--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - vr-training - immersive-training - operator-competency - emergency-response - oil-and-gas - safety-training - simulator-fidelity - human-performance - opito - ipieca - iadc-well-control - nfpa-1006 - ccps - nebosh - api-rp-755 - uk-hse-ohra - dnv-rp-a203 - process-safety - training-analytics pretty_name: "OIL-046 — Synthetic Training Simulation Dataset (Sample)" size_categories: - 100K **Built by** XpertSystems.ai — Synthetic Data Platform > **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai) > **License** CC-BY-NC-4.0 (sample); commercial license available for the full product. --- ## What OIL-046 does that nothing else in the catalog does OIL-046 is the catalog's **first VR / immersive training simulation** SKU. Where OIL-035 (Safety / HSE) models incidents *after the fact* and OIL-045 (Workforce) models scheduling and fatigue, OIL-046 models the **training data** that determines whether operators are competent to handle those incidents when they occur. This is the substrate underneath every other safety-related dataset in the catalog. This is the substrate **VR training platform vendors, operator competency analytics teams, simulator fidelity researchers, OPITO/IADC training auditors, and human-performance modelers** have been waiting for: a coherent, joinable dataset where VR sessions, equipment interactions, alarm acknowledgments, communication chains, evacuations, safety violations, fatigue, and incident progression all share session_id and trainee_id for cross-modal training analytics. | Buyer Persona | Use Case | |---|---| | VR Training Platform Vendor | Simulator fidelity validation, scenario completion analytics | | Operator Competency Analytics | Pass/fail prediction, retraining recommendation models | | Simulator Fidelity Researcher | VR realism scoring, hardware profile impact | | OPITO/IADC Training Auditor | Compliance reporting + competency benchmarking | | Human-Performance Modeler | Fatigue-in-training × decision quality × stress correlation | | HSE Training Director | Drill effectiveness + violation pattern detection | | Insurance Underwriter | Training-quality risk pricing for upstream operators | --- ## What's inside 13 CSV tables organized around `session_id` / `trainee_id` / `facility_id` join keys: facility master → trainee master → training sessions → VR movements (3D position + head rotation) → equipment interactions → alarm events → emergency response → communication logs → evacuation sequences → safety violations → fatigue profiles → incident progression → pre-built ML training labels. | Table | Rows (sample) | What it represents | |---|---:|---| | `facility_master.csv` | 30 | 10-class facility × 10-region × VR environment version + operational complexity | | `trainee_master.csv` | 500 | 8-class role × skill level × certifications + fatigue + stress susceptibility | | `training_sessions.csv` | 2,500 | 20-class scenario × severity × fatigue × stress × completion score + grade | | `vr_movements.csv` | 15,000 | 3D position (x, y, z) + head rotation (yaw, pitch) + hazard proximity + collision flag | | `equipment_interactions.csv` | ~29,000 | 15 equipment types × 15 interaction types with correct-action flag + quality score | | `emergency_response.csv` | ~14,800 | Multi-step response workflows with delay + success + containment status | | `alarm_events.csv` | ~12,800 | ISA 18.2-aligned alarm priority + acknowledgment time + alarm flood flag | | `communication_logs.csv` | ~16,200 | Communication type × clarity score × failure flag × command chain level | | `evacuation_sequences.csv` | ~760 | Route × muster point × expected vs actual completion time | | `safety_violations.csv` | ~810 | 10-class violation × severity × correction × coach intervention | | `fatigue_profiles.csv` | ~7,500 | Per-session × 3-stage fatigue + reaction delay + cognitive load | | `incident_progression.csv` | ~11,900 | Cascade staging × escalation probability × stabilization probability | | `ai_training_labels.csv` | 2,500 | **Pre-built ML labels: 8 columns spanning hazard prob + response grade + containment + retraining flag + VR realism** | Total: ~115,000 rows, ~14 MB. The full OIL-046 product is ~85 million sessions and ~950 million VR movement records. --- ## Calibration sources Every distribution and ratio is anchored to **named public references**. Highlights: - **IPIECA Competency Framework** — upstream operator competency classification and scenario taxonomy. - **IOGP Process Safety Fundamentals** — facility classification and scenario severity bands. - **IADC Well Control + WellCAP** — well-control training scenario taxonomy. - **NFPA 1006** Technical Rescue Personnel Professional Qualifications — emergency responder training standards. - **OPITO** Offshore Petroleum Industry Training Organization — VR- augmented offshore training requirements + session duration norms. - **CCPS Process Safety + LOPA** — containment success benchmarks and rare-event drill scheduling. - **NEBOSH** International General Certificate — safety violation taxonomy. - **ISA 18.2 / EEMUA 191** — alarm priority bands and acknowledgment conventions. - **UK HSE OHRA** + **API RP 755** — fatigue management applied to training environments. - **DNV-RP-A203** simulator validation + emerging VR training-fidelity standards — realism scoring conventions. - **ISO 14224:2016** — equipment classification compatible taxonomy. --- ## Validation scorecard The wrapper ships a 10-metric scorecard (`validation_scorecard.json`) that re-scores the dataset on every generation. Default seed 42 result: | ID | Metric | Target | Observed | Source | |---|---|---|---:|---| | M01 | Facility-Type Taxonomy (floor) | ≥ 10 | **10** | IPIECA / IOGP | | M02 | Scenario Taxonomy (floor) | ≥ 20 | **20** | IADC / IPIECA / NFPA 1006 | | M03 | Equipment Taxonomy (floor) | ≥ 15 | **15** | ISO 14224 / IADC | | M04 | Violation Taxonomy (floor) | ≥ 10 | **10** | NEBOSH / CCPS | | M05 | Session Duration Median (min) | 30–90 | **65** | OPITO / IADC | | M06 | Containment Success Rate | 0.65–0.85 | **0.727** | IPIECA / CCPS LOPA | | M07 | Fatigue Exceedance Share | 0.06–0.18 | **0.110** | UK HSE OHRA / API RP 755 | | M08 | VR Realism Score (mean, floor) | ≥ 0.87 | **0.918** | DNV-RP-A203 / OPITO | | M09 | Rare-Event Label Rate | 0.005–0.045 | **0.022** | IPIECA / CCPS | | M10 | Response Grade (mean) | 0.45–0.75 | **0.582** | IPIECA / IADC | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** Note: 6 of 10 metrics fall within 5% of target midpoint, and all 4 floor metrics deliver complete taxonomy coverage at sample scale. The scorecard is anchored to **11 distinct training-industry standards** spanning IADC, IPIECA, OPITO, NFPA, CCPS, NEBOSH, ISA, UK HSE, API, DNV, and ISO — the deepest standards-anchoring of any SKU in the catalog. --- ## Suggested use cases - **Pass/fail prediction** — pre-built `training_pass_label` in `ai_training_labels.csv` enables binary classifier training for competency assessment. - **Recommended retraining detection** — `recommended_retraining_flag` + `response_grade_score` × scenario_type supports retraining-recommender model training. - **VR realism × performance correlation** — `vr_realism_score` per session × `procedural_accuracy` × `pass_label` enables simulator- fidelity ROI studies. - **Multi-modal training event prediction** — join `alarm_events` + `communication_logs` + `equipment_interactions` + `vr_movements` on session_id to train multi-modal trainee-behavior models. - **Fatigue-in-training analytics** — `fatigue_profiles` 3-stage scoring × session severity × procedural accuracy enables fatigue-aware training scheduling models. - **Cascade-failure response training** — `incident_progression.csv` cascade staging × escalation probability × emergency response actions enables Bow-Tie / LOPA training-effectiveness modeling. - **Equipment-interaction quality scoring** — per-interaction `correct_action_flag` × `actual_response_time_sec` vs `expected_response_time_sec` enables interaction-quality ML. - **Evacuation timing prediction** — `evacuation_sequences.csv` expected vs actual completion time + route clear flag enables evacuation effectiveness modeling. - **Cross-vertical immersive-training methodology** — the OIL-046 generator architecture (20 scenarios × 15 equipment × VR movements × labels) ports directly to Aviation, Maritime, Healthcare, Defense, Mining, and Manufacturing VR training research. --- ## Loading ```python from datasets import load_dataset trainees = load_dataset( "xpertsystems/oil046-sample", data_files="trainee_master.csv", split="train", ) sessions = load_dataset( "xpertsystems/oil046-sample", data_files="training_sessions.csv", split="train", ) labels = load_dataset( "xpertsystems/oil046-sample", data_files="ai_training_labels.csv", split="train", ) ``` Or with pandas directly: ```python import pandas as pd from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="xpertsystems/oil046-sample", filename="vr_movements.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` All 13 tables share these primary join keys: - `trainee_id` → trainee_master ↔ sessions ↔ vr_movements ↔ equipment_interactions ↔ emergency_response ↔ communication_logs ↔ evacuations ↔ violations ↔ fatigue ↔ labels - `facility_id` → facility_master ↔ trainee_master (home) ↔ sessions ↔ alarms ↔ evacuations ↔ labels - `session_id` → sessions ↔ all event tables ↔ labels (1:1 or 1:N alignment) - `incident_id` → emergency_response ↔ incident_progression (1:N cascade staging) --- ## Schema highlights **`training_sessions.csv`** — `session_id`, `trainee_id`, `facility_id`, `scenario_type` (20-class), `severity_level` ∈ {low, medium, high, critical}, `severity_score`, `rare_event_flag`, `fatigue_score`, `stress_score`, `mean_response_time_sec`, `procedural_accuracy`, `communication_failure_flag`, `safety_violation_flag`, `containment_success_flag`, `completion_score`, `training_grade`, `ai_assist_enabled`, `vr_hardware_profile`. **`vr_movements.csv`** — `movement_id`, `session_id`, `trainee_id`, `timestamp`, `position_x`, `position_y`, `position_z`, `movement_vector`, `head_rotation_yaw`, `head_rotation_pitch`, `proximity_to_hazard_m`, `collision_or_trip_flag`, `safe_zone_flag`. **`equipment_interactions.csv`** — `interaction_id`, `session_id`, `trainee_id`, `equipment_id`, `equipment_type` (15-class), `interaction_type` (15-class), `expected_response_time_sec`, `actual_response_time_sec`, `correct_action_flag`, `manual_override_flag`, `equipment_state_before/after`, `interaction_quality_score`. **`alarm_events.csv`** — `alarm_id`, `session_id`, `facility_id`, `alarm_type`, `severity_level` (ISA 18.2), `acknowledged_flag`, `acknowledgment_time_sec`, `alarm_flood_flag`, `false_alarm_flag`. **`safety_violations.csv`** — `violation_type` (10-class: wrong_valve_sequence, incorrect_ppe, missed_loto_step, incomplete_permit_check, delayed_alarm_acknowledgment, failed_communication_protocol, missed_gas_test, unauthorized_override, + 2 more), `procedure_breached`, `severity`, `coach_intervention_required_flag`, `repeat_violation_flag`. **`ai_training_labels.csv`** — pre-built ML labels: `hazard_probability` ∈ [0, 1], `response_grade_score` ∈ [0, 1], `operator_error_probability` ∈ [0, 1], `containment_success_label` (binary), `emergency_escalation_label` (binary), `rare_event_label` (binary), `training_pass_label` (binary), `vr_realism_score` ∈ [0, 1], `recommended_retraining_flag` (binary). --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Training pass rate is ~14% — much lower than industry-mature 60–80%.** The generator's `training_pass_label` requires containment success AND procedural accuracy AND low fatigue AND correct emergency response all combining; this multi-AND gate produces a low pass rate by design, biased toward identifying improvement opportunities. The scorecard validates the more useful **response_grade_score mean (0.58)** which sits in the IPIECA/IADC competency-development band. For pass-rate modeling work, threshold `response_grade_score > 0.65` directly to recover an industry-realistic ~60% pass rate. 2. **Operator error probability mean is ~71%.** Again, this is a training environment — operators are learning. Real-world (post-certification) operator error rates are much lower (~1–5%). For deployed-operator modeling, use OIL-038/039/040/045 which carry calibrated steady-state error rates. 3. **Recommended retraining flag ~86%.** This flag identifies *any* improvement opportunity, not just material competency gaps — most training sessions identify *something* to improve. For "actual retraining required" subset, intersect with `training_pass_label == 0` AND `response_grade_score < 0.50`. 4. **Violation severity is approximately uniform (~25% each across LOW / MEDIUM / HIGH / CRITICAL).** Industry-mature operations have pyramid- shaped violation distributions. The uniform distribution is intentional for balanced ML training; for pyramid-shaped sampling, use OIL-037 (Regulatory Compliance) or OIL-045 (Workforce Safety Violations). 5. **VR movement data uses simple 3D position + head rotation.** No hand/controller pose data, no eye tracking, no biometric streams. For research requiring full VR biometric channels, the full product includes optional hand-tracking + eye-tracking + heart-rate streams. 6. **Equipment interactions assume binary correct/incorrect action.** Real operator training systems use graded correctness (e.g., "partially correct, sequenced wrong"). The full product carries a 5-tier correctness scale; sample uses the binary collapse. 7. **HF preview sizing** — default sample mode is 5K trainees × 25K sessions × 150K VR rows producing ~134 MB. The HF preview is reduced to 500/30/2,500/15,000, ~14 MB. All schemas, taxonomies, and scorecard calibrations are preserved at the smaller scale. 8. **Deterministic seeding.** All 13 tables are deterministic on `--seed`. Catalog default is seed 42. Seed sweep verifies Grade A+ across {42, 7, 123, 2024, 99, 1}. --- ## Commercial / full product The full **OIL-046** product covers ~350,000 trainees × ~8,500 facilities × ~85 million sessions × ~950 million VR movement records across a 5-year horizon, with optional hand-tracking / eye-tracking / heart-rate biometric streams, 5-tier graded correctness on equipment interactions, calibrated industry-realistic pass-rate distributions, and configurable scenario- portfolio composition for industry-specific competency stress testing. Available under commercial license — contact [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai). XpertSystems.ai also publishes synthetic data products across Cybersecurity, Healthcare, Insurance & Risk, Materials & Energy, and Oil & Gas verticals. Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).