Mätdata

Mätdata refers to values derived from measurements performed with instruments, sensors, or observational methods. It includes numerical or categorical values and is usually accompanied by metadata that documents when, where and how the measurement was taken, as well as the instrument or method used. Measurement data may be raw or processed to derive quantities of interest.

Types: Quantitative data includes continuous and discrete measurements such as temperature, pressure, or counts. Qualitative data records categories or labels. Time-series data captures the evolution of a measurement over time. Spatial data may include location coordinates tied to measurements.

Quality and metadata: Data quality depends on accuracy, precision, completeness, consistency, and timeliness. Calibration, traceability to standards, and documented uncertainty are important. Metadata typically includes timestamp, instrument ID, unit, location, sampling rate, and conditions; it enables reproducibility and data reuse.

Acquisition and processing: Mätdata is collected with sensors, probes, or experimental setups. Raw data often undergo preprocessing such as filtering, drift correction, unit conversion, and aggregation. Provenance tracking and versioning are used to maintain data lineage.

Storage and standards: Data are stored in databases or file formats such as CSV, JSON, NetCDF, or HDF5, sometimes with accompanying metadata records. Standards and ontologies promote interoperability and reuse, including metadata standards (e.g., ISO 19115 for geospatial data) and domain-specific schemas.

Applications and challenges: Mätdata underpins scientific research, engineering, environmental monitoring, agriculture, healthcare, and economics. Common challenges include measurement bias, sensor drift, missing values, unit inconsistencies, and ensuring privacy and governance in sensitive domains.