Benefits of good data management

Good research data management involves activities on several levels. This includes creating a data management plan, considering what needs to be in place for collaborative working, working with sensitive data, and ensuring data security, as well as data storage and backup.

This also involves practical issues, such as how to approach the documentation of research data, file naming and versioning, and deciding which data to keep and which data to discard.

Following good research data management procedures has many benefits:

  • Increasing the impact of your research. Good research data management (RDM) supports and enhances your research. Making research data open and verifiable gives your research greater visibility and increases the number of citations.
  • Avoiding duplication of effort. Good RDM ensures you can revisit and reuse your data later on instead of repeating the work. It also enables others to reuse and acknowledge your data, which rewards your efforts, provides feedback sooner and saves time and effort, saving public money and enabling scientific advances.
  • Making it easier to share. Documenting your data throughout its lifecycle makes it easier for you to share and for others to understand and reuse your data. 
  • Ensuring research integrity and validation of results. Good RDM makes it easier to fulfil the commitments of responsible research, by making it repeatable, reproducible, replicable, and reusable.
  • Ensuring accountability. Good RDM demonstrates your desire to create high-quality output from your research and to be accountable for its integrity. It also shows awareness of the responsible use of public resources that fund it. 
  • Complying with the University's and funders' research data policies
  • Complying with obligations regarding data security, data legislation and institutional policies 
  • Saving you time. Doing the work now to plan for your expected data, back them up and document them in detail preserves time that is otherwise lost in searching for, recovering and deciphering data in the future.