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Research Data Management: RDM in Research Life Cycle

RDM in Research Life Cycle

Research data are involved in all stages of the research life cycle. In each stage, a data life cycle is evolved simultaneously.

Research Life Cycle and Data Life Cycle

When you plan your research, you should also plan to manage your research data and prepare a data management plan.

When you undertake your research, data will be collected, analysed, given descriptions and stored.

When you publish your research outputs, your data should also undergo the peer review process and be published and shared.

When the research outputs are ready for preservation and dissemination, the data should be well preserved and disseminated for reuse in a research data repository.

FAIR Data Principles

The FAIR Data Principles are published in the journal Scientific Data (2016). They are a set of guiding principles proposed by a consortium of scientists and organizations to support scientific data management, stewardship, and the reusability of digital assets. The four foundational principles are:

  • FAIR data principlesFindable​
    • Data and supplementary materials should have sufficiently rich metadata as well as a unique and persistent identifier such as DOI.​
    • Metadata are machine-readable to support automatic discovery.​
  • Accessible​
    • Metadata and data should be understandable to humans and machines.​
    • Data are stored in a trusted repository.​
  • Interoperable​
    • Metadata should use a formal, accessible, shared, and broadly applicable language for knowledge representation.​
    • Data are interoperable with applications, or workflows for analysis, storage, and processing.​
  • Reusable​
    • Data and collections should have a clear usage license and provide accurate information on provenance.​
    • Meta(data) meet the domain-relevant community standards.

The original statement of the FAIR data principle

Benefits of RDM

A good research data management plan in the research life cycle is not only beneficial to the researcher, but also helpful to the scholarship and the society.

Benefits of RDM – For Researchers

  • Meeting the requirements of publishers and funding agencies
  • Saving time
  • More visibility and citations of research outputs
  • Opportunities for collaboration
  • Career recognition
  • Minimizing non-compliance risks (legal, ethical, institutional, and funders’ policies)

Benefits of RDM – For Scholarship

  • Facilitating data finding and re-use
  • Ensuring data could be cited
  • Enabling new research and new insights on the data
  • Valuable data are protected
  • Supporting research integrity and reproducibility

Benefits of RDM – For Society

  • Efficient use of public resources
  • Better quality research can lead to better decision-making
  • Opportunities for citizen science
  • Increased transparency and trust in scholarship