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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 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: 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 softwares 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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Experts that may need to be involved, as described in Metroline Step: Build the Team, are described below.
Data manager/Data steward/Researcher (Scientist). Someone or someone else who knows the context and content of the project and resource generation.
Practical examples from the community
This section should show the step applied in a real project. Links 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.”https://carpentries-incubator.github.io/scientific-metadata/instructor/data-metadata.html#types-of-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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