---
title: Spikenaut SNN Telemetry Data
emoji: 🧠
license:
- mit
- apache-2.0
task_categories:
- reinforcement-learning
- time-series-forecasting
size_categories:
- 100K<n<1M
configs:
- config_name: gpu_telemetry
  data_files:
  - split: train
    path:
    - v2_parquet/gpu_telemetry/train-*.parquet
- config_name: mining
  data_files:
  - split: train
    path:
    - v2_parquet/mining/train-*.parquet
- config_name: hft
  data_files:
  - split: train
    path:
    - v2_parquet/hft/train-*.parquet
- config_name: qubic_ticks
  data_files:
  - split: train
    path:
    - v2_parquet/qubic_ticks/train-*.parquet
- config_name: gpu_telemetry_v3
  data_files:
  - split: full
    path:
    - v3/gpu_telemetry/full-*.parquet
- config_name: qubic_signals
  data_files:
  - split: full
    path:
    - v3/qubic_signals/full-*.parquet
- config_name: state_telemetry
  data_files:
  - split: train
    path:
    - v3/state_telemetry/train-*.parquet
  - split: validation
    path:
    - v3/state_telemetry/validation-*.parquet
  - split: test
    path:
    - v3/state_telemetry/test-*.parquet
- config_name: outcomes
  data_files:
  - split: train
    path:
    - v3/outcomes/train-*.parquet
  - split: validation
    path:
    - v3/outcomes/validation-*.parquet
  - split: test
    path:
    - v3/outcomes/test-*.parquet
- config_name: encoding_params
  data_files:
  - split: train
    path:
    - v3/encoding_params/train-*.parquet
dataset_info:
- config_name: gpu_telemetry
  features:
  - name: vddcr_gfx_v
    dtype: float64
  - name: vram_temp_c
    dtype: float64
  - name: gpu_temp_c
    dtype: float64
  - name: power_w
    dtype: float64
  - name: gpu_clock_mhz
    dtype: float64
  - name: mem_clock_mhz
    dtype: float64
  - name: clock_mhz
    dtype: float64
  - name: fan_speed_pct
    dtype: float64
  - name: mem_util_pct
    dtype: float64
  - name: qubic_tick_trace
    dtype: float64
  - name: qubic_tick_rate
    dtype: float64
  - name: qubic_epoch_progress
    dtype: float64
  - name: row_index
    dtype: int64
  splits:
  - name: train
    num_bytes: 84653192
    num_examples: 813973
  download_size: 6864325
  dataset_size: 84653192
- config_name: mining
  features:
  - name: hashrate_mh
    dtype: float64
  - name: power_w
    dtype: float64
  - name: gpu_temp_c
    dtype: float64
  - name: reward_hint
    dtype: float64
  - name: qubic_tick_trace
    dtype: float64
  - name: qubic_tick_rate
    dtype: float64
  - name: qubic_epoch_progress
    dtype: float64
  - name: timestamp
    dtype: string
  - name: blockchain
    dtype: string
  - name: block_height
    dtype: int64
  - name: chain_epoch
    dtype: int64
  splits:
  - name: train
    num_bytes: 12670089
    num_examples: 120322
  download_size: 5763683
  dataset_size: 12670089
- config_name: hft
  features:
  - name: timestamp
    dtype: string
  - name: step
    dtype: int64
  - name: action
    dtype: string
  - name: asset
    dtype: string
  - name: price_usd
    dtype: float64
  - name: quantity
    dtype: float64
  - name: trade_value_usdt
    dtype: float64
  - name: realized_pnl_usdt
    dtype: float64
  - name: balance_usdt
    dtype: float64
  - name: balance_dnx
    dtype: float64
  - name: balance_sol
    dtype: float64
  - name: balance_render
    dtype: float64
  - name: balance_asi
    dtype: float64
  - name: balance_near
    dtype: float64
  - name: balance_btc
    dtype: float64
  - name: balance_pepe
    dtype: float64
  - name: render_qty
    dtype: float64
  - name: asi_qty
    dtype: float64
  - name: near_qty
    dtype: float64
  - name: btc_qty
    dtype: float64
  - name: pepe_qty
    dtype: float64
  - name: cumulative_pnl
    dtype: float64
  - name: portfolio_value
    dtype: float64
  - name: ch2_mode
    dtype: string
  - name: reason
    dtype: string
  splits:
  - name: train
    num_bytes: 7591295
    num_examples: 31573
  download_size: 2604075
  dataset_size: 7591295
- config_name: qubic_ticks
  features:
  - name: timestamp
    dtype: timestamp[ms]
  - name: tick
    dtype: int64
  - name: tick_rate
    dtype: float64
  - name: qubic_tick_trace
    dtype: float64
  - name: hashrate_mh_derived
    dtype: float64
  - name: power_w_derived
    dtype: float64
  - name: gpu_temp_c_derived
    dtype: float64
  - name: reward_hint_derived
    dtype: float64
  splits:
  - name: train
    num_bytes: 1755520
    num_examples: 27430
  download_size: 637843
  dataset_size: 1755520
- config_name: gpu_telemetry_v3
  features:
  - name: row_index
    dtype: int64
  - name: ts_utc
    dtype: int64
  - name: ts_synthetic_offset_ms
    dtype: int64
  - name: schema_version
    dtype: string
  - name: vddcr_gfx_v
    dtype: float64
  - name: vram_temp_c
    dtype: float64
  - name: gpu_temp_c
    dtype: float64
  - name: power_w
    dtype: float64
  - name: gpu_clock_mhz
    dtype: float64
  - name: mem_clock_mhz
    dtype: float64
  - name: fan_speed_pct
    dtype: float64
  - name: mem_util_pct
    dtype: float64
  splits:
  - name: full
    num_bytes: 79158875
    num_examples: 813973
  download_size: 6090892
  dataset_size: 79158875
- config_name: qubic_signals
  features:
  - name: row_index
    dtype: int64
  - name: ts_utc
    dtype: int64
  - name: ts_synthetic_offset_ms
    dtype: int64
  - name: schema_version
    dtype: string
  - name: qubic_tick_trace
    dtype: float64
  - name: qubic_tick_rate
    dtype: float64
  - name: qubic_epoch_progress
    dtype: float64
  splits:
  - name: full
    num_bytes: 46599955
    num_examples: 813973
  download_size: 3734815
  dataset_size: 46599955
- config_name: state_telemetry
  features:
  - name: ts_utc
    dtype: int64
  - name: episode_id
    dtype: string
  - name: step_idx
    dtype: int32
  - name: schema_version
    dtype: string
  - name: window_ms
    dtype: int32
  - name: gpu_util_pct
    dtype: float32
  - name: mem_util_pct
    dtype: float32
  - name: fb_used_mib
    dtype: float32
  - name: fb_total_mib
    dtype: float32
  - name: power_w
    dtype: float32
  - name: power_limit_w
    dtype: float32
  - name: gpu_temp_c
    dtype: float32
  - name: vram_temp_c
    dtype: float32
  - name: sm_clock_mhz
    dtype: float32
  - name: mem_clock_mhz
    dtype: float32
  - name: fan_speed_pct
    dtype: float32
  - name: vddcr_gfx_v
    dtype: float32
  - name: pcie_replay_counter
    dtype: int64
  - name: ecc_sbe_vol
    dtype: int64
  - name: ecc_dbe_vol
    dtype: int64
  - name: throttle_reasons
    dtype: int64
  - name: thr_sw_power_cap
    dtype: bool
  - name: thr_hw_slowdown
    dtype: bool
  - name: thr_sw_thermal
    dtype: bool
  - name: thr_hw_thermal
    dtype: bool
  - name: thr_hw_power_brake
    dtype: bool
  - name: num_requests_running
    dtype: int32
  - name: num_requests_waiting
    dtype: int32
  - name: gpu_cache_usage_perc
    dtype: float32
  - name: ttft_p50_s
    dtype: float32
  - name: ttft_p99_s
    dtype: float32
  - name: itl_p50_s
    dtype: float32
  - name: tokens_per_s
    dtype: float32
  - name: prompt_tokens_total
    dtype: int64
  - name: generation_tokens_total
    dtype: int64
  - name: num_preemptions_total
    dtype: int64
  - name: e2e_latency_p99_s
    dtype: float32
  - name: request_timeouts
    dtype: int64
  - name: cpu_util_pct
    dtype: float32
  - name: cpu_temp_c
    dtype: float32
  - name: ram_used_pct
    dtype: float32
  - name: board_power_w
    dtype: float32
  - name: chassis_fan_rpm
    dtype: int32
  - name: fpga_spike_rate_hz
    dtype: float32
  - name: fpga_membrane_q88
    dtype: int16
  - name: fpga_clock_gated
    dtype: bool
  - name: fpga_temp_c
    dtype: float32
  - name: fpga_uart_frame_seq
    dtype: int64
  - name: source_host
    dtype: string
  - name: gpu_uuid
    dtype: string
  - name: regime
    dtype: string
  - name: synthetic
    dtype: bool
  splits:
  - name: train
    num_bytes: 136002048
    num_examples: 569344
  - name: validation
    num_bytes: 28374528
    num_examples: 118784
  - name: test
    num_bytes: 28104378
    num_examples: 117653
  download_size: 3857852
  dataset_size: 192480954
- config_name: outcomes
  features:
  - name: episode_id
    dtype: string
  - name: step_idx
    dtype: int32
  - name: ts_utc
    dtype: int64
  - name: schema_version
    dtype: string
  - name: reward
    dtype: float32
  - name: d_gpu_temp_c
    dtype: float32
  - name: d_ttft_p99_s
    dtype: float32
  - name: d_tokens_per_s
    dtype: float32
  - name: d_kv_cache_usage
    dtype: float32
  - name: thermal_violation
    dtype: bool
  - name: oom
    dtype: bool
  - name: timeout_burst
    dtype: bool
  - name: sla_met
    dtype: bool
  - name: discount
    dtype: float32
  - name: is_terminal
    dtype: bool
  - name: is_first
    dtype: bool
  - name: is_last
    dtype: bool
  splits:
  - name: train
    num_bytes: 34748277
    num_examples: 569344
  - name: validation
    num_bytes: 7249672
    num_examples: 118784
  - name: test
    num_bytes: 7180671
    num_examples: 117653
  download_size: 1396646
  dataset_size: 49178620
- config_name: encoding_params
  features:
  - name: feature_name
    dtype: string
  - name: encoder_type
    dtype: string
  - name: min
    dtype: float64
  - name: max
    dtype: float64
  - name: base_rate_hz
    dtype: float32
  - name: max_rate_hz
    dtype: float32
  - name: dt_seconds
    dtype: float32
  - name: q8_8_scale
    dtype: int32
  - name: threshold
    dtype: float32
  - name: schema_version
    dtype: string
  splits:
  - name: train
    num_bytes: 2587
    num_examples: 36
  download_size: 3510
  dataset_size: 2587
language:
- en
tags:
- neuromorphic
- spiking-neural-networks
- snn
- liquid-state-machine
- leaky-integrate-and-fire
- stdp
- e-prop
- hardware-aware-ai
- fpga
- q8-fixed-point
- rust
- julia
- cuda
- gpu-telemetry
- blockchain
- cryptocurrency
- dynex
- qubic
- kaspa
- monero
- quai
- verus
- hft
- time-series
- bci
- med-tech
- neuromorphic-computing
- spike-train
- feature-extraction
- real-time-inference
- mining
- hashrate
- telemetry
- reinforcement-learning
- rlds
- action-proposal
- safety-filter
- vllm
- nvml
datasets:
- rmems/Spikenaut-SNN-Telemetry
library_name: spikenaut
pipeline_tag: reinforcement-learning
---

# 🧠 Spikenaut SNN Telemetry Dataset

> **"The threshold at which stimulus becomes perceptible"**

Telemetry for the **Spikenaut Supervisor control stack**: v3 restructures this
corpus from time-series forecasting into an **action-proposal trajectory
dataset** — states, proposed actions, safety-filter verdicts, and outcomes —
while every v2 config remains published, byte-identical and loadable.

The control hierarchy this dataset serves:

```
learned policy → action proposal → deterministic safety filter → execution → logged trajectory
```

**The deterministic safety filter is always the final authority.** Spikenaut
(the learned SNN policy) only *proposes*. This dataset is structured so that
training, evaluation, and audit all respect that boundary.

---

## 📊 Dataset Overview

### v3 trajectory configs (Parquet, splits: train / validation / test)

| Config | Rows | Splits | Description |
|--------|-----:|--------|-------------|
| `state_telemetry` | 805,781 | 569,344 / 118,784 / 117,653 | Aligned state windows in the full v3 supervisor schema (52 columns) |
| `outcomes` | 805,781 | 569,344 / 118,784 / 117,653 | Horizon-H deltas, event flags, RLDS step flags |
| `gpu_telemetry_v3` | 813,973 | `full` (complete capture) | v2 GPU sensors, cleanly separated from Qubic signals |
| `qubic_signals` | 813,973 | `full` (complete capture) | The `qubic_*` columns split out of the GPU stream |
| `encoding_params` | 36 | `train` | Per-feature spike-encoding sidecar (axon-encoder parameters) |

`gpu_telemetry_v3` and `qubic_signals` are flat corpus derivations — they
deliberately cover *all* episodes (train, validation, test, and embargo), so
they publish under a single `full` split rather than a misleading `train`
label. Episode membership is `row_index // 4096` against the block boundaries
in *Episode & Split Design* below. For leakage-safe training, use
`state_telemetry`.

