News – Jean Golding Institute News

Ask-JGI Example Queries from the Faculty of Arts, Social Sciences and Law

Posted on 24 March 202524 March 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 Arts, Law and Social Sciences here at the University of Bristol.

YouTube Comment Scraping

One researcher got in touch for advice about scraping data from the YouTube comment section. They were interested in collecting all the comments for a set of videos so that they could analyse sentiment and engagement with the videos’ content. While this wasn’t something we’d done before, we spent some time reading about the subject and found that the official YouTube Data API (https://developers.google.com/youtube/v3) was suitable for this work (no 3rd party tools needed!). We discussed this with the client, and based on their needs, suggested that we use the official Python client as a simple and flexible way to interact with this data source.

While the researcher was relatively new to Python, they expressed an interest in learning for the project. While we wrote the code and documentation for the comment scraping pipeline, the researcher went through some of the Python courses that the JGI offers (https://bristol-training.github.io/). This way, we were able to meet with them again after a few weeks to go through the code together, and make sure everything was understood and in a usable state.

Table of 'Scraped YouTube Comments'. The table shows the channel name, title, author, like count and text. — *Example of the kind of YouTube comment data accessible via the official API.*

Cross-platform comparison of social media posts quoting a Greek poet

One query we supported revolved around a cross-platform comparison of social media posts quoting a specific Greek poet. The study aimed to collect posts from TikTok, Tumblr, and Pinterest to identify the most popular poem quotes and analyse how frequently they were misattributed. While researchers working with platforms like X, Facebook, or YouTube can often find established data collection methods, niche platforms pose unique challenges. A key difficulty was determining the right data sample size across platforms. Three of them form unique social networks with different engagement metrics, making it unclear how many posts would be sufficient for a meaningful analysis. Through collaboration, we worked together to understand the research question better and adapted methodological aspects of this research design. We also explored alternative analysis approaches, including network analysis, to better understand how posts spread on these platforms and to assess the reach of these quotations.

Code review for cross-sectional survey on food insecurity

A PhD student working in anthropology and social policy attended some of the free coding courses the JGI offers (https://bristol-training.github.io/). Since this initial encounter with R, they have been using R for their data analysis. As their supervisors do not work with R, the student found themselves in need of additional feedback on their R based project. Specifically, they wanted to make sure that their approach to and interpretation of Principle Component Analysis is on the right track. So the student contacted Ask-JGI for a second opinion on their analysis, and they wish to have their R code reviewed to make sure it was all working correctly. We are happy to have offered them the support they needed and to confirm that they were on the right track!

People sat at computers looking at code on a projector screen — *R training session led by JGI Data Scientists*.

Fuzzy Matching for Job Postings Analysis

We assisted researchers from the Business School with the data collection process for their job postings analysis. This involved extracting and analysing job postings data to understand how companies invest in specific skill sets, especially those related to cutting-edge technologies like AI.

One of the initial hurdles we faced was matching company names from the provided list with those found in job postings. Even though this might sound straightforward, company names can vary significantly. We encountered abbreviations and slight variations in spelling. A simple exact match would not be sufficient. That’s where fuzzy matching came into play. We used algorithms that can identify similar strings, even with minor differences. This allowed us to accurately link our company list to job postings, even when the names weren’t perfectly aligned. This was crucial for capturing the broadest possible range of relevant data.

The sheer volume of job posting data presented another significant challenge. We were dealing with potentially millions of records, processing this data requires substantial computational resources. To tackle this, we utilized High-Performance Computing (HPC). HPC allows us to distribute the workload across multiple processors, significantly accelerating the data processing and analysis. This was essential for handling the massive datasets and complex algorithms involved in fuzzy matching.

Visualising historical networks of Chinese and Eurasian elites in the British Empire

We are working with a PhD researcher in the History department. In this case, the Ask-JGI team is offering assistance in exploring the use of network visualisation and analysis tools. These might be otherwise not as easily accessible to researchers when the methods are considered interdisciplinary in their home discipline. And Ask-JGI helps to bridge that gap. The PhD project involves mapping the network of powerful individuals in the British Empires across the late 19th and early 20th centuries. This network is complex, as individuals are connected with one another through different types of ties, such as family relations, alumni networks, business partnerships, and political organisations. Visualising these ties as a network of heterogenous nodes and edges helps the researcher to effectively communicate the subject of the research. Through our conversations, we bring clarity to concrete next steps in the analysis of the dataset. We also offered learning resources and advice on alternative analytical methods that can be applied to distil insights on how interpersonal connections and social capital might have translated to power in the historical context.

