The QUEST Center conducts projects in various areas such as Open Science, Stakeholder Engagement, Incentives & Indicators, and Quality Assurance. We are developing new approaches in design, analysis and reporting in biomedical, both preclinical and clinical research to improve research. To learn more about our projects, please use our filter options below.
Complementary to our implementation activities, we have established the Monitoring & Evaluation System COMPASS. For more information, please see here.
In the scope of COMPASS, we have developed a modular self-evaluation tool on responsible research practices. Whether you are a student, want to develop as a researcher/research team, or want to improve research evaluation from a reviewer's perspective, with our COMPASS on Research Quality, you can start your journey toward more impactful research today. Once you have completed and submitted your assessment, you’ll receive a visual summary.
ANONY-MED creates an ethically aligned, trustworthy AI toolbox for privacy-preserving data synthesis using generative AI methods like GANs and homomorphic encryption in healthcare. This advances AI models in radiology, cardiology, and stroke therapy, while ensuring transparency and robustness.
The project aims to include FAIR criteria in the Charité Metrics Dashboard and co-develop prototype open science dashboards for other research fields.
The project "Berlin University Alliance Open Science Magnifiers" continues and further develops the systematic monitoring of Open Science practices. This includes visualizations of indicators for open practices, as well as highlighting of specific best-practice examples.
The CARDS project aims to develop and expand tools, services, and training offered to researchers of the Berlin University Alliance (BUA) on the topic of research data management (RDM).
In winter 2021/22, the current status of research data management (RDM) was surveyed throughout the Berlin University Alliance (BUA), i.e. at the Charité and the three large Berlin universities.
This project will develop recommendations for actions that researchers, research institutions, funders publishers can use to promote the sharing of detailed methods and reusable step-by-step protocols in the life sciences.
The project aims to optimize trustworthiness, robustness, usefulness, and transparency of data and algorithm-driven biomedical research. Through conceptual development, meta-research, and stakeholder engagement, we aim to generate evidence-based best practices for high research standards.
Training biomedical researchers for a deep understanding of processes, values, and merits of clinical trial data sharing.
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