Describe with metadata

Metadata (literally ‘data about data’) is information about a piece of data, an object, a dataset, a research project or any other physical or virtual entity. Here are some examples of metadata for different ‘objects’: 

  • Research project: Project title, project leader, people involved, funding body, start date, duration… .
  • Data set: Name, description, access URL, format, licence, size, production date(s), publication date...
  • Photograph: Photographer, date and location of the photograph, make of camera and lens, aperture and exposure time...
  • Article: Author, title, date of creation, newspaper title, page numbers, DOI...

Metadata is readable by both humans and computers, as it specifies, in a simple, concise and structured manner, the context in which a piece of data was created. It therefore makes it possible to determine the reliability of a piece of data and the extent to which it can be reused.

data_metadata_6-1.png

"The value of the message (data) may depend a lot on the information of where does the message come from (metadata)."

Source : Dataedo, 2022, licensed under a Creative Commons Attribution-NoDerivs 3.0 License

Metadata standards

Selecting the metadata required for a research project is a challenging task, as this decision must be made before the first data is collected. This is why it is recommended to use existing templates tailored to the needs of the discipline. These templates are known as metadata standards. For example:

  • Dublin Core : a standard consisting of 15 elements and generally used for the exchange of information on works (written, photographic, scenographic, etc.) between the systems of different institutions.
  • Darwin Core : a standard derived from the Dublin Core and developed for the specific needs of biodiversity informatics for describing and facilitating information sharing.
  • Data Documentation Initiative (DDI) : an international standard for describing data produced in the social, behavioral, economic, and health sciences. The specifications and tools are available on the DDI website.
  • Digital Imaging and Communications in Medicine (DICOM) : an international standard accredited by ISO 12052 specific to medical images and their related information.
  • Text Encoding Initiative (TEI): a standard used in the digital humanities that enables the structure and semantics of a text to be described in great detail.

On its website, the Digital Curation Centre (DCC) offers a page gathering these standards with general information for each of them, tools to implement them and use cases of data repositories that currently use them. The FAIRSharing initiative also provides a list of metadata standards.

Metadata schemas

Metadata schemas represent an evolution of standards, with constraints added for the values entered. The schema specifies whether an element is mandatory or optional, unique or repeatable, the valid values, the syntax to be used, etc. For example, when formatting dates and times, it is possible to follow the ISO 8601 schema and standard, whilst for the description of citable items, the DataCite schema may be chosen.

When and how create metadata

Metadata should be created as early as possible and over the course of the project. This ensures that essential information is captured at the right time, prevents details from being overlooked, and avoids an excessive workload towards the end of the project. They will adhere to the FAIR principles, just as the objects they describe do.

Metadata complements documentation, which is written in a more literary style and intended for human readers. It can be created manually or using software to facilitate or automate its creation. The Digital Curation Centre (DCC) provides a list of such tools.

Typology

Metadata can be grouped into several broad categories, the number of which varies depending on the classification system used.

This metadata is used to facilitate the discovery, evaluation and understanding of content.

Examples include:

  • Title
  • Author(s)
  • Description
  • Date(s)
  • Language(s)
  • Identifiers (ISBN, DOI...)
  • etc.

This metadata, some of which is generated by the measuring instruments themselves, enables data to be shared across different systems and projects. It ensures that the data can continue to be read by both humans and machines.

Examples:

  • How is the data configured?
  • What formats and versions of formats are used?
  • How is the database configured?
  • etc.

 

 

This metadata describes the origin of the data and the processes it has gone through. It is necessary for a good understanding, interpretation and reuse of the data. This metadata concerns human actions as well as very technical aspects and often requires some knowledge of the research domain to be filled in.

Examples:

  • Where did the data come from?
  • Why was it collected?
  • Who collected it, where and when?
  • What instruments or technologies were used to collect the data and how were they set up?
  • How was the data processed?
  • etc.

 

 

This metadata describes how the data can be accessed and used, the copyright status, the licence conditions and the rights holders.

Examples:

  • How can someone access the data?
  • Who is authorised to read or modify the data or metadata, and under what conditions?
  • Who is responsible or has authority over the data?
  • Are there any costs associated with accessing the data?
  • Under what licence is the data available?
  • etc.

 

 

This metadata comprises the information required to manage the long-term availability of the data. It is based on provenance, technical, and rights and access metadata.

Example:

  • Have the files undergone any restructuring or other changes, for example in connection with migration to new file formats? (checksums)
  • What software is required to access the data?
  • What is their retention period?
  • etc.

 

 

To learn more

video-metadata.pngCapsule vidéo - Data Science for All : "Metadata, an underestimated facilitator

23 June 2026