Interactive visualisation of the network dataset, highlighting the family ties. Each node is an individual. Made using Rhumbl.com — A screenshot of an interactive visualisation of the network dataset, highlighting the family ties. Each node is an individual. The following figure was not produced by Ask-JGI, it is an illustration provided by the researcher in the above query, Ryan Lu.

Book Launch – AI and Literature Routledge Handbook

Posted on 17 March 202517 March 2025 by kerry.turcsi

In January 2025, the Turing Liaison team, based in the Jean Golding Institute for interdisciplinary data science, organised a book launch for the new AI and Literature Routledge Handbook.

The Handbook was co-edited by Genevieve Liveley, a Professor of Classics at the University of Bristol, and a Turing Fellow. “It brings together 30 new and exciting ideas about the incredible intersection between AI and Literature”, says Genevieve. “We’ve got Computer Scientists, artists, poets and some of the leading names in AI Science”.

Genevieve Liveley standing at a lectern presenting at the book launch to a seated audience — *Genevieve Liveley, co-editor of The AI and Literature Routledge Handbook.*

This Handbook combines early career contributors with some of the best-known names in the digital humanities and computational literary studies. “I think the book does this amazing thing where it has all these different minds and ways of coming at the topic,” says Victoria Punch, a book contributor and a PhD researcher at the Universities of Bristol and Exeter. “I think there is always something really exciting about getting together with people from different disciplines.”

Victoria Punch standing at a lectern presenting at the book launch to a seated audience. Projector screen is showing an image of the book and Victoria's name beside it. — *Victoria Punch, contributer to The AI and Literature Routledge Handbook.*

The launch was hosted at the SS Great Britain in Bristol – a heritage site and one of the most important engineering experiments that changed the flow of information, ideas, fashion, culture and literature. Similarly, this Handbook was another important experiment in what feels like a transformative moment in history – the rise of AI, and how this intersects across sectors.

“There’s never been a better time to look at AI,” says Kate Devlin, another book contributor and Professor of Artificial Intelligence & Society, King’s College London. “This book deals with many different aspects of how AI intersects with literature in a way that it has had its origins in the past in the stories we tell, right through to the science fiction fields we have today.”

Kate Devlin standing at a lectern presenting at the book launch to a seated audience — *Kate Devlin, contributer to The AI and Literature Routledge Handbook.*

AI and Literature explores a variety of theories and approaches when AI is deployed in literary contexts. “One of the reasons why science fiction is so important is that it helps us understand the stories that we tell about AI,” says co-editor, Will Slocombe, Reader in English, University of Liverpool. “We talk about technologies as if they are neutral things but they are surrounded by stories and discourse.”

Will Slocombe standing at a lectern presenting at the book launch to a seated audience. The screen is showing a slide titled 'AI Interdisciplinarities' — *Will Slocombe, co-editor of The AI and Literature Routledge Handbook.*

It offers a fresh perspective on the past, present, and future of AI and literature that will appeal to students and scholars with relevant interests across a range of subjects, including AI Engineering, Classics, Computing, Digital Humanities, English, Ethics, Film and Television, Law, and Narratology.

Pick up your copy now: AI and Literature Routledge Handbook

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.

Meet the Research Data Advocate team

Posted on 11 February 202512 February 2025 by kerry.turcsi

We are delighted to announce a new pilot training scheme led by our newly-appointed JGI Research Data Science Advocates. This is a new way to take part in training in a low-stress, collaborative and supportive environment, and at the same time form a community of data scientists in your area.

The pilot will run JGI training events over a whole week in Schools, supported by a local Data Science Advocate. They will run sessions to support a cohort to undertake the training together, over the course of a week. The formal training takes only around 2-3 hours to complete, but it is anticipated that this format will allow deeper learning and more useful application to research.

