datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
clinical-false-stability-sepsis-v1Clinical False Stability Sepsis Detection
Overview
This dataset tests whether a model can detect false stability in a clinical system.
False stability occurs when a system appears stable based on surface indicators, while deeper structural signals reveal that the system is already drifting toward collapse.
In real clinical environments this phenomenon appears frequently during severe infections such as sepsis. A patient's vital signs may temporarily stabilize even while the underlying… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-false-stability-sepsis-v1.false-stability-collapse-benchmark-v0.1
False Stability Collapse Benchmark v0.1
Overview
This benchmark evaluates whether machine learning systems can detect future collapse when surface indicators still appear stable.
Many complex systems show calm behavior immediately before sudden failure.
These conditions are often referred to as false stability or metastable states.
Examples include:
financial markets before crashes
ecosystems approaching tipping points
infrastructure networks prior to cascading failures… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/false-stability-collapse-benchmark-v0.1.