  Over the last five years, research on Relation Extraction (RE) witnessed
extensive progress with many new dataset releases. At the same time, setup
clarity has decreased, contributing to increased difficulty of reliable
empirical evaluation (Taill\'e et al., 2020). In this paper, we provide a
comprehensive survey of RE datasets, and revisit the task definition and its
adoption by the community. We find that cross-dataset and cross-domain setups
are particularly lacking. We present an empirical study on scientific Relation
Classification across two datasets. Despite large data overlap, our analysis
reveals substantial discrepancies in annotation. Annotation discrepancies
strongly impact Relation Classification performance, explaining large drops in
cross-dataset evaluations. Variation within further sub-domains exists but
impacts Relation Classification only to limited degrees. Overall, our study
calls for more rigour in reporting setups in RE and evaluation across multiple
test sets.
