Research Data Management (RDM)

Research data management

Research data management (RDM) is about how you collect, care for, use, preserve and share the data that support your research.

Research data lifecycle

The research data lifecycle includes everything from planning how data will be collected, to publication, to long term data preservation, to possible reuses of data. Steps of the lifecycle include: Plan, Acquire, Process, Analyze, Preserve, Share Results, and Reuse, but terms may differ based on institution, field of research, or model author. 

Importance of RDM

There are several reasons RDM is important. Here are some:

• Funding agencies increasingly turn to data and reproducible results to approve research grants.
• Journal publishers require authors to provide a data availability statement (DAS).
• Data is a transient product and can be easily lost if not saved properly.
• Managing research data correctly saves time and money.
• Data that can be referenced, verified, and validated increases the accuracy and quality of the research. Sharing data can also often lead to developments and insights from its readers, even if they are outside the original research.