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, methods and strategies for handling digital research data are to be continuously developed - from generation and management to evaluation and subsequent use. The spectrum ranges from classic data management to semantic modelling, automated information extraction and metadata enrichment to complex analyses, knowledge representation and data-driven visualization.
A particular focus is on the testing and further development of artificial intelligence and machine learning methods, including large language models, in order to improve data processing 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 procedures as well as critical reflection on the role of digital infrastructures for knowledge production.
We develop new approaches as part of collaborations and externally funded projects, combining scientific research with library infrastructure. Our work is aimed at all disciplines and thus creates interfaces between different scientific communities.
In addition, the SUB Göttingen makes an important contribution to strategic scientific issues and to the further development of national data infrastructures and participates in research networks, for example. In this way, it combines explorative research, conceptual groundwork and practical infrastructure development into an integrative approach for the sustainable handling of digital research data.
