Add Bio-Pulse sample (10K sequences) with README, SCHEMA, parquet, JSONL

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - synthetic
+  - healthcare
+  - digital-health
+  - wearables
+  - remote-patient-monitoring
+  - precision-medicine
+  - physiological-telemetry
+  - early-warning-detection
+  - clinical-decision-support
+  - time-series
+  - hipaa-safe
+pretty_name: Bio-Pulse Physiological Telemetry Pack
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: bio_pulse_sample.parquet
+---
+
+# Bio-Pulse Physiological Telemetry Pack (Sample)
+
+**A synthetic wearable-sensor physiological telemetry dataset for early-warning detection, digital-health model training, and remote patient monitoring research.** Each row is a complete physiological event sequence — from baseline vitals through a progressing clinical scenario (drug interaction, septic cascade, nocturnal hypoglycemia) — with phenotype context, causal-chain labels, and anomaly-signature detection metadata.
+
+Built by [SolsticeAI](https://www.solsticestudio.ai/datasets) as a free sample of a larger commercial pack. 100% synthetic. No real patient, wearable, or clinical data — fully HIPAA-safe: no PHI, no identifiers, no re-identification risk.
+
+## What is included
+
+| File | Rows | Format | Purpose |
+|---|---:|---|---|
+| `bio_pulse_sample.parquet` | 10,000 | Parquet | Columnar, typed, best for analytics |
+| `bio_pulse_sample.jsonl` | 10,000 | JSON Lines | Streaming / LLM training friendly |
+
+**This sample:** 10,000 physiological event sequences, balanced across 4 criticality tiers and 3 diagnostic classes.  
+**Criticality classes:** `low`, `medium`, `high`, `life_threatening` (~2,500 each)  
+**Diagnostic classes:** `Drug_Interaction_Toxicity`, `Septic_Shock_Cascade`, `Nocturnal_Hypoglycemia` (~3,300 each)  
+**Patient phenotypes:** `pediatric_asthmatic`, `active_longevity`, `high_stress_corporate`, `sedentary_diabetic`, `geriatric_frailty`  
+**Sensor assets covered:** Continuous Glucose Monitor, Smart Watch v3, O2 Ring, Neural Patch v2
+
+## Record structure
+
+Each record is one physiological event sequence with 7 top-level fields:
+
+| Field | Type | Contents |
+|---|---|---|
+| `schema_version` | string | Pack schema version (`1.0.0-bio-pulse-sample`) |
+| `event` | struct | `id`, `trace_id`, `timestamp`, `criticality`, `outcome`, `confidence` |
+| `patient_context` | struct | `phenotype`, `age_group`, `existing_conditions[]` |
+| `medical_logic` | struct | `diagnostic_class`, `causal_chain[]`, `prediction_window_minutes`, `tracked_biomarkers[]` |
+| `vitals_telemetry` | list<struct> | Ordered sensor readings: `timestamp`, `sensor_id`, `sensor_asset`, `event_name`, `vitals` (hr, glucose, spo2, temp_c, cortisol_ugdl, systolic_bp, qt_interval_ms), `patient_id` |
+| `detection_logic` | struct | `signature`, `anomaly_score`, `baseline_deviation` |
+| `simulation` | struct | `synthetic`, `model`, `fidelity`, `target_diagnoses[]` |
+
+See [SCHEMA.md](./SCHEMA.md) for the full nested field breakdown.
