JGI Seed Corn Funding Project Blog 2022-2023: Sydney Charitos & Lauren Thompson

An exploration of how primary school children want to view their health data: a co-design study


Due to the growth of self-tracking health devices, greater attention has been paid to how individuals view their health data. However, young children are often not the focus of these investigations. Instead, adult tools are applied and validated rather than starting from the children’s perspectives.


This project aimed to explore children’s views around visualising health data and to co-produce a set of designs/prototypes illustrating how health data can be better visualised for children. To do this, we are running a series of creative workshops with a class of 10–11-year-olds from a local school. We had both research aims and social aims throughout the project. These included:

Workshop 1:

Research Aim: Generate a range of data visualisations based on personas with invisible disabilities who represent ‘clients’. These personas represented children with different health conditions, with different motivations and requirements for tracking.

Social Aim: Educate the children about invisible disabilities, foster empathy towards the personas, provide healthy children with a broader understanding of the experiences of non-healthy children, and introduce the concept of co-design by involving them in the research process.

Workshop 2:

Social Aim: Teach the children to use BBC Micro:bits which are small electronic devices created to support children in developing electronics and computer science skills.

Workshop 3:

Research aim: Enable children to provide feedback and compare designs created by artists and academics interested in data visualisation. Subsequently, the children would create visualisations guided by designs based on their workshop 1 displays.

Social aim: Through comparison, children evidence and develop their skills of analysis and evaluation. These are higher-order processing skills which reflect a thorough understanding of the topic and are key steps on the way to intentional creation. Intentional creation is the ultimate goal as it evidences the full understanding of the context and application in education.

Workshop 4:

Research aim: Create a poster synthesising the children’s ideas into one display per persona.

Social aim: Children will show a full understanding of health data visualisation. They will do this by comprehending and responding to the three personas and adapting their ideas to those needs and preferences. This will show their ability to generate and adapt ideas when visualising health data and will be confirmed by their creation of a poster, on which they have also provided rationale for their choices – proving their consideration and conceptualisation. Children will furthermore work collaboratively, evidencing that skill of teamwork, to combine all of their ideas on each of the ‘clients’ into a singular large display. Through synthesising ideas, they will simultaneously evaluate and create in order to reflect their full understanding of both health data visualisation and how to adapt it to requirements.


Since the project is still ongoing, we do not currently have results. However, we can share some displays that highlight interesting concepts. For more details, please reach out to us.

Figure 1, Child's drawing from workshop 1 based on the personas.
Figure 1: Child’s drawing from workshop 1 based on the personas.

Future plans

After we complete all of the intended workshops, we will create a display to be placed on a wall at the school we have been working with. The children will then be able to show their work to their teachers, friends and parents. We intend to write up these results into a paper focusing on the methodology of the study as well as interesting and unexpected findings.

Contact details and links

Contact Sydney Charitos (sydney.charitos@bristol.ac.uk) or Lauren Thompson (lauren.thompson@bristol.ac.uk) for more information about the project.

Ask JGI Student Experience Profiles: Ben Anson

JGI Student Experience Profiles: Ben Anson (Ask-JGI Data Science Support 2022-23)
Ben Anson, 1st year PhD Student in the School of Mathematics at the University of Bristol

JGI Student Experience Profiles: Ben Anson (Ask-JGI Data Science Support 2022-23)

Ask-JGI was advertised to my CDT by one of the staff members in the School of Mathematics. It sounded like a fun way to make the most of my MSc in statistics and get some ‘real world’ practice with statistics, all whilst doing my PhD! So, I applied, and it was one of the easiest application forms I have ever filled in. I was offered the job almost instantly and started 2 or 3 weeks after.

It has been really beneficial for me to chat with people from disciplines, e.g. biological sciences, psychology, education. It makes it much easier to understand the jargon from field to field (and hopefully it was helpful for them to understand more about data science), and gives interesting insights into what other research are up to. A lot of the statistics I’ve studied has been within a fairly theoretical framework, so it has been challenging and rewarding to see how this theory applies in practice. Queries are also quite varied, in the sense that some people know exactly what model they want to use, but want advice on how to perform inference, and other queries are about selecting the right method, or model, or even about framing their problem in a sensible way.

