Cross-Cutting Research

The main goal of this research group is to design, launch and operate an integrative environment that fosters efficient, sustainable and secure data and analysis management throughout the research data cycle, to implement procedures and regulations for sharing and combining data, to facilitate matchmaking between data producers and data users, and to add value to existing data by data augmentation. We envision an environment that complements conventional pooled anonymized data with innovative ideas on data ownership and distribution. This infrastructure is meant to support the entire Healthy Longevity and Aging research process from initial planning to publication and translation with the particular focus on sustainability and innovation. Go to projects

The research group Law & Ethics of the URPP DynAge investigates ethical and legal challenges of datarich research. Topics include compliance of such research with data protection law, fair and transparent data management, ensuring informed consent in datarich research or protecting the contextual integrity of data for preventing misuse. Among others, the project aims to develop novel approaches for applying the principles of data protection law the requirements for the lawfulness of data processing and the granting of data subject rights (e.g. the right to object) in a research environment. Go to projects

This research group uses tracking data from GPS and IMU (inertial measurement unit) sensors to study how spatial mobility and physical activity contribute to health and well-being at older age. The sensor data are used to extract a broad set of indicators of individual daily mobility and physical activity, and link the movement of individuals to the semantics of places visited and paths traveled. This rich information contributes to within-person mobility and activity profiles, which may then be associated with a wide range of situational characteristics and dispositional quality of life and performance measures to explain interindividual differences in the intraindividual profiles. Go to projects

Health data acquisition has been profoundly boosted by recent advances in computation, storage, and especially sensing technologies. With ever more data being collected and shared across the world, a vision is emerging which is to conduct research based on the integration of datasets that come from different research projects. Our ultimate goal is to form a coherent picture that reveals the relationship between variables that are distributed across different datasets. We will focus on the issues that arise along with this vision, including multimodal data organization, representation, imputation, and more. Go to projects

This group develops novel methodological approaches to study variations in cognitive performance across the lifespan and along the continuum from healthy to pathological functioning. Specifically, we investigate the potential for plasticity, mechanisms for stabilization and compensation across the lifespan. For this, we acquire and analyze multimodal data sets, such as simultaneous EEG and eye-tracking, structural MRI and diffusion tensor imaging (DTI) as well as behavioral data. From these rich data sets, we extract multivariate parameters and apply state-of-the-art methods, such as machine learning, functional network modelling, and longitudinal analyses. Go to projects