Collections¶
Dataverse collections are a key feature that allow users to organize and structure data hierarchically. A collection in Dataverse can contain datasets and/or sub-collections, offering flexible organization for various research projects or institutions. Key points about Dataverse collections include:
- Hierarchy: Collections can be nested, meaning a collection can contain multiple sub-collections. This helps organize data by project, research team, topic, or department.
- Grouping of data: A collection acts as a "container" for related datasets. This is useful for large research projects generating many datasets or institutions wanting to organize data by topic or research group.
- Customization and control: Collection owners can customize permissions and access. They can set rules about who can view, download, or collaborate on datasets, maintaining detailed control over privacy or open access.
- Custom metadata: Each collection can have its own metadata schema, making it easier to describe the data appropriately based on the discipline or project's needs.
- Search and discovery: Dataverse collections are designed to facilitate searching and data discovery within the repository. Users can search within specific collections or across all public collections in a Dataverse installation.
- Citation and permalinks: Just like individual datasets, collections can also receive unique persistent identifiers, such as a DOI (Digital Object Identifier), allowing them to be formally cited in academic publications.
Dataverse collections help efficiently manage large volumes of research data, enhancing visibility and accessibility.
Create a new Dataverse collection¶
Original documentation from the Dataverse project here.
Edit a Dataverse collection¶
Original documentation from the Dataverse project here.