  Unlike previous works, this open data collection consists of X-ray cone-beam
(CB) computed tomography (CT) datasets specifically designed for machine
learning applications and high cone-angle artefact reduction. Forty-two walnuts
were scanned with a laboratory X-ray set-up to provide not only data from a
single object but from a class of objects with natural variability. For each
walnut, CB projections on three different source orbits were acquired to
provide CB data with different cone angles as well as being able to compute
artefact-free, high-quality ground truth images from the combined data that can
be used for supervised learning. We provide the complete image reconstruction
pipeline: raw projection data, a description of the scanning geometry,
pre-processing and reconstruction scripts using open software, and the
reconstructed volumes. Due to this, the dataset can not only be used for high
cone-angle artefact reduction but also for algorithm development and evaluation
for other tasks, such as image reconstruction from limited or sparse-angle
(low-dose) scanning, super resolution, or segmentation.
