Documentation and metadata

What to consider in your DMP

Rich documentation and metadata should accompany your data so that others can find, understand and reuse it. Consider:

• What information is needed for the data to be read and interpreted? 
• How will you capture this information?
• What metadata standards, if any, do you plan to use?

What are documentation and metadata?

Good metadata facilitates the discovery and reuse of your research data. When considering what to include, remember the FAIR Data Principles. Rich metadata and documentation will ensure your data is Findable, Accessible, Interoperable, Reusable.

Metadata is structured, machine-readable data that provides information about other data. Examples of metadata include: 

• data type
• authorship
• file formats
• dataset title
• abstract/description
• access conditions
• terms of use.

Documentation is the systematic recording of the research process which enables us to understand and reuse data. Examples of documentation include: 

• README file
• Data dictionary
• Protocol
• Lab notebook
• Consent documentation.

Examples

The authors of this dataset shared in the Open Research Data Repository have provided helpful documentation in the form of a data dictionary to explain what the variables in the data mean. They've also highlighted where data might be missing or incomplete.

Rich metadata has also been provided. The title is a useful summary of the data being presented. The details section includes lots of information about the objective and methodology of the project. Targeted keywords have been provided to improve the findability of the data.

Metadata standards

A metadata standard is a common set of guidelines which define the structure and format of metadata. Standards make data more consistent, enabling integration and comparison with other datasets. They're a vital step towards interoperable data.

General metadata standards are available, such as Dublin Core and MODS as well as discipline-specific standards such as Darwin Core for biological sciences and the Data Documentation Initiative for social and behavioural sciences.

FAIRsharing is a resource that describes and links:

• community-driven standards
• databases
• repositories
• data policies.

You can use FAIRsharing to search over 1,700 standards across natural sciences, engineering, and humanities and social sciences. The Research Data Alliance have also created a catalogue of metadata standards which you may find helpful.