The work has spanned over many areas, I’ve worked on several queries from psychologists about how to fit models and/or test hypotheses for experiments they have run, and helping them explore ways of dealing with problematic datasets (e.g. missing data). I’ve helped analyse survey data from taught courses at the university, advised on how to process football sentiment text data, and discussed the best way to visualize results from non-destructive testing methods!

My experience of Ask-JGI has been mostly with statistics queries, but there are also queries about data visualization, data management, ML applications, etc, which are also super interesting. I’d recommend anyone who has experience in any of the above to apply, as the role is very varied and fits around PhD work.

Ask JGI Student Experience Profiles: Vanessa Hanschke

Ask JGI Student: Vanessa Hanschke
Vanessa Hanschke, 3rd year PhD student, in the School of Computer Science at the University of Bristol

JGI Student Experience Profiles: Vanessa Hanschke (Ask-JGI Data Science Support 2022-23)

I initially wanted to join the Ask-JGI because I thought it would be a great opportunity for me to keep my coding skills alive. I studied computer science and have worked in the data and AI industry for three years before starting my PhD in Interactive Artificial Intelligence. My PhD looks at supporting data science teams to reflect on the social impact of their applications through roleplay and although my technical background is helpful, I don’t actually write any code on a day-to-day.

The JGI experience definitely gave me opportunities to practise some coding, but it also gave me so much more. I was able explore all the interesting research work happening around the university, whether it was fish genetics, appetite psychology, analysing racist discourse or history video games. It’s inspiring to see the many different things researchers can do with data and how different their data sets look: qualitative data in the form of survey responses, hand curated excel sheets manually extracted from historical archives or long lists of numbers collected with environmental sensors. My biggest takeaway is that, because academia can be such a competitive environment, having a place that will give you constructive feedback and support is invaluable. It was very rewarding to facilitate connections between researchers who could collaborate or to provide a piece of advice or that little snippet of code that helped researchers become unstuck.

Another big highlight of being part of the JGI was participating in public outreach events such as AI UK in London or Bristol Data Week. It is such an exciting space and so much fun to speak to people who are just learning about data concepts and are curious to know if it will benefit their lives or if the perceived harms will manifest. The media buzz around data and AI means there is a lot of important work that needs to be done to both demystify the hype, while also opening up opportunities for people to be creative with the possibilities that data science can provide.

Ask JGI Student Experience Profiles: Matt Chandler

Ask JGI Student: Matt Chandler
Matt Chandler, 3rd year PhD student in the Department of Mechanical Engineering

JGI Student Experience Profiles: Matt Chandler (Ask-JGI Data Science Support 2022-23)

Over the past year I’ve found my experience working with the Ask-JGI service really rewarding. I was keen to apply as I was looking for an exposure to the wider world of research being done at Bristol, which is something I have definitely achieved along the way. An aspect I was most surprised by was how relevant a lot of my previous experience in data analysis was, in topics very far removed from my own area of research. Whether it be statistics, coding advice, data ethics or visualization, data is data regardless of where it came from. And when things came up which I had not encountered before, having a team there with a range of different backgrounds made it a lot easier to get up to speed.

A part of the job I’ve most enjoyed was helping out with a range of events throughout the year. The highlights include assisting in the delivery of one of UKRN’s Train-the-Trainer workshops, working a stall at AI UK 2023 on sensing air quality, and the launch of the Ask-JGI Roadshow this year in which the team would visit departments across the university to have a more informal opportunity to engage with researchers about their data. A few of these conversations then lead to more in-depth assistance and advice. If you see an upcoming Roadshow in your department and have a question you would like answering (or even if you don’t!), I would definitely recommend going along.

The Ask-JGI team has made this year a really enjoyable experience. As a cohort, we’ve come together to deliver much better advice than any individual would be able to, and it means we’ve been able to rely on one another when our individual research projects took up more time. I would strongly recommend applying to anyone with even a vague interest in data science. It’s an amazing opportunity for development and networking, and allows you to immerse yourself in the wider community at Bristol.

