Datasets:

ClarusC64
/

clinical-temporal-5node-pressure-buf-lag-cpl-competitive-landscape-v0.1

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-T001	Yield declines and QA backlog rises. Inventory shrinks. Second source slow.	months	0.5	0.64	0.78	0.9	0.66	0.54	0.38	0.26	0.3	0.56	0.78	0.92	0.52	0.7	0.86	0.94	2	lock-in trajectory	1
MSD-T002	Stable production and timely releases with healthy inventory.	months	0.28	0.34	0.36	0.38	0.84	0.82	0.8	0.78	0.18	0.22	0.24	0.26	0.3	0.34	0.36	0.38	0	stable	0
MSD-T003	Critical deviation delays release. Downstream trials depend on lots. Remediation slow.	months	0.56	0.72	0.84	0.94	0.62	0.48	0.32	0.2	0.36	0.66	0.84	0.94	0.6	0.78	0.9	0.96	1	early crossing	1
MSD-T004	Extra QC capacity added at t2. Inventory rebuild begins. Second source activated.	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 works	0
MSD-T005	Inventory depleted. QA throughput constrained. Supply disruption likely.	months	0.6	0.76	0.9	0.98	0.6	0.44	0.26	0.16	0.34	0.66	0.9	0.98	0.58	0.78	0.94	1	2	visible lock-in	1
MSD-T006	Out-of-spec trend resolved through rapid CAPA. Release schedule holds.	months	0.36	0.44	0.48	0.46	0.78	0.74	0.72	0.7	0.22	0.26	0.28	0.26	0.34	0.38	0.4	0.38	0	containment	0
MSD-T007	Change control slow. Supplier dependency high. Downstream schedule rigid.	months	0.54	0.68	0.82	0.92	0.68	0.54	0.34	0.22	0.32	0.6	0.82	0.94	0.54	0.72	0.9	0.96	3	late lock-in	1
MSD-T008	Deviation trend flagged early. Batch prioritization restores supply flow.	months	0.4	0.5	0.56	0.54	0.74	0.7	0.72	0.74	0.34	0.3	0.26	0.24	0.5	0.48	0.44	0.42	0	recovered	0
MSD-T009	Repeated release failures. Inventory collapses. Remediation delayed.	months	0.58	0.72	0.88	0.96	0.62	0.46	0.28	0.18	0.34	0.64	0.88	0.96	0.56	0.76	0.92	0.98	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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