Presentation of QUEST activities to research groups

The focus topics are:

• Incentives and indicators in research

  Considerations about a more quality-oriented assessment of research performance and indicators for the performance-based allocation of funds (LOM).

• Electronic lab notebook

  An introduction to the eLN, highlighting the advantages of switching from the traditional paper book to a digital documentation format and addressing common issues when implementing it. For groups already using the eLN, specific issues encountered during its use can be addressed. 

• Research data management & Open Data

  Introduction to practices and infrastructures for handling research data to make it more efficient and ensure long-term usability of the data. In addition, we will present ways in which data can be made available to third parties to ensure transparency and enable reuse. This can be done in a completely open way or with access constraints.

• Reproducibility and Quality in Preclinical Research

  The discussion will focus on key factors influencing the reproducibility of preclinical research, such as sample size calculation, blinding, randomization, and the application of appropriate statistical tests for data analysis. These elements are essential to ensuring reliable, valid, and unbiased results. The QUEST Center supports with several tools and is engaged in initiatives aimed at promoting standardized procedures and maintaining high-quality standards in academic biomedical research, fostering greater transparency, reliability, and reproducibility across the scientific process.

• Preclinical systematic reviews & meta-analyses

  Introduction to these two meta-research methods for summarising research findings from different sources, and for evaluating both range and quality of available evidence addressing specific research questions.

• Patient and Stakeholder Engagement

  Considerations on why and how to involve patients and other stakeholders in your research projects.

• Causal Inference for Valid Interpretation of Results

  How does causal inference differ from description and prediction in health data science and why is it essential for producing meaningful and decision-relevant results? This discussion focuses on clearly defined causal questions, the importance of transparent and explicit assumptions, and how study design and analytic choices can mitigate biases and improve validity.

• Trustworthy AI/AI Ethics

  Considerations regarding the interface between trustworthy AI and meta-research, particularly with regard to methods for evaluating, improving, and responsibly implementing robust, transparent, and ethically accountable AI systems in  healthcare.

• Scientific Peer Review and Editing

  Why peer review and editorial processes matter for research quality, and how clear communication, constructive critique, and transparency improve scientific publications. The presentation highlights the roles of authors, peer reviewers, and editors within the scientific ecosystem, including emerging models of peer review as well as issues of credit, contributorship, and incentives.