Skip to Main Content

Research Data Management

Metadata and metadata standards

 

UGent Open Science. Knowledge clip: Metadata (8:20)

Metadata is commonly defined as 'data about data'.

Creating good metadata about the data about the generated research data is often described as 'a love letter to the future'. Memory is unreliable and what an individual remembers tomorrow, in 2 weeks, or 5 years time will be very different.

Metadata provides descriptive information and context about research data, making it easier to understand, organise, and manage throughout its lifecycle. It serves as essential documentation that describes the various aspects of data, facilitating data discovery, interpretation, and reuse.

Some funders or publishers may contractually require research data to be made available where possible. If such is the case its best practice to create the metadata as the data is generated.

 

Types of metadata

There are different types of metadata. The ARDC Metadata page describes the following types of metadata:

  • Descriptive metadata, such as the photographer, location, subject, date and time
  • Technical metadata, such as the type of camera used to take the photograph, the file format, the exposure time and the photo’s dimensions. Some instrumentation used to gather information may output useful technical metadata such as settings and software versions that should be included to increase reproducibility.
  • Access and rights metadata will describe legal permissions regards data access and use, along with licences and conditions
  • Preservation metadata
  • Provenance metadata, including the source of the photo and any changes to its ownership.

Some sites have additional metadata types:

  • Administrative metadata, which is information related to the management and adminstration of a dataset. Technical metadata, Preservation metadata, and Access and Rights metadata may be classed as subsets of Administrative metada
  • Structural metadata which describes the internal organisation and structure of the data which assists with understanding the connections between different datasets. There may be overlap with Descriptive metadata fields with titles of related datasets and names.
  • Discovery metadata, which are aids such as tags and keywords that aid in making the data discoverable
  • Usage metadata captures information about download counts and user interactions with the data.

 

Metadata standards

When creating metadata it is useful to have selected a 'metadata standard'. The Digital Curation Centre (DCC) describe metadata standards as

Specifications for the minimum information that should be collected about research data in order for it to be re-used.

 

Adopting a metadata standard as a template for capturing metadata is preferable to using improvised and possibly inconsistent methods of recording the metadata.

There are general metadata standards such as Dublin Core which has “15 core elements (properties) for describing resources”.
The DCC lists discipline specific metadata standards that may be more appropriate and supportive of the needs of your project.
Additional metadata that should be collected that may affect reproducibility are external variables such as machinery settings, software versions, and environmental conditions

Resources

https://mozillascience.github.io/open-data-primers/2.2-metadata.html

ABN 51 818 692 256 | CRICOS provider number 00103D | RTO code 4909 | TEQSA PRV12151 Australian University
   Federation University Australia acknowledges the Traditional Custodians of the lands and waters where our campuses, centres and field stations are located and we pay our respects to Elders past and present, and extend our respect to all Aboriginal and Torres Strait Islander and First Nations Peoples.