’s next steps include testing the tool on large and diverse databases of pathogens of public health concern, updating and streamlining the codebase for broader and easier use by other researchers, and development of a web-portal that would allow users to upload their samples for testing against a comprehensive example datasets.

Staying Connected

For more details about our project and future updates, please contact either Sion Bayliss or Daniel Lawson, and feel free to engage with us for any queries or discussions.

Figure 1. Example of a phylogenetic tree or ‘family tree’ of bacterial genomes with an example of a long-branch isolate (red circle).
Figure 1. Example of a phylogenetic tree or ‘family tree’ of bacterial genomes with an example of a long-branch isolate (red circle).
Figure 2. Example output of the software tool on small dataset with a simulated hybrid strain. The simulated hybrid strain is indicated in red. The correctly identified recipient strain is shown in blue and the donor strain is shown in green. The donor strain contributed approximately 20% of their genome to the recipient strain in randomly sized DNA tracts.
Figure 2. Example output of the software tool on small dataset with a simulated hybrid strain. The simulated hybrid strain is indicated in red. The correctly identified recipient strain is shown in blue and the donor strain is shown in green. The donor strain contributed approximately 20% of their genome to the recipient strain in randomly sized DNA tracts.