Ask JGI Student Experience Profiles: Lily Tu

Ask JGI Student: Lily Tu
Lily Tu, 3rd year PhD student, in the Department of Mechanical Engineering at the University of Bristol

JGI Student Experience Profiles: Lily Tu (Ask-JGI Data Science Support 2022-23)

I came across machine learning models in a summer project just before my PhD and have since been looking to incorporate it into my PhD project, as data analysis forms a big part of my research field. I applied to join the Ask-JGI team so that I can share my experience and understanding of data science with other researchers across the university, as well as expand my horizon of how data science is applied in other disciplines. I found that the Ask-JGI team is a really good community of PGRs at similar stages as me. Although no one is supposed to know every aspect of data science, it was a great experience to discuss the queries over the weekly meetings and learn from each other.

From the technical aspect, I have worked with queries ranging from machine translation, data visualisation for biology experimental data, to spectral data in earth science. It was a great opportunity to hone my communication skills through talking to people from unfamiliar research backgrounds, getting hold of the key question that they want support from and sending through my thoughts and ideas. I think it’s an essential skill to have and prepares me well in future inter-disciplinary collaborations.

From the non-technical aspect, there’re lots of great initiatives and ad-hoc activities coming through the mailbox. I’ve attended the AI UK conference in London this year and helped at the e-scooter stand. It was good to have the exposure to communities outside my research field. I also took part in the DataFace project and did some filming for data analysis skill videos – that was some media experience I’d otherwise not have gained just from my PhD.

Ask JGI Student Experience Profiles: Marina Vabistsevits

ASK-JGI Student: Marina Vabistsevits
Marina Vabistsevits, Final year PhD student in Population Health Sciences at Bristol Medical School

JGI Student Experience Profiles: Marina Vabistsevits (Ask-JGI Data Science Support 2022-23)

What made you decide to apply to join the Ask-JGI team?

I applied to join the Ask-JGI team because I wanted to be involved in a wider data science community at the University of Bristol. I wanted to learn about data-intensive research in other disciplines and possibly gain data science skills outside my PhD project.

What did you find most rewarding about your Ask-JGI experience?

Being a part of the Ask-JGI team meant helping other researchers at the University with their data science queries. The queries we received involved anything from statistics, programming, data visualisation, machine learning, NLP, but also data ethics, data storage, software development, and even public engagement. I enjoyed being exposed to many aspects of data science, which would otherwise be impossible during one PhD programme. The most rewarding part of this experience was researching how to best help the person with their query to find the solution that would work for them, thereby helping them to advance their research at the University.

What sort of work did you do as a part of your Ask-JGI experience?

My favourite queries involved helping people with R programming, often including statistics, data visualisation, and app development. I was also involved in a long-term project with the Professional Services team at the University with another Ask-JGI student. We helped the team with analysing and producing insights from an extensive survey. This allowed the team to make data-informed decisions about the next steps, which may have a University-wide impact.

Another exciting part of my Ask-JGI experience was going to the AI UK 2023 conference in London to present and promote the Data Hazards framework developed by former colleagues at the JGI.

Would you recommend this experience to other students?

I would absolutely recommend joining the Ask-JGI Data Science support team. It is an excellent opportunity to learn about data science outside your immediate field, gain practical knowledge in new areas, and experience a data science consulting role! You also get to meet really interesting people and learn about the broader application of the skills you’re gaining in your current PhD programme.

JGI Seed Corn Funding Project Blog 2022-2023: Dr Wenzhi Zhou and Prof. Xibo Yuan

Partial Discharge Analysis in Wide Bandgap-Based Motor Drive Systems – Dr Wenzhi Zhou and Prof. Xibo Yuan

The power electronics industry is currently experiencing significant changes due to the emergence of ultra-fast transistors made from wide bandgap (WBG) materials, which are replacing silicon transistors. These new transistors have a switching speed that is 10 times faster, resulting in a 70% reduction in energy loss in converters and enabling motor drive systems to be reduced to less than half their previous size. However, the short voltage rise times (<20ns) and high frequencies (up to MHz) of WBG converters pose new challenges for the insulation of motor drive systems, leading to a drastic decrease in their lifespan.

