Project overview
The modern physical scientist cannot perform their research without generating significant quantities of data, having recourse to related/prior data, significant data analysis and integrating results with other data. This requires a range of skills and resources that are not available to the majority of physical scientists. There is therefore an urgent need in the physical sciences for providing access to data and integrating them with data science approaches. This requires building a new skills base that enables and empowers working in a data science way. The Physical Sciences Data-science Service (PSDS) will provide a single place where existing databases, open data sources and data that is still being worked on can be stored and searched in a unified way. This means that it will become trivial to find and combine different types of physical sciences data - from details on structure to measured physical properties of materials. It will also make possible instant comparison of and context for experiment data with that already available. This is just the start however. There is enormous potential for being able to perform data science across all of these data, that is for example, Machine Learning and Artificial Intelligence approaches, which are becoming a new avenue of research in their own right. It is vital that data science becomes a routine tool for all physical scientists. For many this will mean learning new skills. The PSDS will therefore develop a training programme around the four main competencies (statistics, programming/tools, computational methods & data visualisation) required to perform data science. Identified links with networks and postgraduate training will enable PSDS users to gain deeper skills in various aspects of data science. The long-term aim is for the PSDS, and therefore data science, to become a seamless, key part of the research infrastructure for physical scientists.
Staff
Lead researchers
Other researchers
Collaborating research institutes, centres and groups
Research outputs
Samantha Kanza, Cerys Willoughby, Nicola Knight, Colin Leonard Bird, Jeremy G. Frey & Simon J. Coles,
2023, Digital Discovery, 2(3), 602-617
DOI: 10.1039/d2dd00121g
Type: article
Charlie Hadley, Jeremy G. Frey, Samantha Kanza & Nicola Knight,
2022
Type: conference
Sarah Callaghan, Samantha Kanza, Nicola Knight & Jeremy G. Frey,
2022
Type: conference
Samuel Munday, Jeremy G. Frey, Samantha Kanza & Nicola Knight,
2022
Type: conference
Rita Borgo, Jeremy G. Frey, Samantha Kanza & Nicola Knight,
2022
Type: conference
Victoria Greenacre, Andrew L. Hector, Ruomeng Huang, William Levason, Vikesh Sethi & Gillian Reid,
2022, Dalton Transactions, 51(6), 2400-2412
DOI: 10.1039/d1dt03980f
Type: article