+
+## Why this dataset is useful
+
+Most public physiological datasets are either highly-scoped single-signal recordings (MIMIC ICU, PhysioNet challenges) or flat aggregated reports. This pack is shaped around what digital-health and wearable-AI teams actually need to train predictive monitoring models:
+
+- Multi-signal sequences rather than single-vital recordings
+- Balanced criticality tiers across low → life-threatening
+- Causal-chain labels that connect trigger events to outcomes (not just end-state labels)
+- Phenotype-aware variability so models don't overfit one population
+- Explicit prediction-window fields for early-warning benchmarks
+- Cross-sensor coverage (CGM, smart watch, pulse ox, wearable patch)
+- Synthetic-by-design — safe to share across vendors, regulators, and research teams
+
+## Typical use cases
+
+- Early-warning detection model training
+- Wearable-device AI and firmware validation
+- Remote patient monitoring (RPM) pipelines
+- Clinical decision support prototypes
+- Digital-health analytics and dashboards
+- Phenotype-aware risk stratification
+- LLM fine-tuning on physiological narratives + reasoning
+- Benchmarking anomaly scores and false-alarm reduction
+
+## Quick start
+
+```python
+import pandas as pd
+import pyarrow.parquet as pq
+
+df = pq.read_table("bio_pulse_sample.parquet").to_pandas()
+
+# Criticality distribution (balanced)
+print(df["event"].apply(lambda e: e["criticality"]).value_counts())
+
+# Average anomaly score by diagnostic class
+df["dx"] = df["medical_logic"].apply(lambda m: m["diagnostic_class"])
+df["anomaly"] = df["detection_logic"].apply(lambda d: d["anomaly_score"])
+print(df.groupby("dx")["anomaly"].mean().round(3))
+
+# Pull the QT-interval trajectory for one episode
+row = df.iloc[0]
+traj = [(v["timestamp"], v["vitals"]["qt_interval_ms"]) for v in row["vitals_telemetry"]]
+print(traj)
+```
+
+Streaming form:
+
+```python
+import json
+
+with open("bio_pulse_sample.jsonl") as f:
+    for line in f:
+        episode = json.loads(line)
+        # one physiological sequence per line
+```
+
+## Responsible use
+
+This dataset is intended for **digital-health research and model development**. It contains synthesized physiological sequences and clinical-scenario labels — it does **not** contain real patient records, PHI, wearable-device telemetry, or identifiable data of any kind. It is HIPAA-safe by construction (no covered data is present). Models trained on this data must be independently validated against real clinical data before any deployment in patient care.
+
+## License
+
+Released under **CC BY 4.0**. Use freely for research, prototyping, education, and commercial development with attribution.
+
+## Get the full pack
+
+This Hugging Face repo is a **10K-episode sample**. The production pack scales to 1M+ episodes, wider phenotype and age coverage, additional diagnostic classes (atrial fibrillation, asthma exacerbation, hypertensive crisis, post-op recovery, stroke precursors), richer biomarker panels, device-specific signal-fidelity variants, parquet + JSONL + FHIR-aligned formats, and buyer-specific configurations.
+
+**Self-serve (Stripe checkout):**
+- [**Sample Scale tier — $5,000**](https://buy.stripe.com/7sY5kD2j85QTfSb5lfeEo03) — ~25K records, one subject, 72-hour delivery.
+
+**Full pack + enterprise scope:**
+- [www.solsticestudio.ai/datasets](https://www.solsticestudio.ai/datasets) — per-SKU pricing across Starter / Professional / Enterprise tiers, plus commercial licensing, custom generation, and buyer-specific variants.
+
+**Procurement catalog:**
+- [SolsticeAI Data Storefront](https://solsticeai.mydatastorefront.com) — available via Datarade / Monda.
+
+## Citation
+
+```bibtex
+@dataset{solstice_bio_pulse_pack_2026,
+  title        = {Bio-Pulse Physiological Telemetry Pack (Sample)},
+  author       = {SolsticeAI},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/solsticestudioai/bio-pulse-pack}
+}
+```

SCHEMA.md

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+# Bio-Pulse Physiological Telemetry Pack — Schema
+
+One row = one physiological event sequence, end-to-end. All records share the same seven top-level fields.
+
+Schema version: `1.0.0-bio-pulse-sample`
+
+## Top-level fields
+
+### `schema_version` — string
+Schema identifier. Constant within a sample release. Used to detect pack version drift.
+
+### `event` — struct
+Identifier fields and the overall clinical outcome for the sequence.
+
+| Field | Type | Notes |
+|---|---|---|
+| `id` | string | Stable episode identifier, e.g., `BIOPULSE-100000`. |
+| `trace_id` | string (UUID) | Cross-links telemetry steps within the sequence. |
+| `timestamp` | string (ISO-8601) | Episode start time. |
+| `criticality` | string | `low`, `medium`, `high`, `life_threatening`. |
+| `outcome` | string | `patient_stabilized`, `intervention_successful`, `morbidity_risk_detected`. |
+| `confidence` | double | 0–1. Confidence of the outcome label. |
+
+### `patient_context` — struct
+Synthetic patient archetype and baseline risk factors.
+
+| Field | Type | Notes |
+|---|---|---|
+| `phenotype` | string | One of: `pediatric_asthmatic`, `active_longevity`, `high_stress_corporate`, `sedentary_diabetic`, `geriatric_frailty`. |
+| `age_group` | string | `18-30`, `31-50`, `51-70`, `70+`. |
+| `existing_conditions` | list<string> | Conditions carried by the synthetic patient (e.g., `asthma`, `type-2-diabetes`). |
+
+### `medical_logic` — struct
+Clinical scenario driving the sequence and the biomarkers being tracked.
