datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
qatar-environmental-monitoring-register-template
Environmental Monitoring Register Template for Qatar Projects
This resource provides a practical structure for recording environmental monitoring activities during construction and operational projects.
The template can help project teams organize monitoring dates, locations, environmental parameters, results, observations, compliance status, corrective actions, responsibilities and close-out records.
What the Template Covers
The environmental monitoring register… See the full description on the dataset page: https://huggingface.co/datasets/waeyqatar/qatar-environmental-monitoring-register-template.NK-Oil-Well-Sensor-Monitoring
Oil Well Sensor Monitoring Dataset - NK Field
Dataset Description
This dataset contains hourly sensor readings from 10 oil wells at the NK field.
The monitoring period covers approximately seven months, from January 1, 2026, to July 20, 2026.
The data were provided by Galaz and Company LLP
(ТОО «Галаз и Компания») within the research project:
“Development and Implementation of Control Algorithms for Low-Production-Rate Wells in Mechanized Oil Production Systems… See the full description on the dataset page: https://huggingface.co/datasets/xiva15/NK-Oil-Well-Sensor-Monitoring.NK-Oil-Well-Sensor-Monitoring
Oil Well Sensor Monitoring Dataset - NK Field
Dataset Description
This dataset contains hourly sensor readings from 10 oil wells at the NK field.
The monitoring period covers approximately seven months, from January 1, 2026, to July 20, 2026.
The data were provided by Galaz and Company LLP
(ТОО «Галаз и Компания») within the research project:
“Development and Implementation of Control Algorithms for Low-Production-Rate Wells in Mechanized Oil Production Systems… See the full description on the dataset page: https://huggingface.co/datasets/Arailym-tleubayeva/NK-Oil-Well-Sensor-Monitoring.metacognitive-monitoring-battery
Metacognitive Monitoring Battery
A cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework.
Paper: The Metacognitive Monitoring Battery: A Cross-Domain Benchmark for LLM Self-Monitoring
Code: github.com/synthiumjp/metacognitive-monitoring-battery
Author: Jon-Paul Cacioli (Independent Researcher, Melbourne, Australia)
Overview
The battery comprises 524 items across six cognitive domains… See the full description on the dataset page: https://huggingface.co/datasets/synthiumjp/metacognitive-monitoring-battery.clinical-monitoring-order-device-activation-coherence-risk-v0.1What this repo is for
This dataset tests whether a model can detect coupling failures between monitoring orders and actual device activation.
It measures alignment between
the clinical decision to monitor
and
the operational reality that monitoring is active with alarms configured and responded to.
It predicts
unmonitored deterioration
false reassurance when alarms are not handled
missed escalation after alarm events.
You use it for
patient safety monitoring
clinical AI eval for operational… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-monitoring-order-device-activation-coherence-risk-v0.1.spaces-monitoringclinical-analgesia-opioid-risk-monitoring-coherence-risk-v0.1What this repo is for
Detect when
opioids are given
but monitoring and rescue readiness
do not match risk
Common breaks
no sedation score recorded
resp rate not monitored
SpO2 not monitored
naloxone not available
deterioration not escalated
Examples
ward morphine dose with no obs for hours
recovery opioid with no sedation scoring
ED opioid given then patient desaturates with no response
You use it to flag
opioid safety risk
clinical-fluid-balance-order-monitoring-coherence-risk-v0.1What this repo is for
Detect when
fluid monitoring is ordered
but charting
is missing
incomplete
or too infrequent
before
AKI risk
fluid overload
missed deterioration
clinical-quad-safety-underreporting-conmed-misattribution-monitoring-lag-governance-interim-v0.1Clarus Clinical Quad Coupling Safety Signal Integrity v0.1
PurposeDetect safety signal distortion driven by four interacting nodes.
Quad nodes
Apparent AE decline or mismatch
Conmed masking or missing timing
Data entry or monitoring lag
Governance or interim timing pressure
InputOne vignette.
OutputStrict JSON only.
