@article{501,
  author = {Samuel Nastase and Yun-Fei Liu and Hanna Hillman and Asieh Zadbood and Liat Hasenfratz and Neggin Keshavarzian and Janice Chen and Christopher Honey and Yaara Yeshurun and Mor Regev and Mai Nguyen and Claire Chang and Christopher Baldassano and Olga Lotisky and Erez Simony and Michael Chow and Yuan Leong and Paula Brooks and Emily Micciche and Gina Choe and Ariel Goldstein and Tamara Vanderwal and Yaroslav Halchenko and Kenneth Norman and Uri Hasson},
  title = {The {\textquotedblleft}Narratives{\textquotedblright} fMRI dataset for evaluating models of naturalistic language comprehension},
  abstract = {<p>The {\textquotedblleft}Narratives{\textquotedblright} collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.</p>
},
  year = {2021},
  journal = {Scientific Data},
  volume = {8},
  doi = {10.1038/s41597-021-01033-3},
}