Skip to main content

COVID-19 update: Library spaces are open to students and staff only, with precautions in place for your safety. Learn more.

Manage research data

Essential practices so that data can be found and reused

Research data is valuable and needs to be well managed to ensure that it can be used to the fullest extent. Implementing good research data management practices:

  • allows you to collaborate more seamlessly
  • means your data is well organised and easy for you to find
  • enables your data to be easily understood if you share or publish data
  • reduces the risk of data loss

The University Research Data Management Policy and Procedures outline expectations regarding research data management at the University. Further information for University researchers can be found on the research data management intranet page.

Organise data
  • Meaningful file names and a strategically organised folder structure can save you time and ensure that your collaborators can understand and locate your data files when they need them.

    File naming

    Select an appropriate naming convention for your files as early as possible and follow it throughout your research consistently.

    • You may wish to start your title with the date, formatted as YYYYMMDD, to display your files in chronological order
    • Choose useful keywords that you or others might use to search for your files, separating each word or section with a hyphen or underscore. Document the keywords you choose to use, so that you can interpret your file names later. Useful keywords may include:
      • project acronym
      • location
      • data type
      • data collection methods

      Example: [Date]_[Project]_[Location]_[Method]_[Run]

    Things to avoid

    • Don’t manually change or delete the file extension suffix (eg .docx, .pdf, .csv) which is usually generated automatically
    • Avoid the use of special characters such as \ / : * ? " < > |, apart from hyphens and underscores, in file names
    • Don’t make file names too long

    Folder structure

    A well organised folder structure can save you time and ensures that other researchers you work with can easily locate your data files

    Key considerations:

    • Keep your raw data in a separate folder from working data
    • Store consent forms separately for ethics and privacy reasons
    • Nest your folders in the direction that best suits to how you plan to use them, eg Location > Method > Date or Method > Date > Location
    • Don’t create too many empty folders ahead of time

    Example: [Project] > [Experiment] > [Instrument or Type of file] > [Location]

    Further information:

  • Some storage systems enable automatic versioning. Alternatively, you can use a version control table to thoroughly document the versioning process. If necessary, you can use manual versioning by adding version terms to the file name, such as:

    • author name or initials
    • date last modified
    • version number

    For advanced versioning needs, consider version control software such as Git or Mercurial.

Describe data
  • Accurately describing your data is essential for ensuring that you, and others who may need to use the data, can make sense of your data and understand the processes that have been followed during data collection, processing and analysis. Well-described data is more easily discoverable, verifiable, and reusable by other researchers if the data are published or shared.


    Metadata is descriptive and contextual information about your data. It may include title, creator(s), date produced/ collected, location, abstract, subject, method, process, quality, format, rights and ownership.

    To determine what metadata to keep, it’s useful to think about what information would help someone else understand and reuse your data. You may consider using a metadata standard (also called a metadata schema), a defined set of fields that can either be general or discipline-specific. Using a standard will not only provide a rich description of your data, but also increases the likelihood of people finding your data.

    For more information on metadata, check out Dublin Core, which is a commonly used general metadata standard, or find a metadata standard related to your discipline by searching the Digital Curation Centre’s disciplinary metadata directory.

    Creating data documentation

    Once you’ve decided what metadata you need to keep for your data, you should record this information and store it with the data. Some storage systems, like the University’s eNotebook, provide mechanisms for you to do this when you save your data. In other storage systems, like the Research Data Store and CloudStor, you may have to record your metadata manually in a README document (a text document) or a version control table.

    Further information: