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Short description
Common Data Elements (CDEs) are standardised, precisely defined question paired with specific allowable responses. These CDEs can be used systematically across different sites, studies, or clinical trials to ensure consistent data collection [NIH]. These CDEs can be annotated with an ontological model to define their meaning and facilitate integration of CDEs from different registries [de novoDe Novo]. The ontological model is essential for computers to assess that common data elements are indeed common [de novoDe Novo].
By openly publishing the CDEs and the ontologies to annotate them openly, they can be reused to build case report forms for data collection [de novoDe Novo].
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See the Register record level metadata step?
[NIH] https://www.nlm.nih.gov/oet/ed/cde/tutorial/03-100.html
Why is this step important
By using common data elements, it is ensured that the data being collected uses the exact same definitions and, if annotated, ontological model. This is an important step for the interoperability of the data.
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
[De Novo]
Furthermore, we have made our eCRF interoperable and reusable, as the codebook describing the eCRF templates containing the CDEs and the ontologies to annotate them is openly available in ART-DECOR [34]. Via the openly available iCRF Generator tool [35], the codebook can be directly implemented in other EDC systems such as OpenClinica and REDcap.
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We could say something about setting up new CDEs and reusing existing ones?
Practical Examples from the Community
This section should show the step applied in a real project. Links to demonstrator projects.
Further reading & References
[FAIRopoly] FAIRopoly https://www.ejprarediseases.org/fairopoly/
[Generic] A Generic Workflow for the Data FAIRification Process: https://direct.mit.edu/dint/article/2/1-2/56/9988/A-Generic-Workflow-for-the-Data-FAIRification
[Elixir] https://faircookbook.elixir-europe.org/content/recipes/introduction/fairification-process.html
[Elixir2] A framework for FAIRification processes: https://faircookbook.elixir-europe.org/content/recipes/introduction/metadata-fair.html
[GOFAIR] https://www.go-fair.org/fair-principles/f2-data-described-rich-metadata/
[RDMkit] https://rdmkit.elixir-europe.org/machine_actionability.html
[NIH] https://www.nlm.nih.gov/oet/ed/cde/tutorial/03-100.html
Authors / Contributors
Experts 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;
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.
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
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