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.
Metadata Standards
Metadata is commonly defined as “data about data”, meaning it provides descriptive or contextual information about a dataset. It helps explain the structure, content, and purpose of the data, making it easier to understand, manage, and process. Metadata are machine-readable, facilitating organization, discovery, and interoperability across systems.
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.
DataCite Metadata schema
Useful standard for describing general research datasets when there is no data category or discipline specific standard.
Details
The DataCite Metadata Schema for Publication and Citation of Research Data distinguishes between 3 different levels of obligation for the metadata properties:
- Mandatory (M) properties must be provided,
- Recommended (R) properties are optional, but strongly recommended and
- Optional (O) properties are optional and provide richer description.
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
Formalized specific metadata standards
Available for particular file formats and disciplines.
Details
FAIRsharing and Digital Curation Center are two resources to identify disciplinary metadata standards.
Practical courses about these aspects are provided by our service on a regular basis.