Research integrity is the art of doing research well and responsibly, grounded in shared scientific values. Generally, people entering academic research have an intrinsic motivation to live up to these ideals. Guidelines and frameworks for good scientific practice give direction for how to do that. Training on this matter is often mandatory. However, learning about research integrity should not be memorising a list of abstract rules. Good scientific practice is contested terrain, with some consensus and many grey zones, personal dilemmas, competing incentives and interpersonal conflicts. Understanding the fundamental principles of research integrity, the academic system in which we operate, and the resulting controversies is crucial for researchers to derive strategies for implementing good scientific practice in their own workflows.

Open Science
Research integrity cannot be discussed without the Open Science movement. Open Science is an ambition as well as a set of tools to uphold the scientific value of transparency and supports reproducibility (the ability to repeat analyses or entire studies, which is one marker of quality and reliability in many fields). Open Science also aims to make publicly funded research more collaborative and accessible. There are many myths and some valid controversies around this topic, including concerns about data privacy. During my time in the Open Science Initiative of the MPI-CBS and as Open Science officer at the University of Leipzig, I have accumulated a vast amount of knowledge on that subject that I integrate in my trainings.

AI and integrity
New technologies raise integrity questions faster than any research integrity guideline can answer them, from authorship and disclosure to the potential bias and reliability of results. Research integrity can no longer be discussed without considering generative AI tools and their widespread use in the academic system. In my workshops, we apply general principles of integrity to the use of AI and look for strategies to work responsibly in specific use cases.

Training approach
My workshops on research integrity take a systemic approach, widening the perspective from individual conduct to the structures around it. That also means that I encourage participants not only to take responsibility for implementing good scientific practice in their own workflows, but for maintaining integrity in their fields and to contribute to discussion around field-specific standards. We work through dilemmas, debate them, and connect abstract rules back to the situations in which they have to hold.