Datasets:
trial_id string | batch_id string | month int64 | scaleup_stage_0_1 int64 | batch_variability_cv_pct float64 | potency_pct int64 | label_claim_deviation_pct float64 | impurity_pct float64 | exposure_auc_variance float64 | clinical_response_sd float64 | response_noise_z float64 | phase3_fail_risk_next_90d int64 | label_phase3_fail_risk_next_90d int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
TRIAL_CMC01 | B001 | 1 | 0 | 3.8 | 98 | 0.6 | 0.1 | 0.08 | 0.22 | 0.2 | 0 | 0 |
TRIAL_CMC01 | B002 | 3 | 0 | 4.2 | 97 | 0.8 | 0.12 | 0.1 | 0.24 | 0.3 | 0 | 0 |
TRIAL_CMC01 | B003 | 6 | 1 | 6.5 | 95 | 1.5 | 0.18 | 0.15 | 0.3 | 0.6 | 0 | 0 |
TRIAL_CMC01 | B004 | 9 | 1 | 8.2 | 93 | 2.3 | 0.26 | 0.22 | 0.38 | 1 | 1 | 1 |
TRIAL_CMC01 | B005 | 12 | 1 | 9 | 92 | 2.8 | 0.32 | 0.26 | 0.44 | 1.3 | 1 | 1 |
TRIAL_CMC02 | B010 | 2 | 0 | 3.6 | 99 | 0.5 | 0.09 | 0.07 | 0.21 | 0.2 | 0 | 0 |
TRIAL_CMC02 | B011 | 5 | 1 | 6 | 96 | 1.4 | 0.16 | 0.14 | 0.28 | 0.6 | 0 | 0 |
TRIAL_CMC02 | B012 | 8 | 1 | 7.8 | 94 | 2.1 | 0.24 | 0.2 | 0.35 | 0.9 | 1 | 1 |
TRIAL_CMC02 | B013 | 11 | 1 | 8.9 | 93 | 2.6 | 0.3 | 0.24 | 0.41 | 1.2 | 1 | 1 |
TRIAL_CMC02 | B014 | 4 | 0 | 4.4 | 97 | 0.9 | 0.13 | 0.1 | 0.24 | 0.3 | 0 | 0 |
Clinical Quad Manufacturing Scale Up Batch Variability Potency Drift Clinical Response Noise v0.1
Each row is a batch monthly snapshot.
Core quad
Manufacturing scale up
Batch variability
Potency drift
Clinical response noise
Target
label_phase3_fail_risk_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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