  The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography
(MEG) recordings of 27 English speakers who listened to two hours of
naturalistic stories. Each participant performed two identical sessions,
involving listening to four fictional stories from the Manually Annotated
Sub-Corpus (MASC) intermixed with random word lists and comprehension
questions. We time-stamp the onset and offset of each word and phoneme in the
metadata of the recording, and organize the dataset according to the 'Brain
Imaging Data Structure' (BIDS). This data collection provides a suitable
benchmark to large-scale encoding and decoding analyses of temporally-resolved
brain responses to speech. We provide the Python code to replicate several
validations analyses of the MEG evoked related fields such as the temporal
decoding of phonetic features and word frequency. All code and MEG, audio and
text data are publicly available to keep with best practices in transparent and
reproducible research.
