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- We need to identify our research data context in our target scientific domain. By context, we mean all the related machines, samples, lab protocols, and generally everything that is related to the experiment that is not reflected in the data itself.
- Example: John Doe performs an experiment in the lab and generates some data and uploads them to the data repository in CKAN. However, we have no idea what was the process by which this data is generated, As a result, the data is not understandable.
- The next step is to semantically describe the identified contextual data in the previous step. Here basically we model our data and annotate them to be machine-actionable also.
- Why annotated? Humans are not the only data users. As matter a of fact, soon most mostly machines (AI for instance) are supposed to perform actions on data. Without annotation, the machine's precision and comprehension weaken.
- How to annotate? Ontologies and Vocabularies are rich sources to find domain-specific annotation.
- Where to find these annotations? Terminology Services. TIB already has one: https://terminology.tib.eu/ts
- What if I cannot find a proper annotation? Contribution. You can develop your own vocabulary for your specific domain. Benefit? people in your domain will use it.
- RDM has to serve Linked Data. The last step is to Link your contextual metadata and data. This means you need an actual link that connects your SMW graph to the CKAN graph. For example a link from a Sample page in SMW to the related dataset in CKAN.
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