rawdata

Raw data refers to data in its original, unprocessed form as collected from sources such as measurement devices, observations, or user inputs. It has not been cleaned, normalized, filtered, or aggregated, and may require substantial processing before it can be analyzed or used for decision making. The term is often used in contrast with processed, cleaned, or derived data. In programming contexts, rawData is a common field name for unprocessed measurements or inputs.

Characteristics of raw data include a variety of formats, potential noise, missing or inconsistent values, and a lack of context or metadata. Raw data may be voluminous and high-velocity, depending on the source. An important companion is metadata that describes origin, units, sampling rate, and methods of collection.

Common formats and examples include raw sensor streams (temperature, acceleration), scientific instrument outputs (spectra, chromatograms), raw image data from cameras (RAW files), and log files or unparsed data dumps. In many domains, raw data is stored for provenance and reproducibility, while derived data is created for analysis.

Processing and quality considerations involve steps such as data cleaning, normalization, calibration, synchronization, and transformation. Data quality issues include noise, outliers, drift, missing values, and biases. Provenance tracking and versioning are important to trace how raw data becomes analytical results.

Applications and considerations include their role in reproducible research, machine learning training with original signals, and forensic data analysis. Privacy and ethics must guide sharing of raw data, particularly when it contains sensitive information. Proper storage, backups, and documentation are essential.