The files required to transform the dataset will be generated in
You will be prompted to enter some metadata about the dataset to create.
The dataset mappings, metadata, notebook and download files are created in the
The dataset folder is generated based on this template folder. Example mapping files are provided for DrugBank XML data and Columbia Open Health clinical Data TSV data.
Let us know if those examples are helpful, or if they would need to be more explicit.
You are encouraged to improve the metadata description of your dataset by editing the 2 metadata files generated in
A dozen of metadata are defined using a SPARQL query for the summary of the dataset, and for each distribution.
- SPARQL insert dataset summary metadata (once by dataset).
- SPARQL insert dataset distribution metadata (for each new version).
Change the URIs between
<>and strings between
We recommend using
Stardog RDF Grammarsextension in Visual Studio Code to edit SPARQL queries (
You can define the files to download using:
- a Bash file
- Download with
d2s download $dataset_id
- a Jupyter Notebook
The files will be downloaded in
A template is provided with examples to download, unzip or add column labels provided.
d2sextract data from csv/tsv files based on their column label. If your tabular doesn't have column you can add them at the end of the download.sh file by using the
Multiple solutions are available to integrate data in a standard Knowledge Graph:
- RML mappings (RDF Mapping Language)
- CWL workflows defined to convert structured files to RDF using SPARQL queries
- BioThings Studio to build BioThings APIs (exposed to the Translator using the ReasonerStd API)
- DOCKET to integrate omics data
- Python scripts and notebooks
- Define new CWL workflows to build and share your data transformation pipelines
- See the CWL workflows defined for d2s.