In the Data Science Methods department, we are working on the design and implementation of innovative tools and workflows that enable the structured and automated storage, documentation, processing and analysis of data from laboratory experiments and simulations. These initiatives are crucial for promoting data-driven research and development. We provide a variety of analysis tools that are combined with modern, AI-based methods to effectively evaluate large amounts of data. These technologies are continuously being developed to meet the changing needs of research.

A central goal of our work is the innovative linking and integrated analysis of laboratory and simulation data. In doing so, we are guided by the FAIR data principles to ensure transparency and accessibility of research data. For this purpose, we at INP are developing comprehensive workflows, tools, and standards for a unified research data management that enables data to be organized and used efficiently. On this basis, we use modern data science methods, including automated and AI-supported analysis procedures.

A particular focus is on the application of machine learning methods and neural networks, which not only revolutionize data analysis but also create new and efficient possibilities for simulations. With these developments, we strive to push back the boundaries of research and make a lasting contribution to the science and technology of the future.

• Research data management tools
• Semantic information infrastructure
• Machine learning models
• Research software engineering