What is research data management?

Research thrives on curiosity, creativity, and collaboration. At the heart of every successful project lies data, whether gathered through interviews, measured in labs, or generated by simulations. This data forms the foundation for insights and discoveries.

But data alone isn’t enough. Managing it well is what transforms raw information into meaningful knowledge. That’s where Research Data Management comes in.

 

🗂️ Research Data Management (RDM) is the thoughtful process of planning, organizing, storing, analyzing, sharing, and preserving research data—not just during a project, but long after it ends. This applies to all types of data: from raw measurements and transcripts to images, audio and video files, computer code, and geographic coordinates.

 

The main goal of RDM is to make working with research data as easy, safe, and efficient as possible. Good data management prevents data loss, supports collaboration, and ensures that research can be understood and reused in the future. Careful planning from the start not only saves time and effort, but also helps you meet legal requirements and the expectations of funding agencies. Good RDM practices are also closely aligned with the FAIR principles: data should be Findable, Accessible, Interoperable, and Reusable.

 

👉 In short: how do we treat the data that fuels discovery? Not just as files to be stored, but as valuable assets to be respected, protected, and used responsibly—for science, for society, and for the future.

The Benefits of Responsible Data Management

 

Stay compliant and ethical:

  • Design your research to meet ethical standards
  • Carefully protect the privacy of your research participants

Increase your impact:

  • Share and reuse data to support new research
  • Make your work more visible and discoverable by citing your research data

Promote transparency:

  • Make your research verifiable and replicable
  • Clearly show how you reached your results

Collaborate effectively with companies:

  • Work confidently and securely with sensitive company data
  • Plan ahead for how intellectual property will be managed

Save time and avoid frustration:

  • Use metadata and version control to make collaboration easier
  • Prevent data loss or unusable files through careful documentation and archiving

Meet funder requirements:

  • Make your data as FAIR as possible
  • Enable other researchers and students to reuse your data

Contents of this Library Guide

This Library Guide provides insight into the various ethical, legal, but also practical challenges you may face when working with research data. In addition, it offers guidance on how to organize data management so that your research runs smoothly. The guide is organized by research phase.

 

Click on the links below or go to the drop-down menu at the top of the page:

 

Research Data Management Preparation & planning During your research After the research

 

FAIR data

 

FAIR data – a framework for proper datamanagement

Knowledge grows when data is shared, understood, and reused. That’s why more and more research institutions, funders, and governments are embracing the FAIR principles of data management: making data Findable, Accessible, Interoperable, and Reusable.

 

These principles don’t just apply to the data itself, but also to the context around it, the metadata that describes it, and the systems that store and share it. Together, they ensure that research data doesn’t disappear into a drawer or a forgotten folder, but remains usable and meaningful over time.

 

At Hanze University of Applied Sciences, the goal is clear: to make research data as FAIR as possible. That means documenting it carefully, storing it securely, and making it available in ways that others can understand and build upon.

 

Because in the end, data is more than numbers or files, it’s the raw material of progress. And when managed with care, it becomes a bridge to new questions, deeper insights, and shared understanding.

 

🔎 Findable
Your data must be easy to find, both for people and computers.
How do you achieve this?
Give your data and associated metadata clear, descriptive names and use a reliable storage location or repository. Ensure that your metadata (the description of your data) is well structured and searchable.

 

📂 Accessible
Your data must be stored securely and be easily accessible to others, with clear agreements about who is allowed to access it.
How do you achieve this?
Store your data in a reliable repository, specify who has access and under what conditions, and add a clear user licence.

 

⚙️ Interoperable
Your data must be combinable with other data and usable in different systems or software.
How do you approach this?
Use open, standard file formats and describe your data and metadata according to common standards so that others can easily link or reuse them.

 

♻️ Reusable
Your data must be described well enough that others can reuse it in other research.
How do you approach this?
Provide your data and metadata with sufficient context and explanation so that others understand how the data was collected and can be used. Clearly indicate the conditions under which reuse is permitted.

 

 

A common misconception is that FAIR data always means open data.

 

However, FAIR data does not have to be freely accessible to everyone. In fact, sometimes it’s not advisable to make your data completely open, for example, when dealing with privacy-sensitive information, ongoing follow-up research, or when multiple rounds of data collection are still planned.

 

When you deposit data in an online repository, you often have the option to control who can access it. Others cannot simply download your data, but may need to request permission first. This way, you remain in control over who can use your data.

 

That’s why the guiding principle for research data is:

 

"As open as possible, as closed as necessary"

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