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Research Data Management

Sharing good practices for Research Data Management

What is Research Data Management


Research data management (RDM) refers to activities and practices that support preserve, 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.

Research Data Life Cycle


RDM involves different activities during and after the research project that generates data. The research data life cycle shown below identifies eight key stages in RDM, which are planning, collectingorganizing, processingdocumenting, preserving, sharing, and reusing. It is important to note that research data management is not always a linear process. In reality, you may need to revisit some stages and redo some processes throughout your project. We will share some good practices in these areas in the following sections of this online guide.


• Data Citation



  • DMP Tools
  • DMP Examples



 

• Data Repository
• Data Journal
• Data Licensing

  • Data Sources
  • Web Scraping

 

 


 
 

• Backup Strategies
• Storage Media

• File Format

  • Folder Structure
  • File Naming
  • Version Management

• Documentation
• Metadata


 

  • Data Provenance
  • Data Cleaning
  • Data Analysis
  • Data Visualization

 

Why RDM Matters


Even if you are not going to share your research data, effective RDM brings you a lot of benefits:

Increase research efficiency

Staying organized with your research data from the start can save your time and effort in the long run.

Ensure data security

Minimizing the risk of data loss, human errors, unauthorized access, or other unwanted risks.

Preserve data for future use

Ensuring your data is accessible and understandable by researchers, including yourself, in the future.

Improve the reproducibility of your research

Providing accurate, complete, and reliable data to reproduce your research findings when needed.

RDM may be tedious and time-consuming at the beginning. However, an aggregation of small routine practices can help to build good RDM habits. It is never too late to develop good RDM habits today!


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

Scenario 1:

 

A researcher saved all of his collected data in his notebook. Unfortunately, his notebook was stolen before he could complete the data analysis.

 

Scenario 2:

A researcher worked with a group of collaborators in a research project. He was responsible to collect and analyze a part of the research data. Three months after he finished his part, he was asked to clarify some variables in his data file. Unfortunately, he was unable to recall what the variables meant and where they came from. There was also no notes or documents he could refer to.

 


RDM Online Training Course