We used the instance segmentations and the accompanying un-smoothed 8-bit tomograms to extract characteristics of each particle. Query-able tabular data frames were populated with these characteristics on a particle-by-particle basis where each particle was indexed by its label in the instance segmentation. The characteristics included volume, surface area, sphericity, mean grey level, maximum grey level, minimum grey level, centroid co-ordinates, and other characteristics that where indicative of cracking in the particles. For each particle we also included an 8-bit grey level image of the particle in a bounding box and a distance transform image of the particle in a bounding box. These images allowed us to quickly calculate other bespoke particle characteristics by querying the table. These tables were calculated by our grey level analysis algorithm, which was able to analyse ~ 84,000 particles (7000 particles × 12 SoC) in minutes. Such tables were calculated for each tomogram (thus for each SoC). When comparing between tables it was important to make sure that comparisons were made between identical sets of particles. To achieve this an inner join of each table to the pristine SoC table was performed on the particle index column. This dropped any rows that couldn't be tracked through all SoC. Additionally, any particles that shared an edge with particles that were dropped during this inner join operation were also dropped from the tables. This was because there was a tendency for neighbouring particle instances to 'absorb' the voxels previously labelled as another particle at a different SoC if an edge was shared between the particles. Finally, any particles that overlapped with the edge of the image are removed as these were likely to be partial particle volumes. These steps filtered the dataset from ~10,000 particles to ~7,600 particles.