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Short description 

This step focuses on the processes required to transform existing metadata, already mapped to a metadata schema (see previous step). It provides detailed guidance on how to expose metadata to meet these standards, ensuring that data can be easily discovered and utilized by others. By exposing metadata in a FAIR manner, the research resource (e.g. Dataset) is made findable and accessible to a wider audience through appropriate channels and platforms such as the Health-RI Catalog.

This step also emphasizes the impact of adhering to the FAIR principles and the standards that compile them. It offers practical advice and tools to help users achieve compliance, thereby enhancing the reusability and interoperability of their data. This step is essential for those looking to improve the visibility and accessibility of their metadata, ensuring that it can be effectively matched and reused within the scientific community and beyond.

Transform data:
FAIR in a box - csv -> rdf part (add link)
open refine - transformation part
Castor EDC - swag template

Store and expose data:
Triple store in a FAIR data point

Transform, store and expose metadata:
FAIR data point reference implementation

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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 [GOFairGOFAIR_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. 

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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] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/ 

[GraphDB] http://graphdb.ontotext.com/   

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.