Find a Data Champion

Alexandra Gushurst-Moore is the Research & Impact Coordinator of the Fitzwilliam Museum and Coordinator of Cambridge Visual Culture. Her doctoral thesis, entitled “The Making of Modern Fantasy in the Visual Arts of England, ca. 1850–1920”, was supervised by Elizabeth Prettejohn at the University of York. She holds an MA(Hons) from the University of Edinburgh and an MSt from the University of Oxford. Beyond her research, she has professional experience of and interest in British educational policy, the strategy and operation of HEIs and the facilitation of inclusive and supportive research cultures.

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.

I’ve been working in the Department of Psychiatry for almost a decade now. I specialise in improving process and workflow and in research data management. I also work in branding, digital recruitment, website design and administration, social media management, and data architecture. I am particularly good at thinking of new and creative solutions to emerging problems. I am currently working on ways to govern and organise the many research projects within my department and in the School of Clinical Medicine, and on creating ways to share research and imaging data (MRI, PET etc) openly.  

I am a neuroscientist studying human speech perception with the aim of advancing assistive speech (neuro)technologies and AI speech recognition systems. My background is in cognitive neuroscience and medicine.

I am an expert in human neurophysiology methods (electro- and magnetoencephalography, EEG/MEG) combined with machine learning and artificial neural network modelling (deep learning).

Since the beginning of my PhD, I’ve been passionate about reproducible and open science practices such as data and code sharing, and good coding practices including version control. In my department I am leading the adoption of the standardised Brain Imaging Data Structure (BIDS) for storing and sharing human neurophysiology (MEG/EEG) data.