Enhancing the Sensorium Dataset for FAIR Neuroscience Research

The FAIR principles Findable, Accessible, Interoperable, and Reusable provide a framework for improving the scienti c value of research data. Although adoption is increasing across disciplines, neurophysiology datasets often do not meet these standards, which restricts their reuse beyond the initial study. This thesis uses the Sensorium dataset, released as part of a challenge to develop predictive models of the mouse primary visual cortex, as a case study in dataset FAIRi cation. The Sensorium dataset comprises calcium imaging recordings from ap proximately 8,000 neurons per mouse, collected from ten mice exposed to both natural and parametrically generated video stimuli. When the dataset was accessed for a novel analysis, several essential metadata elements were found to be missing, including stimulus labels, unique video identi ers, segment level descriptions, and data quality ags. The rst part of this thesis ad dresses these gaps by systematically enriching metadata. A custom algo rithm was developed to classify videos based on the labels reported in the associated publication. Duplicate videos within and across recordings were identi ed and assigned unique identi ers; equivalent video segments in para metric stimuli were detected and catalogued; and data quality issues, includ ing missing trials and corrupted videos, were documented. All metadata was organised into a machine-readable, extensible schema using JSON and CSV formats, and released openly with a Python toolkit for data management, ltering, and visualisation, under permissive open-source and CC0 licences. The second part of the thesis demonstrates the scienti c value of this FAIRi cation e ort by applying Topological Data Analysis (TDA), speci cally zigzag persistence, to characterise the dynamics of neural population activity in response to the di erent stimuli. Classi cation analyses show that zigzag persistence vectorisations capture meaningful and generalisable topological features of neural activity. These results were enabled by the structured metadata generated in the initial phase of the thesis. Collectively, this work highlights both the challenges of retroactive FAIR i cation and the scienti c bene ts it allows.