--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - regulatory-compliance - audit-trails - capa - permit-violations - environmental-reporting - safety-compliance - cybersecurity-compliance - oil-and-gas - iso-45001 - ccps - phmsa - bsee - epa-cwa - api-rp-754 - api-rp-1173 - nist-csf - iec-62443 - sox - grc - enforcement-risk pretty_name: "OIL-037 — Synthetic Regulatory Compliance 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's inside 12 CSV tables covering the complete GRC (governance / risk / compliance) lifecycle: facility master → audit trails → inspection findings → permit violations → CAPA workflows → environmental reporting → safety compliance → training → contractor compliance → escalation chains → cybersecurity compliance → pre-built ML labels. | Table | Rows (sample) | What it represents | |---|---:|---| | `compliance_master.csv` | 500 | 7-type facility master with regulation type, base risk, maturity | | `audit_trails.csv` | 2,250 | 6-type audits (internal, third-party, regulatory, ESG, cyber, PSM) | | `inspection_findings.csv` | 2,700 | 7-class findings with severity score and root cause | | `permit_violations.csv` | 8,800 | 7-type permits × 7 violation codes; shutdown/override/referral flags | | `capa_workflows.csv` | 5,000 | ISO 45001 CAPA lifecycle: opened/target/actual close, aging, effectiveness | | `environmental_reporting.csv` | 64,000 | 8-type exceedances (CO₂/CH₄/VOC/SOₓ/NOₓ/produced water/flaring/spill) × 6 agencies | | `safety_compliance.csv` | 20,000 | OSHA recordable flags + API 754 Tier 1–4 PSE classification | | `training_records.csv` | 30,000 | 8-module training × contractor flag × expiration tracking | | `contractor_compliance.csv` | 3,000 | ISNetworld/Avetta-style pre-qualification audits | | `escalation_chains.csv` | 2,500 | 6-level escalation (site → regional → legal → exec → regulator → board) | | `cybersecurity_compliance.csv` | 6,000 | NIST CSF / NERC CIP / IEC 62443 / SOX ITGC × 7 control families | | `compliance_labels.csv` | 500 | Pre-built ML labels: risk score, enforcement & shutdown probabilities, priority segment | Total: ~145,000 rows, ~13 MB. The full OIL-037 product is ~6 million rows. --- ## Calibration sources Every distribution and ratio is anchored to **named public references**. The validation scorecard (see below) re-scores observed vs. target for 10 industry-anchored metrics, every one citing its source. Highlights: - **ISO 45001:2018 Clause 10.2** — OH&S corrective action overdue rate benchmark. - **CCPS Risk-Based Process Safety** — repeat audit finding rate maturity bands. - **CCPS Auditing Guidelines** — CAPA closure aging distribution. - **API RP 754** — Process Safety Performance Indicators (Tier 1–4 event mix). - **API RP 1173** — Pipeline Safety Management System audit benchmarks. - **ISO 19011** — Guidelines for auditing management systems. - **BSEE / PHMSA inspection statistics** — critical finding severity ratio. - **EPA NEI / TRI compliance reporting** — environmental exceedance baselines. - **PHMSA HL pipeline + BSEE OCS enforcement data** — shutdown order frequency. - **IPIECA / OGUK incident-management benchmarks** — escalation resolution rate. - **ISNetworld / Avetta** — contractor pre-qualification insurance-validity baseline. --- ## 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 | CAPA Overdue Rate (ceiling) | ≤ 0.25 | **0.184** | ISO 45001:2018 | | M02 | Repeat Audit-Finding Rate (ceiling) | ≤ 0.25 | **0.067** | CCPS RBPS | | M03 | Critical Finding Share (ceiling) | ≤ 0.075 | **0.034** | BSEE / PHMSA | | M04 | Env Exceedance Rate (ceiling) | ≤ 0.035 | **0.026** | EPA NEI / TRI | | M05 | Enforcement Shutdown Rate (ceiling) | ≤ 0.055 | **0.017** | PHMSA / BSEE | | M06 | API 754 PSE Tier 1+2 Share | 0.035–0.065 | **0.049** | API RP 754 | | M07 | CAPA Aging Days (median) | 30–60 | **42** | CCPS / ISO 45001 | | M08 | Escalation Resolution Rate | 0.62–0.78 | **0.709** | IPIECA / OGUK | | M09 | Contractor Insurance Valid (floor) | ≥ 0.89 | **0.928** | ISNetworld / Avetta | | M10 | Audit Score Median (floor) | ≥ 80 (0–100) | **93.8** | API RP 1173 / ISO 19011 | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** --- ## Suggested use cases - **Enforcement-risk prediction** — train classifiers that predict `enforcement_probability` or `regulatory_shutdown_probability` from facility context, finding history, environmental exceedances, and CAPA closure patterns. Pre-built labels in `compliance_labels.csv`. - **CAPA closure-aging forecasting** — regression on `aging_days` from finding severity, facility maturity, contractor ratio, and effectiveness-check history. Practical for compliance-team capacity planning. - **GRC operational dashboards** — `compliance_master.csv` × `compliance_labels.csv` on `facility_id` builds a complete priority-segmented portfolio view across 500 facilities for portfolio-level risk benchmarking. - **Audit-finding root-cause modeling** — 7-class `root_cause_category` × 4-class severity ladder on ~2,700 findings enables HUMAN_ERROR vs. PROCEDURE_GAP vs. EQUIPMENT_CONDITION decomposition for audit-program improvement studies. - **Escalation pathway analysis** — `escalation_chains.csv` has 6-level escalation ladder + response-time-hours + whistleblower/legal flags, enabling temporal-process-mining and bottleneck analytics. - **Multi-framework cyber compliance benchmarking** — NIST CSF / NERC CIP / IEC 62443 / SOX ITGC × 7 control-family decomposition supports cross- framework gap-analysis modeling. - **Environmental exceedance early-warning** — 8 exceedance types × 6 agencies × 64K reporting periods enables agency-specific exceedance and late-submission classifiers. --- ## Loading ```python from datasets import load_dataset facilities = load_dataset( "xpertsystems/oil037-sample", data_files="compliance_master.csv", split="train", ) findings = load_dataset( "xpertsystems/oil037-sample", data_files="inspection_findings.csv", split="train", ) labels = load_dataset( "xpertsystems/oil037-sample", data_files="compliance_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/oil037-sample", filename="capa_workflows.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` All 12 tables join on `facility_id`. Cross-table joins also available on `audit_id` (audits ↔ findings), `finding_id` (findings ↔ violations ↔ capa), `violation_id` (violations ↔ capa ↔ safety ↔ escalation), and `capa_id` (capa ↔ escalation). --- ## Schema highlights **`compliance_master.csv`** — `facility_id`, `facility_type` (7-class: REFINERY / OFFSHORE_PLATFORM / LNG_TERMINAL / PIPELINE_SYSTEM / TANK_FARM / DRILLING_SITE / PETROCHEMICAL_PLANT), `region` (9-class), `operator_entity`, `regulation_type` (16-class), `operational_maturity_score` ∈ [0.05, 0.99], `base_compliance_risk` ∈ [0, 1], `contractor_ratio`, `environmental_sensitivity`, `cyber_maturity_score`, `compliance_status` ∈ {COMPLIANT, WATCH, NON_COMPLIANT, ENFORCEMENT_RISK}, `last_major_audit_date`, `active_permit_count`, `employee_count`. **`inspection_findings.csv`** — `finding_id`, `audit_id`, `facility_id`, `detected_date`, `finding_type` (7-class: DOCUMENTATION_GAP / TRAINING_EXPIRY / EQUIPMENT_CERTIFICATION / PERMIT_BREACH / ENV_EXCEEDANCE / CYBER_CONTROL_GAP / PROCESS_SAFETY_GAP), `severity_level` ∈ {LOW, MEDIUM, HIGH, CRITICAL}, `severity_score` ∈ [0, 