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

Samuel A. Nastase; Yun Fei Liu; Hanna Hillman; Asieh Zadbood; Liat Hasenfratz; Neggin Keshavarzian; Janice Chen; Christopher J. Honey; Yaara Yeshurun; Mor Regev; Mai Nguyen; Claire H.C. Chang; Christopher Baldassano; Olga Lositsky; Erez Simony; Michael A. Chow; Yuan Chang Leong; Paula P. Brooks; Emily Micciche; Gina Choe; Ariel Goldstein; Tamara Vanderwal; Yaroslav O. Halchenko; Kenneth A. Norman; Uri Hasson

doi:10.1038/s41597-021-01033-3

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

Samuel A. Nastase, Yun Fei Liu, Hanna Hillman, Asieh Zadbood, Liat Hasenfratz, Neggin Keshavarzian, Janice Chen, Christopher J. Honey, Yaara Yeshurun, Mor Regev, Mai Nguyen, Claire H.C. Chang, Christopher Baldassano, Olga Lositsky, Erez Simony, Michael A. Chow, Yuan Chang Leong, Paula P. Brooks, Emily Micciche, Gina ChoeAriel Goldstein, Tamara Vanderwal, Yaroslav O. Halchenko, Kenneth A. Norman, Uri Hasson

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41 Scopus citations

Abstract

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.

Original language	English (US)
Article number	250
Journal	Scientific Data
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41597-021-01033-3
State	Published - Dec 2021

All Science Journal Classification (ASJC) codes

Statistics and Probability
Information Systems
Education
Computer Science Applications
Statistics, Probability and Uncertainty
Library and Information Sciences

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10.1038/s41597-021-01033-3

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Nastase, S. A., Liu, Y. F., Hillman, H., Zadbood, A., Hasenfratz, L., Keshavarzian, N., Chen, J., Honey, C. J., Yeshurun, Y., Regev, M., Nguyen, M., Chang, C. H. C., Baldassano, C., Lositsky, O., Simony, E., Chow, M. A., Leong, Y. C., Brooks, P. P., Micciche, E., ... Hasson, U. (2021). The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension. Scientific Data, 8(1), Article 250. https://doi.org/10.1038/s41597-021-01033-3

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title = "The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension",

abstract = "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.",

author = "Nastase, {Samuel A.} and Liu, {Yun Fei} and Hanna Hillman and Asieh Zadbood and Liat Hasenfratz and Neggin Keshavarzian and Janice Chen and Honey, {Christopher J.} and Yaara Yeshurun and Mor Regev and Mai Nguyen and Chang, {Claire H.C.} and Christopher Baldassano and Olga Lositsky and Erez Simony and Chow, {Michael A.} and Leong, {Yuan Chang} and Brooks, {Paula P.} and Emily Micciche and Gina Choe and Ariel Goldstein and Tamara Vanderwal and Halchenko, {Yaroslav O.} and Norman, {Kenneth A.} and Uri Hasson",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = dec,

doi = "10.1038/s41597-021-01033-3",

language = "English (US)",

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Nastase, SA, Liu, YF, Hillman, H, Zadbood, A, Hasenfratz, L, Keshavarzian, N, Chen, J, Honey, CJ, Yeshurun, Y, Regev, M, Nguyen, M, Chang, CHC, Baldassano, C, Lositsky, O, Simony, E, Chow, MA, Leong, YC, Brooks, PP, Micciche, E, Choe, G, Goldstein, A, Vanderwal, T, Halchenko, YO, Norman, KA & Hasson, U 2021, 'The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension', Scientific Data, vol. 8, no. 1, 250. https://doi.org/10.1038/s41597-021-01033-3

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AU - Nastase, Samuel A.

AU - Liu, Yun Fei

AU - Hillman, Hanna

AU - Zadbood, Asieh

AU - Hasenfratz, Liat

AU - Keshavarzian, Neggin

AU - Chen, Janice

AU - Honey, Christopher J.

AU - Yeshurun, Yaara

AU - Regev, Mor

AU - Nguyen, Mai

AU - Chang, Claire H.C.

AU - Baldassano, Christopher

AU - Lositsky, Olga

AU - Simony, Erez

AU - Chow, Michael A.

AU - Leong, Yuan Chang

AU - Brooks, Paula P.

AU - Micciche, Emily

AU - Choe, Gina

AU - Goldstein, Ariel

AU - Vanderwal, Tamara

AU - Halchenko, Yaroslav O.

AU - Norman, Kenneth A.

AU - Hasson, Uri

PY - 2021/12

Y1 - 2021/12

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AB - 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.

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ER -

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

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