Skip to main content

ACU Research Data Management Toolkit

This toolkit will help you to determine your research data management needs and create a data management plan that documents how data will be managed during the research process and after the project is completed.

Describe your data

How will you describe your data? The term 'metadata' refers to descriptive information about your data. It includes information such as:

  • the creation date of the data 
  • the version
  • what the data is
  • who can use it
  • when it can be used
  • how it can be used
  • what it might be used for
  • where it can be found
  • how long it will be available 

Metadata should be descriptive, consistent and meaningful so that you and other people can interpret the data at a later date.

Appropriate metadata will also facilitate depositing your data into a repository at the end of the project. 

Metadata Standards

Metadata standards have been developed to specifically address data description and enable sharing of data within any given discipline.

The UK Digital Curation Center's Directory of Disciplinary Metadata provides information about a range of disciplinary standards, including profiles, tools to implement the standards and case studies of data repositories currently implementing them.

Types of metadata

Usually, metadata are standards-based and serve a particular purpose in data processing and machine-to-machine interoperability. Metadata may be stored as XML, in an accompanying document, a set of data fields in a repository, or a README file.  Three broad categories of metadata are:
  • Descriptive - common fields such as title, author, abstract, keywords which help users to discover online sources through searching and browsing.
  • Administrative - This type helps manage the dataset. It includes rights management, access control, use requirements, technical data on file creation and quality control, file formats, software/hardware for access and use, and any information relevant to archiving and preservation.
  • Structural - how different components of a set of associated data relate to one another, such as tables in a database, that chapter 1 comes before chapter 2 in a book, or that file x is the JPEG format of the archival TIFF image file z.

    • Data description examples

    Engaging to learn: Increasing the engagement of children with autism in learning activities. Research Data Australia

    This research addresses the ARC national research priority of promoting and maintaining good health and well being for all Australians by enabling children with autism and their families to lead more productive and fulfilling lives. Children with autism are amongst the most challenging of all students for educators. Improving educational outcomes by engaging these children in learning, the aim of this research, is critical if these children are to achieve their full potential. This will benefit the Australian community by increasing independence, reducing barriers to inclusion, and improving the quality of life for children with autism and their families.

    Young people's perceptions of their spirituality. Research Data Australia

    An investigation in the elements that have contributed to the spiritual wellbeing of 16-20 year-olds in regional Victoria, based on their own perceptions: A pilot study. Voluntary participation by twenty-four young people aged 16-20 who came from faith based schools, government sponsored alternative school, and transition to work programs.