The FAIR principles Findable, Accessible, Interoperable, and Reusable provide a framework for improving the scientic 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 FAIRication.
The Sensorium dataset comprises calcium imaging recordings from approximately 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 identiers, segmentlevel descriptions, and data quality ags. The rst part of this thesis addresses these gaps by systematically enriching metadata. A custom algorithm was developed to classify videos based on the labels reported in the associated publication. Duplicate videos within and across recordings were identied and assigned unique identiers; equivalent video segments in parametric stimuli were detected and catalogued; and data quality issues, including 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 scientic value of this FAIRication eort by applying Topological Data Analysis (TDA), specifically zigzag persistence, to characterise the dynamics of neural population activity in response to the dierent stimuli. Classication 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 FAIRication and the scientic benets it allows.