What is Research Data?

The Research Data Management applies to all research data used or produced in the course of your project, whether you generate them yourself or reuse them from external sources. It also includes associated research outputs necessary for understanding and reusing these data. Here are some examples of data and related materials you should consider:

• Experimental or observational data (measurements, sensor data, imaging, physical samples, field notes, etc.)
• Simulation or modelling data (outputs from computational models, synthetic datasets)
• Processed results and analysis outputs (data processing files, graphs, tables, visualisations)
• Code, scripts, and notebooks (Python, R, MATLAB, Julia, etc.), including configuration files or data pipelines
• Textual or qualitative data (interviews, surveys, annotated corpora, ethnographic records)
• Lab notebooks, field journals, observation notes
• Experimental protocols, study designs, metadata files
• Reused data from external sources (public or licensed databases), with proper legal verification of reuse conditions

💡 All these elements are considered research data as long as they are used to produce, support, or validate scientific results. The RDM ensures that such data are properly documented, traceable, of high quality, and used in accordance with scientific, legal, and ethical standards.

What Is Research Data Management And Why It Matters?

Research data management (RDM) means the managing and organisation of data during and after the active phase of a research project. It includes many aspects, e.g. the planning of data collection or generation, organising data, documentation and description, storage, version control, and after the conclusion of the research project, such as what data should be discarded, what should be kept and what can be shared. It is beneficial to plan how the data should be managed at the very start of a research project.

For PhD students and early-career researchers, managing data well brings tangible advantages:

1. It structures the research workflow and facilitates writing publications or a thesis.
2. It saves time later, as data are easier to retrieve, interpret, and reuse.
3. It increases visibility, because well-documented and shared data can be cited and contribute to scientific reputation.
4. It creates new opportunities for collaboration and funding, as data sharing is often encouraged or required by funders.
5. It helps anticipate legal, ethical, and contractual requirements, avoiding compliance issues.

Additional Resources :

Self-Assessment Quiz