Research Data Management

What is Research Data?

Research data is defined here as any information that has been collected, observed, generated or created to validate original research findings. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media. Data can be, for example:

• Instrument measurements
• Experimental observations
• Images, video and audio
• Text documents, spreadsheets, databases
• Survey results and interview transcripts
• Simulation data, models and software
• Slides, artifacts, specimens, samples
• Sketches, diaries, lab notebooks

Source: ANDS Guide. What is research data?
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What is Research Data Management?

Research Data Management (RDM) is a process that includes all activity surrounding the data, e.g. planning, collecting, analyzing, organizing, describing, sharing, and preserving your research data. It includes the management of active data during your research project and also how the processed and analyzed data is preserved and shared after the project. Research Data Management is often presented as a cycle with the following or similar steps (Image Source: University of California, Santa Cruz).

Why Should I Manage my Data?

• It's good scientific practice: you ensure research integrity and reproducibility
• You increase your own research efficiency
• You save time and resources in the long run
• You enhance data security and minimize the risk of data loss
• You prevent duplication of effort by enabling others to use your data
• You meet funding body grant requirements (if applicable)
• Publishing datasets is an academic merit of its own!

FAIR Principles of Research Data Management

When working with research data, you should always try to follow the FAIR principles. These are guidelines on how to make your data Findable, Accessible, Interoperable and Reusable. See Share and Publish Data for more information.