Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. This spring's hot topic of conversation for my colleagues in higher ed was that "Everyone Is Cheating Their Way Through College" article in New York magazine. Most of the fellow professors I spoke with about this were horrified by how often students now can and do let A.I. write their papers. Others are joining their students in asking, Why not? A surprising coalition--William Shakespeare and 17th-century scribes, as well as 21st-century elementary school teachers, anti-fascist scholars, and epidemiologists--would tell you why not. A key principle for 17th-century scholars transcribing or translating classical or biblical texts was lectio difficilior potior: The reading that is stranger is stronger.

We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. Leveraging the data released in Hyperbard, we demonstrate that many solutions to popular graph mining problems are highly dependent on the representation choice, thus calling current graph curation practices into question. As an homage to our data source, and asserting that science can also be art, we present all our points in the form of a play.