
2.5 hours per module: 9:30 a.m. - 12:00 p.m. | Online
Module 1: 26.03.2026
Module 2: 21.04.2026
Module 3: 26.05.2026
Module 4: 25.06.2026
The short workhops provide you with subject-specific knowledge in research ethics, offer space to reflect on your values and attitudes as researchers as well as supervisors of qualification theses, and enable you to practice skills for dealing with conflict-laden situations in your research practice. All in all your will strengthen your competencies for making responsible decisions in everyday research activities.
The content and scope of the training are aligned with the DFG Memorandum as well as the respective institutional guidelines for safeguarding Good Scientific Practice (GSP).

This module conveys the fundamental principles of good scientific practice and highlights typical grey areas and conflicts that may arise in everyday research. It addresses both rule violations and scientific misconduct as well as possibilities for prevention. A particular focus is placed on the ombudsman system and the competent handling of conflicts. Participants learn to make responsible decisions in the scientific work context and gain an overview of national and international frameworks.

This module addresses challenges related to authorship, publications, and scientific integrity. Issues such as the order of authors, responsibilities within author teams, and the boundary between legitimate citation practices and plagiarism are discussed. Participants develop strategies for responsibly dealing with conflicts and learn to identify risks such as predatory journals.

This module addresses both the role of supervisors and the perspective of supervised researchers. Participants reflect on what constitutes good supervision, which expectations and responsibilities exist on both sides, and how challenges and conflict situations can be professionally supported. In addition, various forms of plagiarism are explained and common detection and screening tools are presented. Another focus is on the use of AI tools, their possibilities and limitations, and the associated requirements for transparency and scientific integrity.