Update README.md

README.md

@@ -1,52 +1,43 @@
 ---
 language:
 - en
-license: mit
-task_categories:
-- time-series-forecasting
-- feature-extraction
-pretty_name: "Spikenaut V2 Telemetry Data"
+pretty_name: Spikenaut V2 Telemetry Data
 ---
+⚠️ 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.
 
-# Spikenaut V2 Telemetry Data
+Current Objective: Re-validating the hardware telemetry layer using Dynex mining as the ground-truth baseline.
 
-Multivariate time-series dataset for training a hardware-aware Spiking Neural Network (SNN) across 7 blockchain networks.
+🧠 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.
 
-## Dataset Description
+Research Focus: Neuromorphic High-Frequency Data Processing.
 
-- **Total Events:** 1,132,861 validated neuromorphic events
-- **Format:** JSONL (JSON Lines)
-- **Pre-scrubbed:** All sensitive identifiers (wallets, IPs, hostnames) removed
+Methodology: "Measure twice, spike once."
 
-## Files
+Hardware Baseline: Pure Dynex mining telemetry (GPU/CPU/Efficiency).
 
-| File | Rows | Size | Description |
-|---|---|---|---|
-| `neuromorphic_data.jsonl` | 789,114 | 363 MB | GPU telemetry: voltage, temps, hashrate, power, clocks, fan speed, utilization |
-| `ghost_market_log.jsonl` | 223,020 | 174 MB | Live crypto market ticks: price & volatility for DNX, Qubic, Kaspa, Monero, Ocean, Verus |
-| `node_sync_harvest.jsonl` | 120,334 | 55 MB | Blockchain node sync events: block acceptance rates for Kaspa, Monero, Dynex |
-| `qubic_ticks.jsonl` | 27,430 | - | Qubic epoch & tick traces |
-| `snn_model.json` | - | 3.8 KB | Snapshot of trained SNN model weights |
-| `parameters.mem` | - | - | FPGA Q8.8 membrane potential parameters |
-| `parameters_decay.mem` | - | - | FPGA Q8.8 decay parameters |
-| `parameters_weights.mem` | - | - | FPGA Q8.8 synaptic weights |
+Infrastructure: Developed on the Ship of Theseus workstation (Fedora 43).
 
-## Channels (V2: 16-channel)
+📊 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.
 
-V2 processes 16 parallel channels across 4 trading lobes (Scalper, Day, Swing, Macro):
-- Hardware telemetry (GPU/CPU temp, power, clocks, fan)
-- Blockchain sync metrics (block acceptance, tick rates)
-- Market data (price, volatility across 6+ assets)
-- Qubic epoch/tick traces
+1. Raw Telemetry Data
+Timestamps: Microsecond-precision Unix epochs.
 
-## Use Cases
+Compute Metrics: GPU NVML (Power, Temp, Clocks) and CPU k10temp/powercap.
 
-- Multivariate time-series forecasting for cryptocurrency markets
-- Hardware telemetry anomaly detection
-- FPGA co-processor deployment (Xilinx Artix-7)
-- SNN training with E-prop + OTTT learning rules
+Algorithm Efficiency: Hashrate fluctuations and mining pool volatility.
 
-## Related
+2. Spiking Features
+Poisson Encodings: Data translated into spike trains for SNN-native processing.
 
-- **V2 Model:** [Spikenaut-SNN-v2](https://huggingface.co/rmems/Spikenaut-SNN-v2)
-- **V1 Dataset:** [Spikenaut-v1-Telemetry-Data](https://huggingface.co/datasets/rmems/Spikenaut-v1-Telemetry-Data)
+Neuromodulator Signals: Reward/Pain signals derived from efficiency vs. thermal overhead.
+
+
+⚖️ License & Research Ethics
+This dataset is part of the open-source research initiative by Monty’s Engineering Technology.
+
+License: GNU General Public License v3.0 (GPL-3.0)
+
+Intent: Transparency and reproducibility in neuromorphic engineering.