Metadata and standards

In order to be able to reuse research data sets, they need to be described with “metadata”.


Specifications for the minimum information that should be collected about research data is available from for example:

Different types of metadata

Metadata can be divided to different categories according to their function:

  • Describing (e.g. title, author)
  • Structural (e.g. variables)
  • Administrative (e.g. licences, versions)

Metadata can exist on different levels:

  • Project/study level (e.g. aim)
  • File/dataset/database level (e.g. file format, checkum)
  • Variable/object level (e.g. variable description)

Standards for data and metadata 

Across research disciplines there are many standards for data and metadata, designed to assist data management, from collection through preservation and publication to subsequent sharing and reuse. A standard contains specifications and guidelines that can be used for the description, interoperability, citation, sharing, publication, or preservation of research data and metadata. Broadly, these standards allow data and metadata to be harmonized with respect to structure, format and annotation. This opens their content to transparent interpretation, reuse, integrative analysis and comparison. Data and metadata standards are essential for the implementation of the FAIR principles. 

Standards for data and metadata may include elements such as organization of data files in a folder structure, file naming, variable naming, mandatory and optional metadata fields and a file structure for the metadata, among other things.  

Data and metadata standards for particular kinds of data may be found e.g. at  

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If you have questions regarding  research data management please contact