datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sepsis-omics-datasets
脓毒症 (Sepsis) 公共组学与临床数据集合
冻结快照 · 2026-06-20 · 共 500 个数据集 · 7.4 GB · 全部带文件、信息卡与元数据
本仓库系统收集与脓毒症 / 败血症 / 脓毒性休克 / 菌血症 / 内毒素血症 / SIRS 相关的公开数据，
覆盖转录组、单细胞、空间转录组、蛋白质组、代谢组、外泌体、微生物组、表观（甲基化/染色质）以及
临床试验与药物数据。检索关键词：sepsis, septic shock, septicemia, septicaemia, bacteremia, endotoxemia, SIRS。
数据来源
数据库
数据集数
GEO
133
ClinicalTrials.gov
121
ArrayExpress/BioStudies
108
PRIDE/ProteomeXchange
75
Metabolomics Workbench
38
MetaboLights
25
数据类型… See the full description on the dataset page: https://huggingface.co/datasets/wei82/sepsis-omics-datasets.physionet-sepsis-2019SepsisPrediction_Dataset_Full
Dataset Card for "SepsisPrediction_Dataset_Full"
More Information needed
sepsis-cases-event-log
Sepsis Cases Event Log
Anonymized hospital event log of ~1,050 patient trajectories suspected of
sepsis, recorded from a hospital's ERP system. Each trajectory tracks
admission, triage, lab orders (e.g. lactate, leucocytes), antibiotic
administration, IV fluid administration, return-to-ER events, and discharge.
Widely used as a benchmark for process mining tasks: process discovery,
conformance checking, predictive process monitoring.
Re-host notes
This Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/kondratevakate/sepsis-cases-event-log.africa-synth-neonatal-sepsis-dataset-all
African Neonatal Sepsis Synthetic Dataset | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Health datasets… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-neonatal-sepsis-dataset-all.neonatal-sepsis-care
Neonatal Sepsis & Newborn Care (Blood Culture, Pathogens, KMC, Outcomes) | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: health - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/neonatal-sepsis-care.SepsisPrediction_DatasetSepsisPrediction_Dataset
Dataforce Team - DATATHON Competition by RISTEK FASILKOM Universitas INDONESIA (2025)
sepsis_dataclinical-sepsis-trajectory-instability-v0.1
clinical-sepsis-trajectory-instability-v0.1
What this dataset does
This dataset tests whether a model can classify sepsis trajectory instability from short clinical proxy sequences.
Each row describes a patient-like scenario across three time points.
The task is to predict whether the scenario is moving toward instability or remaining stable.
Core stability idea
Sepsis instability does not depend on one variable alone.
A patient may show an abnormal value and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-sepsis-trajectory-instability-v0.1.clinical-five-node-sepsis-cascade-boundary-v0.7
What this repo does
This repository contains a Clarus v0.7 dataset modeling sepsis cascade boundary detection using a five-node cascade representation.
The dataset extends the earlier cascade-boundary structure by introducing uncertainty geometry.
The earlier question was:
Where is the cascade boundary?
v0.7 adds a second critical question:
How confident are we in that boundary and intervention judgment?
This allows Clarus to distinguish:
• confident boundary proximity• confident… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-five-node-sepsis-cascade-boundary-v0.7.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.sepsis_with_notes_20260327clinical-recovery-window-sepsis-v1Clinical Recovery Window Sepsis Detection
Overview
This dataset tests whether a model can detect when a clinical system is still recoverable.
In severe infections such as sepsis, patients often pass through a finite period during which intervention can still reverse the trajectory toward collapse. This period is known as the recovery window.
Once the recovery window closes, physiological deterioration becomes increasingly difficult to reverse and the system moves toward irreversible failure.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-recovery-window-sepsis-v1.clinical-control-sequence-sepsis-v1
Clinical Control Sequence Sepsis Detection
Overview
This dataset tests whether a model can detect whether a proposed intervention sequence is the correct stabilizing control sequence for a sepsis-like clinical system.
Complex systems are often not stabilized by a single action. They require the correct sequence of interventions delivered in the correct order as the system evolves.
The goal of this benchmark is to determine whether the control sequence meaningfully guides… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-control-sequence-sepsis-v1.clinical-control-horizon-sepsis-v1
Clinical Control Horizon Sepsis Detection
Overview
This dataset tests whether a model can detect whether a proposed control strategy has a sufficient stabilization horizon in a sepsis-like clinical system.
Some control strategies stabilize a system only briefly. Others maintain enough forward influence to guide the system into a durable recovery basin.
The goal of this benchmark is to determine whether the controller can reliably sustain stabilization across a meaningful… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-control-horizon-sepsis-v1.sepsis_data_2_14clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.0
ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.0
What this repo does
This repository provides a Clarus v1.0 benchmark for sepsis transition under a four-variable clinical quad:
infection_load
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 right path… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.0.physionet-sepsis-2019sepsis-population-drift-benchmark-300
HipAAsynth Dataset
Summary
This dataset is a validation artifact generated by HipAAsynth.
HipAAsynth is a deterministic testing and validation service that simulates real-world variability to evaluate how healthcare systems perform under deployment conditions.
Description
This dataset represents a controlled cohort used for testing and benchmarking.
