The University of Bristol's central hub for data science and data-intensive research, connecting a multidisciplinary community of experts across the University and beyond.
Rachael Laidlaw (Ask-JGI Data Science Support 2023-24)
I first came into contact with the Jean Golding Institute last year at The Alan Turing Institute’s annual AI UK conference in London, and then again in the early stages of the DataFace project in collaboration with Cheltenham Science Festival. This meant that before I officially joined the team back in October, I already knew what a lovely group of people I’d be getting involved with! Having nice colleagues, however, was not my only motivation for applying to be an Ask-JGI student. On top of that, I’d decided that whilst starting out in my ecological computer-vision PhD niche, I didn’t want to forget all of the statistical skills that I’d developed back in my MSc degree. Plus, it sounded really fun to keep myself on my toes by exercising my mind tackling a variety of data-oriented requests from across the university’s many departments.
Rachael Laidlaw (centre), second-year PhD student in Interactive Artificial Intelligence, and other JGI staff members at the JGI stall
During the course of my academic life, I’ve taken the plunge of changing disciplines twice, moving from pure mathematics to applied statistics and then again to computer science, and I liked the idea of supporting others to potentially do the same thing as they looked to enhance their work by delving into data. Through Ask-JGI, I kept my weeks interesting by having something other than my own research to sometimes switch my focus to, and it felt very fulfilling to be able to offer useful technical advice to those who were in the same position that I myself had been in not so long ago too! I therefore got stuck in with anything and everything, from training CNNs for rainfall forecasting or performing statistical tests to compare the antibiotic resistance of different bacteria, to modelling the outcomes of university spinouts or advising on the ethical considerations and potential bias present when designing and deploying a questionnaire-based study. And, of course, by exposing myself to these problems (alongside additional outreach initiatives and showcase events), I also learned a lot along the way, both from my own exploration and from the rest of the team’s insights.
One especially exciting query revolved around automating the process of identifying from images which particular underground printing presses had been used to produce various historical political pamphlets, based on imperfections in the script. This piqued my interest immediately as it drew parallels with my PhD project, highlighting the copious amount of uses of computer vision and how it can save us time by speeding up traditionally manual processes: from the monitoring of animal biodiversity to carrying out detective work on old written records.
All in all, this year has broadened my horizons by giving me great consultancy-style work experience through the opportunity to share my expertise and help a wide range of researchers. I would absolutely encourage other curious PhD students to apply and see what they can both give to and gain from the role!
JGI Seed Corn Funding Project Blog 2023/24:Mark Mumme, Eleanor Walsh, Dan Smith, Huw Day and Debbie Johnson
What is Children of the 90s?
Children of the 90s (Co90s) is a multi-generational population-based study following the health and development of nearly 15,000 families living around Bristol, whose children were born in 1991 and 1992.
Co90s initially recruited its participants during the early stages of the mum’s pregnancy and captures information prospectively, at key time points, using self-reported questionnaires, interviews, clinics and electronic health records (EHR).
The Co90s supports about 20 project teams using NHS data at any one time.
What is Synthetic Data?
At its most basic, synthetic data is information generated artificially rather than recorded directly from real-world events. It is essentially a computer-generated version of the data that doesn’t contain any real data and preserving privacy and confidentiality.
Privacy vs Fidelity
Generating synthetic data is frequently a balancing act between fidelity and privacy (Figure 1).
“Fidelity”: how well does the synthetic data represent the real-world data?
“Privacy”: can personal information be deduced from the synthetic data?
Figure 1: Privacy versus fidelity
Why synthetic NHS data:
EHR data are incredibly valuable and rich data sources, yet there are significant difficulties to accessing this data, including financial costs and the time taken to complete multiple application forms and have these approved.
Because the authentic NHS data is so difficult to access, it is also not unusual for researchers to have never worked with, or possibly even seen, this type of data before. They often face a learning curve to understand how the data is structured, what variables are present in the data and how those variables relate to each other.
The journey for a project to travel (Figure 2) just to get NHS data typically goes through the following stages:
Figure 2: The stages a project goes through to get NHS data
Each of these stages can take several months and are usually sequential. It not unheard of for projects to run out of time and/or money due to these lengthy timescales.
