Metadata (data documentation) are absolutely necessary for a complete understanding of the research data content and to allow other researchers to find and re-use your data.
Metadata should be as complete as possible, using the standards and conventions of a discipline, and should be machine readable. Metadata should always accompany a dataset, no matter where it is stored.
Practical courses about these aspects are provided by our service on a regular basis.
Tool
For help on documenting your data before depositing it on data repository, have a look at:
- DataCite Metadata Schema useful for describing general research data based on European recommendation.
Details
Table 1 and table 2 list the different items you should document about your dataset based on the 3 different levels of obligation.
Table 1: DataCite Mandatory Properties
Table 2: DataCite Recommended and Optional Properties
The DataCite Metadata Schema for Publication and Citation of Research Data distinguishes between 3 different levels of obligation for the metadata properties:
- Formalized specific metadata standards available for particular file formats and disciplines.
Details
Disciplines specific metadata standards.
FAIRsharing is a resource to identify the standards, databases or repositories that exist for their data and discipline.
It can be use for example, when creating a data management plan for a grant proposal or funded project; or when submitting a manuscript to a journal, to identify the recommended databases and repositories, as well as the standards they implement to ensure all relevant information about the data is collected at the source.
Today’s data-driven science, as well as the growing demand from governments, funders and publishers for FAIRer data, requires greater researcher responsibility. Acknowledging that the ecosystem of guidance and tools is still work in progress, it is essential that researchers develop or enhance their research data management skills, or seek the support of professionals in this area.