Now you can start the validation and aggregation of validation results.
Therefore run the Jupyter Notebook [fhir-validation.ipynb](home/fhir-validation.ipynb).
#### Navigate validation results
You can navigate the validation results by "Table of Content" of Jupyter Lab. Therefore switch the left navigation bar from "File browser" to "Table of Contents".
### User documentation
The further user documentation is embedded in the Jupyter Notebook:
The different outputs are described in markdown cells and used parameters are described in the code cells.
### Select resources to be validated by FHIR Search parameters
You can select/filter the resources to be validated by [FHIR search](https://www.hl7.org/fhir/search.html) parameters.
For filter options you can set `search_parameters`, see [FHIR search common parameters for all resource types](https://www.hl7.org/fhir/search.html#standard), as well as additional FHIR search parameters for certain resource types like [Patient](https://www.hl7.org/fhir/patient.html#search), [Condition](https://www.hl7.org/fhir/condition.html#search), [Observation](https://www.hl7.org/fhir/observation.html#search), ...
Download the FHIR NPM Packages of the [German MII Core Dataset modules](https://www.medizininformatik-initiative.de/de/uebersicht-ueber-versionen-der-kerndatensatz-module) (Kerndatensatz der Medizininformatik Initiative) to the directory `packages`.
If you want to use other FHIR packages, download the FHIR NPM packages to the packages directory and set them up by the environment variables of the HAPI validation service.
The environment variable names is derived from config section `implementationguides` in HAPIs [application.yaml](https://github.com/hapifhir/hapi-fhir-jpaserver-starter/blob/master/src/main/resources/application.yaml)
- Loading FHIR resources to be validated by [FHIR search](https://www.hl7.org/fhir/search.html) (for documentation see section "Select resources to be validated by FHIR Search parameters" below)
If you don't want to use Jupyter Lab as a user interface (e.g. if you want to generate markdown for CI/CD reports), you can use the Python library [fhirvalidation.py](home/fhirvalidation.py) returning a [pandas](https://pandas.pydata.org/docs/user_guide/index.html) dataframe independent from Jupyter Lab.