During research gives an overview of what you need to do to store and secure your research data during your project.

  • Before you start covers grant applications, the UQ Research Data Manager and ethics for research data management
  • Ongoing looks at sharing and re-using your research data once your project is completed

1. Metadata and managing data files

Metadata

Metadata is often referred to as "data about data". It is information used to describe your research data, making it easier to discover, retrieve and reuse.

The UQ Research Data Management policy requires metadata be collected and stored along with the research data.

Metadata is important for:

  • discovery (title, keywords, project description)
  • evaluation (methods, dates)
  • re-use (information on variables, software or hardware required, access and reuse conditions)

Read the Australian National Data Service (ANDS) Guide to Metadata for an overview of metadata creation and collection for research data.

Collect metadata that is relevant for your discipline and within the standards of your research area. You will also need to be aware of the requirements for metadata by the journals and data repositories to which you intend to submit your research.

Managing data files

Managing your project's data files is an important part of organising, using, sharing and keeping track of your research. Decisions made about data files may impact how the data can be analysed, stored or used in the future.  You will need to consider:

Data types and formats

Where possible choose data formats that are non-proprietary or open and sustainable, as this improves the chances of interoperability and re-use of the data in the future.

For more advice on formats:

File naming conventions

File and folder naming conventions support efficient use of your project's data, and make the data accessible to researchers over time. Choose descriptive, meaningful file names that can be clearly understood. Document the convention chosen and ensure it is followed consistently.

Version control

Research data can undergo a number of changes throughout a project, and managing the versions and iterations of your project's dataset is important to ensure the integrity and validity of your work. Document a system for tracking versions, updates, and changes made, and ensure it is followed consistently. Where possible, look at version control tools or software. Get more ideas on data versioning.