  Nature has inspired various ground-breaking technological developments in
applications ranging from robotics to aerospace engineering and the
manufacturing of medical devices. However, accessing the information captured
in scientific biology texts is a time-consuming and hard task that requires
domain-specific knowledge. Improving access for outsiders can help
interdisciplinary research like Nature Inspired Engineering. This paper
describes a dataset of 1,500 manually-annotated sentences that express
domain-independent relations between central concepts in a scientific biology
text, such as trade-offs and correlations. The arguments of these relations can
be Multi Word Expressions and have been annotated with modifying phrases to
form non-projective graphs. The dataset allows for training and evaluating
Relation Extraction algorithms that aim for coarse-grained typing of scientific
biological documents, enabling a high-level filter for engineers.
