datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
clinical_structural_drift_detection_v0.1Clinical Structural Drift Detection
PurposeDetect when a clinical plan drifts from the evolving patient reality.
You get a case with time change signals.You decide if drift exists.You label the drift type.You propose the corrective adjustment.
Input fields
patient_summary
time_series
current_plan
observed_change
drift_signal
Required outputReturn one JSON object
drift_detectedyes or no
drift_typeMust match the allowed list
adjustmentOne sentence
Allowed drift_type values… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_structural_drift_detection_v0.1.clinical_site_quality_drift_detection_v0.1Clinical Site Quality Drift Detection v0.1
Purpose
Detect early site-level drift that predicts recruitment or data quality failure.
Model task
Return one JSON object
risk_levellow, medium, high
failure_modeone allowed label
correct_actionone short paragraph
Scoring
0 to 100
risk accuracy 30
failure mode accuracy 35
action similarity 25
format pass 10
Run
python scorer.py --predictions predictions.jsonl --test_csv data/test.csv
selfsupervised-selfmonitoring-drift-detection-v0.1Self-Monitoring Drift Detection v0.1
What this tests
Whether a system detects drift from its own baseline using a defined threshold.
Rules
dropdrift if baseline - current >= threshold
risedrift if current - baseline >= threshold
absdrift if abs(current - baseline) >= threshold
Failure modes
false_stability_claimDrift is present but the response claims stability
threshold_handled_okResponse matches the threshold rule
How it works
baseline_value is the prior stable value
current_value is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/selfsupervised-selfmonitoring-drift-detection-v0.1.ai-goal-misgeneralization-drift-detection-v0.1What this dataset is
Detects early goal drift when an agent leaves its training setting
Names the drift type, how strong it is, and what to do next
Inputs
setting
env_shift_event
training_objective
deployment_task
internal_goal_signal_t0
internal_goal_signal_t1
behavior_t0
behavior_t1
Required output
Return JSON only
drift_type_labelOne… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-goal-misgeneralization-drift-detection-v0.1.