DBIS

Cancer remains one of the most devastating diseases worldwide, with high mortality rates and limited long-term treatment success. While therapies have advanced, most current interventions only provide marginal improvements in overall survival and progression-free survival. A major reason for this limited success lies in the heterogeneity of cancer cells—even within a single tumor, cells can differ dramatically in their molecular states, drug sensitivities, and potential for metastasis.

Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to study this complexity. Unlike traditional bulk RNA sequencing, scRNA-seq allows us to profile gene expression at the resolution of individual cells, revealing subtle differences in cell populations, lineage trajectories, and cell–cell interactions. Recent advances in platforms such as 10x Genomics have enabled the profiling of millions of cells in a single experiment, offering unprecedented insights into disease biology.

Despite progress, one crucial gap remains: the lack of a comprehensive, cancer-focused cell atlas that integrates diverse datasets to capture malignant cell heterogeneity across tissues. Moreover, while large foundation models have transformed fields like natural language processing and computer vision, their potential in single-cell biology—and specifically cancer biology—remains largely unexplored. Early efforts such as scGPT and Geneformer demonstrate the promise of large-scale, self-supervised learning in biology, but no existing cross-organ, cancer-specific foundation model currently exists to connect cellular states with patient outcomes such as survival and treatment resistance.

The wood industry presents an ideal case for intelligent and sovereign data use along the value chain, especially in light of the shift toward a sustainable economy. Data offers numerous opportunities, such as improving traceability, optimizing machinery, and reducing waste and transport. The diversity in digital maturity among companies poses challenges but also provides transferable insights for other sectors. The champI4.0ns initiative is building on previous projects and fostering tools that demonstrate the potential of cross-company data use in production.

Topics of interest:

-Data Spaces and Data Ecosystems;

-Semantic modelling;

-Federated Learning.

The SAFERWATER project addresses major challenges in water management, such as climate change, population growth, and increasing cyber threats to critical infrastructure. It focuses on modernizing dams and water supply systems while leveraging digitalisation to enhance security and efficiency.

Water supply systems are facing major challenges due to climate change, population growth, and the increasing threat of (cyber)attacks on critical infrastructure. Reservoirs and water treatment facilities in particular need to be modernized, operated in an environmentally and climate-friendly manner, and simultaneously protected against digital threats.

While ongoing digitalization offers opportunities for more efficient operations and management, it also introduces new vulnerabilities to cyberattacks. The joint research project SAFERWATER is therefore developing a secure, AI-driven approach aimed at significantly enhancing the resilience and efficiency of reservoirs and water supply infrastructures.

This project develops an open-source prototype to enable a blockchain-based business model for Datenmarktplatz.NRW. The platform provides data scientists in the Rhenish mining area (Rheinisches Revier) with access to historical IoT datasets for AI development and integrates real-time IoT streams into AI systems for continuous value creation. The prototype ensures tamper-proof data handling, automates secure processes, and implements a seamless reward system using blockchain and smart contracts, fostering trust and efficiency in the marketplace.

NFDI4DS is a national research data infrastructure for Data Science and AI project. The overarching objective of the project is the development, establishment, and sustainment of a national research data infrastructure (NFDI) for the Data Science and Artificial Intelligence community in Germany.

RWTH-Teilvorhaben: Sozio-technische Systemgestaltung des Train-the-Trainer-HotSpots der NBP

„Personalisierte Kompetenzentwicklung durch skalierbare Mentoringprozesse – tech4comp“ ist eines von derzeit neun Projekten, die in der Förderlinie „Innovationspotenziale digitaler Hochschulbildung“ in interdisziplinären Forschungskonsortien didaktische, technologische und organisationale Gestaltungsaspekte der Hochschulbildung beleuchten und so zur nachhaltigen Veränderung der Hochschullandschaft beitragen werden.