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Library Notice

Understanding Research Data Management (RDM)

Research Data Management (RDM) refers to all aspects of data management throughout the Research Data Lifecycle, from planning through collection, processing & analysis, storage & preserve, sharing and reuse. RDM aims to ensure that research data is Findable, Accessible, Understandable and Reusable over time. It is an essential component of responsible research, and researchers have the responsibility to follow good RDM practices to ensure the sustainability of their research data.

What is Research Data Lifecycle?

RDM covers all aspects of data management activities during and after a research project, addressing every stage of the Research Data Lifecycle, including:

  1. Planning: Develop a Data Management Plan (DMP)
  2. Collecting: Gather valid and relevant research data
  3. Processing & Analysis: Clean, transform, and analyze data
  4. Storage & Preservation: Securely store and preserve data
  5. Sharing: Share data to enhance visibility and impact
  6. Reuse: Ethically reuse and cite existing data sources

What are FAIR Principles?

Throughout the research data lifecycle, it is recommended to adhere to the FAIR Principles to ensure data is Findable, Accessible, Interoperable, and Reusable (FAIR).

  • Findable: Ensure data is discoverable by both humans and machines
  • Accessible: Make data accessible to others in appropriate forms
  • Interoperable: Ensure data compatibility with other data, applications, and workflows
  • Reusable: Ensure data is understandable and reusable in the future

  

For more information about RDM, please visit our RDM LibGuide.