Two further v3 tables ship as **typed, empty Parquet schema artifacts** and are
deliberately *not* listed as loadable configs yet:

| Schema artifact | Path | Why empty |
|-----------------|------|-----------|
| `action_proposals` | `v3/action_proposals/` | The deterministic teacher refuses to label states whose core signals (NVML throttle mask, ECC counters) were never collected — the v2 backfill therefore has **zero** labels, and a guessed label would be worse than none. |
| `safety_filter_log` | `v3/safety_filter_log/` | No safety filter ran during the v2 captures; there are no verdicts to publish. |

They are excluded from the viewer configs because `datasets` (≤ 5.0.x) cannot
batch a 0-row Parquet file (`ArrowInvalid: BatchSize must be greater than 0`).
Each gains a config entry with its first populated release; the schemas are
already frozen in the Parquet files and in
[`v3_build.py`](https://github.com/rmems/spikenaut-telemetry-etl/blob/main/src/spikenaut_etl/v3_build.py).

### v2 configs (same records, still authoritative for the raw captures)

| Config | Records | Window | Description |
|--------|--------:|--------|-------------|
| `gpu_telemetry` | 813,973 | — (no timestamps in source) | RTX 5080 sensors: power, temps, clocks, utilization |
| `mining` | 120,322 | 2026-03-19 11:55 → 03-20 14:05 | Multi-coin node sync telemetry |
| `hft` | 31,573 | 2026-03-11 18:22 → 03-12 02:40 | Ghost Money paper-trading log (**simulated, not live capital**) |
| `qubic_ticks` | 27,430 | 2026-03-20 08:55 → 03-21 08:46 UTC | Qubic tick stream in SNN format |

These are **disjoint capture windows**, not one continuous run.

**Serving format:** the v2 configs load from verified Parquet conversions
under `v2_parquet/` — a script-less Hub dataset gets exactly one builder,
inferred from the first config's files, so JSONL and Parquet configs cannot
share a card (see the 2026-08-16 changelog). The original JSONL files remain
in `full_data/`, byte-untouched and canonical; the conversions are produced
and fidelity-checked by the ETL (identical rows, columns, and nulls; the one
representation change is `qubic_ticks.timestamp`: `timestamp[s]` →
`timestamp[ms]`, same values at finer unit).

```python
from datasets import load_dataset

state = load_dataset("rmems/Spikenaut-SNN-Telemetry", "state_telemetry")   # train/validation/test
outcome = load_dataset("rmems/Spikenaut-SNN-Telemetry", "outcomes")
enc = load_dataset("rmems/Spikenaut-SNN-Telemetry", "encoding_params", split="train")
v2 = load_dataset("rmems/Spikenaut-SNN-Telemetry", "mining", split="train")
```

All v3 trajectory tables join on **(`episode_id`, `step_idx`)**; `ts_utc` is the
time key where it exists.

---

## 🛡️ Control Hierarchy & Safety

- **The deterministic layer is final.** A learned proposal is data *into* the
  safety filter, never a command. `safety_filter_log.filter_verdict` records
  what the filter did with each proposal: `allow`, `modify`, `veto`, or
  `substitute`, plus the `filter_rule_id` that fired.
- **Deployment modes** (`safety_filter_log.mode`): `shadow` (policy proposes,
  filter logs, humans/heuristics act), `assist` (filtered proposals surface as
  suggestions), `active` (filtered proposals execute). Promotion between modes
  is a human decision informed by this dataset.
- **Shadow KPI: filter-override rate** — the share of proposals the filter did
  *not* pass through unchanged (`verdict != "allow"`). A policy is not a
  candidate for `assist` while its override rate is materially nonzero on the
  scenarios that matter.
- `override` / `override_reason` record the rare, logged case of a human
  overriding the filter — the field exists so that such events are data, not
  folklore.

## 📐 Telemetry Contract

- **`schema_version`** (column in every v3 config, currently `3.0.0`) is the
  semantic version of the *telemetry contract*: **PATCH** = docs/units
  clarifications, **MINOR** = additive nullable columns, **MAJOR** = anything
  breaking (rename, retype, resample).
- **Missing means `null`** — never `0.0`, never `""`. Columns whose collectors
  do not exist yet are present, typed, and null (schema first, backfill later).
- **`synthetic`** flags rows not produced by a real collector; the entire
  current backfill is measured data, so it is `false` everywhere. `regime` is
  null in the backfill: classifying idle/ramp/overload from values would be
  inference, not provenance.

