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| ⚠️ Status: Ground Zero Rebuild (April 2026) |
| This dataset has been intentionally purged and reset. After initial prototyping and large-scale data ingestion, the research has transitioned into a strict verification phase. To ensure the scientific integrity of the Spikenaut ecosystem, all previously uploaded data was deleted to make way for a verified, high-fidelity baseline. |
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| Current Objective: Re-validating the hardware telemetry layer using Dynex mining as the ground-truth baseline. |
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| 🧠 Project Context |
| This telemetry data serves as the "Sensory Input" for the Spikenaut Neuromorphic Architecture. Before infusing SNN logic into high-level models like OLMoE-7B, the underlying temporal signals must be absolute. |
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| Research Focus: Neuromorphic High-Frequency Data Processing. |
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| Methodology: "Measure twice, spike once." |
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| Hardware Baseline: Pure Dynex mining telemetry (GPU/CPU/Efficiency). |
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| Infrastructure: Developed on the Ship of Theseus workstation (Fedora 43). |
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| 📊 Dataset Structure (WIP) |
| Once the verification process is complete, this dataset will contain high-resolution temporal features formatted for Liquid State Machine (LSM) and STDP training. |
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| 1. Raw Telemetry Data |
| Timestamps: Microsecond-precision Unix epochs. |
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| Compute Metrics: GPU NVML (Power, Temp, Clocks) and CPU k10temp/powercap. |
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| Algorithm Efficiency: Hashrate fluctuations and mining pool volatility. |
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| 2. Spiking Features |
| Poisson Encodings: Data translated into spike trains for SNN-native processing. |
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| Neuromodulator Signals: Reward/Pain signals derived from efficiency vs. thermal overhead. |
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| ⚖️ License & Research Ethics |
| This dataset is part of the open-source research initiative by Raul Montoya Cardenas. |
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| License: GNU General Public License v3.0 (GPL-3.0) |
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| Intent: Transparency and reproducibility in neuromorphic engineering. |
