The Physical Sciences Data-science Service (PSDS) will create a single searchable hub where physical scientists can store, find, and combine data on everything from molecular structures to material properties. Modern physical scientists generate enormous amounts of data, but most lack the tools or skills to analyse it effectively or connect it with existing datasets. This gap means valuable experimental results remain isolated, and opportunities for discovery—through machine learning or artificial intelligence—are missed. The PSDS aims to make data science a routine, seamless part of the research infrastructure. If successful, the service could accelerate the discovery of new materials, catalysts, or energy storage systems by allowing researchers to instantly compare fresh experimental data against all prior knowledge. It also includes a training programme in statistics, programming, computational methods, and data visualisation, building a workforce equipped to use data science as a standard tool. Over time, this could transform how physical sciences research is conducted, making data-driven discovery the norm rather than the exception.
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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.
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