RDM Training

Context

Training in research data management (RDM) is a cornerstone of building capacity for FAIR and Open Science. For researchers, it provides the skills and confidence to apply best practices in their own work; for data stewards and support staff, it ensures they can deliver consistent, high-quality guidance across projects and disciplines. As expectations around data sharing, reproducibility and compliance grow, so too does the need for accessible, well-targeted, and sustainable RDM training for data stewards or complementary profiles.

The RDM training provision is shaped by its scope, accessibility and integration into institutional processes. Training remains ad hoc and siloed in many contexts, driven by individual enthusiasm or urgent need, dependent on short-term funding, or limited to single events. The materials developed are often valuable, yet they also reflect this ad hoc nature. Moving toward a structured, continuous training portfolio requires strategic planning, dedicated resources and alignment with national or thematic infrastructure. This includes clear definitions of audience, targeted expertise levels and learning objectives; and feedback loops to keep material relevant. For training aimed at professional roles, such as data stewards, embedding Train-the-Trainer approaches and recognising participation through certificates or ECTS points can further strengthen uptake and impact.

In federated and cross-institutional environments, harmonising training presents both challenges and opportunities. Centralised catalogues, shared curricula and collaborative delivery can reduce duplication and increase reach, but this requires coordination and clear responsibilities. Whether delivered in person, online, as e-learning or as blended formats, effective RDM training is not only about knowledge transfer; it also helps enable culture change, embedding shared principles and practices that persist beyond the training itself.

Guidelines

RDM training strengthens skills development and embeds good data practices across an organisation. These tips are organised around four core drivers: strategic planning, accessible delivery, quality assurance and sustainable capacity, reflecting organisational experience and established good practice.

1. Plan strategically for training delivery

✔ Identify and analyse target audiences

Map the personnel requiring RDM training - such as PhD candidates, postdoctoral researchers, senior researchers, technical staff, or project managers - and assess their existing knowledge, roles and needs in their respective positions. Training design should also consider domain-specific knowledge and requirements to ensure that learning objectives are properly aligned with disciplinary contexts.

✔ Integrate training into institutional cycles

Align training with research milestones to maximise participation:

• Onboarding programmes
• Grant application and submission deadlines
• Doctoral training requirements
• Ethics review processes

2. Deliver through accessible and consistent formats

✔ Offer predictable schedules

Publish an annual or semester-based training calendar to enable staff to plan their attendance well in advance.

✔ Use varied formats for accessibility

Combine in-person workshops, online modules and hybrid sessions to reach participants across locations and time zones:

• Short “microlearning” sessions to complement full-day courses
• Recorded versions of live sessions for flexible follow-up

✔ Promote through multiple channels

Use diverse communication channels to ensure awareness and uptake:

• Institutional newsletters
• Intranet announcements
• Faculty mailing lists
• Social platforms

3. Maintain quality and relevance of materials

✔ Align with recognised standards

Base content on institutional, national and international resources such as RDMkit, FAIR principles and domain-specific standards.

✔ Regularly update content

Review and refresh materials at least annually, or sooner if new tools, policies or best practices emerge. Feedback from participants regarding the materials should be reviewed immediately after each training session, and, where relevant, used to update the content before the next course delivery. Include the content update in the course delivery procedures, enabling enough time for this process.

✔ Integrate feedback into development

Collect participant evaluations systematically and use them to refine content, improve delivery methods and identify emerging needs:

• Highlight unclear topics
• Identify demand for new training areas

4. Build sustainability and institutional capacity

✔ Share delivery responsibility

Develop a small team or network of trainers to reduce reliance on individuals and ensure continuity during staff changes.

✔ Adopt Train-the-Trainer models

Equip colleagues in different faculties or departments to deliver sessions locally, increasing reach without overburdening central support.

✔ Recognise and reward participation

Provide recognition mechanisms for both trainers and participants:

• Certificates
• Digital badges
• Formal academic credits (e.g. ECTS)