PSDI is so much more than a Digital Infrastructure. It’s also about building and connecting with our community. PSDI runs webinars, workshops, community townhalls and training events, we invite you to attend have a look at our upcoming events, look at our past events or check out our event recordings.
This Webinar, presented by Dr. Samantha Pearman-Kanza, explores the sociotechnical challenges of implementing Electronic Lab Notebooks (ELNs) to achieve FAIR (Findable, Accessible, Interoperable, Reusable) data in research labs. Through PSDI, she has evaluated diverse ELN solutions, uncovering key barriers and strategies for success.
Join us in Birmingham for the official PSDI Showcase — a one-day, in-person event designed to introduce researchers, data professionals, and institutions working at the forefront of physical sciences, to our Physical Sciences Data Infrastructure.
This Webinar, presented by Dr Matthew Partridge, delves into how quality checks were applied to physical chemistry data, uncovering surprising flaws in reputable datasets and showing why such review processes are essential to ensuring scientific accuracy.
This is the 4th Workshop on Ontologies4Chem, aimed at recommending a set of ontologies for FAIR data management in the chemistry community and addressing metadata annotation challenges. For details, please refer to the NFDI4Chem event page: https://www.nfdi4chem.de/event/4-workshop-ontologies4chem/.
This webinar, presented by Dr Sathya Sai Seetharaman, explores the development of a dedicated materials database for the UK solid-state NMR community. It highlights how evolving data-sharing practices are shaping the way researchers manage and access information, demonstrating how the platform upholds the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) to enhance collaboration and research efficiency.
This webinar, presented by Dr Aileen Day, introduces the first version of the PSDI launched in spring 2025, explaining how to integrate physical sciences resources like data, services, tools, and guidance into its resource catalogue and cross-data search using PSDI’s metadata framework, making it ideal for new partners and researchers eyeing future contributions.
This webinar, presented by Elliott Kasoar and Dr Alin-Marin Elena, explores recent advances in machine learnt interatomic potentials (MLIPs) that revolutionize atomistic simulations with ab initio accuracy and expanded scales, while introducing software frameworks such as janus-core, aiida-mlip, and ML-PEG for data generation, benchmarking, training, and workflow integration within the PSDI ecosystem.
This half-day, in-person workshop at IDCC26 will explore how data management plans (DMPs) can evolve from static, compliance-driven documents into smart, active, and integrated tools that better support research, reuse, and reproducibility. Through short talks and group discussions, participants will share experiences and collaboratively imagine the future of DMPs in practice.
This webinar, presented by Samuel Munday, shows how computer vision and domain-specific AI unlock chemical “dark data,” transforming static literature, patents, and notebooks into interoperable, AI-ready research assets.
Save the date for our next community Electronic Research Notebooks event! This half day webinar will share success stories of real-world ERN implementations, demonstrating how different institutions have successfully implemented different tools to enhance their research practices. This event is intended for anyone interested in implementing ERNs or in understanding the value that they can bring.
Save the date for our one-day workshop at the University of Bath on paramagnetic NMR! Expert talks will cover experimental and computational pNMR, followed by a hands-on session with support on your own data. Ideal for researchers working with paramagnetic metal complexes.
This machine learning for materials training course is being run at Daresbury Laboratory by the Physical Sciences Data Infrastructure (PSDI) initiative in collaboration with AIChemy, STFC-SCD, PSDS, CCP5 and CCP9. This training is targeted towards PhD students, in particular those in the Materials and Molecular Simulations field. The aim of this training is to introduce attendees to the latest methods of machine learning applied to atomistic simulation of materials.