The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

The “Narratives” 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 (~43,0...

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Veröffentlicht in:Scientific data 2021-09, Vol.8 (1), p.250-250, Article 250
Hauptverfasser: Nastase, Samuel A., Liu, Yun-Fei, Hillman, Hanna, Zadbood, Asieh, Hasenfratz, Liat, Keshavarzian, Neggin, Chen, Janice, Honey, Christopher J., Yeshurun, Yaara, Regev, Mor, Nguyen, Mai, Chang, Claire H. C., Baldassano, Christopher, Lositsky, Olga, Simony, Erez, Chow, Michael A., Leong, Yuan Chang, Brooks, Paula P., Micciche, Emily, Choe, Gina, Goldstein, Ariel, Vanderwal, Tamara, Halchenko, Yaroslav O., Norman, Kenneth A., Hasson, Uri
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Sprache:eng
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Zusammenfassung:The “Narratives” 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. Measurement(s) functional brain measurement Technology Type(s) functional magnetic resonance imaging Factor Type(s) story • native language Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14818587
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-021-01033-3