Research Data
Data is a cross-cutting topic in many research disciplines. It has become significantly more relevant in recent years. Libraries have traditionally focused on data, and digitalization is also increasing awareness in research and society.
Working methods, approaches and strategies for handling digital research data should be continuously developed – from generation and management to evaluation and reuse. This ranges from classic data management to semantic modelling, automated information extraction and metadata enrichment to complex analyses, knowledge representation and data-driven visualisation.
Focus
A particular focus is on testing and further developing artificial intelligence and machine learning methods, including large language models, in order to improve data preparation and analysis as well as the reusability of scientific data. This also includes the transfer of formal scientific-theoretical concepts – for example, from model theory – into algorithmically usable methods, as well as critical reflection on the role of digital infrastructures in knowledge production.
Cooperation
We develop new approaches within the framework of collaborations and third-party funded projects, combining scientific research with library infrastructure. Our work is interdisciplinary and addresses all disciplines, thus creating interfaces between different scientific communities.
Infrastructures
In addition, the SUB Göttingen makes an important contribution to scientific strategy issues and the further development of national data infrastructures and participates in research networks, for example. In this way, it combines exploratory research, conceptual groundwork and practical infrastructure development into an integrative approach for the sustainable handling of digital research data.