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Even though this step has no clearly defined deliverable, several of the steps that follow rely on being familiar with your data. For example, in order to create or reuse your semantic (meta)data model, it is important to understand the elements and structure of your existing data, or data to be collected. Furthermore, a good understanding of your data is closely connected to the FAIRification goals, since these can depend on the data elements.
Expertise requirements for this step
Experts that may need to be involved, as described in Metroline Step: Build the Team, include:
Data specialist: can help with understanding of data structure,
Domain expert: can help with understanding of data elements.
How to
While performing this step, keep your FAIRification goals in mind, since e.g., selecting a relevant subset of the data and defining driving user questions(s) depend on a thorough understanding of the data.
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Define or align common data elements (CDEs). For a new data collection: define CDEs whose semantics are clear and unambiguous; for an existing data set, existing data elements can be aligned to CDEs.
References & Further reading
[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
[GOFAIR_Process] https://www.go-fair.org/fair-principles/fairification-process/
[CDE] https://cde.nlm.nih.gov/home
Authors / Contributors
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Expertise requirements for this step
Experts that may need to be involved, as described in Metroline Step: Build the Team, include:
Data specialist: can help with understanding of data structure,
Domain expert: can help with understanding of data elements.
Practical examples from the community
Examples of how this step is applied in a project (link to demonstrator projects).
Training
Relevant training will be added in the future.