Trainings

This free course introduces researchers to the principles and practices of FAIR Research Data Management (RDM). The FAIR principles—Findable, Accessible, Interoperable and Reusable—are essential for ensuring high-quality, well-managed research data. The course aims to equip participants with the knowledge and tools to implement FAIR RDM practices effectively within their research.

The course consists of five sessions:

1. What is FAIR RDM and why should we do it? – Introduction to FAIR principles, their relationship with Open Science and RDM.
2. Planning for FAIR: Introduction to RDM and Data Management Plans (DMPs) – Understanding RDM, creating DMPs and funder requirements.
3. Getting started with putting FAIR RDM into practice – Metadata, file formats and data publication.
4. A deeper dive into putting FAIR RDM into practice (Part 1) – Metadata, persistent identifiers (PIDs) and knowledge organisation systems.
5. A deeper dive into putting FAIR RDM into practice (Part 2) – Repositories, licensing, data citation and research integrity.

Each session includes interactive discussions, exercises and project-based learning activities. Participants will apply their learning through a structured project, allowing them to implement FAIR principles in a practical context.

By the end of this course, participants will be able to:

• Explain the importance of research data sharing and the benefits of FAIR principles.
• Create a Data Management Plan (DMP) and understand funder requirements.
• Identify metadata standards and documentation practices that enhance data FAIRness.
• Recognise the role of file formats in making data interoperable and reusable.
• Use repositories and persistent identifiers (PIDs) to ensure data accessibility.
• Apply appropriate licences and data citation practices.
• Understand research integrity considerations in FAIR RDM.

This course is designed for:

Master’s students and PhD candidates

• Require foundational knowledge on RDM and FAIR principles.
• Face challenges in managing research data effectively.
• Motivated by institutional policies and funder requirements.

Early-career and established researchers

• Need to integrate FAIR principles into their research workflows.
• Seek to improve data sharing practices and research reproducibility.
• Motivated by the potential for increased citation and research visibility.

Research support professionals (librarians, data stewards, administrators)

• Require a deeper understanding of FAIR principles to support researchers.
• Face challenges in implementing and enforcing RDM policies.
• Motivated by the need to align institutional policies with best practices in FAIR data management.