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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RFI-0001 | Enrollment on plan. Site activation smooth. Runway healthy. Vendor deliverables on time. | 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 execution | 0 |
RFI-0002 | Enrollment slightly behind. Rapid outreach. Extra sites planned. Burn monitored. | 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 | 0 |
RFI-0003 | Recruitment slows. Screen failures rise. Decision cadence slows. CRO timeline rigid. | 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 | runway erosion | 1 |
RFI-0004 | Site activation delayed. Budget reforecast late. Vendor obligations lock in. | 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 |
RFI-0005 | Runway tight. Bridge financing uncertain. Enrollment misses widen. Contract penalties trigger. | 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 |
RFI-0006 | Early under-enrollment detected. Fast site expansion. Burn reduced. Runway restored. | 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 |
RFI-0007 | Recruitment lag persists. Leadership review delayed. Vendor coupling tight across sites. | 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 |
RFI-0008 | Enrollment dips at t1. Rapid eligibility tweak improves flow. Costs controlled. | 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 |
RFI-0009 | Enrollment misses widen. Spend continues. Financing slow. Insolvency risk 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 | trial insolvency lock-in | 1 |
What this repo does
This dataset tests whether a model can detect a clinical trial drifting over time from recruitment failure into trial insolvency lock-in by reading a short ordered window of signals and predicting whether the program crosses into cascade lock-in by the final step.
Core quad
pressure
buffer
lag
coupling
Prediction target
label_cascade_state
Row structure
One row represents a short time window (t0–t3) across trial months. It includes time-series values for pressure (enrollment shortfall and burn), buffer capacity (funding runway and operational margin), governance lag (decision and remediation latency), and coupling tightness (vendor contracts and site dependency). The label marks whether insolvency lock-in is reached 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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