GRM ™- TRX - MLCI - DATASET 3.0

This dataset accompanies the technical specification GRM™ – TRX–MLCI™ Temporal Computing Architecture (2025), which introduces a deterministic, multi-layer framework for representing, analyzing, and stabilizing time-dependent processes in physical, biological, synthetic, and AI systems. The data included here makes the computational structure of that architecture fully transparent, reproducible, and available for research. The dataset does not require prior knowledge of GRM™. Instead, it provides all temporal structures in open formats so researchers can directly explore: how temporal signals evolve across layers how drift and resonance propagate how multi-layer coherence is computed how system stability is evaluated how deterministic predictions are generated Everything needed to understand and work with the TRX–MLCI framework is provided in the data itself. What the dataset contains The dataset includes all core components defined in the architecture specification : Raw temporal data (TSV, TMAP) – time-indexed state vectors and derivative maps Derived fields – coherence fields, divergence metrics, stability indicators Full MLCI computational matrix – deterministic multilayer coherence index Predictive temporal outputs – forward temporal projections generated by the deterministic model Unified multi-scale integration data – combined micro/meso/macro temporal structures 2D and 3D models – coherence fields, drift tensors, unified temporal geometries Complete documentation – dataset manual and the GRM™–TRX–MLCI™ whitepaper All files use open formats (CSV, SVG, OBJ, STL, GLTF, PDF), ensuring compatibility with Python, R, MATLAB, Mathematica, ParaView, Blender, Unity, and standard scientific workflows. Purpose of the dataset This dataset allows researchers to: examine deterministic temporal behavior independent of probabilistic models evaluate stability, resonance alignment, drift propagation, and collapse dynamics validate their own implementations of TRX–MLCI components explore multi-layer temporal computation for physics, biology, AI systems, and engineered processes build, compare, and test new temporal algorithms using reproducible ground-truth data The dataset serves as the experimental foundation of the GRM™ – TRX–MLCI™ architecture and enables anyone to study, replicate, and extend the framework’s temporal computations. Why this dataset matters It exposes a new deterministic temporal modeling approach, defined rigorously and reproducibly. It provides layered temporal data that is uncommon in traditional datasets, which typically depend on probabilistic inference. It enables the scientific community to explore coherence, stability, and prediction in a structured and transparent way. It is suitable for research, va lidation, benchmarking, model development, and educational use.