Define CWL workflows for RDF conversion

Click on the Use this template button to create a new repository used by the d2s Command Line Interface tool.

See d2s.semanticscience.org for detailed documentation to run CWL workflows to transform structured data to a target RDF knowledge graph and deploy services.

Requirements

• Docker: see the d2s Docker installation documentation for quick install on various systems.

• pip install the d2s client and cwlref-runner (run workflows of Docker containers) withon Python 3.6+

  sudo apt install d2s cwlref-runner

Create a project

Follow the prompt instructions to create a project in the provided folder:

d2s init project-folder-name

See the d2s.semanticscience.org for the complete documentation.

Edit this template

You might want to edit or modify this template:

git clone --recursive https://github.com/MaastrichtU-IDS/d2s-project-template.git

Update submodules

You might want to update the d2s-core submodule to get the latest updates for the docker deployments definitions:

./update_submodules.sh

About

We use the Common Workflow Language to describe workflows to transform heterogeneous structured data (CSV, TSV, RDB, XML, JSON) to a target RDF data model (BioLink in those examples).

The user can transform the input data as RDF using various solutions:

• RML mappings
• CWL workflows executing SPARQL queries to transform the generic RDF generated depending on the input data structure (AutoR2RML, xml2rdf) to the target model of his choice. See documentation to run CWL workflows
• BioThings Studio (web UI and API)
• DOCKET multiomics provider (Python notebooks and Nextflow)