Datasets:
trial_id string | site_id string | month int64 | irb_cycle_days int64 | contracting_lag_days int64 | site_activation_delay_days int64 | activation_drift_index float64 | enrollment_target_n int64 | enrolled_n int64 | enrollment_shortfall_pct int64 | power_loss_z float64 | label_power_loss_next_90d int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
TRIAL_ACT01 | S01 | 1 | 28 | 35 | 20 | 0.18 | 40 | 38 | 5 | 0.2 | 0 |
TRIAL_ACT01 | S01 | 3 | 32 | 40 | 25 | 0.22 | 80 | 74 | 8 | 0.3 | 0 |
TRIAL_ACT01 | S02 | 6 | 45 | 55 | 40 | 0.35 | 120 | 98 | 18 | 0.6 | 0 |
TRIAL_ACT01 | S02 | 9 | 60 | 70 | 55 | 0.55 | 160 | 120 | 25 | 1 | 1 |
TRIAL_ACT01 | S03 | 12 | 75 | 85 | 70 | 0.7 | 200 | 140 | 30 | 1.3 | 1 |
TRIAL_ACT02 | S01 | 2 | 26 | 34 | 18 | 0.16 | 40 | 39 | 3 | 0.2 | 0 |
TRIAL_ACT02 | S02 | 5 | 40 | 50 | 35 | 0.3 | 100 | 86 | 14 | 0.5 | 0 |
TRIAL_ACT02 | S03 | 8 | 58 | 66 | 52 | 0.52 | 150 | 112 | 25 | 0.9 | 1 |
TRIAL_ACT02 | S03 | 11 | 72 | 82 | 68 | 0.68 | 190 | 132 | 31 | 1.2 | 1 |
TRIAL_ACT02 | S04 | 4 | 34 | 42 | 28 | 0.24 | 70 | 64 | 9 | 0.3 | 0 |
Clinical Quad IRB Delay Contracting Lag Site Activation Drift Enrollment Shortfall v0.1
Each row is a site monthly snapshot.
Core quad
IRB delay
Contracting lag
Site activation drift
Enrollment shortfall
Target
label_power_loss_next_90d
Files
data/train.csv
data/tester.csv
scorer.py
Evaluation
Run model on data/tester.csv
Return predictions row aligned
Score with scorer.py
License
MIT
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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