Mini Worldlit: A Dataset of Contemporary Fiction from 13 Countries, Nine Languages, and Five Continents - Andrew Piper; David Bamman; Christina Han; Jens Bjerring-Hansen; Hoyt Long; Itay Marienberg-Milikowsky; Tom McEnaney; Mathias Iroro Orhero; Emrah Peksoy; Pallavi Rastogi; Sebastian Rasmussen; Roel Smeets; et al. (Journal of Open Humanities Data)
World literature plays a key role in understanding the global diversity of human storytelling. However, datasets suitable for large-scale cross-cultural analysis remain limited. Responding to the increasing digitization of literary texts and the need for more diverse and multilingual resources, we introduce Mini Worldlit, a manually curated dataset of 1,192 works of contemporary fiction from 13 countries, representing nine languages across five continents. Mini Worldlit employs consistent cross-cultural selection criteria, overseen by scholarly experts, to ensure geographic, linguistic, and stylistic coherence. The dataset provides a foundation for future comparative studies of global literary cultures, offering a template for cross-cultural sampling. Our methodology pairs geographic boundaries with linguistic communities, enabling a structured exploration of world literature. This dataset is designed to facilitate a comparative approach to understanding literature and support the growing field of multilingual digital humanities.