Semantic Relativity Theory — Simulation Dataset and Scripts (v1.0)

This dataset accompanies the paper “Semantic Relativity Theory: An Information-Geometric Extension to Cognitive Gravity and Advertising Optimization” (v1.0). It contains all materials required to reproduce the AI-based simulations reported in the paper: the Python script, generated figures, and SHA256 checksum files.Contents- simulation.py — main script to generate synthetic semantic scores for 200,000 virtual users, compute uplift U = 1 + α tanh(β S), and export figures.- Figures — ctr_comparison.png, uplift_histogram_sim.png, uplift_convergence.png.- Checksums — *.sha256 files for integrity verification.- README.md — environment and step-by-step instructions.How to reproduce1) Python 3.9+ with NumPy, SciPy, Matplotlib (versions as in README).2) Verify integrity: `sha256sum -c .sha256`3) Run: `python simulation.py`Distribution noteThis dataset supersedes the previous Zenodo record for long-term stability on figshare. Scientific content is unchanged; only citation/back-matter are updated.How to cite (dataset)PSBigBig (2025). Semantic Relativity Theory — Simulation Dataset and Scripts (v1.0). figshare. https://doi.org/10.6084/m9.figshare.30351523