Tables of particles and particle characteristics were calculated from the unsmoothed grey-level images and the corresponding label segmentations for both case studies by using the grapes toolkit, see figure . A critical step in processing data like this is to clean the dataset and remove edge cases. In case study 2 (the operando and time resolved case) one of the most critical steps was to make sure that particle labels were consistent between the tomograms that represented the ROI at different SoC. This was mostly achieved by the segmentation; however, segmentation errors still had to be removed from the analysis. This was achieved by removing any particle labels from the analysis that did not exist in all the tables representing different SoC (by dataframe merging). Furthermore, it was found that it was necessary to remove any particles that shared an edge or were close neighbours with a particle label that was not consistent across the dataset (i.e. one of the particles removed in the previous step). This was because particle labels surrounding these inconsistent labels often absorbed the pixels otherwise assigned to the inconsistent label. In addition to this, in both case examples, any particle labels from the analysis that shared an edge with the edge of the image ROI were removed. This was important as it removed partial particle volumes from the analysis. The data was then ready to be queried in order to extract insights on NMC secondary particle degradation.