Data communication – Jean Golding Institute News

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.

PhD Connect Conference

Posted on 10 December 202411 December 2024 by kerry.turcsi

Our Turing Liaison team recently funded a number of PhD students to go to the 2024 Alan Turing PhD Connect Conference. This is one of the range of approaches we are taking to bridging the gap between the Turing’s goals and the University’s research and academics who reflect these goals.

Attendees sat around circular tables with sheets of paper and pens on the table — *Attendees at a PhD Connect conference. Photo provided by Jingrong Bai*.

Supporting PhD students to make connections and discover new collaborations through the Turing will hugely benefit students and the wider data science and AI community, and is an important part of our objective. Below are some statements from the students we funded about their experience at the conference.

Damien Wang

I am Damien, a first-year PhD student from University of Bristol and SWDTP who specializing in psychology and artificial intelligence. The past two days at PhD Connect 2024 have been incredibly fulfilling. I had the opportunity to explore a wide range of PhD projects in AI and data science, engaging in discussions with other attendees and collaboratively tackling problems by leveraging our diverse backgrounds.

Conversations with peers and insights from the panel discussions were truly enlightening. I was also fortunate to represent my group during the Mini-DSG session and deliver my own poster presentation. These experiences have boosted my confidence and skills in presenting, and I’m grateful for the valuable feedback I received on my research.

This two-day journey has inspired me to push forward with even greater motivation. A heartfelt thanks to the Alan Turing Institute and everyone I met along the way!

Ming Chen

The PhD Connect 2024 conference was an incredible opportunity to engage with peers, learn from industry experts, and explore real-world applications of data science and AI. My research interests include learning sciences and emerging technologies in language learning. I would say that one of the highlights for me was participating in group research discussions, which broadened my understanding of AI’s role in addressing societal challenges.

I also appreciated the networking opportunities and the chance to discuss my research with fellow attendees and professionals from diverse sectors. Another interesting part of the conference is the Research Karaoke, which is a great experience for people to have fun and practise doing presentations.

Jizhao Niu

I am grateful to The Jean Golding Institute for funding my attendance at the conference. It was a fantastic opportunity to meet many PhD students from Bristol and beyond, engaging in discussions on health sciences-related projects.

A highlight for me was the training session on how to pitch research effectively, which provided valuable insights and practical skills. We worked as a team to sell an item to other groups, which was both enjoyable and educational.

I learned the importance of tailoring research presentations to audiences with diverse backgrounds — a skill I look forward to applying in the future!

Jingrong Bai

During the conference, we got insightful points on AI-human by Piotr Mirowski from DeepMind. Then, we interacted with the group work and presented karaoke, which was good for us to connect with other PhD students across the UK, also, learned how to prepare a good presentation by Beatriz Costa Gomes. Last but not the least, we shared our research ideas through the poster session. All in all, it is a valuable experience for me to know the AI field and meet all of the awesome people, really appreciate all of the speakers, organizers and students.

Jingrong Bai (left) standing in front of a poster with two other individuals discussing the posters on display — *Jingrong Bai (second on the left) with other PhD students at a networking sessio*n.

Zia Saylor

Zia Taylor (left), Kerstin Nothnagel (centre) and Michael Rumbelow (right) at PhD Connect — *Zia Saylor (left), Kerstin Nothnagel (centre) and Michael Rumbelow (right) at PhD Connect*.

Perhaps my favorite session was the one on day 2 morning of the conference when we discussed the principles of a good academic presentation. Focusing on basics like practice, maintaining relevancy to the audience, and ensuring that materials were packaged in an alluring way were key methods discussed. Looking at the AI aspect of our learning opportunities, much of the conference consisted of hands-on opportunities to engage with the materials, from designing a workflow that would integrate AI into academia without infringing on the rights and words of academics to developing a mechanism to integrate data on building pricing into an AI cost estimation algorithm that could be made. This enabled us as students to learn more about AI in its many forms and potential for interdisciplinary applications.

Jay Liu

It has been a wonderful journey for me to attending the 2024 Alan Turing AI PhD Conference at Horizon Leeds. It is my first time travelling to Leeds, a fantastic city with fancy malls and restaurants. I am grateful for the great opportunity and generous funding for the program!

I am a PhD student in Finance at the University of Bristol Business School, focusing on understanding the effects of AI and algorithmic decision making in the financial markets. I believe the conference can further improve my understanding on AI and the application of AI on interdisciplinary research!

