Speakers
Description
The rapid changes in the way we undertake scientific research driven by digitalisation and artificial intelligence means we need to look again at the basis of scientific methods, requiring the input from the philosophy of science and one end while being pragmatic about the uses of technology at the other. This approach will provide a way to ensure that our research and teaching is relevant, effective and efficient.
More specifically, this means revisiting the topics of quality and provenance frameworks as developed by certain disciplines; exploring the role of metadata and semantics in such frameworks and in the context of use of data by AI systems; exploring how the digital representation of units is best managed in such frameworks.
Consequently, the session will address the following questions:
- How should we best interact with AI systems, how can they contribute
to scientific discovery? - How can we ensure that sensible
provenance is provided with data that is consumed and produced by AI
systems? Why is it so hard to get quality metadata? - How do we ensure that the metadata and semantic framework is useful and used by researchers and not seen as yet another barrier to work?
- The role of metadata standards and ontologies. How do we evolve
the publication and dissemination framework to provide these
details? - What software tools are needed to support researchers
to use digital terminology and units? What support can be built
into programming languages?
This session builds on the agenda set by a successful small conference held at the Royal Society of Chemistry in London in March 2025, and convened by the UK Physical Science Data Infrastructure (PSDI, www.psdi.ac.uk), the International Union for Pure and Applied Chemistry (IUPAC, Green and Gold Book projects) and CODATA (DRUM Task Group).
Session Programme
Talk Titles & Speakers
1. Scientific discoveries in the age of AI (Vanessa A Seifert)
2. A knowledge model for the SI brochure: the SI Reference Point (Dr. Maximilian Gruber)
3. Quantities, Not Quantities, and Making Sure Our Data is Useful (Dr. Cerys Willoughby & Elizabth Newbold)
4. Sharing measurement data: enabling interoperability (Blair Hall)
5. Practical Application of the Definition and Conversion of Units in the Physical Sciences Data Infrastructure (PSDI) (Dr. Aileen Day)
6. Thoughts on AI and Scientific Data (Dr. Samantha Pearman-Kanza & Prof. Stuart Chalk)
Title: Scientific discoveries in the age of AI
Speaker: Vanessa A Seifert
Abstract: Forms of AI based on Machine learning algorithms (MLAs) are rapidly developing and already being used widely in science. Philosophers and scientists are grappling with how to understand AI-driven science, its capabilities and limitations, and its role in society. Despite the AI revolution in science being well underway, it is still unclear to what extent we should expect AI to truly revolutionise science and contribute to its progress. In this talk, I attempt to demystify the role of AI in chemical progress by presenting some standard philosophical views on scientific progress and spelling them out from the perspective of AI. I argue that it is far from uncontroversial what the true contribution of AI can be to science, thus warranting an interdisciplinary approach towards understanding the role of AI in science
Bio: Marie Sklodowska curie fellow, University of Athens/Bristol
Title: A knowledge model for the SI brochure: the SI Reference Point
Speaker: Dr. Maximilian Gruber
Abstract: In 2022, the General Conference on Weights and Measures (CGPM) encouraged the wider metrological community to develop "a globally accepted digital representation of the SI". This presentation provides an overview of the development process, fundamental ideas, outcomes, current state and next steps of this endeavour.
Bio: I am Maximilian Gruber, researcher/postdoc at the national metrology institute of Germany. Coming from an engineering background, computational methods and information modelling lead me through my PhD in computer science. I research on topics of semantic knowledge models and (industrial) sensor networks, with a focus on unit representations and enabling interoperability of measurement data.
Title: Thoughts on AI and Scientific Data
Speakers: Dr. Samantha Pearman-Kanza & Prof. Stuart Chalk (online)
Bio: Stuart Chalk is a Professor of Chemical Informatics at the University of North Florida focusing on scientific data models, knowledge representation and digital metrology. Samantha Pearman-Kanza is a principle enterprise fellow at the University of Southampton, https://www.southampton.ac.uk/people/5xm57p/doctor-samantha-pearman-kanza
Abstract: We are now in an AI society, so what can the research community do to make scientific data available to AI in a way that it can be better understood. This talk will briefly discussion ideas around semantification of scientific data and what that means for the research community.
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