...
| Status | ||||
|---|---|---|---|---|
|
Short description
Once machine-readable (meta)data is exposed, it can be used. Triple stores, such as GraphDB [GraphDB] and Blazegraph [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_sparql]. 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.
...
Nice read: https://data.persee.fr/understanding/what-is-a-triplestore/?lang=en
[Blazegraph] https://blazegraph.com/
[eu] https://data.europa.eu/data/datasets/eu-open-data-portal-sparql-endpoint
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[FAIRopoly] https://www.ejprarediseases.org/fairopoly/
[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[GraphDB] http://graphdb.ontotext.com/
[w3_sparql] https://www.w3.org/TR/sparql11-federated-query/
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
...
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
References & Further reading
[Blazegraph] https://blazegraph.com/
[eu] https://data.europa.eu/data/datasets/eu-open-data-portal-sparql-endpoint
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[FAIRopoly] https://www.ejprarediseases.org/fairopoly/
[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[GraphDB] http://graphdb.ontotext.com/
[w3_sparql] https://www.w3.org/TR/sparql11-federated-query/
Authors / Contributors
Experts who contributed to this step and whom you can contact for further information The How to section should:
be split into easy to follow steps;
Step 1
Step 2
etc.
help the reader to complete the step;
aspire to be readable for everyone, but, depending on the topic, may require specialised knowledge;
be a general, widely applicable approach;
if possible / applicable, add (links to) the solution necessary for onboarding in the Health-RI National Catalogue;
aim to be practical and simple, while keeping in mind: if I would come to this page looking for a solution to this problem, would this How-to actually help me solve this problem;
contain references to solutions such as those provided by FAIR Cookbook, RMDkit, Turing way and FAIR Sharing;
contain custom recipes/best-practices written by/together with experts from the field if necessary.
Expertise requirements for this step
Describes the expertise that may be necessary for this step. Should be based on the expertise described in the Metroline: Build the team step.
Practical examples from the community
Examples of how this step is applied in a project (link to demonstrator projects).
Training
Add links to training resources relevant for this step. Since the training aspect is still under development, currently many steps have “Relevant training will be added in the future if available.”
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.