authors update

foundation-frontiers/posts/2026/08/05/small-data-high-quality.qmd

@@ -5,7 +5,7 @@ description: With compute resources in the UK at a premium — and with energy a
 categories:
   - Viewpoints
   - AI
-author: A. Rosemary Tate
+author: A. Rosemary Tate, Eugenie Hunsicker and Francis Osei
 date: 2026-08-05
 date-format: long
 bibliography: ref3.bib
@@ -59,10 +59,11 @@ With compute resources in the UK at a premium—and with energy and water costs
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 About the author:
 : **A. Rosemary Tate** is a Chartered Biostatistician and Computer Scientist with over 30 years of experience in medical research and statistical consulting. She has a BSC in mathematics and a DPhil in Computer Science and AI, and an MSc in Medical Statistics. She has been scientific manager of a large EU-funded project and held lectureships at the Institutes of Child Health and Psychiatry. An independent  statistical consultant since 2016, she now spends most of her time as a "Data Quality Agent Provocateur". 
-
+: **Eugenie Hunsicker** is the Lead Data Scientist at The Access Group, where she develops AI, ML and statistical solutions to support a range of business functions and where she supports early career data professionals. She has held roles in sigma, the LMS and the RSS, including current membership in the AI Task Force, and is a member of the IMA. She is a Visiting Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University, and currently sits on the Strategic Advisory Board for the School of Science and Technology at City St. George’s University of London. She was awarded the Suffrage Science Award in 2018, was a member of the 2021 class of Fellows of the Association for Women in Mathematics and was awarded the Senior Anne Bennet Prize in 2023 by the LMS.
+: **Francis Osei** is the Lead Clinical AI Scientist and Researcher at Bayezian Limited, where he designs and builds intelligent systems to support clinical trial automation, regulatory compliance, and the safe, transparent use of AI in healthcare. His work brings together data science, statistical modelling, and real-world clinical insight to help organisations adopt AI they can understand, trust, and act on.
 
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-**Copyright and licence** : © 2026 A. Rosemary Tate 
+**Copyright and licence** : © 2026 A. Rosemary Tate, Eugenie Hunsicker and Francis Osei. 
 <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer" style="display:inline-block;">
   <img style="height:22px!important;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1">
   <img style="height:22px!important;margin-left:3px;vertical-align:text-bottom;" src="https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1">
@@ -73,7 +74,7 @@ This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0)
 
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 **How to cite** :  
-Tate, A. Rosemary 2026. “**Small Data, High Quality: a winning combination for the UK**.” *Real World Data Science*, 2026. [URL](https://realworlddatascience.net/foundations-frontiers/posts/2026/05/small-data-high-quality.html)
+Tate, A. Rosemary, Hunsicker, Eugenie and Osei Francis, 2026. “**Small Data, High Quality: a winning combination for the UK**.” *Real World Data Science*, 2026. [URL](https://realworlddatascience.net/foundations-frontiers/posts/2026/05/small-data-high-quality.html)
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