Project Details
Projekt
Data management, analysis and integration
Applicant
Professor Dr.-Ing. Jan Hasenauer
Subject Area
Cell Biology
Bioinformatics and Theoretical Biology
Bioinformatics and Theoretical Biology
Term
since 2025
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 533863915
The revolution of sequencing, mass spectrometry and imaging techniques observed during the last decade resulted in an unprecedented acceleration of data generation and provided a tremendous amount of novel insights, e.g., in pathology-associated phenotypes and cellular heterogeneity. However, this would not have been possible without the development of new tailored computational methods. These computational methods build on community standard for the exchange of data. Furthermore, their development benefited from the implementation of the FAIR data principles, which promotes findable, accessible, interoperable and reproducible data. The computational methods are mostly implemented in open-source software tools, which provides a rich research software infrastructure and enables experimental groups to conduct data exploration and statistical analysis. Yet, in-depth dataset analysis of large-scale datasets benefit from expert-level knowledge in statistics, computer science, and mathematical modeling. Thus, the management, analysis and integration of research data remains a critical component of scientific projects, yet these aspects are often underestimated, leading to challenges during and at the conclusion of projects. This Z project will support research data management, analysis and integration within the MagNet. We will enhance the quality, reproducibility, reusability, and impact of scientific results while ensuring adherence to FAIR principles. Building on our expertise, we aim to address the integration of diverse datasets to identify common mechanisms of macrophage effector functions. Specifically, we will: 1. Provide a central data management infrastructure to facilitate standardized, high-quality data collection, storage, and sharing. 2. Provide statistical and bioinformatic data analysis as well as mechanistic modelling to support experimental groups in extracting meaningful insights from their data. 3. Integrate datasets to define conserved and delineate common from distinctive macrophage functions across tissues. Overall, our approach will enhance our understanding of macrophage biology and provide insights into their roles in various physiological and pathological contexts.
DFG Programme
Research Units
