Quantum Coherence Preservation in Fibonacci-Structured Microtubules During HIV-Induced Neuroinflammation: Computational Modeling Data and Analysis

Quantum Coherence Preservation in Fibonacci-Structured Microtubules During HIV-Induced Neuroinflammation: Computational Modeling Data and Analysis Description: This dataset includes comprehensive computational modeling data, Python simulation scripts, detailed analysis results, and validation materials supporting the manuscript titled “Quantum Coherence Preservation in Fibonacci-Structured Microtubules During HIV-Induced Neuroinflammation.” The dataset explicitly addresses the longstanding cognitive paradox observed during acute HIV infection, wherein patients maintain cognitive function despite severe neuroinflammatory cytokine responses. Employing rigorous computational modeling and Monte Carlo simulations, this study demonstrates that quantum coherence is significantly preserved within microtubules arranged in Fibonacci-based geometries compared to regular structures. Contents: Raw Data Files: Detailed simulation output data (CSV, JSON, NPZ formats). Simulation Scripts: Clearly documented Python scripts for reproducibility of quantum coherence modeling and Monte Carlo analysis. Figures and Visualizations: High-resolution images illustrating key computational findings, quantum coherence dynamics, and parameter sensitivity analyses. Validation Materials: Comprehensive parameter derivations, sensitivity analyses, and robustness checks explicitly validating computational integrity. README Documentation: Detailed explanations of dataset contents, file structures, methodologies, and explicit references to parameter sources from established scientific literature. Clarification Note: This dataset was previously retracted due to initial concerns regarding model parameter accuracy. Following comprehensive review and validation, all parameters and computational methodologies have been explicitly confirmed as scientifically justified, accurate, and reproducible. This re-uploaded dataset reflects rigorous scientific standards and methodological transparency. Usage: Researchers interested in quantum biology, computational neuroscience, neuroinflammation modeling, or quantum coherence phenomena are encouraged to use this dataset for further analysis, validation, and comparative studies.