Datalogging

Data logging, or datalogging, is the automatic collection and storage of measurements over time by devices such as data loggers or data acquisition systems. It enables long-term monitoring of physical quantities—such as temperature, humidity, pressure, electrical current, or motion—by recording observations with time stamps that tie each Measurement to a precise moment. Datalogging can operate autonomously or be integrated with IT systems for centralized access.

A typical datalogging setup comprises sensors, signal conditioning, a data collection unit, and storage media. Sampling rate and duration are configured by the user and depend on the phenomenon and required resolution. Data can be transmitted in real time or retrieved later via USB, Ethernet, Wi‑Fi, or cellular networks, or stored on removable media for offline collection. Common formats include CSV, JSON, or XML; more advanced systems use binary formats or scientific standards such as HDF5 or NetCDF.

Key considerations include accurate timekeeping, calibration, and data integrity. Synchronization (for example with NTP or GPS time) ensures timestamps remain consistent across devices. Handling missing values, sensor drift, and outliers is common in analysis. Metadata—sensor type, units, location, and installation context—improves data quality and reuse. Security and privacy considerations arise for networked deployments and cloud storage.

Applications span environmental monitoring, industrial automation and maintenance, energy metering, agriculture, transportation, and scientific research. Datalogging supports trend analysis, anomaly detection, process optimization, and retrospective studies, making it a foundational tool in research, operations, and monitoring programs.