### Sampling rates and time

| Source | Cadence | Time columns |
|--------|---------|--------------|
| v2 GPU capture (→ `state_telemetry` backfill) | **undocumented** — the collector emitting these columns is not in `rmems/Theseus-Quarry`, and no interval is recorded anywhere | `ts_utc` null, `ts_synthetic_offset_ms` null |
| v2 `mining` | irregular (~1 Hz bursts) | real `timestamp` strings (see v2 section) |
| v2 `hft`, `qubic_ticks` | event-driven | real timestamps |
| Future v3 collectors | must publish `window_ms` per row | `ts_utc` int64, ns since Unix epoch |

**No timestamp in this dataset is ever synthesized.** `ts_utc` is nullable
int64 nanoseconds since epoch. `ts_synthetic_offset_ms` was designed as
`row_index × documented sampling interval`; because **no sampling interval is
documented** for the GPU capture, it ships null rather than encoding a guess.
(The dataset's own history shows why: 114,250 fabricated timestamps were once
published as a real collection window. Never again.) When the cadence is
confirmed from the collector source, populating the column is a MINOR bump.
Ordering within an episode is still exact via `step_idx`.

### `state_telemetry` columns (52)

| Block | Columns | Units / notes | v2 backfill? |
|-------|---------|---------------|--------------|
| Keys | `ts_utc` (int64 ns), `episode_id` (str), `step_idx` (int32), `schema_version` (str), `window_ms` (int32) | alignment window length | keys yes; `ts_utc`/`window_ms` null |
| GPU/NVML | `gpu_util_pct`, `mem_util_pct`, `fb_used_mib`, `fb_total_mib`, `power_w`, `power_limit_w`, `gpu_temp_c`, `vram_temp_c`, `sm_clock_mhz`, `mem_clock_mhz`, `fan_speed_pct`, `vddcr_gfx_v` (float32) | %, MiB, W, °C, MHz, V | `mem_util_pct`, `power_w`, `gpu_temp_c`, `vram_temp_c`, `sm_clock_mhz`\*, `mem_clock_mhz`, `fan_speed_pct`, `vddcr_gfx_v` |
| GPU counters | `pcie_replay_counter`, `ecc_sbe_vol`, `ecc_dbe_vol` (int64) | monotone counters | null |
| Throttle | `throttle_reasons` (int64 bitmask) + decoded `thr_sw_power_cap`, `thr_hw_slowdown`, `thr_sw_thermal`, `thr_hw_thermal`, `thr_hw_power_brake` (bool) | NVML `nvmlClocksThrottleReasons` bits `0x4/0x8/0x20/0x40/0x80` | null |
| Inference server (vLLM metric names) | `num_requests_running`, `num_requests_waiting` (int32), `gpu_cache_usage_perc` (float32, 0–1), `ttft_p50_s`, `ttft_p99_s`, `itl_p50_s`, `e2e_latency_p99_s` (s), `tokens_per_s`, `prompt_tokens_total`, `generation_tokens_total`, `num_preemptions_total`, `request_timeouts` | s, tokens/s, counts | null |
| CPU/board | `cpu_util_pct`, `cpu_temp_c`, `ram_used_pct`, `board_power_w` (float32), `chassis_fan_rpm` (int32) | %, °C, W, RPM | null |
| FPGA | `fpga_spike_rate_hz` (float32), `fpga_membrane_q88` (int16, **Q8.8**), `fpga_clock_gated` (bool), `fpga_temp_c` (float32), `fpga_uart_frame_seq` (int64) | Hz, Q8.8, °C | null |
| Provenance | `source_host`, `gpu_uuid`, `regime` ∈ {idle, ramp, steady, overload, fault, synthetic}, `synthetic` (bool) | | `synthetic=false`; rest null |

\* `sm_clock_mhz` is backfilled from v2 `gpu_clock_mhz` (the collector sampled
a single graphics clock; NVML SM and graphics clocks are reported separately on
paper but were one reading here). The redundant v2 `clock_mhz` duplicate was
verified equal and dropped.

### Q8.8 convention

FPGA-side quantities use **Q8.8 signed fixed point**: value = `int16 / 256`,
range −128.0 … +127.99609375, 1 LSB = 1/256 ≈ 0.0039. `fpga_membrane_q88`
stores the raw int16; `encoding_params.q8_8_scale = 256` records the scale so
any backend can reproduce the mapping. This matches the Limen-Neural hardware
contract (256-neuron / 1024-weight envelope, `.mem` export in Q8.8).

