  Recent research in computational imaging largely focuses on developing
machine learning (ML) techniques for image reconstruction, which requires
large-scale training datasets consisting of measurement data and ground-truth
images. However, suitable experimental datasets for X-ray Computed Tomography
(CT) are scarce, and methods are often developed and evaluated only on
simulated data. We fill this gap by providing the community with a versatile,
open 2D fan-beam CT dataset suitable for developing ML techniques for a range
of image reconstruction tasks. To acquire it, we designed a sophisticated,
semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray
CT setup. A diverse mix of samples with high natural variability in shape and
density was scanned slice-by-slice (5000 slices in total) with high angular and
spatial resolution and three different beam characteristics: A high-fidelity, a
low-dose and a beam-hardening-inflicted mode. In addition, 750
out-of-distribution slices were scanned with sample and beam variations to
accommodate robustness and segmentation tasks. We provide raw projection data,
reference reconstructions and segmentations based on an open-source data
processing pipeline.
