Short
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description
The next step in the process is to make your data and metadata linkable, i.e. transform them to a machine readable knowledge graph representation [Generic]. Currently, this is done using Semantic Web and Linked Data technologies [GOFAIR_process]. An example of a linkable machine-readable global framework is the Resource Description Framework (RDF). It provides a common and straightforward underlying model and creates a powerful global virtual knowledge graph [Generic]. To transform the metadata and data into this linkable representation requires the semantic models defined in step X and step Y respectively. See the How to section for practical information.
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It is essential to properly define your access conditions – see Step X.
[Blazegraph] https://blazegraph.com/
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[FDP] https://direct.mit.edu/dint/article/5/1/184/113181/The-FAIR-Data-Point-Interfaces-and-Tooling
[FDP_spec] https://fairdatapoint.readthedocs.io/_/downloads/en/latest/pdf/
[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[GOFAIR_process] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/
[GraphDB] http://graphdb.ontotext.com/
Why is this step important
By completing this step, you will have FAIRified your (meta)data and exposed it to the world.
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
[Generic]
In order to transform the data into a machine-readable form (Step 5a) the semantic data model defined (or chosen) in Step 4a is required. Specialized tools are available for this process such as the FAIRifier, which provides insight into the transformation process and makes the process reproducible by tracking intermediate steps [6]. Other similar tools are Karma [16], Rightfield [17], and OntoMaton [18].
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After transforming the eCRF data into a machine-readable RDF representation (step 11), it is stored in a triple store. This is done via the data transformation application upon data entry (collected or updated) in the EDC system (step 10). The URL providing access to the machine-readable data in the triple store is made available in the FAIR Data Point as an access URL in the Distribution layer (Figure S2).
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/
[De Novo] https://ojrd.biomedcentral.com/articles/10.1186/s13023-021-02004-y
[FDP] https://direct.mit.edu/dint/article/5/1/184/113181/The-FAIR-Data-Point-Interfaces-and-Tooling
[FDP_spec] https://fairdatapoint.readthedocs.io/_/downloads/en/latest/pdf/
[Generic] https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[GOFAIR_process] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/
[GraphDB] http://graphdb.ontotext.com/
Authors / Contributors
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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.”