Dataset used in the manuscript 'Digitally Deconstructing Leaves in 3D Using X-ray microcomputed Tomography and Machine Learning'. Please cite the paper presenting this dataset: Citation: Théroux-Rancourt, G., M. R. Jenkins, C. R. Brodersen, A. McElrone, E. J. Forrestel, and J. M. Earles. 2020. Digitally deconstructing leaves in 3D using X-ray microcomputed tomography and machine learning. Applications in Plant Sciences 8(7): . Description of the dataset A 'Cabernet Sauvignon' grapevine (Vitis vinifera L.) leaf from a plant of the BOKU experimental vineyard in Tulln, Austria, was scanned using microCT at the Swiss Light Source. The original reconstructions of the scans are using the gridrec (Gridrec_reconstruction_downsized.zip) and the paganin, or phase-contrast, algortithm (Phase_contrast_reconstruction_downsized.zip). To facilitate automated segmentation, the size of the image in the x and y dimensions have been halved, so that the size of the pixels is 0.325 µm in those dimensions, but 0.1625 µm in the z (slices) dimension. A binary image segmenting the leaf cells and the airspace for each gridrec and phase-contrast stacks are created, and both are combined together (Binary_stack_for_local_thickness.zip), a map of the local thickness is created (Local_thickness_map.zip). This map gives information on the largest diameter of the pixels labeled as cells in the binary stack. Hand-labeled slices or ground truths were drawn on the following slices: 80, 140, 200, 260, 340, 400, 440, 540, 620, 740, 800, 860, 940, 1060, 1140, 1240, 1300, 1400, 1480, 1540, 1600, 1690, 1740, 1840 (Hand_labelled_slices.tif). Using the hand-labeled slices and the different images, a random-forest model was trained, which allowed to automatically segment the remaining slices of the stack (Fullstack_Prediction_Example-6_training_slices-6...). The source code for the segmentation program is available here, and the source code for the testing used in the paper is available here.