Repository of data shown in paper "Mechanical waveform memory in in an athermal random medium" by Eamon Dwight and D. Candela. To download the data files, use the green Code button to select Download ZIP.

ss6ffgg.xlsx is a spreadsheet of the gain $\mathcal{G}$ and the fidelity $\mathcal{F}$ of the granular memory measured for the suite of six input signals shown in Fig. 5(a). The gain column of this spreadsheet lists $\mathcal{G}E\gamma_0$ where $\mathcal{G}$ is the dimensionless gain defined in the paper, $E = 1.0\times 10^7$ Pa is the grain-material Young modulus, and $\gamma_0$ is the shear-input amplitude. These data are plotted in:

• Fig. 8 (main figure), fidelity $\mathcal{F}$ versus friction coefficient $\mu$.
• Fig. 11 (main figure), fidelity $\mathcal{F}$ versus compression $\delta_0$.
• Fig. 11 (inset), fidelity $\mathcal{F}$ versus grain shape.
• Fig. 12(a) gain $\mathcal{G}$ versus compression $\delta_0$.
• Fig. 12(b) gain $\mathcal{G}$ versus number of grains $N$.

ffnn.xlsx is a spreadsheet of the data shown in Fig. 14, fidelity $\mathcal{F}$ and neural-net recognition accuracy for binary-word input signals versus number of input signals $S=2^L$.