RDM explained
What is Research Data Management (RDM)?
Research Data Management is a broad term encompassing all practices and actions to ensure that research data are:
- Secure
- Sustainable
- Easy to find, understand, and (re)use
RDM includes activities such as planning, collecting and organizing, documenting, storing and backing up, preserving, and sharing research data.
It is about taking proper care of data, not only during a research project, but also in the longer term.
Data as first-class research objects
Research data are not a mere by-product of scientific research, nor a simple means to (article) publication. They often have a much longer shelf life than the scientific publications they underpin:
- They constitute the evidence needed to verify and validate published claims.
- They can be reused for follow-up or new research, for teaching, etc.
Therefore, research data should be cared for as first-class research objects. RDM is about exactly that.
What are research data?
The term 'research data' is hard to define, because it is highly domain- and context-specific. We refer to research data as any information collected or generated for the purpose of analysis, in order to generate or validate scientific claims.
Besides research data, RDM also requires you to manage the documentation needed to make those data understandable.
Types of research data
There is a huge variety of data types. Research data can be classified in different ways, for example based on their:
- Content: numerical, textual, audiovisual, multimedia…
- Format: spreadsheets, databases, images, maps, audio files, (un)structured text…
- Mode of data collection: experimental, observational, simulation, derived/compiled from other sources
- Digital (born-digital or digitized) or non-digital nature (e.g. paper surveys, notes…)
- Primary (generated by the researcher for a particular research purpose or project) or secondary nature (originally created by someone else for another purpose)
- Raw or processed nature
The research data lifecycle
The research data lifecycle is a key concept within RDM. It describes the different stages research data go through before, during, and after a research project. Each stage of the research data lifecycle entails various data management activities, and the choices made in one phase influence the next one.
Thinking about the data lifecycle for your own project is often the first step to draft a high-quality Data Management Plan.
