Research Data Management: RDM Explained

Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).

Research data is any information that has been collected, observed, generated or created to validate original research findings.  Although often digital, research data can come in a variety of formats.  For example:

• Video and audio files
• Lab notebooks
• Images
• Physical artefacts
• Documents, spreadsheets and databases
• Interview transcripts
• Textual source material

There are many reasons why research data management is important:

• Data, like journal articles and books, is a scholarly product.
• Data (especially digital data) is fragile and easily lost.
• There are growing research data requirements imposed by funders and publishers.
• Research data management saves time and resources in the long run.
• To comply with legislative requirements such as Data Protection and Freedom of Information Acts.
• Good management helps to prevent errors and increases the quality of your analyses.
• Well-managed and accessible data allows others to validate and replicate findings.
• RDM is a way to ensure research integrity and avoid reputational risk for researcher and institution.
• Research data management facilitates sharing of research data and, when shared, data can lead to valuable discoveries by others outside of the original research team.

DMU will shortly be putting in place a new Research Data Management Policy.  In the meantime, please refer to the 'Good Practice in Research Data Management' Guidance.