Find a Data Champion

Can Jin completed her PhD in Education at the University of Cambridge and is currently a researcher at Cambridge University Press & Assessment. In this role, she supports good research practice, designs and manages research projects, conducts appropriate data analyses, and disseminates findings through reports, research papers, and presentations.

I'm a research fellow working to improve data management practices broadly across the materials and chemical sciences, with the aim to accelerate discovery and enable new data-driven approaches in the physical sciences. I lead the development of the decentralised open source RDM platform, datalab, used by several labs in Cambridge and beyond to track sample provenance, ingest raw characterisation data from instruments and easily disseminate data within and between labs. datalab has been designed primarily with materials chemistry and battery groups in mind, but has been extended to work for polymer research, direct air capture and autonomous labs. I also work extensively on API standardisation for crystal structure databases, via my involvement in OPTIMADE. I am very happy to discuss how people can engage with this work if they are in this domain, or how the approach could be adopted by other domains.

Before starting my current role, I completed my undergraduate studies in Theoretical Physics at the University of Manchester, after which I joined the University of Cambridge for an MPhil in Scientific Computing and subsequent PhD in Physics where I worked on crystal structure prediction for battery materials. I then joined UCLouvain in Belgium for a postdoc and research fellowship, before making my way back to Cambridge in 2025 as a Leverhulme Trust Early Career Research Fellow.

I am a postdoctoral researcher working on data and wet lab projects at the Wellcome Sanger Institute. I combine hands-on laboratory work with computational analysis, helping to generate, process, and interpret biological data. My work sits at the interface of experimental biology and data science.

Roberto Ragno is a Marie Sklodowska-Curie European Fellow at the McDonald Institute for Archaeological Research. His primary area of expertise is the application of Bayesian statistical modelling to archaeological data, with a focus on long-term human–environment dynamics in the Roman Mediterranean. He is committed to open and reproducible research practices, including the publication of open-access datasets, code repositories, and software workflows.