  Modern performance on several natural language processing (NLP) tasks has
been enhanced thanks to the Transformer-based pre-trained language model BERT.
We employ this concept to investigate a local publication database. Research
papers are encoded and clustered to form a landscape view of the scientific
topics, in which research is active. Authors working on similar topics can be
identified by calculating the similarity between their papers. Based on this,
we define a similarity metric between authors. Additionally we introduce the
concept of self-similarity to indicate the topical variety of authors.
