This archive contains raw data, fitted parameters, reproducible code, and the full protocol document for the evaluation of the CPA + Constraint (CPA + C) viscosity model against VFT, MYEGA (Mauro et al. 2009), and Avramov–Milchev (1988) across five canonical glass-forming datasets spanning fragile molecular liquids and an intermediate network glass-former. The accompanying paper is available at arXiv:2511.16791. CPA + C outperforms VFT, MYEGA, and Avramov–Milchev on four of five datasets, with margins reaching ΔAIC = 140.9 over MYEGA and 124.4 over Avramov–Milchev on the largest dataset (salol, n = 95). On one dataset (Laughlin OTP, n = 35, the narrowest temperature range), Avramov–Milchev achieves the best fit, as expected when the measurement window is too narrow for the constraint transition to be resolved. BIC confirms the same ranking on all five datasets. Leave-one-out cross-validation on the salol dataset shows CPA + C generalizes to held-out data with mean absolute prediction error 3× lower than the next-best model. A nonparametric bootstrap on the Laughlin salol dataset (1000 resamples) confirms the ΔAIC margins are robust: every resample yields ΔAIC well above the conventional strong-evidence threshold for all three comparators. A smooth sigmoid replacement for the piecewise constraint function yields equivalent or improved fit quality, confirming insensitivity to the functional form. The bootstrap and sigmoid-variant analyses are reported in two manuscript appendices. All code is provided. Run the Python scripts to reproduce each number. The fitting code can be applied directly to any viscosity–temperature series in the same format, facilitating independent replication and extension to additional glass families.

Version History v4 (June 2026): Theoretical framing clarified: the Continuous Present Actualization (CPA) basis of the model is now explained directly in the motivation, and the efficient-continuance behavior is presented as emergent from CPA under constraint rather than as a separate principle, consistent with the current formulation of Dynamic Present Theory I. Data, fits, code, and all reported numbers are unchanged from v3. v3 (March 2026, updated May 2026): Multi-start optimization corrects Laughlin and Plazek CPA + C fits from local minima to global optima. Salol added as fourth dataset (n = 95). Casalini & Roland (2004) added as fifth dataset for digitization cross-validation. Reproducible Python scripts added. Data extraction provenance documented. Nonparametric bootstrap robustness analysis added on Laughlin salol (1000 resamples) confirming the ΔAIC margins are tight; manuscript updated with two appendices (Bootstrap Robustness; Sensitivity to the Constraint Function Form); Casalini Salol VFT ΔAIC corrected from a transcription discrepancy; Conclusions, Abstract, and Limitations sections updated to use consistent "weakly identified and compatible with zero" language for the A parameter on Plazek and Laughlin salol; new scripts added: bootstrap_chunk.py, analyze_bootstrap.py, plazek_with_anomaly.py. v2 (2025): Terminology standardized to align with Dynamic Present Theory I. v1 (2025): Initial release.