Find a Data Champion

I work in cognitive neuroscience and neuroimaging, with a strong focus on research data management, reproducible analysis, and open science practices. Before training in cognitive neuroscience, I was educated in computer science and trained as a systems analyst, which continues to shape how I approach research data, workflows, and computational methods. In my current work, I focus on brain imaging data, including data organisation in BIDS (Brain Imaging Data Structure), preprocessing and analysis pipelines, and the development of practical training materials to support researchers in using modern computational tools and adopting good data practices.

Marina has a PhD from the Chemical Engineering and Biotechnology Department at the University of Cambridge. She has previous industrial experience in R&D by working with multinational companies and start-ups. Currently, Marina is Commercialisation Associate in the Physical Sciences team at Cambridge Enterprise. She identifies and evaluates new technologies arising from University research, supporting academics with the protection of their intellectual property and commercialisation of their research outputs.

Prior to joining Cambridge Enterprise, Marina worked as a Research Analyst at the University of Cambridge. Amongst her main responsibilities, Marina conducted research and analysis of international projects and partnerships, providing analytical trends and insightful data that allowed the University to forge strategic partnerships with external institutions.

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.