Datasets:
id string | context string | step_unit string | pressure_t0 float64 | pressure_t1 float64 | pressure_t2 float64 | pressure_t3 float64 | buffer_t0 float64 | buffer_t1 float64 | buffer_t2 float64 | buffer_t3 float64 | lag_t0 float64 | lag_t1 float64 | lag_t2 float64 | lag_t3 float64 | coupling_t0 float64 | coupling_t1 float64 | coupling_t2 float64 | coupling_t3 float64 | cross_step int64 | notes string | label_cascade_state int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MSD-0001 | Batch release on schedule. QA stable. Inventory healthy. Suppliers diversified. | months | 0.22 | 0.26 | 0.28 | 0.3 | 0.86 | 0.84 | 0.82 | 0.8 | 0.18 | 0.2 | 0.22 | 0.24 | 0.3 | 0.34 | 0.36 | 0.38 | 0 | stable supply | 0 |
MSD-0002 | Minor out-of-spec trend. QA investigates quickly. Inventory buffer adequate. | months | 0.34 | 0.4 | 0.44 | 0.46 | 0.78 | 0.74 | 0.72 | 0.7 | 0.22 | 0.24 | 0.26 | 0.28 | 0.34 | 0.38 | 0.4 | 0.42 | 0 | recoverable drift | 0 |
MSD-0003 | Yield decline persists. Deviation backlog grows. CAPA delayed. Single-site coupling increases. | months | 0.48 | 0.6 | 0.72 | 0.84 | 0.7 | 0.6 | 0.46 | 0.34 | 0.28 | 0.44 | 0.66 | 0.84 | 0.42 | 0.58 | 0.74 | 0.88 | 2 | drift to shortage | 1 |
MSD-0004 | Critical deviation. Release delayed. Alternate supplier qualification slow. Downstream trials depend on lots. | months | 0.52 | 0.64 | 0.76 | 0.88 | 0.66 | 0.54 | 0.4 | 0.28 | 0.32 | 0.54 | 0.74 | 0.88 | 0.5 | 0.66 | 0.8 | 0.9 | 2 | cross t1-t2 | 1 |
MSD-0005 | Inventory depleted. Trial dosing at risk. Contract penalties trigger. QA throughput constrained. | months | 0.56 | 0.7 | 0.82 | 0.92 | 0.62 | 0.5 | 0.34 | 0.22 | 0.36 | 0.6 | 0.82 | 0.92 | 0.56 | 0.72 | 0.88 | 0.94 | 1 | early crossing | 1 |
MSD-0006 | Drift detected early. Extra QC capacity added. Inventory rebuilt. Coupling reduced via second source. | months | 0.44 | 0.52 | 0.5 | 0.48 | 0.72 | 0.76 | 0.78 | 0.8 | 0.3 | 0.26 | 0.22 | 0.2 | 0.46 | 0.44 | 0.4 | 0.38 | 0 | intervention holds | 0 |
MSD-0007 | QA backlog grows. Change control slow. Supplier dependency tight. Downstream schedule rigid. | months | 0.5 | 0.62 | 0.78 | 0.9 | 0.68 | 0.56 | 0.38 | 0.26 | 0.28 | 0.5 | 0.76 | 0.9 | 0.48 | 0.64 | 0.82 | 0.92 | 1 | lag compounding | 1 |
MSD-0008 | Deviation trend at t1. Rapid CAPA and batch prioritization restores flow. | months | 0.46 | 0.58 | 0.6 | 0.56 | 0.66 | 0.62 | 0.68 | 0.7 | 0.4 | 0.34 | 0.28 | 0.24 | 0.52 | 0.5 | 0.46 | 0.42 | 0 | recovery path | 0 |
MSD-0009 | Release failures accumulate. Inventory collapses. Remediation slow. Supply disruption locks in. | months | 0.58 | 0.72 | 0.86 | 0.94 | 0.6 | 0.48 | 0.32 | 0.2 | 0.34 | 0.62 | 0.86 | 0.94 | 0.58 | 0.74 | 0.9 | 0.96 | 2 | supply disruption lock-in | 1 |
What this repo does
This dataset tests whether a model can detect manufacturing drift forming over time and predict whether the program crosses into supply disruption lock-in by the final step.
Core quad
pressure
buffer
lag
coupling
Prediction target
label_cascade_state
Row structure
One row represents a short temporal window (t0–t3) across program months. It includes time-series values for pressure (deviations and schedule stress), buffer capacity (inventory and QA margin), governance lag (CAPA and change control latency), and coupling tightness (single-site and downstream dependency). The label marks whether supply disruption lock-in occurs by t3.
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run predictions on tester.csv
Score with scorer.py
License
MIT
Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.
What Production Deployment Enables
• 50K–1M row datasets calibrated to real operational patterns
• Pair, triadic, and quad coupling analysis
• Real-time coherence monitoring
• Early warning before cascade events
• Collapse surface and recovery window modeling
• Integration and implementation support
Small samples reveal structure.
Scale reveals consequence.
Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com
Instability is detectable.
Governance determines whether it propagates.
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