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Short description
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'Metadata is the descriptor, and data is the thing being |
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described '(https://doi.org/10.1162/dint_r_00024 ) |
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 type of metadata you might be most familiar with is the descriptive metadata 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). Other types of metadata commonly used are:
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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 step), whether you aim to:
gain a clear view of what metadata currently describes your resource
expand your current metadata
ensure compliance with requirements to publish it in a metadata catalogue (see also the Metroline Step: Register resource level metadata step )
follow a semantic model to describe your metadata
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metadata
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Why is this step important
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To be able to
Furthermore it is:
Beneficial for you and your team: Having comprehensive and detailed metadata ensures that anyone, including yourself, can understand and work on the data effectively even when some time as has passed since collection. This is an example of good data management practices and contributes to data remaining usable and meaningful over time and saves time when setting up new projects.
Beneficial for the organisation: Complete and error-free metadata makes it easier for organisations to migrate information about its projects between systems, especially when newer software versions are available.
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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Regarding the National Health Data catalogue: Health-RI is in the process of defining a metadata scheme for adding metadata ( |
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Example: Eva, a researcher at Radboudumc, wants to assess what metadata is available about her project. She starts by consulting her Data Management Plan (DMP). She then remembers that she added metadata about her project to the PaNaMa registry and the Radboud Data Repository.
Step output: Systems and documents identified, where metadata are stored (for instance the DMP, Research Management system such as PaNaMa, and (local) data repositories).
Step 2: Extract and evaluate your metadata
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Share or publish your metadata (:Metroline stepStep: Register your resource - level metadata)
Expand your metadata to include domain specific metadata(check this page: Petal instructions) : Domain-specific metadata schema development
Step 4 (Bonus Step!): Enhance Your Metadata
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Experts that may need to be involved, as described in https://health-ri.atlassian.net/wiki/spaces/FSD/pages/273350662/Metroline+Step: +Build+the+Team , are described below.
Data manager/Data steward/Data librarian, Researcher (Scientist)or someone else who knows the context and content of the project.
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This section should show the step applied in a real project. Links to demonstrator projects.
Training
https://carpentries-incubator.github.io/scientific-metadata/instructor/data-metadata.html#types-ofhtml#metadata
https://howtofair.dk/how-to-fair/metadata/#what-are-metadata
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.
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