To take part in the pilot (which is aimed at relatively inexperienced coders within a discipline), please email to jgi-training@bristol.ac.uk. If your school doesn’t have a volunteer, you would be welcomed at a research-adjacent community. Bios for our Advocates are below and even if you don’t need this particular training, they would love to include you in an ongoing data science community, so please get in touch.

Ruolin Wu

I am a PhD student of paleobiology diving into the mysteries of evolutionary history. Armed with code, fossils, and molecular data, I craft stories about topological and temporal pattern of animals and plants. Outside of academia, I like climbing, handcrafts, succulents and ferns of any kind.

Zhiyuan Xu

I am a 1st year PhD student focusing on data science and artificial intelligence, with a particular focus on large language models and their applications. My background includes experience in machine learning, data-driven research, and interdisciplinary collaboration to address complex problems.

Bryony Clifton

I’m a PhD student in Biochemistry, studying the molecular details underpinning neurotransmission. My project focuses on identifying the biological role for an uncharacterised intramembrane protease found in the human brain. During my PhD, I have become aware of the importance of developing tools to present complex datasets in a clear and informative way. I am excited to begin my role with the JGI where I can support others to build these skills too.

Catherine Upex

I’m Catherine and I’m a first year PhD student based in the medical school. I’m using data science and AI to understand the shape and movement patterns of the heart over different disease states. I’m also currently working on a mini-project using AI protein folding tools, like AlphaFold, and computer simulations to uncover interactions between synthetic cannabinoids and the hERG potassium channel and its relation to arrythmia risk.

Kaan Deniz

Aerospace Engineer who has intensive industrial experience in numerical modelling with a MSc degree from the University of Bristol/ Aerospace Engineering. Current PhD student in Aerospace Engineering at the University of Bristol. Research focus is numerical modelling of composite manufacturing processes.

Boy Li

I study how to synergize domain-specific knowledge with data-driven deep learning models to extract information from remote sensing imagery.

Vaishnudebi Dutta

I am an Engineering Mathematics PhD student working on model and data-driven design of combination therapies for non-small cell lung cancer. Beyond my research, I serve as the School of Engineering Mathematics and Technology (SEMT) PhD Student Representative, advocating for and supporting the academic community. I also hold a key position as the PhD Representative for the Bristol Cancer Research Network where I get the opportunity to share research updates to Clinicians, and others in the network. Additionally, I manage the network’s official X (formerly Twitter) presence, helping to disseminate research developments and maintain engagement with the broader scientific community.

Zhengzhe Peng

I am a PhD student with a diverse background in computer science, business, and over a year of IT work experience. My research applies advanced data science methods, with a focus on AI, to explore real-world challenges. I am dedicated to expanding my knowledge in these fields and eager to help others who are new to data science, working together to advance and explore new possibilities in this ever-evolving domain.

Winfred Gatua

Winfred Gatua is a PhD Fellow at the University of Bristol, specializing in Molecular Genetics and Life Course Epidemiology. Her research focuses on the triangulation of evidence between Mendelian randomization and randomized controlled trials for complex diseases. She holds an MSc in Bioinformatics, a Postgraduate Diploma in Health Research Methods, and a BSc in Biomedical Science and Technology. Transitioning from wet lab biomedical sciences to dry lab bioinformatics, Winfred is a self-taught coder passionate about open science, automation, and reproducible research in genetics. Beyond research, Winfred is dedicated to capacity building, particularly in increasing computational and data literacy among non-computer science researchers. Since 2021, she has been a volunteer instructor with The Carpentries, securing funding, hosting and instructing carpentries lessons that equip researchers with essential skills in data analysis, open science, reproducible research and best practices in scientific computing in different institutions across the globe.

Meet the Ask-JGI team – Mirah, Tao, Yueying & Dan

Posted on 22 January 202514 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! Meet some of the team below:

Mirah Zhang (she/her) – Ask-JGI PhD Student

Mirah Zhang, PhD candidate in Geographic Data Science in the School of Geographical Sciences

I am currently a PhD candidate in Geographic Data Science in the School of Geographical Sciences. My PhD work is methodologically focused. It involves elements of counterfactual prediction, and information theory based causal discovery. While a big part of causal inference is ‘normal’ statistics, I am particularly interested in scenarios where standard statistical models struggle in handling causal relations entangled with spatial structures.

