Run of digital pathology tissue/tumor prediction workflow
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of the tissue/tumor prediction workflow for digital pathology from <a href="https://github.com/crs4/deephealth-pipelines/tree/c54840df08742e3aa454394e0e74d15fbd640f07">crs4/deephealth-pipelines</a>. It follows the <a href="https://w3id.org/ro/wfrun/provenance/0.1">Provenance Run Crate</a> profile. The workflow has been run with <a href="https://github.com/common-workflow-language/cwltool/tree/3.1.20230213100550">cwltool</a>, using the --provenance option to generate a <a href="https://doi.org/10.1093/gigascience/giz095">CWLProv</a> RO bundle, and then converted to an RO-Crate using <a href="https://github.com/ResearchObject/runcrate/tree/755fb7f0a8ba6fc238a2cb7a3218175644eb78b5">runcrate</a>. The input dataset is <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/Mirax2-Fluorescence-2.zip">Mirax2-Fluorescence-2</a> by Yves Sucaet, from the <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/">MIRAX test data</a>.<br> </p>
ShareScore
36/100
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
- 0