Simulated nanoCT Datasets
<p>Simulated parallel-beam and fan-beam nano-CT datasets with per-angle random phantom shifts and rotations.</p> <p>The dataset generation is described in the paper <a href="https://arxiv.org/abs/2307.10474">Learning-based approaches for reconstructions with inexact operators in nanoCT applications</a>.</p> <p>Code to use the data is available at <a href="https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct">https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct</a> (in particular the module src/learned_reco/data.py).</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
Topics
ScienceDex for faculty
Your academic CV and NIH biosketch
Make your CV in your school's format and your NIH biosketch from it, then download both as Word files.