Current synthetic NHS data:
Recently, the NHS has released synthetic Hospital Episode Statistics (HES) data (available here; https://digital.nhs.uk/services/artificial-data) which is, unfortunately, quite limited for practical purposes. This is because a very simple approach was adopted; each variable is randomly generated independently from all others. While it is possible to infer broadly accurate descriptive statistics for single variables (e.g., age or sex), it is impossible to infer relations between variables (e.g., how the number of cancer diagnoses increases with age). In the terms introduced above, it has high privacy but low fidelity. As shown in the heatmap, Figure 3, we observe practically no association between diagnosis and treatment because synthetic NHS data is randomly generated variable-by-variable.
Figure 3: Heatmap displaying the relations between disease groupings (right side) and treatment (bottom) from the synthetic NHS data. The colour shadings represent the number of patients (e.g., the darker the shading, the higher the number). The similarity in shading within each diagnosis row shows that treatment and diagnosis were largely independent in this synthetic dataset.
What do researchers want from synthetic data?
We developed an anonymous survey and asked 230 researchers experienced with EHR data, what would be important to them when considering using synthetic EHR data. Out of the 24 responding most were epidemiologists at fellow or professor level. Researchers were then invited to an online discussion group to expand on insights from the survey. Seven researchers attended.
Most researchers had a more than 3 years of experience using EHRs both within and outside of cohort studies. Although few had much knowledge of synthetic EHR data, many had heard of synthetic EHR data and were interested in its application, particularly as a tool for training and learning about EHRs generally.
The most important issues to researchers (Figure 4) were consistent patient details and having all the additional diagnosis & treatment codes rather than just the main ones:
Figure 4: What researchers look for in synthetic EHRs
The most important utility for these researchers was to test/develop code and understand broad structure of the data, as shown below (Figure 5):
Figure 5: Priorities of researchers when using synthetic data
This was reflected in their main concerns about maintaining the utility of the data in the synthetic version by producing high level of accuracy and attention to detail.
During the discussion it was recognised that EHRs are “messy” and synthetic data should emulate this, providing an opportunity to prepare for real EHRs.
Emulate “messy” real data discussion visual
Being able to prepare for the use of real EHRs was the main use case for synthetic data. No one suggested using the synthetic data as the analysis dataset in place of the real data.
Preparation for using real EHR data visual
It was suggested, in both survey responses and the discussion group, that any synthetic data should be bespoke to the requirements of each project. Further, it was observed that each research project only ever used a portion of the complete dataset, therefore synthetic data should be minimized also.
“I think any synthetic data set based on any of the electronic health records should be stripped back to the key things that people use, because then the task of making it a synthetic copy [is] less.” (online participant)
Summary
Following the survey and discussion with some researchers familiar with EHRs a few key points came through:
Training – using synthetic data to understand how EHRs work, and to develop code.
Fidelity is important – using synthetic data as way for researchers to experience using EHRs (e.g. the real data flaws, linkage errors, duplicates).
Cost – the synthetic data set, and associated training, must be low cost and easily accessible.
Next Steps
There is a demand for a synthetic data set with a higher level of fidelity than is currently available, and particularly there is a need for data which is much more consistent over time.
The Co90s is well placed to respond to this demand, and will look to:
Obtain approximately 10 years’ worth of NHS data – record level but pseudonymised.
Mike Nsubuga(Ask-JGI Data Science Support 2023-24)
Embarking on a New Path
Mike Nsubuga, first year PhD Student in Computational Biology and Bioinformatics
In the early days at Bristol, even before I began my PhD, I stumbled upon something extraordinary. AskJGI, a university initiative that provides data science support to researchers from all disciplines, caught my attention through a recruitment advert circulated by my PhD supervisor for support data scientists.
My journey started with hesitation. As a brand-new PhD student, who had just relocated to the UK, I questioned whether I was ready or suitable for such a role. Despite my reservations, my supervisor saw potential in me and encouraged me to seize this opportunity. Yielding to their encouragement, I applied, not fully realizing then how this decision would profoundly shape both my academic and professional paths.
A World of Opportunities
Joining AskJGI opened a door to a dynamic world brimming with ideas and innovations. My background in bioinformatics and computational biology meant that working on biomedical queries was particularly rewarding. These projects varied from analyzing protein expression data to studying infectious diseases, allowing me to use data science in meaningful ways.
