In recent years, the scientific community has confronted significant challenges that threaten its credibility and its contribution to society. The so-called reproducibility crisis has shown that a substantial share of published findings cannot be reliably replicated, raising concerns about the robustness of current research practices. At the same time, growing awareness of questionable research practices has highlighted the urgent need for stronger norms of integrity, transparency and responsible conduct.
This course engages with these challenges by examining the principles of research ethics and the transformative potential of open science. Openness and transparency are not abstract ideals; they are essential for rebuilding trust in scientific work and ensuring that research genuinely serves the public good. Participants will explore how to implement responsible data management, share research outputs openly, and navigate complex ethical dilemmas, particularly in a landscape increasingly shaped by artificial intelligence. By fostering integrity, accountability and critical reflection, this course prepares future researchers to lead with rigor, openness, and responsibility in a rapidly evolving scientific environment.
3 ECTS
See the full course description here
CONTENT
This course has two primary objectives:
To examine the fundamental principles of scientific integrity and research ethics, addressing key challenges, common pitfalls, and best practices for ensuring responsible and ethical research conduct.
To examine the core dimensions of Open Science – its rationale, benefits and limitations - and provide practical guidance on how researchers can meaningfully and effectively implement open science practices throughout the research lifecycle.
Content covered includes:
Foundations of research ethics and responsible conduct of research
Principles and norms of scientific integrity (e.g., honesty, transparency, accountability, rigor)
Open Science: concepts, societal value, and current policy landscape
Open Access to scientific publications: models, rights retention, and strategic choices
Open and FAIR data: principles, benefits, and challenges
Responsible research data management, including Data Management Plans
Preregistration and registered reports: purposes, platforms, and practical implementation
Ethical and methodological challenges in an era of AI‑assisted research
Tensions and trade‑offs between openness, privacy, intellectual property, and research ethics
EVALUATION METHODS
As part of this course, students are assessed on a continuous basis. The evaluation includes:
(i) Group activities such as reading, analyzing, presenting and discussing scientific articles related to research ethics and open science;
(ii) Individual assignments, including the development of a robust Data Management Plan (DMP) for their master thesis or doctoral project (applying FAIR principles), the drafting of a preregistration, and/or the creation and presentation of an Open science integration plan;
(iii) active class participation.
Important note: By submitting an assignment for evaluation, students affirm that (i) the submitted work accurately reflects the facts. To ensure this, students must have verified all factual claims, especially those originating from generative AI tools (which must be explicitly acknowledged as support tools if used); and (ii) they have complied with all specific requirements of the assignment, particularly those concerning transparency and documentation of the process.
Failure to meet any of these commitments - whether through intent or negligence – constitutes a breach of the students’ obligation to truthfulness and may violate broader principles of academic integrity. Such breaches constitute academic misconduct.