Joining the Ask-JGI team has been an amazing opportunity for me to interact with researchers from a wide range of different backgrounds, and in different stages of their research. I am constantly learning on the job, not just acquiring new skills but also whole new perspectives!

Over the past few months, I have come to the understanding that there is more value to our work here than the code solutions we provide. It is an empowering experience, being able to interact with people, to empathize, and to lift them with the skills I have. It also gives me a sense of pride, being part of the stubborn human element in data science/AI that cannot be automated away. All of these have made my Ask-JGI role a uniquely fulfilling experience both academically and at a personal level.

Tao Zhou (he/him) – Ask-JGI PhD Student

Tao Zhou, PhD student in Advanced Quantitative Methods in the School of Geographical Sciences

I’m a final-year PhD student in Advanced Quantitative Methods in the School of Geographical Sciences, where my research focuses on the socio-economic determinants of health, especially health inequalities from a life-course and geographical perspective. Methodologically, I am mainly interested in Econometrics, structural equation modelling, multilevel modelling and survival analysis. In the meantime, I’m also passionate about exploring the variations and combinations of these models, such as latent growth curve modelling, intersectional MAIHDA, and longitudinal age-period-cohort analysis with spatial effects. Before the doctoral journey, I’ve got my BSc degree in Economics and MSc degree in Social Statistics.

As a member of the Ask-JGI team, I really enjoy discussing with researchers from a variety of disciplines across the university about their projects. These interactions help resolve their queries, while at the same time enhancing my own understanding of particular research areas.

The Ask-JGI helpdesk has created a platform for interdisciplinary communication through data science, which I highly recommend if you have any relevant enquiries or would like to apply to join our team for the next cohort.

Yueying Li (she/her) – Ask-JGI PhD Student

Yueying Li, PhD student in Population Health Sciences in the Bristol Medical School

I joined the Ask-JGI team as a PhD student in Population Health Sciences. Over the course of my academic journey, I’ve progressively narrowed my focus from public health during my bachelor’s, to epidemiology in my master’s, and now to genetic epidemiology for my PhD. This field deals with vast amounts of data, and leveraging data science techniques for efficient management and analysis can make a tremendous impact.

Before applying for this position, I heard glowing recommendations from colleagues and former Ask-JGI helpers, and I’m happy to say the experience has been incredibly rewarding. It’s a fantastic opportunity to sharpen my coding skills and refresh my statistical knowledge. During my education, I learned tools like SPSS, SAS, Stata, R, and Python, but not all of them are frequently used in my projects. Working at the Ask-JGI helpdesk has allowed me to hone those skills and expand my expertise. Beyond the technical growth, one of the most exciting parts of the job is engaging with researchers from diverse disciplines. It’s inspiring to contribute to their fascinating and valuable projects while learning from their unique perspectives. It is even more beneficial to do things in a team where everyone is talented, supportive, and respectful.

Dan Collins (he/him) – Ask-JGI Coordinator

Dan Collins, PhD student on the Interactive AI CDT in the School of Computer Science

I’m currently in the final year of my PhD with the Interactive AI CDT. While my research involves abstract simulation experiments and exploring conflicts and cooperation in populations of AI agents, I have a keen interest in the broader applications and impact of data science in the real world. Working with Ask-JGI has been a fantastic opportunity to explore this interest further.

I joined Ask-JGI last year as a student data scientist and had a great experience in the role. I’ve particularly enjoyed the collaborative nature of the work, and the exposure it has given me to different data science techniques and research problems across a variety of specialisms. This year, I’ve had the opportunity to continue working with Ask-JGI as a Coordinator. In this role, I’ve been able to draw on my experiences to help support a new team of Ask-JGI PhD students, while continuing to deliver data science support through the helpdesk.

I believe Ask-JGI is a truly valuable program. It enables PhD students with data science expertise to develop their skills and gain experience collaborating on interdisciplinary research, and it encourages researchers at the University to explore how data science techniques can be used to support their work.

If you’re a PhD student interested in joining the Ask-JGI team (or you know someone who might be good for it), 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.