X-ray tomography image data of a graphite foam block (KFoam) and tortuosity analysis
<p>X-ray tomography (CT) image data of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1).</p> <p>The dataset includes: raw radiographs; scan & reconstruction parameter settings file; reconstructed 3D volume. To visualise the 3D volume use software such as ImageJ (https://imagej.net/Fiji/Downloads). The volume image data (NMT_15_229_LLME_DivInterlayer.raw) is in binary format and has the following characteristics: 1586 x 1567 x 1588; 8-bit; little-endian byte order.</p> <p>The second .zip file is a 200 x 200 x 200 subset of this dataset. This was used to perform a tortuosity analysis on the foam. This dataset includes three sets of tiff images; tomographic slices; binarised slices; skeletonised slices. It also includes an excel file with the results of the tortuosity analysis performed with ImageJ.</p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4