Lesson plan 6: Metadata

FAIR elements:

Findable

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource

Accessible

Once the user finds the required data, she/he/they need to know how they can be accessed, possibly including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A1.1. The protocol is open, free, and universally implementable

A1.2. The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata are accessible, even when the data are no longer available

Interoperable

The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (Meta)data use vocabularies that follow FAIR principles

I3. (Meta)data include qualified references to other (meta)data

Reusable

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

R1. (Meta)data are richly described with a plurality of accurate and relevant attributes

R1.1. (Meta)data are released with a clear and accessible data usage license

R1.2. (Meta)data are associated with detailed provenance

R1.3. (Meta)data meet domain-relevant community standards

Primary audience(s): Bachelor's, master's, PhD degree students

Learning outcomes:

  • Can describe types of metadata

  • Can recognise metadata formats

  • Can identify metadata standards

  • Can use metadata standards to describe resources

  • Can explain what metadata registries are

  • Can search and find data and metadata standards in registries

  • Can articulate metadata of different types to describe a resource

  • Can write metadata in a relevant format

  • Can appraise the usefulness of metadata standards to describe a resource

Summary of tasks/actions:

  1. Metadata are 'data about data'

    1. Present and describe the different types of metadata (can present the whole list, or pick specific elements relevant to your audience).

      1. Metadata are:

        1. standardised

        2. structured

        3. machine- and human-readable

        4. a subset of documentation

    2. Documentation (descriptive and/or technical info)

    3. Controlled vocabularies and ontologies

    4. Persistent identifiers (PIDs)

    5. Licences

  2. Learn syntax of example metadata standards:

    1. Dublin Core is general and applicable to all datasets on a project level; on a data level there are discipline-specific standards to branch into such as:

      1. Data Documentation Initiative (DDI) – social science

      2. Ecological Metadata Language (EML) – ecology

      3. Flexible Image Transport System (FITS) – astronomy

    2. Minimum information standards

  3. Use metadata catalogues/registries and search for suitable standards

Metadata form the core of machine- and human-readable descriptions of data, be they technical information or annotations, and cover all aspects of the FAIR principles. Metadata is an umbrella term that includes file formats, ontologies and licences, and documentation in general. For each of the principles, metadata can be used at different granularities and domain specificity, with more general metadata not providing as much usefulness and value to the underlying data than domain-specific metadata.

References:

Take-home tasks:

Exercises:

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