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Short Description 

Once machine-readable (meta)data is exposed, it can be used. Triple stores, such as GraphDB[] and Blazegraph[], provide a SPARQL endpoint. This endpoint allows the (meta)data stored in the triple store to be queried using the SPARQL query language, provided the user has been granted access[De novo]. Queries can be performed in a variety of ways, for example via a form provided by the endpoint or programmatically using e.g. JavaScript, C#, Java, or Python. Query results can be returned in a variety of formats, such, such as HTML, JSON, XML and CSV. Furthermore, since the introduction of SPARQL 1.1, federated querying is supported [w3]. With federated querying a user can direct a portion of a query to a particular SPARQL endpoint. Results are returned to the federated query processor, which combines the results. 

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Nice read: https://data.persee.fr/understanding/what-is-a-triplestore/?lang=en  

Working Group

Why is this step important 

By completing this step you will know how your FAIR data can be used in practicse.  

Expertise requirements for this step 

This section could describe the expertise required. Perhaps the Build Your Team step could then be an aggregation of all the “Expertise requirements for this step” steps that someone needs to fulfil his/her FAIRification goals.  

How to 

Some libraries / clients for programming languages 

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https://faircookbook.elixir-europe.org/content/recipes/applied-examples/fair-data-matrix/2-rose-metabolites-Python-RDF-querying-analysis.html  

Practical Examples from the Community 

This section should show the step applied in a real project. Links to demonstrator projects. 

References & Further

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reading

[FAIRopoly] FAIRopoly https://www.ejprarediseases.org/fairopoly/ 

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FAIR Data Point: https://fairdatapoint.readthedocs.io/_/downloads/en/latest/pdf/  

Authors / Contributors 

Experts who contributed to this step and whom you can contact for further information