---

## 🎛️ Action Taxonomy

`*_action` / `*_action_id` columns use this frozen mapping (append-only, never
renumbered):

| id | action | id | action |
|---:|--------|---:|--------|
| 0 | `no_op` | 7 | `shed_load` |
| 1 | `throttle_clocks` | 8 | `reroute_request` |
| 2 | `power_gate_fpga` | 9 | `migrate_workload` |
| 3 | `clock_gate_fpga` | 10 | `cap_power` |
| 4 | `reduce_batch_size` | 11 | `raise_fan` |
| 5 | `evict_kv_cache` | 12 | `pause_workload` |
| 6 | `downshift_precision` | 13 | `escalate_to_human` |

### Deterministic teacher rules (v1.0.0)

Implemented and unit-tested in
[`teacher_policy.py`](https://github.com/rmems/spikenaut-telemetry-etl/blob/main/src/spikenaut_etl/teacher_policy.py);
every label records the rule that produced it in
`action_proposals.teacher_rule_id`, with `label_source = "teacher_rule"` and
`label_confidence = 1.0` (deterministic rules are certain by definition).
First match wins:

| Rule | Trigger | Teacher action |
|------|---------|----------------|
| `TR-001-ECC-DBE` | `ecc_dbe_vol > 0` | `escalate_to_human` (+ paired `migrate_workload` in `proposed_params.paired_action`) |
| `TR-002-HW-POWER-BRAKE` | throttle bit `0x80` | `cap_power` |
| `TR-003-HW-THERMAL` | throttle bit `0x40` | `throttle_clocks` |
| `TR-004-HW-SLOWDOWN` | throttle bit `0x8` | `throttle_clocks` |
| `TR-005-SW-THERMAL` | throttle bit `0x20` | `raise_fan` |
| `TR-007-KV-PRESSURE-BATCH` | `gpu_cache_usage_perc ≥ 0.95` sustained 6 windows | `reduce_batch_size` |
| `TR-006-KV-PRESSURE-EVICT` | `gpu_cache_usage_perc ≥ 0.95` sustained 3 windows | `evict_kv_cache` |
| `TR-009-QUEUE-PREEMPT` | queue strictly growing 3 windows, throughput flat (±5%), preemptions rising | `reroute_request` |
| `TR-008-QUEUE-GROWTH` | queue strictly growing 3 windows, throughput flat (±5%) | `shed_load` |
| `TR-000-NOMINAL` | core signals present, nothing above fired | `no_op` |

Rules key off the **NVML throttle bitmask, never hard-coded temperatures** — a
GPU at 85 °C with no slowdown bit set is a GPU doing its job. Idle (`0x1`) and
`sw_power_cap` (`0x4`) bits describe normal operation and map to `no_op`.
**If `throttle_reasons` or `ecc_dbe_vol` is null, the teacher emits no label
at all** — which is why `action_proposals` is currently empty (see Migration).

---

## ⚡ Encoding

Policy: **store raw values plus encoding parameters — never only pre-encoded
spike trains.** Spikes are a lossy function of encoder settings; raw + params
lets [`axon-encoder`](https://github.com/Limen-Neural/axon-encoder) (or any
backend) re-encode for a different simulator, timestep, or hardware target.

`encoding_params` is the sidecar (one row per state feature):

- `encoder_type` ∈ {rate, delta, latency, population, poisson, temporal,
  derivative} — the default assignment is `rate` for continuous magnitudes and
  `delta` (threshold = 1 count) for monotone counters.
- `min` / `max` — the encoder input range, **fitted on the train split only**
  (leakage rule); null for features with no data yet.
- `base_rate_hz` / `max_rate_hz` — firing rates mapped to range endpoints
  (defaults 5 → 100 Hz), matching
  `RateEncoder::try_new(base_rate_hz, max_rate_hz, (min, max), dt_seconds)`.
- `dt_seconds` — **the encoder's replay integration step** (default 0.010).
  This is a prescriptive encoding choice, *not* a claim about collection
  cadence (which is undocumented for the GPU source). Batch encoding uses
  `p = 1 − exp(−rate_hz · dt_seconds)`.
- `q8_8_scale = 256` — see Q8.8 convention above.

---

## 🧩 Episode & Split Design

- **Episodes** are fixed 4,096-step windows over the verified-contiguous
  `row_index` order (`gpu-000000` … `gpu-000198`; the final episode is short:
  2,965 steps). The capture has no wall clock, so bounded windows — not
  load-event segmentation — are the honest boundary choice for the backfill.
- **RLDS flags** live in `outcomes`: `is_first` / `is_last` mark episode
  edges; episodes end by *windowing*, so `is_last = true` is truncation and
  `is_terminal` stays `false`; `discount = 1.0`.
- **Outcome horizon H = 64 steps.** `d_gpu_temp_c[t] = gpu_temp_c[t+64] −
  gpu_temp_c[t]`, computed strictly within an episode; the last 64 steps of
  each episode are null. The other deltas (`d_ttft_p99_s`, `d_tokens_per_s`,
  `d_kv_cache_usage`) and the event flags await their collectors and are null.
  `reward` is null: no reward function is defined yet, and publishing one
  implicitly through data would bypass review.
- **Splits are chronological blocks** (≈70/15/15 by episode):
  train = `gpu-000000…gpu-000138`, validation = `gpu-000140…gpu-000168`,
  test = `gpu-000170…gpu-000198`. Episodes `gpu-000139` and `gpu-000169`
  (8,192 rows) are **embargo gaps published in no split**, so blocks are never
  temporally adjacent and no 64-step outcome window can cross a boundary.
- **Leakage rules:** never random-shuffle across time; no episode spans two
  splits; fit normalization/encoding statistics (including `encoding_params`
  min/max) on **train only**; anything fitted elsewhere is a bug.

