datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
honesty-index
The Kerne Honesty Index
What each synthetic dollar advertises, next to what it actually paid.
Advertised APY versus realized APY for 21 synthetic dollar vaults, recomputed hourly from
ERC-4626 share price growth on chain, and signed.
The realized column is not taken from anybody's dashboard. It is measured directly from the vault
contract: convertToAssets(10**decimals) read at two block heights, divided by 10**asset_decimals,
annualized over the real elapsed time between those… See the full description on the dataset page: https://huggingface.co/datasets/kerne-protocol/honesty-index.solana-yield-honesty
Solana Honesty Index
What each Solana stablecoin product says it pays, next to what it actually
paid, measured from a share price rather than from a claim.
Snapshot generated 2026-09-21T13:24:04.031Z. Window 30 days.
13 products across 3 protocols,
13 comparable, 0 published but not
comparable. Realized figures: 5 by issuer_share_price_history, 2 by onchain_share_price, 6 by issuer_share_price_observed.
product
advertised
realized
gap
delivered
realized method
Kamino… See the full description on the dataset page: https://huggingface.co/datasets/kerne-protocol/solana-yield-honesty.protocols-with-stepsAll protocols from https://github.com/protocolsio/protocols in text form with steps as json list
ProtocolEC
ProtocolEC
A protocol-derived benchmark for complete clinical-trial eligibility-criteria (EC) generation.
ProtocolEC pairs each of 4,302 completed Phase III trials (22 therapeutic areas) with (i) its
ClinicalTrials.gov registry metadata and registry EC, and (ii) a more complete EC set extracted
from the trial's protocol PDF. Protocol EC contain roughly twice the criteria and words of the
registry EC. Splits are 80/10/10, stratified by therapeutic area.
Split
Trials… See the full description on the dataset page: https://huggingface.co/datasets/Konghao/ProtocolEC.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-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-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-therapeutic-protocol-avatar-state-space-construction-v0.1What this dataset tests
Whether a model can construct physiologically plausible patient avatarsas state-spaces suitable for long-horizon protocol stress-testing.
Required outputs
avatar_profile
state_vector_schema
baseline_coherence_score_0_100
fragility_points
Avatar must include
comorbidity stack
baseline state vector
adherence profile
fragility points
Typical failures
avatars that ignore physiology links
schemas missing key streams
fragility points not tied to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-therapeutic-protocol-avatar-state-space-construction-v0.1.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-population-shift-protocol-deviation-site-variance-endpoint-fragility-v0.2Clinical Quad Population Shift Protocol Deviation Site Variance Endpoint Fragility v0.2
What this dataset does
It tests whether a model can detect when clinical trial endpoints lose credibility under quad coupling.
Quad nodes
population_shift
protocol_deviation_rate
site_variance_level
endpoint_fragility
Labels
0 coherent
Stable population
Low deviations
Low site variance
Endpoint robust
1 tradeoff
Some drift exists
Endpoint still usable
Risk is present but not… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-population-shift-protocol-deviation-site-variance-endpoint-fragility-v0.2.clinical-quad-population-shift-protocol-deviation-site-variance-endpoint-fragility-v0.1
Clinical Quad Population Shift × Protocol Deviation × Site Variance × Endpoint Fragility v0.1
What this is
A quad-coupling dataset for trial collapse that happens when:
the enrolled population drifts from the intended cohort
protocol deviations rise
site-to-site variance widens
the primary endpoint is fragile to measurement or baseline imbalance
Task
Input: one row describing the quad stateOutput: label
0 — Stable1 — Drift2 — Collapse
Why it matters… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-population-shift-protocol-deviation-site-variance-endpoint-fragility-v0.1.clinical-quad-protocol-deviation-cluster-staffing-load-training-gap-governance-pressure-v0.1Clarus Clinical Quad Coupling Protocol Deviation Cluster Staffing Load Training Gap Governance Pressure v0.1
What this dataset isThis dataset tests whether a model can detect clustered protocol deviations caused by four interacting nodes.
