datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lc_quad_synth
LC-QuAD 2.0-synth
Dataset Summary
This dataset is an updated version of the LC-QuAD 2.0 dataset which includes LLM-based natural language translations of the corresponding wikidata queries. It also includes
verifier scores for the LLM translations and the original translations indicating the probability that the translation is correct (for details see our linked GitHub Repository).
It contains 19000 examples of queries and translations. It can be used for training and… See the full description on the dataset page: https://huggingface.co/datasets/timschwa/lc_quad_synth.lc_quad2
Dataset Card for LC-QuAD 2.0 with answers
clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1Clarus Clinical Quad Coupling Endpoint Adjudication Integrity v0.1
PurposeDetect adjudication drift driven by four interacting nodes.
Quad nodes
Endpoint cluster shift
Blinding gap or reviewer dominance
Operational or vendor process change
Governance submission or review pressure
InputOne vignette.
OutputStrict JSON only.
Required keys
adjudication_integrity_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale
confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1.clinical-quad-unblinding-sae-cluster-media-leak-trial-halt-decision-v0.1Clinical Quad Unblinding SAE Cluster Media Leak Trial Halt Decision v0.1
Each row is a site weekly snapshot.
Core quad
Emergency unblindingSAE clusterMedia leak riskTrial halt decision risk
Target
label_trial_halt_risk_next_30d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v1.0
ClarusC64/clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v1.0
What this repo does
This repository provides a Clarus v1.0 benchmark for postoperative collapse under a four-variable clinical quad:
surgical_stress
buffer_capacity
lag_burden
coupling_stress
The v1.0 upgrade is Closed-Loop Control Geometry.
The task is no longer limited to detecting deterioration or ranking one intervention against another.
It tests whether a controller can:
choose the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v1.0.epl-inplay-quad-pre-goal-collapse-window-v0.1EPL In-Play Quad Pre-Goal Collapse Window v0.1
What this dataset is
You test whether a model can detect an in-play collapse window before a goal.
Each row represents a live match-state snapshot.
The label asks
Will a goal occur in the next 120 seconds
Core quad coupling
Press intensityDefensive line heightTurnover zonexG per possession
Why this matters
Most football models explain goals after the fact.
This dataset tests pre-goal instability detection.
Intended use
You feed a row.
You output a… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/epl-inplay-quad-pre-goal-collapse-window-v0.1.clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1Clarus Clinical Quad Coupling Informed Consent Integrity v0.1
PurposeDetect consent integrity failures driven by four interacting nodes.
Quad nodes
Consent version drift or addendum mismatch
Re-consent gap after material risk change
Enrollment pressure or incentives
Governance audit or regulator timing
InputOne vignette.
OutputStrict JSON only.
Required keys
consent_integrity_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale
confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1.clinical-quad-investigator-turnover-training-reset-protocol-deviations-data-lag-v0.1Clinical Quad Investigator Turnover Training Reset Protocol Deviations Data Lag v0.1
Each row is a site monthly snapshot.
Core quad
Investigator turnoverTraining resetProtocol deviationsData lag
Target
label_primary_fail_next_90d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-investigator-turnover-training-reset-protocol-deviations-data-lag-v0.1.clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.9
(v0.9)
What this repo does
This dataset implements a Clarus v0.9 intervention-competition benchmark.
Earlier dataset versions focused on detecting:
deterioration
regime transitions
boundary proximity
recovery feasibility
v0.9 extends the ladder.
The benchmark now evaluates whether a model can identify the correct rescue path when multiple interventions compete under narrowing rescue windows.
This reflects real system decision geometry.
In real systems:
several… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.9.epl-inplay-quad-fatigue-sub-error-collapse-v0.1EPL In-Play Quad Fatigue Substitution Error Collapse v0.1
What this dataset is
You test whether a model can detect late-game defensive collapse.
Each row represents a defending team state in minute 65 to 95.