---

## 🔍 v2 Schemas

<details>
<summary>Click to expand the v2 config documentation (unchanged from the v2 card)</summary>

### `gpu_telemetry` — `neuromorphic_data.jsonl`

12 sensor columns + `row_index`. **This source carries no timestamp** — the
collector never emitted one; records are ordered but not time-located, and a
synthetic clock is deliberately not supplied. `qubic_tick_trace`,
`qubic_tick_rate`, `qubic_epoch_progress` ride along in this file for
continuity; v3 splits them into `qubic_signals`.

### `mining` — `node_sync_harvest.jsonl`

Chain attribution comes from the source's timestamp field, which takes four forms:

| Rows | Source form | Result |
|-----:|-------------|--------|
| 114,238 | `2026-03-19 11:55:13.132` | real `timestamp`, `blockchain: null` |
| 5,001 | `dynex:919876` | `blockchain: "dynex"`, `block_height`, `timestamp: null` |
| 1,083 | `qubic:204:46075040` | `blockchain: "qubic"`, `chain_epoch`, `block_height`, `timestamp: null` |
| 12 | trailing placeholders | **excluded** (quarantined by the pipeline; see 2026-08-04 changelog) |

**114,238 rows carry no chain label** — that information does not exist in the
source. They are `null`, never `""`.

### `hft` — `ghost_market_log.jsonl`

25 columns of paper-trading state. Actions: `buy` 14,603 / `sell` 14,580 /
`observe` 2,390. **Simulated trading — not live capital.**

### `qubic_ticks` — `qubic_ticks_snn.jsonl`

**The `_derived` columns are not measurements.** They are a fixed function of
`tick_rate`, kept for continuity. The independent signals are `tick_rate` and
`qubic_tick_trace`.

### Model artifacts — ⚠️ provenance unverified

`full_data/snn_model.json`, `full_data/hybrid_training_results.json`, and
`models/mining_v2/*.mem` are **not reproducible from this repository** and show
signatures consistent with untrained placeholders (identical weight vectors,
denormal weights, contradictory thresholds). They are under audit. Do not treat
them as trained parameters; the previously published 95.2% accuracy figure has
no reproducible basis.

</details>

---

## 📈 Provenance

```
rmems/Theseus-Quarry           Rust collectors → raw JSONL
        ↓
rmems/spikenaut-telemetry-etl  ingest → validate → clean → publish       (full_data/ JSONL)
        ↓                       └─ spikenaut-etl build-v3 ─────────────→ (v3/ + v2_parquet/)
rmems/Spikenaut-SNN-Telemetry  this dataset
        ↓
rmems/Spikenaut-SNN            model training
```

Every file here is **generated**. Do not hand-edit them; report data issues
against [the ETL repository](https://github.com/rmems/spikenaut-telemetry-etl).
The v2 gates (no constant/all-null columns, distinct-row ratio, bounded drift,
non-fabricated timestamps, exact schema match) still run; the v3 builder adds
its own non-degeneracy asserts and refuses to write a broken tree.

---

## 🔀 Migration: v2 → v3

**Everything v2 is preserved.** Same files, same configs, same bytes; v3 is
purely additive under `v3/`. If you consume `gpu_telemetry`, `mining`, `hft`,
or `qubic_ticks` today, nothing changes for you.

| You used | Consider instead |
|----------|------------------|
| `gpu_telemetry` with the interleaved `qubic_*` columns | `gpu_telemetry_v3` + `qubic_signals` |
| ad-hoc train/test splitting of GPU rows | `state_telemetry` splits (chronological, embargoed) |
| hand-rolled spike encodings | raw values + `encoding_params` |

## 📝 Changelog

### 2026-08-16 — all configs Parquet-served; viewer restored

The 2026-08-15 release broke the Dataset Viewer for every v3 config
(`JSON parse error: Invalid value. in row 0`): a script-less Hub dataset
resolves **one** packaged builder from the **first config's** data files
(`datasets/load.py`, `HubDatasetModuleFactoryWithoutScript`) and applies it to
every config — so the JSON builder was fed Parquet bytes. Mixed JSONL/Parquet
configs cannot work on the Hub.

