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Research Data Management: Home

Good Practices in Research Data Management

What is Research Data Management?

Research data management (RDM) refers to activities and practices that support preservation, access, and use of research data in effective ways. This helps to ensure your valuable research data is well organized, understandable, and reusable. This is an essential element of responsible research conduct and is an important skill for all researchers - both academic staff and research students.

Why RDM matters?

Effective RDM benefits you in the following ways even if you may not share your data:

  Increase research efficiency

Staying organized with your research data from the start or along the way can save your time and resources in long run.

  Ensure data security
      Minimizing the risk of data loss.

  Preserve data for future use
      Ensuring your data is accessible and understandable by researchers, including yourself, in future.

  Improve research integrity
      Providing accurate, complete, and reliable data to reproduce your research findings.


RDM may be tedious and time consuming at the start.  However, an aggregation of small routine practices can help to build good RDM habits. This will make RDM second nature as your research progresses.

Good data management helps avoid potentially disastrous scenarios for researchers, like the following examples:

  • Scenario 1:
    A researcher saved all of his collected data in his notebook. Unfortunately his notebook was stolen before he almost completed the data analysis stage.

    Scenario 2:
    A researcher is collaborating with a group of collaborators in a research project.  He is responsible to collect and analyze a part of the research data. Three months after he finished his part, he is asked to clarify some variables in his data file. Unfortunately, he is not able to recall what the variables mean and where they come from. There is also no notes or documents he can refer to.

Research Data Life Cycle

Research data usually have a longer lifespan than the research project that creates them as researchers may continue to use them in a new project or make them reusable for other researchers. The research data life cycle shown below identifies the key stages in research data management, mainly: Data Management Plan, Data Organization, Data Documentation, Data Preservation, Data Sharing and Data Discovery. 

How to ensure
your data is
discoverable?

Discover

 


Plan

How to 
manage your
project's data?

     
 

How to 
share your
data with 
others?

Share

How to
organize
your data?


   
 

How to ensure
your data is
accessible
in long run?

Preserve

 

Documentation

How to ensure
your data
can be
understandable?

Recommended Books

Online Training

Contact Us

 Research Support

 Phone   2766-6863
 Whatsapp   6423-0820
 Email   lbres@polyu.edu.hk
 In Person   Research Help Desk, 3/F, 
  Pao Yue-kong Library

References:

Briney, K. (2015). Data management for researchers : Organize, maintain and share your data for research success. Exeter, UK: Pelagic Publishing.

EDINA and Data Library, University of Edinburgh, Research Data MANTRA [online course], http://datalib.edina.ac.uk/mantra.

JISC, How and why you should manage your research data: a guide for researchers, https://www.jisc.ac.uk/guides/how-and-why-you-should-manage-your-research-data.

Surkis, A., & Read, K. (2015). Research data management. Journal of the Medical Library Association : JMLA, 103(3), 154-6.