Research ethics and open science

Language

English

Main themes

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.

Learning outcomes

At the end of this learning unit, the student is able to :
1	Understand, explain and apply the core principles of research ethics in the design and conduct of their doctoral work. Critically analyze issues related to scientific integrity and develop concrete strategies to uphold ethical standards, including in the context of AI-assisted and AI-generated research. Identify and articulate the key dimensions, concepts, and societal values underpinning Open Science, and assess their relevance to their own research. Evaluate different scientific publishing models and apply Open Access principles to disseminate research transparently and effectively. Design and implement a comprehensive research data management strategy, including the development of a robust, project-specific Data Management Plan. Prepare, document, and share research data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Contribution of the teaching unit to the LSM’s Learning Outcomes framework of the program: Knowledge and reasoning Scientific and systematic approach Personal and professional development Corporate citizenship

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

Teaching methods

The course alternates between theoretical lectures (delivered by the instructor and students), case studies, field observations and guest lectures.

Evaluation methods

As part of this course, students are assessed on a continuous basis. The evaluation includes:

1. (i) Group activities such as reading, analyzing, presenting and discussing scientific articles related to research ethics and open science;
2. (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;
3. (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.

Online resources

The teaching materials are available to students on Moodle, including:

• PowerPoint slides and/or screencasts
• Scientific articles
• Case studies

Bibliography

Relevant references include (but are not limited to):

• Aguinis, Herman, Ravi S. Ramani, and Nawaf Alabduljader (2018). What You See is What You Get? Enhancing Methodological Transparency in Management Research. Academy of Management Annals, 12 (1), 83–110.
• Aguinis, Herman, and Angelo M. Solarino (2019). Transparency and Replicability in Qualitative Research: The Case of Interviews With Elite Informants. Strategic Management Journal, 40 (8), 1291–315.
• Baker, M (2016). 1,500 scientists lift the lid on reproducibility. Nature, 533, 452–454. https://doi.org/10.1038/533452a
• European Code of Conduct on Research Integrity (ALLEA-Code, 2023)
• Fiŝar, Miloš, Ben Greiner, Christoph Huber, Elena Katok, and Ali I. Ozkes and the Management Science Reproducibility Collaboration (2024). Reproducibility in Management Science. Management Science, 70 (3), 1343–56.
• Munafò, M., Nosek, B., Bishop, D. et al. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1, 0021, 1-9. https://doi.org/10.1038/s41562-016-0021
• Chambers, C. D., and Tzavella, L. (2022). The past, present and future of Registered Reports. Nature Human Behaviour, 6, 29-42. https://doi.org/10.1038/s41562-021-01193-7
• Persic, A.; Beigel, F.; Hodson, S. and P. Oti-Boateng (2021). The time for open science is now. In UNESCO Science Report: the Race Against Time for Smarter Development. Schneegans, S.; Straza, T. and J. Lewis (eds). UNESCO Publishing: Paris.
• Van Vaerenbergh, Y., Hazée, S., & Zwienenberg, T. (2025). Open Science: A Review of Its Effectiveness and Implications for Service Research. Journal of Service Research, online first, https://doi.org/10.1177/10946705251338461

Faculty or entity

> CLSM