Research Data Management (RDM): Metadata

Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage a resource.

Metadata describes the who, what, when, where, why, and how of your data in the context of your research and should provide enough information so that users know what can and cannot be done with your data, ensuring that your data is reproducible.

It also facilitates identification, organisation and interoperability of research outputs.

It is a good practice to begin to document your data at the very beginning of your research project and continue to add information as the project progresses. Include procedures for documentation in your data planning.

The existence of comprehensive, standard machine-readable metadata helps data satisfy the FAIR data principles. Read more about FAIR principles

	Findable — metadata and data should be easy to find for both humans and computers
	Accessible — users need to know how the data can be accessed and what authentication/authority is needed
	Interoperable — data and metadata should use standards so it can interoperate with other data and information
	Reusable — reuse is the ultimate goal of FAIR and requires well-described contextual metadata and data, so that it can be reused and/or combined in different settings.

Metadata should be open: To comply with the FAIR Principles, metadata should be accessible, even if the data itself cannot be shared openly.

You can provide lots of descriptive information about the qualities of your dataset, without the need to share the data publicly. Read more about sharing and publishing Sensitive data

Metadata is important for:

	discovery (title, keywords, project description)
	evaluation (methods, dates)
	re-use (information on variables, software or hardware required, access and reuse conditions)

There are number of established metadata standards available for different disciplines. Choosing an appropriate metadata standard may be a discipline specific, or more general.

If your data is assigned to a set of standards, this can allow cross-comparison with another dataset that uses the same standards, and consequently reuse of the data.

Specific disciplines, repositories or data centres may guide or even dictate the content and format of metadata, possibly using a formal standard. Some standards describe general information such as bibliographic metadata, others describe specific data types or are designed for specific disciplines.

The following tools can be useful for identifying a suitable metadata standard for the research you want to describe. A detailed list of discipline-specific metadata standards has been compiled by the Digital Curation Centre (DCC) and Research Data Alliance.

If you would like to deposit your dataset at the CDU data repository, the DataCite metadata standard is of particular significance.

From Knowledge clip: Metadata [Video], by UGent Open Science, 2021, Ghent University. (https://youtu.be/DW2T_cnqKPU?si=1rqDwUk2rVSZDRbg). CC BY.