+
+| Field | Type | Notes |
+|---|---|---|
+| `diagnostic_class` | string | `Drug_Interaction_Toxicity`, `Septic_Shock_Cascade`, `Nocturnal_Hypoglycemia`. |
+| `causal_chain` | list<string> | Ordered causal event labels (e.g., `MEDICATION_INGESTION_A`, `LIVER_ENZYME_DRIFT_SIMULATED`, `ARRHYTHMIA_DETECTION`). |
+| `prediction_window_minutes` | int | Minutes of lead time available before the terminal event. |
+| `tracked_biomarkers` | list<string> | Which biomarkers are reported in `vitals_telemetry.vitals` (e.g., `glucose_mgdl`, `heart_rate_bpm`, `qt_interval_ms`). |
+
+### `vitals_telemetry` — list<struct>
+Ordered sensor readings for the episode. One struct per reading.
+
+Reading struct:
+
+| Field | Type | Notes |
+|---|---|---|
+| `timestamp` | string (ISO-8601) | Reading timestamp. |
+| `sensor_id` | string | Synthetic sensor serial (e.g., `SENS-6ACEC5`). |
+| `sensor_asset` | string | One of: `CGM_Sensor`, `Smart_Watch_V3`, `O2_Ring`, `Neural_Patch_V2`. |
+| `event_name` | string | Label for what happened at this reading (matches one item in `medical_logic.causal_chain`). |
+| `patient_id` | string | Synthetic patient identifier (e.g., `PAT-D884ED0A`). Not real. |
+| `vitals.hr` | int | Heart rate (bpm). |
+| `vitals.glucose` | double | Blood glucose (mg/dL). |
+| `vitals.spo2` | int | Peripheral oxygen saturation (%). |
+| `vitals.temp_c` | double | Body temperature (°C). |
+| `vitals.cortisol_ugdl` | double | Cortisol (µg/dL). |
+| `vitals.systolic_bp` | int | Systolic blood pressure (mmHg). |
+| `vitals.qt_interval_ms` | int | QT interval (ms). |
+
+### `detection_logic` — struct
+Anomaly-signature metadata describing why this sequence is flagged.
+
+| Field | Type | Notes |
+|---|---|---|
+| `signature` | string | Human-readable signature name (e.g., `Predictive Drug_Interaction_Toxicity for pediatric_asthmatic`). |
+| `anomaly_score` | double | 0–1. Higher = more anomalous. |
+| `baseline_deviation` | string | Short English description of the deviation pattern. |
+
+### `simulation` — struct
+Simulation engine provenance.
+
+| Field | Type | Notes |
+|---|---|---|
+| `synthetic` | bool | Always `true`. |
+| `model` | string | Simulation engine label (`bio_pulse_sim_v1` in this sample). |
+| `fidelity` | string | `Clinical_Grade`. |
+| `target_diagnoses` | list<string> | Diagnoses this engine can produce (e.g., `Hypoglycemic_Event`, `Septic_Shock`). |
+
+## Distribution of this sample
+
+- 10,000 sequences total.
+- Criticality: balanced across `low` / `medium` / `high` / `life_threatening` (~2,500 each).
+- Diagnostic class: balanced across 3 classes (~3,300 each).
+- Phenotype: balanced across 5 archetypes (~2,000 each).
+- Age group: skewed toward `70+` (~3,200) to reflect chronic-care monitoring use cases.
+- Simulation engine label constant (`bio_pulse_sim_v1`) after sanitization.
+
+## Sanitization notes
+
+- Internal identifier prefix (`SIMA-V4-BIO-*`) has been normalized to `BIOPULSE-*`.
+- Internal engine code name has been normalized to `bio_pulse_sim_v1`.
+- Wearable device name previously labeled `Neural_Link_Beta` has been renamed to `Neural_Patch_V2` to avoid collision with an unrelated company trademark.
+- No real patient IDs, names, clinic names, device serials, or health records are present. All identifiers are synthetic.
+- Dataset is HIPAA-safe by construction: no covered data is present.
+
+## Relationship to the full pack
+
+The production pack scales to 1M+ sequences with wider phenotype coverage, additional diagnostic classes (atrial fibrillation, asthma exacerbation, hypertensive crisis, post-op recovery, stroke precursors), richer biomarker panels, device-specific fidelity variants, longer causal chains, and FHIR-aligned delivery. See the pack card for commercial access.

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