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Data Management Planning: Research Data Management

Brings together a variety of resources to aid researchers with data management planning.

Tim Berners Lee's Open Data scheme and the benefits of Open Data

What is Research Data Management (RDM)?

Research Data Management (RDM) refers to the organization of research data from its creation in the research process through its dissemination and storage in a reusable format.   RDM practices encompass the entire lifecycle of the data, and makes sure that all legal, ethical, and funding requirements are fulfilled. 

Some specific activities and issues that fall within the category of Data Management include:

  • File structure and organization
  • Quality control
  • Metadata creation
  • Controlled vocabularies
  • Preservation
  • Access, Sharing and Reuse
  • Security and Privacy
  • Notebook protocols (lab or field)

Benefits of Research Data Management

  • Organized data saves time
  • Facilitates writing up research results for publication
  • Supports project continuity through researcher or staff changes
  • Promotes academic research integrity
  • Facilitates validation of results
  • Facilitates reproducibility of experimental and computational outcomes
  • Data can be easily shared, leading to collaboration, dissemination and greater impact
  • Reduces risk of lost, stolen, or mis-used data
  • Helps to guarantee the quality and authenticity of data
  • Comply with institution, funder and journal requirements

Types of Research Data

Data are generated for different research purposes and in different ways. The type of data generated depends on the discipline. In the sciences, data can be generated from:

  • experimental or laboratory produced data
  • simulations generated from test models (climate, mathematical or economic models)
  • derivations or compilations (text and data mining, databases, 3D models)
  • collections of peer reviewed datasets published (chemical structures, gene sequence databanks, statistical datasets)