Required keys
safety_signal_risk
risk_type
driver_nodes
recommended_action
action_detail
rationale
confidence
Filesdata/train.csvdata/test.csvscorer.py… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-safety-underreporting-conmed-misattribution-monitoring-lag-governance-interim-v0.1.Drone-flight-monitoring-reasoning-SFT
Drone-flight-monitoring-reasoning-SFT
Dataset Description
本数据集是一个专注于无人机飞行安全领域的中文问答数据集，采用了Chain-of-Thought (CoT) 的格式。它旨在用于练习大语言模型的微调训练，使其能够模拟专家思考过程，并针对无人机安全相关问题生成包含推理步骤的结构化回答。微调后模型见(GabrielCheng/Deepseek-r1-finetuned-drone-safty) 。
本数据集是基于 Hugging Face 平台上的 skylink-drone-cot-datasets (pohsjxx/default-domain-cot-dataset) 进行处理和衍生的。
Dataset Structure / Data Fields
数据集中的每个样本包含以下字段：
Question (string): 关于无人机飞行安全或风险相关的问题。
Reasoning (string): 模拟模型的推理过程。
Answer… See the full description on the dataset page: https://huggingface.co/datasets/GabrielCheng/Drone-flight-monitoring-reasoning-SFT.clinical-quad-monitoring-frequency-deviation-latency-risk-v0.1Clarus Clinical Quad Coupling Monitoring Frequency Deviation Latency Risk v0.1
What this dataset isThis dataset tests whether a model can detect monitoring and oversight risk driven by four interacting nodes.
Quad coupling nodes
Monitoring frequency or delay
Deviation or anomaly increase
Data latency or missing updates
Governance review or inspection pressure
Input
One vignette
OutputReturn strict JSON only.
Required output JSON keys
monitoring_risk
risk_type
driver_nodes… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-monitoring-frequency-deviation-latency-risk-v0.1.clinical-monitoring-frequency-deterioration-signal-coherence-risk-v0.1What this repo is for
Detect when monitoring frequency
and patient deterioration
fall out of alignment
before
clinical decline
is missed.
ai-self-monitoring-coherence-collapse-risk-v0.1What this repo is for
Detect when systems stop monitoring themselves.
Focus:
silent failure
suppressed uncertainty
detection without correction
monitoring loop collapse
This dataset targets a critical late-stage risk: systems that notice problems but no longer surface them.
clinical-monitoring-failure-instability-v0.1
clinical-monitoring-failure-instability-v0.1
What this dataset does
This dataset evaluates whether models can detect instability caused by monitoring failure rather than direct physiological collapse.
Each row represents a simplified clinical monitoring scenario across three time points.
The task is to determine whether the care system remains able to detect deterioration or is moving toward detection failure.
Core stability idea
Clinical instability can emerge… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-monitoring-failure-instability-v0.1.precision_agriculture_soil_health_monitoringweather-monitoring-dataset
Weather Monitoring Sensor Dataset
Synthetic dataset generated for the Embedded Systems Course Project —
a real-time Weather Monitoring System built on the Tiva C Series
microcontroller with FreeRTOS, ESP8266, and Firebase.
Sensors Simulated
Sensor
Measurement
Range
DHT-11
Temperature
0 – 50 °C
DHT-11
Humidity
0 – 100 % RH
LDR KY-018
Light Intensity
0 – 1023 (ADC)
Labels (5 Classes — 1000 samples each)
Label
Temp (°C)
Humidity (%)
Light… See the full description on the dataset page: https://huggingface.co/datasets/tamimhassan05/weather-monitoring-dataset.heart_disease_monitoring
🩺 Heart Disease Early Risk Detection Dataset
A clean, structured dataset containing patient vital-sign measurements collected from a hospital setting for early detection of heart-related risks.The dataset supports multi-class classification with three risk levels:
0 — Normal
1 — Abnormal
2 — Risk
This dataset is suitable for machine learning, statistical modeling, and health analytics research.
📦 Dataset Summary
This dataset includes physiological features… See the full description on the dataset page: https://huggingface.co/datasets/roronoazoroinsome/heart_disease_monitoring.health_monitoring