Digitised permanent-thinning data and activation-parameter analysis code for viscosity-modifier polymers

Digitised coordinates, tabulated regression output, and Python analysis scripts supporting the stress-activated re-analysis of permanent viscosity-loss data reported in Marx et al. (2017, Tribology Letters, doi:10.1007/s11249-017-0888-7). No new experimental measurements were generated; only numerical coordinates were extracted from previously published figures using automated colour-segmentation digitisation. Scripts reproduce Table 6, Table 7, and Figure 4(c) of the accompanying manuscript, "Polymeric Viscosity Modifiers in Lubricating Oils: A Mechanism-Led Reassessment and a Proposed Stress-Activated, Relaxation-Time Design Framework," submitted to Tribology International. Files: S1_digitised_data_Marx2017.csv — 34 digitised coordinate pairs (shear stress vs. apparent viscosity-loss rate) recovered from Figures 8, 10 and 11 of the source publication. S2_activation_parameters.csv — regression output (slope, activation parameter, standard error, coefficient of determination) for nine fitted series. S3_individual_fits.py — fits the stress-activated rate equation to each series; reads S1, writes S2. S4_pooling_and_statistics.py — pools results across seven unique formulations, performs slope-comparison t-tests and censoring-sensitivity analysis. S5_relaxation_time_test.py — evaluates a Rouse-based relaxation-time expression and tests its correlation with the extracted activation parameter. README.md — full documentation, provenance of the digitisation method, and known limitations. Requires Python 3.9+ with numpy and scipy. No plotting libraries are needed; all scripts print results to standard output. Run S3 first to regenerate S2 from the raw coordinates, then S4 and S5.