The research domains of business, economics, and other social sciences are concerned with the relationships among individuals and organizations within a society. To understand these complex systems, social science disciplines have been using empirical methods for a long time. However, unstructured data (e.g., images or videos from social media), are gaining in relevance. The generation of continuous data streams in society and economy (datafication) strengthens this trend: It is estimated that by 2025 80% of the data processed in economic applications will be available in unstructured form.

Because of the sheer size and, more importantly, the lack of structure and the heterogeneity of raw data, in particular data mined from the Web, the BERD@NFDI community calls for innovative and reusable methods, mostly from artificial intelligence and machine learning, as well as a suitable storage and computing environment to process the data in a way that it can be used for further scientific analyses. Consequently, algorithms become an integral part of the research data life cycle and thus have to be managed in the same way as the data itself. Within this context, the mission of BERD@NFDI is to develop, provide, and maintain a future-oriented research data infrastructure for the integrated management of unstructured research data and related scientific software and machine learning models that will be strictly aligned to the actual needs of the scientific user community.

GESIS' main focus in BERD@NFDI is the development of innovative methods for the extraction of metadata and relevant entities from unstructured sources, the development of a search infrastructure, and the improvement of the findability of data and resources on the Web.