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Metadata refers to the contextual information about a resource (e.g. a dataset), often described as “data about data”. Metadata can come in many different types and forms. The
Descriptive metadata: this is the type of metadata you might be most familiar with as it is
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often collected in repositories such as Zenodo (see the example of how zenodo describes the resources on its repository). This generic metadata includes details on what the resource is about (e.g., data from patient health records), who created it (e.g., a research team at Radboudumc) and when it was collected. Typically, it also discloses information about the possible uses of the resource (e.g., applicable licensing) and access restrictions (e.g., available for public use/restricted access).
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Provenance
Administrative metadata: This refers to how the resource came to be (provenance of the data, FAIR principle R1.2), what protocols were followed, and what tools were used. The purpose of this metadata is to ensure that you, your colleagues or others can reproduce the initial research.
Structural metadata: Depending on the type of resource, this refers to a detailed description of your resource that goes beyond the generic information explained above. For instance, in the context of a dataset containing data collected from a questionnaire, content metadata could include the questions asked and the allowed range of values. Codebooks: A detailed document that provides information about the structure, content, and organization of a dataset. A codebook usually describes information such as variable names, and measurement methods and unitsThis type of metadata are also often described in a code book or data dictionary.
In this step, the focus will be on assessing the availability of your metadata. This involves identifying and collecting all types of metadata gathered for your resource, checking their quality, and ensuring they are as accurate and complete as possible. This step is a good starting point and a common first step for multiple objectives (see also the Metroline Step: Define FAIRification objectives), whether you aim to:
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Promotes higher research impact: Good metadata records reflect well on the researcher’s outputs. Potential data reusers might be put off by documentation issues and may not be inclined to use the data.
Beneficial for the organisation: well curated metadata increases the reuse of datasets. It increases interoperability between systems: Complete and error-free metadata makes it easier to migrate between systems (when newer (versions) of software are available)
Good image: Good metadata records reflects well as reusers of the data might be put off by documentation issues and might not use the data as much (Ig also for researchers?)
Improves the quality of your data: Good metadata should describe the data accurately and unambiguously, which in turn improves the overall quality of the data and enhances transparency and reproducibility. This enables others to verify results and build upon them.
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