Join us for a seminar on the NPL Data Quality Framework, focusing on trustworthy data, cross-sector insights, and practical tools.
Speakers
Dr Paul Duncan
Joao Gregorio
Ignas Pakamore
Hannah Strassburg
Bartlomiej (Bartek) Cieszynski
Harvey Whelan
Agenda
6.00 pm - Talk
7.00 pm - Questions and answers
8.00 pm - Event closes
Synopsis
High-quality data is essential for reliable decision-making. Yet assessing and communicating data quality remains a persistent challenge across sectors.
In this seminar, we present work from the National Physical Laboratory (NPL) on the development of a structured Data Quality Framework grounded in established standards, including ISO/IEC 25012.
To ensure robustness and generalisability, the framework has been developed through a series of cross-sector case studies.
These studies demonstrate how the framework can be used to move from identification of relevant data quality dimensions through to practical assessment and interpretation.
Examples will be drawn from healthcare and autonomous vehicles, as well as software tools, including Data Quality Plans and Data Management Plans.
We aim to gather feedback from participants on the framework’s usability, applicability across domains, and potential areas for refinement, as well as to explore opportunities for future collaboration.
About the speakers
All speakers work in the Data Science and Artificial Intelligence (DS/AI) department at the UK’s National Physical Laboratory (NPL).
Dr Paul Duncan is a Principal Scientist at NPL, where he leads the Informatics group within the DS/AI department. With a background in data modelling and uncertainty evaluation, his work focuses on developing data quality frameworks and methodologies for understanding uncertainty in complex, interconnected systems. He is involved in cross-disciplinary projects that span data science, semantic technologies, and AI governance. He collaborates with government, academia, and industry to advance confidence in the use of AI. Dr Duncan is the Technical Lead for NPL’s involvement in the AI Standards Hub, where he plays a key role in shaping national and international approaches to Trustworthy AI. His current research explores how metrology can underpin the development of reliable, transparent, and accountable AI systems.
Joao Gregorio is a Senior Scientist. He specialises in developing and delivering data quality frameworks that enable trustworthy, evidence-based decision-making. His research explores how organisations can assess, manage, and improve data quality across complex digital environments, with applications spanning industry, healthcare, and environment. Joao holds a PhD in Chemical and Process Engineering from the University of Strathclyde and draws on a multidisciplinary background in chemistry, engineering, data science, and machine learning to help organisations maximise the value and reliability of their data.
Ignas Pakamore is a Higher Scientist, where he focuses on data quality, metadata standards, and data model development. He completed his PhD in Chemistry at the University of Glasgow before continuing as a Postdoctoral Research Associate in Professor Lee Cronin's research group, working in materials informatics. Ignas subsequently joined ChemAI Ltd as a Cheminformatics and Data Scientist, where he delivered customer training on Bayesian optimisation software, developed custom machine learning models, and provided scientific consultancy.
Hannah Strassburg is a Research Scientist. She has a background in both biochemistry and data science. She is currently working on projects that include devising data quality frameworks, semantic technologies, and standard metadata schemas. Her talk is on the data quality framework that was created for an emissions data set spanning the time period 1750 to 2014.
Bartlomiej (Bartek) Cieszynski is a Data Scientist. He specialises in data quality assessment, machine learning reliability, and AI assurance. His research focuses on understanding how data quality affects machine learning performance and robustness, with applications spanning healthcare AI, assured autonomy, simulation-based validation, and safety-critical systems. He holds a First Class MSci in Physics from the University of Nottingham and draws on expertise in data science, machine learning, informatics, and metrology to help organisations improve the reliability and trustworthiness of AI-enabled technologies.
Harvey Whelan is a Data Scientist. He has an academic background in Physics and Chemistry where he conducted a computational physics study investigating semiconductor alternatives to silicon, graphene and h-BN. Since joining NPL, he has applied his scientific experience to data science challenges spanning data quality, semantic technologies, data modelling, and AI/ML assurance. In this talk, he presents a data quality assessment methodology for maritime autonomy.
Our events are for adults aged 16 years and over.
This meeting is conducted in accordance with the BCS Code of Conduct for Meetings.
BCS is a membership organisation. If you enjoy this event, please consider joining BCS. You’ll be very welcome. You’ll receive access to many exclusive career development tools, an introduction to a thriving professional community and also help us Make IT Good For Society. Join BCS today
Please note, if you have any accessibility needs, please let us know via groups@bcs.uk, and we’ll work with you to make suitable arrangements.
BCS privacy notice: your data will be processed by BCS in accordance with our data privacy notice.
Photography: by attending this event, you may be photographed or filmed. Please speak to a member of staff if you do not wish to be included.
For overseas delegates who wish to attend the event, please note that BCS does not issue invitation letters.
This event is brought to you by: FACS (Formal Aspects of Computing Science) SG
Supported by: Data Management SG and Quality SG
