Developing Standardized Testing Datasets for Benchmarking Automated QC Algorithm Performance
收藏DataONE2025-12-11 更新2025-12-20 收录
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Diagnose Aquatic Sensor Data
## Overview
This project is designed to diagnose and flag events in aquatic sensor data based on various conditions and thresholds. It processes raw data from aquatic sites and applies thresholds and logical conditions to identify different types of anomalies. The primary focus is to flag events that may indicate sensor anomalies, environmental conditions (e.g., frozen water), or technician site visits.
### Key Features
1. Event Detection: Detects and flags various event types, such as MNT (maintenance), LWT (low water table), SLM (sensor logger malfunction), PF (power failure), and VIN (visual inspection).
2. Data Quality Control: Uses thresholds to validate sensor readings, ensuring an accurate representation of water conditions.
3. Automated Labelling: Automatically labels events using a set of predefined indicators for anomaly detection.
创建时间：
2025-12-13
