readme.txt for data accompanying PLOS One paper "Collective Behaviour in Video Viewing" master2.mat - data file containing the video ID, title, and Ad Meter rating for each of 65 commercials shown to subjects; a blank ("[]") in the Rating column means no Ad Meter rating was available full_eye_data.mat - data file containing the following Matlab variables: 1. columns, a guide to the columns of... 2. data, divided into ten columns as follows--block samples, the sample number per subject per video; block time, the elapsed time per subject per video; gaze position in x-direction (pixels); goodness of x-gaze data (0 for bad, 1 for good); gaze position in y-direction (pixels); goodness of y-gaze data (binary, as before); pupil area; goodness of pupil data (binary, as before); subject number; video number 3. fs, collections taken per second (250) 4. master, containing just the video ID and title 5. types, the data types of each of the 10 columns in "data" superbowl_demographics_30Mar2015.mat - data file containing the following Matlab variables: 1. column_labels, a guide to the columns of... 2. subject_demographics, a list of certain demographic information about each of the subjects involved in the study, divided into five columns-- subject ID; gender ID (1 for male, 2 for female); age range (1 for 18-25 years, 2 for 26-30 years, 3 for 31-35 years); ethnicity (1 for caucasian, 2 for African-American, 3 for Latinx, 4 for Asian-American, 5 for multiple ethnicities); native English speaker (0 for non-native but proficient, 1 for native speaker) ratings.mat - data file containing each subject's individual ratings of the 65 videos shown (each subject is one column) subs_remove.mat - data file containing the subjects whose data is faulty for each of 65 videos eyedisplay.m - method to clean and display gaze position data, and find correlations in x- and y-directions; also generates an output variable "makeR" with the cleaned x- and y-position data, from which you can generate each viewer's two-dimensional gaze position "r(t)" eyedisplay_split.m - same as eyedisplay.m but just for the longer videos (1, 3, 5, 6, 9 to 13, 18, 20 to 22, 27, 29, 32, 36, 37, 40 to 42, 45, 46, 51, 56, 59, 60) whose data must be split into smaller chunks in order to be analysed in the same manner as the rest of the data nancorrcoef.m - gets correlation coefficient for gaze data in x- or y-direction, disregarding any NaN values inexact_alm_rpca.m - clean data using sparse PCA method; you will also need the folder "PROPACK" for this, and choosvd.m (NOT PROGRAMMED BY ME BUT NECESSARY FOR PROJECT) threshold.m - percolation method to find the threshold at which a phase transition occurs in a network; yields the giant and second components of the network, with the size of each component stored in column 1 mc.c - Monte Carlo algorithm to find interactions J_ij and critical temperature for a network; further details given in the paper random_gen.h - random number generator to be used in conjunction with mc.c read_lib_ORIGINAL.h - reads x- and y- velocity data from .txt format and prepares it for use by mc.c mc_s.c - same as mc.c but for the videos that needed to be split, as defined above mscd_afg.m - method to find community structure and modularity of networks