Find a Data Champion

I received my PhD in cognitive neuroscience from Basque Center on Cognition, Brain and Language (BCBL). I am currently a Research Associate at the Department of Psychology, University of Cambridge. My interests include data processing and analysis methods for neuroimaging techniques employed in developmental research (fNIRS & EEG). I am interested in the application of these methods for investigating functional connectivity and cognitive processes such as social cognition and language acquisition in infant populations.

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.

Laurisa is a PhD Student in Latin American studies focusing on Colombian diasporic narratives and the spatial humanities. She also works as a Digital Preservation trainer at the University of Southampton. She is involved in digital preservation and open research activities in the UK and beyond, and regularly teaches workshops that help information professionals enhance their computational skills, with an emphasis on digital cultural heritage preservation.

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.