Since the v3 contract mandates Parquet, the four v2 configs now load from
Parquet conversions under `v2_parquet/`, generated and fidelity-verified by
`spikenaut-etl build-v3` (ETL PR #9): identical row counts, rows, columns,
and nulls, with one representation change — `qubic_ticks.timestamp` is
`timestamp[ms]` instead of `timestamp[s]` (Parquet has no seconds resolution;
values unchanged). `full_data/*.jsonl` are byte-untouched and remain the
canonical cleaned exports. Same-day earlier fix: `dataset_info` dtypes were
serialized as repr strings (`Value('float64')`), which the viewer's
config-names step cannot parse; now generated with `Features._to_yaml_list()`
and round-trip-validated before every card commit.

### 2026-08-15 — v3.0.0: action-proposal trajectory restructure

Built by [`spikenaut-etl build-v3`](https://github.com/rmems/spikenaut-telemetry-etl)
(PR [#7](https://github.com/rmems/spikenaut-telemetry-etl/pull/7), merged as
`983323b`), teacher policy **v1.0.0**.

- New configs: `state_telemetry`, `outcomes`, `gpu_telemetry_v3`,
  `qubic_signals`, `encoding_params`; schema artifacts `action_proposals`,
  `safety_filter_log` (typed, empty — see Overview). Task category
  `reinforcement-learning` added; `time-series-forecasting` retained for the
  v2 configs. The `metrics: accuracy` frontmatter key was removed — it
  referred to the disavowed 95.2% figure (see Model artifacts).
- **Zero teacher labels emitted over the v2 backfill** — the historical GPU
  capture has no NVML throttle mask and no ECC counters, and the teacher does
  not guess. This is the intended, honest result; labels begin when collectors
  publish the core signals.
- `ts_utc` / `ts_synthetic_offset_ms` ship null for the GPU-derived configs:
  no sampling interval is documented anywhere for that capture, and this
  dataset does not fabricate clocks (see 2026-08-03 entry for what happened
  last time someone did).
- Step-0 verification notes, for the record: `mining`, `hft`, and
  `qubic_ticks` *do* carry real in-band timestamps (only `gpu_telemetry` is
  time-blind); `hft` has 25 columns; `clock_mhz` was verified byte-equal to
  `gpu_clock_mhz` across all 813,973 rows before being dropped from
  `gpu_telemetry_v3`; `row_index` was verified contiguous 0…813,972.
- Decisions taken where the spec left room, recorded here: episodes are fixed
  4,096-step windows (no wall clock to anchor load events); embargo = one full
  episode per boundary (≥ H = 64); `regime` left null in backfill rather than
  inferred; `sm_clock_mhz` backfilled from v2 `gpu_clock_mhz`; v2 `mining` /
  `hft` / `qubic_ticks` keep their in-band timestamps and are not duplicated
  into v3 configs.

### 2026-08-04 — 12 placeholder rows removed from `mining`

Trailing rows appended out of order (~21 h backwards), the only rows carrying a
UTC offset, holding two distinct `(power_w, gpu_temp_c)` pairs. Quarantined;
the pipeline now fails the build on any unquarantined time reversal.
`mining` 120,334 → **120,322**.

### 2026-08-03 — data rebuilt from recovered originals

`neuromorphic_data.jsonl` (813,973 rows of `{"telemetry":{}}`) and
`node_sync_harvest.jsonl` (every numeric `0.0`) were rebuilt from recovered
originals. Cause: a Symbol/String key mismatch in the old Julia cleaning
script; every filter matched nothing, every lookup returned its default, and
nothing asserted the output was non-degenerate. The same script overwrote
114,250 real timestamps with a fabricated `base + 10s × index` sequence.
Also: chain attribution recovered (dynex 5,001 / qubic 1,083); qubic derived
columns renamed `*_derived`; samples regenerated as seeded random draws; 17
dead GPU columns and 13 dead mining columns dropped; model artifacts flagged
unverified. **If you pulled a revision before 2026-08-03, re-download.**

---

## 📜 Citation

```bibtex
@dataset{spikenaut_snn_telemetry,
  author={Montoya Cardenas, Raul},
  title={Spikenaut SNN Telemetry Dataset},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/rmems/Spikenaut-SNN-Telemetry}
}
```

## ⚖️ License

**MIT OR Apache-2.0** — see LICENSE. Dual-licensed: use whichever fits your project.

## 🙏 Acknowledgments

- **Kaspa, Monero, Qubic, Quai, Dynex, Verus communities** for open-source node implementations
- **E-prop authors** (Bellec et al., 2020) and **STDP pioneers** (Bi & Poo, 1998)
- **vLLM** for the inference-server metric vocabulary mirrored in `state_telemetry`

---

*Built by Raul Montoya Cardenas — WGU AI Engineering*