Jay Liu standing in front of a digital screen showing a slide titled 'Welcome to the PhD Connect' — *Jay Liu standing in front of a digital screen at PhD Connect*.

Alan Turing Institute tote bag (right) featuring Alan Turing's face on it and Jay Liu's name badge (left) for PhD Connect — *Jay Liu standing in front of a digital screen at PhD Connect*.

Zhengzhe Peng

Numerous speakers standing at the front of the room in front of a slideshow projected on a wall — *Session from PhD connect with multiple speakers*.

Attending the PhD Connect Conference organized by the Alan Turing Institute was an enriching experience. I particularly appreciated the diverse perspectives shared during interdisciplinary discussions on data science applications. The keynote sessions inspired new ideas for integrating AI into my research, while the networking opportunities allowed me to connect with peers tackling similar challenges. I gained valuable insights into emerging methodologies and practical approaches that will enhance my PhD work.

Boyang Yu

This conference let me engage with the Mini-data group to explore data science applications in real-world challenges, which is what I’m doing as a PhD. I enhanced my presentation skills and learned to communicate complex ideas to a broader audience, inspired by a standout example from the presenter (Dr Beatriz Costa Gomes). I saw some very nice posters and great to have a picture with one of my most favourite poster (and its owner).

Female speaker on stage behind a lectern with a projected slide behind them showing 'Today's agenda' with timings and session titles — *Damien Wang (left) and Boyang Yu (right) at a poster session at PhD Connect*.

Ding Li

Attending the 2024 Turing Phd connect conference is such an unforgettable experience. I have met a bunch of bioinformatics students from various universities and institutions sharing their research with AI and Machine Learning. The poster and presentation session left me with impression on how research from other fields could help with my own PhD project. During the session, I discussed with Mr Muizz who is also from University of Bristol, but another school of Engineering Mathematics, and heard about how he applied AI on topology of insects’ wings in traditional species classification and phylogeny. It would never happen if there were no such an opportunity.

Ding Li standing in front of a poster with two other male individuals conversing about the poster — *Ding Li (Left) listening to talks given by data scientist Dr Piotr Mirowskifrom Google Deepmind*.

Kerstin Nothnagel

Attending the Alan Turing Institute PhD Connect Conference was an incredible experience. Highlights included Dr Piotr Mirowski’s inspiring keynote on human-machine collaboration and the ‘Mini Data Study Group,’ where we tackled real-world challenges like ICU surge prediction and cancer forecasting.

This event was a perfect prelude to my upcoming ATI funded UK-Italy Trustworthy AI Visiting Researcher Programme in Milan, where I’ll collaborate with global researchers to explore ‘Global AI Policies and Regulations and Their Impact on Healthcare.’ The project is reinforced by the importance of unifying AI policies to ensure technology benefits everyone equally, closing economic gaps rather than widening them.

Successful Seedcorn Awardees 2024-2025

Posted on 6 November 20246 November 2024 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.

George Sains – Collecting & Analysing Multilingual EEG Data

George Sains is a Doctoral Teaching Associate in the Neural Computation research group at the School of Computer Science. Their research is focused on the overlap between Computer Science, Neuroscience, and Linguistics. George has worked on developing models to help understand how linguistic traits have evolved. More recently, they have 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.

The Royal Statistical Society Annual Conference 2024

Posted on 13 September 2024 by kerry.turcsi

The Royal Statistical Society meets annually for their internationally attended conference. It serves as the UK’s annual showcase for statistics and data science. This year they met in Brighton for a conference attended by over 600 attendees from around the world, including JGI Data Scientist Dr Huw Day.

The conference had over 250 presentations, including contributed talks, rapid-fire talks, and poster presentations. At any one time, there could be as many as 6 different talks going on, so it was impossible to go to everything but below are some of Huw’s highlights of the conference.

Pre-empting misunderstandings is part of trustworthy communication

*From left to right; Dr Huw Day, Professor Sir David Spiegelhalter and Dr Simon Day (RSS Fellow and Huw’s dad) at the RSS International Conference 2024.*

As part of a session on communicating data to the public, Professor Sir David Spiegelhalter talked about his experiences trying to pre-bunk misinformation when displaying data.

Data in June 2021 showed that the majority of COVID deaths are in the vaccinated group. The Brazilian president President Jair Bolsonaro used this data to support claims that Covid vaccines are killing people. Spiegelhalter and his colleague Anthony Masters tried explaining why this wasn’t a sign the vaccine was bad in an article in The Observer “Why most people who now die with Covid in England have had a vaccination”.