Core quad coupling
Sprint intensityMinutes since last substitutionDefensive duel successError rate
The label asks
Will this team concede a goal in the next 120 seconds
Why this matters
Late goals decide matches.
Defensive collapse is usually a coupling failure between fatigue and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/epl-inplay-quad-fatigue-sub-error-collapse-v0.1.clinical-quad-recruitment-selection-bias-protocol-pressure-operational-drift-v0.1Clarus Clinical Quad Coupling Recruitment Selection Bias Protocol Pressure Operational Drift v0.1
What this dataset isThis dataset tests whether a model can detect recruitment and selection bias caused by four interacting nodes.
Quad coupling nodes
Recruitment speed or site pressure
Eligibility or baseline data gaps
Operational or staffing drift
Governance or milestone pressure
Input
One vignette
OutputReturn strict JSON only.
Required output JSON keys
recruitment_bias_risk… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-recruitment-selection-bias-protocol-pressure-operational-drift-v0.1.clinical-quad-cardiac-load-reserve-lag-coupling-heart-failure-transition-v1.1
Clarus v1.1 — Counterfactual and Adversarial Control Geometry
What this repo does
This dataset evaluates whether a model can select the correct control policy when:
multiple interventions appear viable
early signals suggest improvement
alternative policies produce better long-term outcomes
The task is not prediction.
The task is selecting the correct action under uncertainty, feedback, and misleading signal structure.
Core quad
The system is defined by… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-cardiac-load-reserve-lag-coupling-heart-failure-transition-v1.1.clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.1
Clinical Quad Oxygen Demand Buffer Lag Coupling Respiratory Collapse v1.1
What this repo does
This dataset evaluates whether a model can select the correct control policy when:
multiple respiratory interventions appear viable
early signals suggest improvement
alternative policies produce better long-term outcomes
The task is not prediction.
The task is selecting the correct action under uncertainty, feedback, and misleading signal structure.
Core quad
The… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-oxygen-demand-buffer-lag-coupling-respiratory-collapse-v1.1.clinical-quad-signal-detection-drift-ae-coding-variance-unblinding-risk-dsmb-decision-delay-v0.1
Clinical Quad: Signal Detection Drift × AE Coding Variance × Unblinding Risk × DSMB Decision Delay
This dataset targets safety governance collapse.
Signals weaken or shift.AE coding diverges across sites.Unblinding pressure rises.The DSMB response slows.
The quad can turn a manageable safety issue into a governance failure.
Variables
signal_detection_drift (low | medium | high)
ae_coding_variance (low | medium | high)
unblinding_risk (low | medium | high)… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-signal-detection-drift-ae-coding-variance-unblinding-risk-dsmb-decision-delay-v0.1.clinical-quad-data-integrity-query-backlog-missingness-governance-threshold-v0.1Clarus Clinical Quad Coupling Data Integrity Query Backlog Missingness Governance Threshold v0.1
What this dataset isThis dataset tests whether a model can detect clinical trial data integrity events driven by four interacting nodes.
Quad coupling nodes
Query backlog or data flow delay
Missingness in critical fields or attachments
Conmed or exposure timeline gaps
Governance thresholds such as audits, CAPA, freeze deadlines, or reporting cadence
Input
One vignette in prompt… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-data-integrity-query-backlog-missingness-governance-threshold-v0.1.clinical-quad-data-cut-timing-database-lock-pressure-query-backlog-csr-narrative-drift-v0.1Clinical Quad Data Cut Timing Database Lock Pressure Query Backlog CSR Narrative Drift v0.1
Each row is a trial monthly snapshot.
Core quad
Data cut timingDatabase lock pressureQuery backlogCSR narrative drift
Target
label_regulatory_issue_next_90d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-data-cut-timing-database-lock-pressure-query-backlog-csr-narrative-drift-v0.1.clinical-quad-metabolic-stress-buffer-lag-organ-coupling-mof-transition-v1.0
ClarusC64/clinical-quad-metabolic-stress-buffer-lag-organ-coupling-mof-transition-v1.0
What this repo does
This repository provides a Clarus v1.0 benchmark for multi-organ failure transition under a four-variable clinical quad:
metabolic_stress
buffer_capacity
lag_burden
organ_coupling_stress
The v1.0 upgrade is Closed-Loop Control Geometry.
