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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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