This category focuses on the principles, standards, and responsibilities that underpin trustworthy, ethical, and high-quality research across all scientific disciplines.

The programmes address responsible conduct of research, research ethics, authorship and contribution standards, data integrity, reproducibility, conflict of interest management, and the prevention of scientific misconduct. Participants develop a clear understanding of institutional, funder, and journal expectations, as well as the ethical and legal frameworks governing modern research practice.

Special attention is given to ethical review processes, research involving human participants, data protection and privacy, transparency in reporting, and compliance with international and European research regulations. The trainings emphasize practical decision-making in real research scenarios, helping participants identify risks early and apply ethical principles consistently throughout the research lifecycle.

This category is essential for doctoral candidates, researchers, supervisors, and project leaders seeking to ensure research credibility, protect institutional reputation, meet funder requirements, and uphold the highest standards of scientific integrity.

Certified Training Programme in Research Data, Methods & Reproducibility

This training programme focuses on strengthening the quality, transparency, and reliability of research through robust data practices, sound methodological design, and reproducible research workflows.

Participants develop a systematic understanding of how research data are generated, documented, analyzed, and validated across the full research lifecycle. The programme addresses core principles of research methods, data handling, uncertainty assessment, and reproducibility, with practical guidance on identifying methodological weaknesses, improving data integrity, and ensuring transparent and verifiable research outcomes.

Rather than focusing on specific tools or disciplines, the training emphasizes transferable frameworks and decision-making strategies applicable across data-intensive, model-based, and empirical research. Examples from areas such as GIS, remote sensing, AI, and modelling are used illustratively, without becoming the core focus.

This course is essential for researchers and research support staff seeking to improve research robustness, meet institutional and funder expectations, reduce errors and rejections, and produce research that is credible, reusable, and sustainable over time.