The task is no longer limited to detecting deterioration or ranking one intervention against another.
It tests whether a… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-metabolic-stress-buffer-lag-organ-coupling-mof-transition-v1.0.clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1Clarus Clinical Quad Coupling Enrollment Criteria Drift Site Selection Bias Screening Pressure v0.1
PurposeDetect enrollment population drift driven by four interacting nodes.
Quad nodes
Criteria relaxation or documentation gap
Site selection or recruitment bias
Screening workflow pressure
Governance or interim timing pressure
InputOne vignette.
OutputStrict JSON only.
Required keys
enrollment_drift_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1.clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.3
Clinical Quad Infection Buffer Lag Coupling Sepsis Transition v1.3
Benchmark definition
Benchmark family: ClarusBenchmark layer: v1.3Geometry type: Failure Reconstruction GeometryDomain: Clinical stability systemsStructure: Quad coupling instability model
Primary question:
Can a model reconstruct the causal pathway that produced a failure state?
Evaluation requires identifying:
the ordered failure decision chain
the root policy error
the counterfactual recovery… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.3.clinical-quad-enrollment-protocol-deviation-site-variance-endpoint-integrity-v0.1
Clinical Quad Enrollment–Protocol Deviations–Site Variance–Endpoint Integrity v0.1
What this is
A quad-coupling dataset for trial collapse driven by the interaction of:
enrollment pattern changes
rising protocol deviations
site-to-site variance
endpoint integrity degradation
Task
Input: one quad state rowOutput: label
0 — Stable1 — Drift2 — Collapse
Why it matters
Trials often fail through operational pressure:
recruitment becomes spiky or slow… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-protocol-deviation-site-variance-endpoint-integrity-v0.1.clinical-quad-dose-selection-suite-v0.1Clarus Clinical Quad Coupling Dose Selection Suite v0.1
What this dataset isThis dataset tests dose selection under four-node coupling pressure.
Quad coupling nodes
Patient biology and organ reserve
Exposure and metabolism constraints
Concomitant drugs and interaction risk
Governance constraints that limit changes or force holds
Input
One clinical vignette in prompt
OutputReturn strict JSON only.
Required output JSON keys
recommended_dose_mg
dose_schedule
hold_or_adjust… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-dose-selection-suite-v0.1.clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1Clarus Clinical Quad Coupling Safety Signal Latency Reporting Lag Conmed Confound v0.1
What this dataset isThis dataset tests whether a model can detect latent safety signals when four interacting nodes create uncertainty.
Quad coupling nodes
Emerging safety event pattern
Reporting or entry latency
Concomitant medication or behavior confound
Governance decision timing such as DSMB, batch release, or safety review
Input
One vignette
OutputReturn strict JSON only.
Required output… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-safety-signal-latency-reporting-lag-conmed-confound-v0.1.epl-inplay-quad-field-tilt-box-load-break-v0.1EPL In-Play Quad Sustained Pressure Breakthrough v0.1
What this dataset is
You test whether a model can detect when sustained attacking pressure converts into a breakthrough goal.
Each row represents a live attacking sequence snapshot.
Core quad coupling
Field tiltCross frequencyBox occupancyClearance success rate
The label asks
Will the attacking team score in the next sequence
Why this matters
Pressure alone does not cause goals.
Pressure combined with clearance instability and second-ball… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/epl-inplay-quad-field-tilt-box-load-break-v0.1.clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.1What this repo is for
This dataset tests whether a model can detect quad coupling coherence risk in hospital critical care flow.
