Tag: data science

JGI Seed Corn Funding Project Blog 2023/24: Naomi Scott

In the second century A.D., Julius Pollux, Professor of Rhetoric at the Academy in Athens, wrote the Onomasticon (‘Book of Words’), and dedicated it to the Emperor Commodus. The work sits somewhere between an encyclopaedia and a lexicon. Chapters are organised by topic, and Pollux lists appropriate words on diverse themes such as ‘The Gods’, ‘Bakery Equipment’, ‘Diseases of Dogs’, and ‘Objects Found On Top Of Tables’. Throughout his work, Pollux quotes canonical authors such as Homer, Aeschylus, and Sappho in support of what he considers correct and elegant linguistic usage. This means that in addition to providing a wealth of information on everyday life in the ancient world, the Onomasticon is also one of our best sources of quotations from otherwise lost works of ancient Greek literature.   

Despite Pollux’s obvious importance, his work has not been translated into any modern language. The vast size of the Onomasticon (10 books in total, each comprised of around 250 chapters) means that it is unwieldy even for researchers able to study the original ancient Greek text. With seed-corn funding from the Jean Golding Institute, my project ‘Crowd Sourcing Julius Pollux’s Onomasticon’ has set to work on filling this gap. Eventually, my aim is to use crowd-sourcing to produce not only a translation of the Onomasticon, thereby making it accessible to researchers in a wide variety of disciplines, but an edition of the work which is fully data-tagged, so that researchers can better navigate the text, and produce key data about it: Which ancient authors and genres are most frequently cited as sources and in what contexts; what topics are granted the most or least coverage within the text; and how are different lexical categories distributed within the encyclopaedia? Without the answers to questions such as these, any individual chapter or citation within the Onomasticon cannot be placed in the wider context of the work as a whole.  

Creating a New Digital Edition 

While a digitised version of the ancient Greek text of the Onomasticon exists, it is based on the work of Erich Bethe, whose early twentieth-century edition of Pollux removed all the chapter titles which have been used to organise the text since it was first published as a printed book in 1502. Bethe did this because he did not consider the chapter titles to be Pollux’s own. Both for the purpose of splitting the text up into manageable short chunks for translation, and for the purpose of data-tagging, I decided it was essential to reinstate the titles. Additionally, my own examination of manuscripts of the Onomasticon dating as far back as the 10^th century has revealed that the chapter titles are in fact much older than first thought, and that the text as we currently have it (abridged from Pollux’s even longer original!) may even have been conceived with the chapter titles. 

The first step in producing a digital edition suitable for crowd-sourcing and data-tagging is therefore to reinsert the titles into the text. This would be an enormous undertaking if done manually. Working with a brilliant team from Bristol’s Research IT department, led by Serena Cooper, Keiran Pitts, and Mike Jones, we have set about automating this process. Ancient Greek OCR (Optical Character Recognition) software designed by Professor Bruce Robertson at the University of Mount Allison in Canada, two editions of the text were scanned — one Bethe’s chapterless version, and the other by Karl Wilhelm Dindorf, whose 1824 edition of the text includes the titles.  The next step is to use digital mapping software to combine the two texts, inserting the titles from Dindorf into the otherwise superior version of the text produced by Bethe.  

Next Steps 

Once the issue of the chapter titles has been resolved, the next step will be to create a prototype of around 20 chapters, which can then be made available to the scholarly community to begin translating and data-tagging the text. A prototype would allow us to get feedback from researchers around the world working with Pollux, and to better understand what kinds of data would be most useful to those seeking to understand the text. This feedback can then be integrated into an eventual complete edition of the text which can then be translated and data-tagged as a whole.  

Eventually, this project will not only make the Onomasticon more accessible to researchers, and help to revolutionise our understanding of this important work. A complete translation and data-tagged edition complete with chapter titles will also allow the Onomasticon to have an impact beyond the academic community. The eventual plan is to train arts professionals engaging with the ancient Greek world to use the digital edition and translation. The Onomasticon’s remarkably detailed picture of ordinary life and ordinary stuff in antiquity makes it a vital resource for anyone trying to recreate the ancient Greek world on stage, on screen, or in novels. The hope is that this project will therefore not only change the way that scholars understand the Onomasticon and its place in the history of the encyclopaedia. It can also offer artists a window onto antiquity, and through its impact on art, shape the public understanding of the ancient world.  

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? 

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: 

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.  

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: 

The most important utility for these researchers was to test/develop code and understand broad structure of the data, as shown below (Figure 5): 

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. 

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.   

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. 
• Explore and evaluate new methods for synthesising complex EHR data (for example statistical and machine learning methods from reviews such as: https://www.sciencedirect.com/science/article/pii/S0925231222004349?casa_token=PctLs_5KiZYAAAAA:VvibS1nKYZ6uZHuYKwkprs2Aah4C33lY-riaS0bwX801IyNP9pZ7Pw_rR__9quz0hp0HTe_0vQ). 
• Focus on synthesizing the key aspects requested by the researchers and maintaining the nuances and errors of the original EHR data. 
• Develop an outline syllabus for a short course on NHS EHRs with synthetic data at the centre.