This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910). ## Data For each measured combination of the confidence and system size, there is one gzipped file. For different ensembles, we collected data in different ranges and quality. The paramters are: * Number of samples `m` per parameter combination * Range `r` of confidences epsilon * Distances `d` between values of epsilon (basically the resolution of the data) * Largest size `N_max` The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where `{N}` signals the system size of the simulation and `{epsilon}` is the confidence value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`). The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two). We present the data for each ensemble in one archive. * Fully connected `full.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144` * Barabasi Albert with a mean degree of 4 `BA4.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768` * Barabasi Albert with a mean degree of 10 `BA10.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536` * Square lattice with first nearest neighbors `lat1.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384` * Square lattice with second nearest neighbors `lat2.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384` * Square lattice with third nearest neighbors `lat3.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536` * Square lattice with fourth nearest neighbors `lat4.tar` * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536` * Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar` * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384` * connected Erdos Renyi with mean degree of 10 `ER10.tar` * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768` ## Data format Each final state is encoded as three lines: * The convergence time is a single integer with a line prefix '# sweeps: ' * The positions of all clusters in opinion space with a line prefix '# ' (unsorted) * The number of agents in each of the clusters without a line prefix ## Python example for reading the format An example script, which visualizes the S vs eps graph for the largest size of the fully connected case, with a function to read this format is given in `example.py`.