Among the initiatives I was involved in was developing models to predict protein production efficiency in cells from their genetic sequences. Our goal was clear yet impactful: to identify patterns in genetic sequences that indicate protein production efficiency. We employed advanced data analysis and machine learning techniques to achieve effective predictions.
Additionally, I contributed to a project analyzing the severity of dengue infections by using statistical models to identify key biological markers. We pinpointed certain markers as critical for distinguishing between mild and severe cases of the infection.
These projects showcased the transformative power of data science in understanding and potentially managing diseases, directly impacting public health strategies.
Making Science Accessible: Community Engagement at City Hall
A highlight of my tenure with AskJGI was participating in Data Science Week at bustling Bristol City Hall. The event was not merely a showcase of data science but an opportunity to demystify complex concepts for the public. Engaging in lively discussions and simplifying intricate algorithms for curious visitors was incredibly fulfilling, especially seeing their excitement as they understood the concepts that are often discussed in our professional circles.
AI and the Future of Society event as part of Bristol Data Week 2024
Fostering Connections and Gaining Insights
AskJGI enhanced my technical skills and broadened my understanding of the academic landscape at the University of Bristol. The connections I forged were invaluable, sparking collaborations that would have been unthinkable in the more isolated environment of my earlier academic career. Reflecting on my transformative journey with AskJGI, I am convinced more than ever of the importance of interdisciplinary collaboration and the critical role of data science in tackling complex challenges. I encourage any researcher at the University of Bristol who is uncertain about their next step to explore what AskJGI has to offer. For PhD students looking to get involved, it represents not just a learning opportunity but a chance to make a significant societal impact.
Emma Hazelwood (Ask-JGI Data Science Support 2023-24)
Emma Hazelwood, final year PhD Student in Population Health Sciences
I am a final year PhD student in Population Health Sciences. I found out about the opportunity to support the JGI’s data science helpdesk through a friend who had done this job previously. I thought it sounded like a great way to do something a bit different, especially on those days when you need a bit of a break from your PhD topic.
I’ve learnt so many new skills from working within the JGI. The team are very friendly, and everyone is learning from each other. It’s also been very beneficial for me to learn some new skills, for instance Python, when considering what I want to do after my PhD. I’ve been able to see how the statistical methods that I know from my biomedical background be used in completely different contexts, which has really changed the way I think about data.
I’ve worked on a range of topics through JGI, which have all been as interesting as they have been different. I’ve helped people with coding issues, thought about new ways to visualise data, and discussed what statistical methods would be most suitable for answering research questions. In particular, I’ve loved getting involved with a project in the Latin American studies department, where I’ve been mapping key locations from conferences throughout the early 20th century onto satellite images, bringing to life the routes that the conference attendees would have taken.
This has been a great opportunity working with a very welcoming team, and one I’d recommend to anyone considering it!
Emilio Romero (Ask-JGI Data Science Support 2023-24)
Emilio Romero, 2nd year PhD Student in Translational Health Sciences
Over the past year, my experience helping with the Ask-JGI service has been really rewarding. I was keen to apply as I wanted to get more exposure to the research world in Bristol, meet different researchers and explore with them different ways of working and approaching data.
From a technical perspective, I had the opportunity to work on projects related to psychometric data, biological matrices, proteins, chemometrics and mapping. I also worked mainly with R and in some cases SPSS, which offered different alternatives for data analysis and presentation.
One of the most challenging projects was working with chemometric concentrations of different residues of chemical compounds extracted from vessels used in human settlements in the past. This challenge allowed me to talk to specialists in the field and to work in a multidisciplinary way in developing data matrices, extracting coordinates and creating maps in R. The most rewarding part was being able to use a colour scale to represent the variation in concentration of specific compounds across settlements. This was undoubtedly a great experience and a technique that I had never had the opportunity to practice.
ASK-JGI also promoted many events, especially Bristol Data Week, which allowed many interested people to attend courses at different levels specialising in the use of data analysis software such as Python and R.
The Ask-JGI team have made this year an enjoyable experience. As a cohort, we have come together to provide interdisciplinary advice to support various projects. I would highly recommend anyone with an interest in data science and statistics to apply. It is an incredible opportunity for development and networking and allows you to immerse yourself in the wider Bristol community, as well as learning new techniques that you can use during your time at the University of Bristol.