Quad coupling nodes
Deviation rate or severity cluster
Staffing or workload pressure
Training gap or outdated materials
Governance or compliance review 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-protocol-deviation-cluster-staffing-load-training-gap-governance-pressure-v0.1.clinical-quad-site-training-protocol-complexity-error-rate-data-usability-v0.1Clinical Quad Site Training Protocol Complexity Error Rate Data Usability v0.1
Each row is a site week snapshot.
Core quad
Site training intensityProtocol complexityOperational error rateData usability
Target
label_data_collapse_next_60d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1Clarus Clinical Quad Coupling Protocol Deviation Staffing Drift Adjudication Variance Missingness Bias v0.1
What this dataset isThis dataset tests whether a model can detect protocol deviation events driven by quad coupling.
Quad coupling nodes
Operational staffing drift or site capacity constraint
Protocol compliance breakdown
Endpoint adjudication variance or bias risk
Data missingness that distorts safety or efficacy interpretation under governance rules
Input
One vignette in… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1.clinical-quad-site-performance-data-integrity-protocol-drift-power-loss-v0.1Clinical Quad Site Data Protocol Power Loss v0.1
Each row is a site week snapshot.
Core quad
Site performanceData integrityProtocol driftStatistical power
Target
label_power_loss_next_60d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
License
MIT
clinical-therapeutic-protocol-protocol-design-tradeoff-navigation-v0.1What this dataset tests
Whether a model can design a multi-step treatment protocolthat manages trade-offs across systems over time.
Required outputs
protocol_timeline
interaction_risk_map
monitoring_plan
adherence_plan
Typical failures
single-step recommendations without time staging
ignoring drug interactions and contraindications
no monitoring cadence
no adherence plan
Suggested prompt wrapper
System
You design a long-horizon treatment protocol under multi-system… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-therapeutic-protocol-protocol-design-tradeoff-navigation-v0.1.linical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1
Clinical Quad: Protocol Deviations × Staffing Drift × Adjudication Variance × Missingness Bias
This dataset targets a “trial looks clean on paper” failure mode.
Sites drift in staffing.Protocol deviations rise.Endpoint adjudication becomes inconsistent.Missing data stops being random.
The four-way coupling can create false stability or false efficacy.
Variables
protocol_deviation (low | medium | high)
staffing_drift (yes | no)
adjudication_variance (low | high)… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/linical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.1.clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.2Clinical Quad Protocol Deviation Staffing Drift Adjudication Variance Missingness Bias v0.2
What this dataset does
It tests whether a model can detect operational collapse risk in trial conduct.
The quad nodes
protocol_deviation
staffing_drift
adjudication_variance
missingness_bias
Labels
coherent
stable staffing
low drift and low bias
operations remain controlled
tradeoff
mixed strain
issues exist but do not meet collapse pattern
collapse_risk
all level nodes high and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-protocol-deviation-staffing-drift-adjudication-variance-missingness-bias-v0.2.clinical-quad-recruitment-protocol-adherence-outcome-drift-v0.1Clinical Quad Recruitment Protocol Adherence Outcome Drift v0.1
Each row is a site week snapshot.
Core quad
Recruitment qualityProtocol deviationVisit adherenceOutcome drift
Target
label_trial_fail_risk_next_30d
Files
data/train.csvdata/tester.csvscorer.py
Evaluation
Run model on data/tester.csvReturn predictions row alignedScore with scorer.py
Columns… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-recruitment-protocol-adherence-outcome-drift-v0.1.protocol-general-by-llmprotocols_and_reportsffr-center-protocol-coherence-baseline-mapping-v0.1Goal
Monitor site-specific protocol coherencefor AI-derived FFR systems.
This dataset maps whether a hospitalis still operating insidethe model’s validated acquisition envelope.
Inputs
scanner vendor and model
reconstruction kernel
slice thickness
heart rate control approach
contrast protocol signature
image quality proxies
motion artifact rate
signal to noise
Required outputs
site_coherence_score
protocol_deviation_index
risk_flag
Interpretation
site_coherence_scorehow aligned this… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-center-protocol-coherence-baseline-mapping-v0.1.kyoto_data_attacks_protocolsprotocolsdefense-sector-cyber-security-protocolscorporate-security-and-protection-protocols