FUNGI-MYCEL Dataset: 2,648 Mycelial Network Units (MNUs) from 39 Protected Forest Sites across 5 Biome Categories — MNIS Scores, Eight-Parameter Measurements, and Validation Records (2007–2026)

This dataset contains the complete observational records underlying the FUNGI-MYCEL framework — the first mathematically rigorous, AI-integrated multi-parameter framework for the quantitative characterization of mycelial network intelligence via the Mycelial Network Intelligence Score (MNIS). Dataset contents: — 2,648 Mycelial Network Unit (MNU) records — 39 protected forest sites across 6 continents — 5 biome categories: temperate broadleaf, boreal conifer, tropical montane, Mediterranean woodland, sub-arctic birch — 19-year observational period: January 2007 – December 2026 — Annual sampling cycles with sub-annual bioelectrical monitoring at 12 priority sites Variables included per MNU per sampling cycle: — η_NW: Mineral Weathering Efficiency (μg mineral · cm⁻² hyphae · day⁻¹) via ICP-MS — ρ_e: Bioelectrical Pulse Density (spikes · cm⁻² · hour⁻¹) via in-situ microelectrode array — ∇C: Chemotropic Navigation Gradient (angular deviation, °) via time-lapse confocal microscopy — SER: Symbiotic Exchange Ratio (dimensionless) via ¹³C/³¹P isotope tracing — K_topo: Topological Fractal Dimension (Hausdorff dimension) via SEM box-counting — ABI: Adaptive Biodiversity Index (H′_rhizo / H′_bulk ratio) via 16S eDNA metabarcoding — BFS: Biological Field Stability (recovery half-time τ½, years) via exponential curve fitting — ARC: Adaptive Resilience Coefficient (DFA scaling exponent α) via detrended fluctuation analysis — MNIS_final: Composite Mycelial Network Intelligence Score [0,1] — AES: Anthropogenic Encroachment Score [0,1] — Site metadata: GPS coordinates, biome category, disturbance status, MAT, MAP, soil pH, SOC, canopy cover — Cross-validation partition assignments (leave-one-site-out folds) — H8 ablation study held-out set flags (397 MNU-years) Key results: — MNIS prediction accuracy: 91.8% (39-site LOSO cross-validation) — Bioelectrical stress detection rate: 94.3% (false alert rate: 4.2%) — Early warning lead time: 42 days before above-ground symptom expression — ρ_e × K_topo Network Intelligence Index: r = +0.917 (p < 0.001, n = 2,648) — SER symbiotic exchange fidelity: 87.4% within ±12% of optimal stoichiometry — ABI biodiversity amplification: H′_rhizo = 1.84 × H′_bulk (mean across intact sites) — BFS field stability half-time: τ½ = 4.1 ± 0.7 years post-disturbance Associated paper: Baladi, S. (2026). FUNGI-MYCEL: A Quantitative Framework for Decoding Mycelial Network Intelligence, Bioelectrical Communication, and Sub-Surface Ecological Sovereignty. Submitted to Nature Microbiology. DOI: 10.14293/FUNGI-MYCEL.2026.001 Python package: pip install fungi-mycel (PyPI: pypi.org/project/fungi-mycel/)