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Research Data Management

Understand what RDM is, why it's important to your research process, and how RRU Library can support you.


Organize, clean, and back up your collected / created data.

What to do:

  • Clean your dataset.
  • Make sure you have documented the description of each of the variables
  • Ensure that you have documented all your decisions related to file naming and version control
  • Aim to create a dataset you would be happy to find

Why do it:

  • So you and other researchers might successfully analyze the dataset(s) later.

How to do it:

  • Preserve a copy of the raw data before progressing with cleaning and validating
  • Clean and validate the data. There are software options such as OpenRefine to help you with that
  • Anonymize the data if and where necessary
  • Ensure the variable names are clear. DDI is one international standard for data documentation, though there are many more
  • Document your version control
  • Maintain a reliable and consistent backup strategy. Consider the 3-2-1 rule:
    • Have at least 3 copies of your data
    • stored on 2 different media
    • with 1 backup kept off site

Things to consider:

  • How will you manage any ethical or privacy issues before analyzing the data?
  • How will you securely store (potentially large and cleaned) data pre-and post-analysis?

RDM step by step

Think of Research Data Management as the organization and maintenance of your research data through the life of a research project. Explore the links below to get a more detailed introduction.