  Peer review constitutes a core component of scholarly publishing; yet it
demands substantial expertise and training, and is susceptible to errors and
biases. Various applications of NLP for peer reviewing assistance aim to
support reviewers in this complex process, but the lack of clearly licensed
datasets and multi-domain corpora prevent the systematic study of NLP for peer
review. To remedy this, we introduce NLPeer -- the first ethically sourced
multidomain corpus of more than 5k papers and 11k review reports from five
different venues. In addition to the new datasets of paper drafts, camera-ready
versions and peer reviews from the NLP community, we establish a unified data
representation and augment previous peer review datasets to include parsed and
structured paper representations, rich metadata and versioning information. We
complement our resource with implementations and analysis of three reviewing
assistance tasks, including a novel guided skimming task. Our work paves the
path towards systematic, multi-faceted, evidence-based study of peer review in
NLP and beyond. The data and code are publicly available.