PD is a well-established diagnostic indicator for assessing the deterioration of electric motor insulation in power converter-fed motor drive systems. It allows for the estimation of service requirements or the prediction of imminent failures. However, the adoption of WBG converters introduces new complexities that can impact the accuracy of PD measurements. Factors such as short voltage rise times (less than 20 ns, comparable to the time it takes light to travel 6 meters) and high frequencies (reaching up to MHz) associated with WBG converters pose significant challenges for insulation within motor drive systems, ultimately leading to a reduction in their lifespan.

To address these challenges and improve PD detection accuracy, we have undertaken research supported by funding from the Jean Golding Institute (JGI). As part of this endeavour, we have developed a specialized PD detection setup, as illustrated in Figure 1. Figure 1a depicts the schematic representation, while Figure 1b showcases the experimental setup we have established. Through this setup, we have conducted preliminary experiments to examine PD behaviour under different excitations, including two-level and three-level pulse width modulation (PWM) waveforms. An example of the PD signal observed under two-level PWM excitations is shown in Figure 2.

Experimental PD setup: (a) schematic


Experimental PD setup: (b) hardware


Fig. 1 Experimental PD setup: (a) schematic and (b) hardware.  


Fig. 2. The PD signal under the two-level PWM excitation.
Fig. 2. The PD signal under the two-level PWM excitation.

The JGI funding has also facilitated collaboration with esteemed experts in the field, including Dr. Jin Zheng from the Engineering Mathematics Department and Dr. Wenbo Wang from Dynex Semiconductor. Their expertise and insights will play a crucial role in advancing our research efforts. Furthermore, we plan to leverage state-of-the-art artificial intelligence techniques to analyse the data we have collected. These techniques will enable us to extract meaningful insights and further enhance our understanding of PD in WBG-based motor drive systems.

Making sea ice thickness maps in the Canadian Arctic operationally available

Making sea ice thickness maps in the Canadian Arctic operationally available – Isolde Glissenaar

Image of western and eastern artic mapSea ice thickness is a key variable when characterising an ice cover and its impact on the local environment and provides important insight into how an ice cover is changing in response to climate change. Unfortunately, observations of ice thickness are sparse, especially in the channels in the Canadian Arctic Archipelago. Sea ice thickness is also an important factor in assessing the safety of shipping in Arctic regions. The Canadian Arctic Archipelago is bisected by the Northwest Passage and is home to many northern communities that rely on marine traffic for resupply. Understanding the changes in ice thickness within the Canadian Arctic Archipelago and monitoring it in the future is therefore of vital importance.

The JGI funded my seed corn project to make sea ice thickness maps in the Canadian Arctic operationally available on a website. The ice thickness maps are created with a machine learning model that uses ice charts (maps of sea ice) from the Canadian Ice Service to estimate sea ice thickness. The ice charts have information about the sea ice concentration, the age of the ice, and the size of the separate ice floes. These characteristics all relate to sea ice thickness and can thus be used to create a proxy for sea ice thickness.

After comparing multiple machine learning methods, a Random Forest Regression model was found to give the best results. For my PhD, I applied this method on the archive of ice charts to determine sea ice thickness for the historic period 1996-2020. However, ice charts continue to be released every week, and this gave the possibility to run the model straight after a new ice chart is released and publish the resulting sea ice thickness map.

In this project I have automized the workflow so that a new sea ice thickness map is created as soon as an ice chart is released. This operational sea ice thickness map is subsequently made available online within 12 hours after the release of the ice chart. A beta-version of the website with both the operational ice thickness maps and the archive of sea ice thickness is now available on https://canadian-sit.streamlit.app/ for shipping navigators, local communities, and climate researchers to use.

Currently, the sea ice thickness maps can only be created for the winter months November-April. Future work would include extending the methods to the summer months when there is more shipping activity. The work can also be extended to the seas around Greenland, as the Danish Meteorological Institute creates similar ice charts for these regions.

For this project I collaborated with the Canadian Ice Service who create the ice charts and are responsible for communicating ice conditions with stakeholders in the region. If the method proves effective and the website reliable, the Ice Service could consider including the sea ice thickness maps in their advice to stakeholders.