indata

Indata, short for input data, is data supplied to a system, program, or model for processing. It is distinguished from the outputs produced by the system and from descriptive metadata. In practice, indata can refer to a single value, a set of values, or an entire dataset used by a software workflow or analytical model.

Common contexts include data processing pipelines, scientific experiments, and machine learning. Indata can be sensor readings, user-entered values, configuration parameters, or training samples. In machine learning, input data forms the features and target values used during model training, evaluation, and prediction.

Formats for indata vary widely. Structured forms include CSV, JSON, and XML; binary formats are used for efficiency. Indata may be structured, semi-structured, or unstructured such as text, images, or audio. Preprocessing steps like cleaning, normalization, encoding, and feature extraction are commonly applied to improve data quality and model performance.

Privacy and governance: Indata handling raises privacy and security concerns. Techniques such as anonymization, access controls, data provenance, and versioning help ensure responsible use and reproducibility. The term is largely generic and is context-dependent, with different roles using it in slightly different ways.