HipAAsynth generates cohorts to simulate how conditions present across:
patient populations
demographic… See the full description on the dataset page: https://huggingface.co/datasets/HipAAsynth/sepsis-population-drift-benchmark-300.clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v0.6
What this repo does
This repository contains a Clarus v0.6 intervention pathway dataset focused on sepsis transition dynamics.
The dataset evaluates whether a model can determine if a proposed intervention meaningfully stabilizes a deteriorating septic system.
The task requires reasoning from:
system state
trajectory toward instability
boundary geometry
recovery geometry
intervention vector
projected trajectory consequence
The model cannot read the answer directly.
It must infer… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v0.6.sepsis-icu-rock-cohort-100
HipAAsynth Dataset
Summary
This dataset is a validation artifact generated by HipAAsynth.
HipAAsynth is a deterministic testing and validation service that simulates real-world variability to evaluate how healthcare systems perform under deployment conditions.
Description
This dataset represents a controlled cohort used for testing and benchmarking.
HipAAsynth generates cohorts to simulate how conditions present across:
patient populations
demographic… See the full description on the dataset page: https://huggingface.co/datasets/HipAAsynth/sepsis-icu-rock-cohort-100.sepsis-icu-minot-cohort-v2-100
HipAAsynth Dataset
Summary
This dataset is a validation artifact generated by HipAAsynth.
HipAAsynth is a deterministic testing and validation service that simulates real-world variability to evaluate how healthcare systems perform under deployment conditions.
Description
This dataset represents a controlled cohort used for testing and benchmarking.
HipAAsynth generates cohorts to simulate how conditions present across:
patient populations
demographic… See the full description on the dataset page: https://huggingface.co/datasets/HipAAsynth/sepsis-icu-minot-cohort-v2-100.clinical-recovery-stability-sepsis-v1Clinical Recovery Stability Sepsis Detection
Overview
This dataset tests whether a model can distinguish between temporary improvement and true structural recovery in a sepsis-like clinical system.
In many complex systems, short-term improvement can occur even while the system remains dangerously close to the instability boundary. Vital signs may improve temporarily, but the underlying dynamics may still favor relapse or collapse.
The task is therefore not simply detecting improvement, but… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-recovery-stability-sepsis-v1.clinical-instability-margin-sepsis-v1
Clinical Instability Margin Sepsis Detection
Overview
This dataset tests whether a model can detect when a clinical system is approaching the instability boundary.
In complex physiological systems, collapse rarely occurs suddenly. Instead the system gradually moves closer to a critical boundary where small disturbances can trigger rapid deterioration.
The goal is to determine whether the system is still safely inside the stability region or is approaching the instability… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-instability-margin-sepsis-v1.clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v0.7
What this repo does
This repository contains a Clarus v0.7 dataset modeling sepsis transition using a quad-coupling system representation.
The dataset extends the v0.6 intervention layer by introducing uncertainty geometry.
The question addressed by earlier versions was:
Can the system be stabilized?
v0.7 adds a second critical question:
How confident are we in that conclusion?
This allows Clarus to distinguish three operational states:
• confident deterioration• confident… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v0.7.sepsis-t-icu-cohort-100
HipAAsynth Dataset
Summary
This dataset is a validation artifact generated by HipAAsynth.
HipAAsynth is a deterministic testing and validation service that simulates real-world variability to evaluate how healthcare systems perform under deployment conditions.
Description
This dataset represents a controlled cohort used for testing and benchmarking.
HipAAsynth generates cohorts to simulate how conditions present across:
patient populations
demographic… See the full description on the dataset page: https://huggingface.co/datasets/HipAAsynth/sepsis-t-icu-cohort-100.clinical-intervention-timing-sepsis-v1
Clinical Intervention Timing Sepsis Detection
Overview
This dataset tests whether a model can detect when an intervention is applied within the effective timing window of a sepsis-like clinical system.
In complex clinical systems, a correct intervention can still fail if it arrives too late relative to the system’s trajectory. The key question is not only whether an action is appropriate, but whether it occurs early enough to alter the path away from collapse.
The goal of… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-intervention-timing-sepsis-v1.clinical-sepsis-screening-antibiotic-escalation-coherence-risk-v0.1What this repo is for
Detect when sepsis signals
and screening plus antibiotic escalation
fall out of alignment
before
delayed treatment
and avoidable deterioration.
clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.1
Clinical Quad Infection Buffer Lag Coupling Sepsis Transition v1.1
What this repo does
This dataset evaluates whether a model can select the correct control policy when:
multiple sepsis 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… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-infection-buffer-lag-coupling-sepsis-transition-v1.1.sepsis-icu-fargo-cohort-v2-100
HipAAsynth Dataset
Summary
This dataset is a validation artifact generated by HipAAsynth.
HipAAsynth is a deterministic testing and validation service that simulates real-world variability to evaluate how healthcare systems perform under deployment conditions.
Description
This dataset represents a controlled cohort used for testing and benchmarking.
HipAAsynth generates cohorts to simulate how conditions present across:
patient populations
demographic… See the full description on the dataset page: https://huggingface.co/datasets/HipAAsynth/sepsis-icu-fargo-cohort-v2-100.