1], `repeat_finding_flag`, `regulation_type` (16-class), `root_cause_category` (7-class), `required_remediation_due_date`, `inspector_variability_score`. **`safety_compliance.csv`** — `osha_recordable_flag`, `process_safety_tier` ∈ {NONE, TIER_4, TIER_3, TIER_2, TIER_1} (API RP 754 performance indicators), 7-class `compliance_gap`, 6-class `corrective_action`. **`cybersecurity_compliance.csv`** — 5-class `framework` ∈ {NERC_CIP, IEC_62443, NIST_CSF, SOX_ITGC, CORPORATE_CYBER}, 7-class `control_family` (ACCESS_CONTROL / ASSET_INVENTORY / CHANGE_MANAGEMENT / LOGGING / INCIDENT_RESPONSE / VULNERABILITY_MGMT / NETWORK_SEGMENTATION), `compliance_gap` ∈ {NONE, MINOR, MAJOR}, `audit_result` ∈ {PASS, CONDITIONAL_PASS, FAIL}, `gap_score`, `ot_asset_exposure_flag`, `remediation_sla_days`. **`compliance_labels.csv` — pre-built ML labels per facility**: `risk_score`, `enforcement_probability`, `regulatory_shutdown_probability`, `compliance_grade` ∈ {A, B, C, D, F}, `priority_segment` ∈ {LOW_RISK, WATCHLIST, HIGH_RISK, CRITICAL_RISK}. --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Training-record certification status skew.** In `training_records.csv`, the `CURRENT` rate at sample scale is ~13% with ~86% `EXPIRED`, well below the API RP T-1 mature target (≥ 80% current). This is a generator quirk in the expiration-date sampling logic (random expirations uniformly drawn from -180 to +365 days around random in-window dates skew most flags to the EXPIRED side). For ML utility, **use `completion_score` and `days_until_expiry` as primary features** rather than the `certification_status` enum, or filter to `days_until_expiry > 0` and treat the rest as a separate compliance-debt subset. The full OIL-037 product ships a re-calibrated training mode-pack. 2. **`compliance_labels.csv` grade-class skew.** ~91% of facilities receive grade "F" because the grade is computed as `compliance_grade(1 - risk_score)` but the risk-score formula at sample scale concentrates between 0.55–0.75, mapping to F under the threshold ladder. For **balanced multi-class training**, use `priority_segment` (4-class: LOW_RISK / WATCHLIST / HIGH_RISK / CRITICAL_RISK), which is well-spread at sample scale, or rebuild a custom grade column from `risk_score` directly with quintile thresholds. 3. **Cybersecurity compliance pass rate.** At sample scale, the cyber audit PASS rate is ~25% and FAIL ~21% — these reflect the generator's strict cyber_gap formula and are **below NIST CSF mature-program baselines** of ≥60% PASS. The 7-control-family taxonomy and 5-framework distribution are accurate; the categorical pass/fail thresholds are intentionally conservative for ML utility. The validation scorecard does **not** validate cyber audit results against NIST CSF maturity claims for this reason. 4. **OSHA recordable rate.** Sample-scale `osha_recordable_flag` rate (~3.8%) is incident-dense relative to BLS oil & gas TRIR (~0.8–1.5% in raw event terms). This is intentional ML-utility scaling (similar to OIL-035) so the 500-facility sample has trainable positive-class density. The full product recovers realistic upstream BLS rates. 5. **Date-window length.** Default window is 2023-01-01 to 2025-12-31 (3 years). For longer-horizon trend modeling, use the full product or override `--start-date` / `--end-date` on the underlying generator. 6. **Deterministic seeding.** All 12 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-037** product covers 8,500 facilities across a configurable multi-year horizon (~6 million rows total), with re-calibrated training certification status logic, balanced compliance-label distributions, and optional NIST CSF maturity-aligned cyber-audit mode-packs. 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).