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 an important skill for all researchers - both academic staff and research students.

Research Data Life Cycle

RDM involves different activities before, during and after a research project which generates data. The research data life cycle shown below identifies eight key stages in RDM: planning, collecting, organizing, processing, documenting, preserving, sharing, and reusing. Research data management is not always a linear process. In reality, you may need to revisit certain stages and perform some processes again during your project period. This guide share some good practices in these stages.

• 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

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 Courses