When you have finished finalizing a dataset and are ready to archive it, there are many options available. Depending on the research and choices made earlier the archive provides the option to fill in descriptive fields for a dataset. The descriptions in the archives often are automatically created using metadata standards like DataCite or Dublin Core, or some other type of standard. See also the item Metadata in this LibGuide.
When registering a dataset in an archive it is important to use unique identifiers to allow for increased findability and easy attribution & citation. Examples of this are:
Some archives also allow you to preregister your project/dataset. Examples are:
Just like your publications, data that you have collected for your research constitute research output, too. Therefore you are required to record your datasets in PURE. Your datasets can be of interest to others, which can in turn lead to new collaboration opportunities. Datasets recorded in PURE also appear in reports that are used for research evaluations. Even if access to your dataset is closed, you are required to register your dataset in PURE. It is a record of the research, data collection and analysis that you have carried out.