Research Data Management (RDM) is the process of organising, storing, documenting, protecting, and sharing research data throughout the research lifecycle. Good data management helps ensure that research data are reliable, secure, accessible, and reusable.

Key aspects of Research Data Management

• Data Management Planning — plan how research data will be handled throughout the project.
• Data Organisation and Documentation — structure and describe data so they can be understood and reused.
• Storage and Security — ensure data are safely stored and protected.
• Data Sharing and Preservation — share and preserve data where appropriate for long-term access and reuse.

Research data can take many forms, including experimental results, survey responses, interview transcripts, measurements, observations, images, audio and video recordings, databases, statistical data, and research software or code.

Open Research Data refers to research data that are made available for others to access and reuse, subject to appropriate legal, ethical, privacy, security, and other conditions.

Open research data are an important part of Open Science.

Important principle:

"As open as possible, as closed as necessary."

This means researchers should make data as openly available as possible while respecting legitimate restrictions.

Some research data should not be publicly available.

The General Data Protection Regulation (GDPR) provides rules for processing personal data in the EU.

Personal Data:

Data identifying individuals may be protected by data protection legislation.

Confidential Data:

Research may involve agreements that restrict sharing.

Intellectual Property:

Some datasets may be subject to intellectual property rights or commercial restrictions.

In these cases, researchers may use:

• Anonymization
• Pseudonymization
• Controlled access
• Data-use agreements
• Restricted repositories