Scripts for the data paper "Le Petit Prince: A multilingual fMRI corpus using ecological stimuli".

1. compute_fd.py:

• Compute the tsnr for the raw and preprocessed fMRI data.

2. compute_tsnr.py:

• Compute the temporal signal-to-noise ratio (tSNR) for the raw and preprocessed fMRI data.

3. compute_isc.py:

• Compute inter-subject correlation (ISC) for each voxel's timeseries across subjects in each language group.

4. get_f0_intensity.m:

• Get the f0 and root-mean-square (RMS) intensity for every 10 ms of the audios using the Matlab toolbox Voicebox.

5. get_word_frequency.py:

• Get word frequency using the Google Books (Version 20120701) unigram frequency counts.

6. get_word_embeddings.py:

• Extract GloVe and BERT embeddings using SpaCy.

7. parse_syntax.sh:

• Get part-of-speech tagging, constituent tree structure and dependency relations for every sentence using the Stanford parser.

8. count_parser_actions.py:

• Syntactic node counts for each word in the audiobooks based on bottom-up, top-down and left-corner parsing strategies as applied to the Stanford-derived constituency trees described above.