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Short description
As already mentioned across many of these chapters, metadata refers to contextual information about a resource, such as dataset. This metadata may come in many formats, the most familiar type of metadata is generic metadata may include details about what the data is about (e.g., patient health records, imaging data), who created it (e.g., a research team at Radboudumc) and when (e.g., collected in 2023) and also disclose information about how can the data be used (e.g., available for public use, restricted access). But there are other types of information that can be useful to include in the metadata:
Content metadata: Refers to what elements are included in the dataset and the possible values (e.g., Imagine questionnaire data is collected it may be relevant to specify in the metadata which questions are being asked and the possible answer choices).
Provenance metadata: Refers to how this metadata came to be. What protocols were followed, for the same example above this might be a specific questionnaire already published (e.g., COVID data collection form).
Metadata is data about data. It comes in many types, such as descriptive metadata, provenance metadata, etc [cb_metadata]. Metadata helps people to locate the data and allows it to be reused and cited [GoFAIR]. Furthermore, metadata can be machine-actionable, allowing for automation of data handling and validation [RDMKit_MachineActionable]. Findability, accessibility and reusability of data can be improved by providing metadata with details about license, copyright, etc., as well as description of use conditions and access of data [Generic].
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