Movies
Here are some statistics and analyses of the neuroimaging datasets, as well as annotations on each movie. Below you will find an interactive map of the Cohen's d effect size for tSNR. We compared tSNR for raw data ('raw'), minimally-preprocessed data up to blurring ('blur') and fully-preprocessed data ('pre').
- cohend raw-pre
- cohend blur-pre
- cohend blur-raw
- MNI152 2mm
Movie List
This table provides information on the movies screened for the NNDb project. We aim to expand this selection over time and make the NNDb even more comprehensive.
| Movie | Year | Length (sec) | Genre | IMDb Score |
|---|---|---|---|---|
| 12 Years a Slave | 2013 | 7715 | Historical | 8.1 |
| 500 Days of Summer | 2009 | 5470 | Romance | 7.7 |
| Back to the Future | 1985 | 6674 | Sci-fi | 8.5 |
| Citizenfour | 2014 | 6804 | Documentary | 8.1 |
| Little Miss Sunshine | 2006 | 5900 | Comedy | 7.8 |
| Pulp Fiction | 1994 | 8882 | Action | 8.9 |
| Split | 2016 | 6739 | Horror | 7.3 |
| The Prestige | 2006 | 7515 | Thriller | 8.5 |
| The Shawshank Redemption | 1994 | 8181 | Drama | 9.3 |
| The Usual Suspects | 1995 | 6102 | Crime | 8.6 |
Data Quality Statistics
Here you will find stats on data quality: the table shows percentage of manually identified independent component analysis (ICA) artifacts out of 250 dimensions, and statistics on the raw (Raw), blurred (Blur) and fully detrended and pre-processed (Pre) timeseries data by comparing temporal signal-to-noise ratios (tSNR).
| Measure | ICA Artifacts (%) | tSNR | |||||
|---|---|---|---|---|---|---|---|
| Mean | Max | ||||||
| Raw | Blur | Pre | Raw | Blur | Pre | ||
| Min | 56.40 | 11.85 | 12.20 | 13.37 | 91.33 | 110.33 | 185.85 |
| Mean | 71.71 | 39.43 | 44.55 | 63.82 | 161.19 | 201.20 | 319.18 |
| SD | 7.47 | 10.17 | 12.73 | 20.79 | 29.15 | 40.16 | 50.58 |
| Max | 87.20 | 60.10 | 68.99 | 98.03 | 218.09 | 310.91 | 431.79 |
Stimulus Annotations
Below are annotations of faces and speech for the movies screened. Click on each movie poster for seeing a table of annotations, and for downloading the data. If you would like to contribute to annotating movies, or you are interested in machine learning competitions, please do get in touch with sarah.aliko.17@ucl.ac.uk or jeremy.skipper@ucl.ac.uk, or head over to our Github page.
