About dataset enhancements
In this guide
Overview
Dataset enhancements are additional metadata components that provide structural and contextual information beyond basic dataset metadata. They make your datasets more comprehensive, discoverable, and valuable to Data Users. Enhancements improve clarity, prevent misuse, enable integration, support governance, and reduce support burden.
You can add the following enhancements to your datasets:
- Distributions: Specify how users can access your dataset. Provide multiple access methods, formats, and technical specifications for different user needs.
- Data dictionary: Document the structure and meaning of dataset fields. Define what each column represents, its data type, and any constraints—improving data understanding and reducing misuse.
- Dataset relations: Connect datasets to related datasets or external resources. Establish meaningful links to show how datasets fit into larger data collections.
- Qualified attributions: Provide detailed attribution information about contributors, sources, or responsible parties beyond basic publisher information.
- Authentic source labels: (For published datasets) Indicate which organisation or entity is the authoritative source for specific data fields within a dataset, enhancing trust and accountability.
How dataset enhancements work
- Dataset enhancements are optional but recommended additions that align with DCAT-AP-LU standards for comprehensive metadata documentation.
- You must be an Editor or have the permission to add or edit datasets to add enhancements.
- You can only add enhancements to datasets in the Draft state.
- Enhancements become part of the dataset's metadata and are visible to users when the dataset is published.
- Enhancements improve dataset discoverability and usability, making them more valuable to Data Users.