StreamFlow run of digital pathology tissue/tumor prediction workflow

<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a>&nbsp;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>&nbsp;profile. The workflow has been run with <a href="http://streamflow.di.unito.it">StreamFlow</a>, using the following commands to produce an RO-Crate bundle:</p> <pre><code class="language-bash"># Run the pipeline streamflow run \ --name ml-predict-pipeline-streamflow \ streamflow.yml # Generate the RO-Crate bundle streamflow prov \ --add-file src=README.md,dst=/README.md,about="{\"@id\":\"./\"}",encodingFormat=text/markdown \ --add-property \./.license=https://spdx.org/licenses/MIT \ --add-property \./.name="DeepHealth Pipeline" \ --add-property \./.description="Run of digital pathology tissue/tumor prediction workflow" \ --file streamflow.yml \ ml-predict-pipeline-streamflow</code></pre> <p>The input dataset is <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/Mirax2-Fluorescence-2.zip">Mirax2-Fluorescence-2</a>&nbsp;by Yves Sucaet, from the <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/">MIRAX test data</a>.</p>