It measures alignment between four signals
patient acuity and ICU demand
staffing coverage
available ICU beds
diagnostic turnaround time
You label each case
coherent when the four signals align and escalation completes
incoherent when any node drifts enough to block escalation or trigger system strain
What it predicts
ICU overflow events
ED boarding spikes
delayed… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-icu-demand-staff-bed-diagnostics-quad-coherence-risk-v0.1.clinical-quad-adherence-dose-ae-efficacy-narrative-collapse-v0.2Clinical Quad Adherence Dose AE Efficacy Narrative Collapse v0.2
What this dataset does
It tests whether a system can detect narrative collapse in a clinical decision loop.
It forces reasoning across four operational drivers plus the narrative layer.
Core quad nodes
Adherence stability
Dose action
AE signal
Efficacy signal
Narrative node
aligned means the story matches the data and governance constraints
spin means the story tries to conceal or reframe misalignment
What the model… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-adherence-dose-ae-efficacy-narrative-collapse-v0.2.clinical-quad-enrollment-protocol-deviation-site-variance-endpoint-integrity-v0.2Clinical Quad Enrollment Protocol Deviation Site Variance Endpoint Integrity v0.2
What this dataset does
It tests whether a model can detect when endpoint integrity degrades under four coupled operational pressures.
Quad nodes
enrollment_pattern
protocol_deviation_rate
site_variance_level
endpoint_integrity
Labels
0 coherent
endpoints clean
enrollment stable
deviations not high
site variance not high
1 tradeoff
strain exists
endpoint softens or system drifts… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-protocol-deviation-site-variance-endpoint-integrity-v0.2.clinical-quad-site-performance-signal-drift-oversight-lag-v0.1Clarus Clinical Quad Coupling Site Performance Signal Drift Oversight Lag v0.1
What this dataset isThis dataset tests whether a model can detect site-level performance drift driven by four interacting nodes.
Quad coupling nodes
Enrollment or reporting signal shift
Data capture or documentation gaps
Operational staffing or monitoring lag
Governance pressure such as reviews, incentives, or interim analyses
Input
One site vignette
OutputReturn strict JSON only.
Required output JSON… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-site-performance-signal-drift-oversight-lag-v0.1.clinical-quad-adjudication-drift-endpoint-reclassification-timing-pressure-v0.1Clarus Clinical Quad Coupling Adjudication Drift Endpoint Reclassification Timing Pressure v0.1
What this dataset isThis dataset tests whether a model can detect endpoint adjudication drift driven by four interacting nodes.
Quad coupling nodes
Clustered endpoint reclassification
Source data delay or missing uploads
Exposure or dose documentation gaps
Governance or interim analysis pressure
Input
One vignette
OutputReturn strict JSON only.
Required output JSON keys… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-adjudication-drift-endpoint-reclassification-timing-pressure-v0.1.clinical-quad-pk-sampling-window-deviation-bioanalytical-variance-dose-adjustment-interim-v0.1Clarus Clinical Quad Coupling PK Integrity v0.1
PurposeDetect PK integrity distortion driven by four interacting nodes.
Quad nodes
Sampling window deviation
Bioanalytical or stability variance
Dose adjustment decisions
Governance interim or submission timing
InputOne vignette.
OutputStrict JSON only.
Required keys
pk_integrity_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale
confidence
Filesdata/train.csvdata/test.csvscorer.py
Run scoringCreate… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-pk-sampling-window-deviation-bioanalytical-variance-dose-adjustment-interim-v0.1.clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.8
What this repo does
This repository provides a Clarus v0.8 clinical quad dataset for detecting and reasoning about postoperative collapse regime transitions.
The dataset models situations where a patient state is no longer contained within a single postoperative recovery basin but is shifting between competing regimes such as:
inflammatory postoperative stress
hemodynamic collapse
septic deterioration
multiorgan instability
This is the conceptual upgrade introduced in Clarus v0.8.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-surgical-stress-buffer-lag-coupling-postop-collapse-v0.8.