zenodoopen
Sandstone rock tomographic data, i12 beamline, DLS synchrotron
<p>This data compliments a paper <i>Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography </i>by D. Kazantsev, L. Beveridge, V. Shanmugasundar and O. Magdysyuk to be published in the <strong>Tomography of Materials and Structures</strong> journal in 2023 (to be updated). This data alongside the data from <a href="https://zenodo.org/records/1443568#.ZBREbUjP1qN">here</a> was used to train the network to remove stripe artefacts. </p><p>The code is written in Python and located at: https://github.com/dkazanc/NoStripesNet</p>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 12
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4