  Large language models (LLMs) have been touted to enable increased
productivity in many areas of today's work life. Scientific research as an area
of work is no exception: the potential of LLM-based tools to assist in the
daily work of scientists has become a highly discussed topic across
disciplines. However, we are only at the very onset of this subject of study.
It is still unclear how the potential of LLMs will materialise in research
practice. With this study, we give first empirical evidence on the use of LLMs
in the research process. We have investigated a set of use cases for LLM-based
tools in scientific research, and conducted a first study to assess to which
degree current tools are helpful. In this paper we report specifically on use
cases related to software engineering, such as generating application code and
developing scripts for data analytics. While we studied seemingly simple use
cases, results across tools differ significantly. Our results highlight the
promise of LLM-based tools in general, yet we also observe various issues,
particularly regarding the integrity of the output these tools provide.
