Fair datapoint
This guide provides steps to install FDP and configure it with GDI-specific SHACL shapes. For a more comprehensive overview, please refer to the existing FDP documentation on exposing metadata.
- Follow the installation guide in the documentation linked above to set up FDP in your environment.
- Ensure that the FDP instance is accessible and that you have administrative rights to configure metadata schemas.
To add GDI-specific SHACL validation, perform the following steps:
- Access the GDI-specific SHACL shapes from this GDI metadata repository.
- Download each SHACL shape file (e.g.,
Resource.ttland others).
- Login to FDP using an admin account.
- Navigate to Metadata Schemas (located in the dropdown under your username).
- For each shape file:
- Open the editor and paste the contents of
Resource.ttl(or other shapes). - Add a description to document the purpose or release information.
- Ensure the abstract checkbox is selected when uploading
Resource.ttl, as most other classes derive from it. - For other shapes, uncheck the abstract checkbox.
- Press Save and Release to finalize the shape.
- Provide a meaningful description and version number for the release.
- Check the public checkbox to make the shape accessible.
- Press Release to complete the upload.
- Open the editor and paste the contents of
Repeat these steps for each SHACL shape file.
The following metadata fields are currently supported:
| Property Name | Example Data |
|---|---|
| contact_point | VCard(hasEmail=[mailto:data-access-committee@xumc.nl], full_name=[Data Access Committee of the x UMC], hasUID=https://ror.org/05wg1m734) |
| creator | Agent(name=[Academic Medical Center], identifier=https://ror.org/05wg1m734) |
| description | This dataset is part of the GDI MS8 milestone, focused on the distributed analysis of COVID-19 cases (GWAS) and allele frequency lookup for infectious diseases. It contains synthetic data designed to replicate COVID-19-related genetic studies, including risk variants associated with severe disease outcomes. The data is used for federated analysis across multiple nodes to identify genomic associations and variant prevalence. |
| number_of_patients | 100 |
| issued | 2024-07-01T11:11:11 |
| keywords | Covid, Smokers, (free to choose) |
| identifier | GDID-[0-9a-f]{8}-[0-9a-f]{4} |
| modified | 2024-06-04T13:36:10.246Z |
| publisher | Agent(name=[Radboud University Medical Center], identifier=https://ror.org/05wg1m734, mbox=[mailto:test@health-ri.nl]) |
| theme | http://publications.europa.eu/resource/authority/data-theme/HEAL |
| title | COVID-19 GWAS and Allele Frequency Lookup Dataset for GDI MS 8 |
| number_of_participant | 100 |
| phenotypes | Age (min and max) |
| accessRights | DUO:0000006, DUO:0000017, DUO:0000018 (General research use, Infectious Disease research use, Genomic research on complex diseases) |
| Property Name | Example Data |
|---|---|
| title | GWAS and Allele Frequency Lookup Data Distribution for GDI MS8 |
| description | VCF file containing COVID-19 case/control data for GDI MS8 demonstration. |
| access_url | https://example.com/dataset/GDI-MS8-COVID19.vcf |
| media_type | https://www.iana.org/assignments/media-types/application/vcf |
| license | https://creativecommons.org/licenses/by-sa/4.0/ |
To onboard large datasets more efficiently, you can use a Jupyter notebook to automate this process. Clone the Sempyro repository and run the notebook using:
hatch run docs:jupyter lab
As a reference, an MS8 template/example is available in the Usage_example_GDI.ipynb notebook for streamlined metadata upload to FDP.