The recently established Leibniz Science Campus “Digital Transformation of Research” (DiTraRe) investigates effects of the broadly understood process of digitalisation of research on a multilevel scale. In a brief introduction, we present the main ideology behind DiTraRe, which is to establish a new research branch. We present different measures via which we are aiming at our main goal as well as discuss different outcomes which will be achieved alongway. We discuss the impact which our consortium may have on researchers and society together with its connections to NFDI initiatives.

Expert talk: Data Infrastructures as a Foundation for AI Projects
This presentation will examine the critical components that drive AI progress, with a focus on the need for large-scale, machine-interpretable data that adapts to the unique demands of different sectors. Key topics will include transparency and reproducibility in AI, as well as the importance of making essential resources—publications, data, models, and code—accessible, interconnected, and scalable.
A robust data infrastructure is essential for the AI community, supporting every stage of the data lifecycle—from collection and creation to processing, analysis, publishing, archiving, and reuse of resources. This talk will highlight the foundational role of these infrastructures in AI-driven initiatives, showcasing several ongoing infrastructure initiatives and illustrating how they contribute to a modern data ecosystem.

DiTraRe Presentation: Usecase "Sensitive data in sports science" 
The MO|RE data platform makes physical fitness data derived from sports science studies available to both academia and the public. Research would strongly benefit from linking health with physical fitness data, e.g. in longitudinal data sets. However, publishing sensitive health (e.g. BMI, blood pressure) and other personal data (e.g. geolocation, social status) is challenging. An overarching concept for the secure handling of sensitive data is lacking, ranging from a trustworthy IT environment to sophisticated access management and auditing mechanisms, which ensures compliance with legal regulations.

Expert Talk
This presentation provides an overview of the development of AI/ML and data analysis in sports science research and teaching in recent years. Based on technological developments in sensor technology, especially in the field of commercial soccer, coupled with changes in the media coverage of sports games (Memmert, 2024b), new types of performance evaluation have become established (Memmert, 2021). The massive increase in available data, subsumed under the term “big data,” enables the calculation of more complex metrics (Memmert, 2025a,b), which has also become important for university research and teaching in sports science. Based on position/event data, significantly faster and more granular analyses are possible than those based on video material. This has led to new research questions, courses, and content in sports science (Memmert, 2024a). Using the example of match analysis, this development is illustrated with future teaching trends and testing potential.
 

Literature
Memmert, (Ed.) (2024a). Computer Science in Sport: Modeling, Simulation, Data Analysis and Visualization of Sports-Related Data. Berlin: Springer-Verlag. 
Memmert, (Ed.) (2024b). Sports Technology. Technologies, Fields of Application, Sports Equipment and Materials for Sport. Berlin: Springer-Verlag. 
Memmert, (Hrsg.) (2025a). Artificial Intelligence and Machine Learning in Sports Science. Berlin: Springer-Verlag. 
Memmert, D. (Ed.) (2021). Match Analysis. Abingdon: Routledge. 
Memmert, D. (Hrsg.) (2025b). Digitalization and innovation in sport and sport science: Handbook of Sport and Sports Science. Berlin: Springer-Verlag.