Consider the following analogy: most car passengers who die in car accidents are wearing seatbelts. Intuitively, we understand that just because these two variables are associated, it doesn’t mean that one causes the other. Having a story like that means you don’t have to talk about base rates, stratification or even start to use numbers in your explanations.

We should try to make the caveats clearer of data before we present them. We should be upfront from what you can and can’t conclude from the data.

Spiegelhalter pointed to an academic paper: “Transparent communication of evidence does not undermine public trust in evidence” where participants were shown either persuasive or balanced messages about the benefits of Covid vaccines and nuclear power. It’s perhaps not surprising to read that those who already had positive opinions about either topic continued to have positive views after reading either messages. Far more interesting is that the paper concluded that “balanced messages were consistently perceived as more trustworthy among those with negative or neutral prior beliefs about the message content.”

Whilst we should pre-empt misconceptions and caveats, being balanced and more measured might prove to be an antidote to those who are overly sceptical. Standard overly positive messaging is actively reducing trust in groups with more sceptical views.

Digital Twins of the Human Heart fueled Synthetic 3D Image Generation

Digital twins are a digital replica/simulator of something from the real world. Typically it includes some sort of virtual model which is informed by real world data.

Dr Dirk Husmeiser at the University of Glasgow has been exploring the application of digital twins of the human heart and other organs to investigate behaviour of the heart during heart attacks, as well as trying to use ultrasound to measure blood flow to estimate pulmonary blood pressure (blood pressure in the lungs). Usually, measuring pulmonary blood pressure is an extremely invasive procedure, so using ultrasound methods has clear utility.

One of the issues of building a digital twin is having data about what you’re looking at. In this case, the data looks like MRI scans of the human heart, taken at several “slices”. Because of limitations in existing data, Dr Vinny Davies and Dr Andrew Elliot, (both colleagues of Husmeiser at the University of Glasgow)have been attempting to develop methods of making synthetic 3D models of the human heart, based on their existing data. They broke the problem down into several steps, working to generate synthetic versions of the slices of the heart (which are 2D images) first.

The researchers were using a method called Generative Adversarial Networks (GANs), where two systems compete against each other. The generator system generates the synthetic model and the discriminator system tries to distinguish between real and synthetic images. You can read more about using GANs for synthetic data generation in a recent JGI blog about Chakaya Nyamvula’s JGI placement.

Slide on “Generating Deep Fake Left Ventricle Images for Improved Statistical Emulation”. — A slide from Dr Vinny Davies and Dr Andrew Elliot’s talk on “Generating Deep Fake Left Ventricle Images for Improved Statistical Emulation”. The slide depicts how progressive GANs work, where the generator learns how to generate smaller, less detailed images first and gradually improves until it can reproduce 2D slices of MRIs of the human heart.

Because the job of the generator is far harder than that of the discriminator (consider the task of reproducing a famous painting, versus spotting the difference between an original painting and a version drawn by an amateur), it’s important to find ways to make the generator’s job easier early on, and the discriminator’s job harder so that the two can improve together.

The researchers used a method called a Progressive GAN. Initially they gave the generator the task of drawing a lower resolution version of the image. This is easier and so the generator did easier. Once the generator could do this well, they then used the lower resolution versions as the new starting point and gradually improved the correlation. Consider trying to replicate a low resolution image – all you have to do is colour in a few squares in a convincing way. This then naturally makes the discriminator job’s harder, as it’s tasked with telling the difference between two, extremely low resolution images. This allows the two systems to gradually improve in proficiency.

The work is ongoing and the researchers at Glasgow are looking for a PhD student to get involved with the project!

Data Hazards

On the last day of the conference, Huw alongside Dr Nina Di Cara from the School of Psychology at the University of Bristol presented to participants about the Data Hazards project.

Participants (including Hadley Wickam, keynote speaker and author of the famous R package tidyverse) were introduced to the project, shown examples of how it has been used and then shown an example project where they were invited to take part in discussions about which different data hazards might apply and how you might go about mitigating for those hazards. They also discussed the importance of focussing on which hazards are most relevant and prominent.

Dr Huw Day (left) and Dr Nina Di Cara in front of a screen that says 'Data Hazards Workshop' — *Dr Huw Day (left) and Dr Nina Di Cara (right) about to give their Data Hazards workshop talk at the RSS International Conference 2024.*

All the participants left with their own set of the Data Hazard labels and a new way to think about communicating hazards of data science projects, as well as invites to this term’s first session of Data Ethics Club.