A dense nerve plexus in the clear outer window of the eye, the cornea, can be imaged in vivo to enable non-invasive monitoring of peripheral nerve degeneration in diabetes. However, a limited field of view of corneal nerves, operator-dependent image quality, and subjective image sampling methods have led to difficulty in establishing robust diagnostic measures relating to the progression of diabetes and its complications. Here, we use machine-based algorithms to provide wide-area mosaics of the cornea’s subbasal nerve plexus (SBP) also accounting for depth (axial) fluctuation of the plexus. Degradation of the SBP with age has been mitigated as a confounding factor by providing a dataset comprising healthy and type 2 diabetes subjects of the same age. To maximize reuse, the dataset includes bilateral eye data, associated clinical parameters, and machine-generated SBP nerve density values obtained through automatic segmentation and nerve tracing algorithms. The dataset can be used to examine nerve degradation patterns to develop tools to non-invasively monitor diabetes progression while avoiding narrow-field imaging and image selection biases.