Numerilised

Numerilised is a term used in data science and information technology to denote data, objects, or processes that have been converted into numerical form to enable computation. Numerisation (or numerilization) means the conversion of qualitative attributes into quantitative representations that can be manipulated by algorithms. The resulting numerilised representation can take many forms, from simple counts and ordinal ranks to high-dimensional vectors produced by embedding models.

Methods include encoding schemes such as one-hot encoding, label encoding, frequency encoding, and continuous scaling; feature extraction from images or text; and signal digitization. The aim is to preserve relevant information while transforming data into a format suitable for analysis.

Applications of numerilised data include statistical analysis, predictive modelling, machine learning, natural language processing, and time-series analysis. In the humanities and social sciences, numerisation supports quantitative research on large corpora; in engineering, sensor readings are naturally numerilised for monitoring and control.

Challenges and considerations arise in choosing an encoding method, as it can affect model bias, interpretability, and performance. Dimensionality and sparsity can increase computational cost, and missing values require imputation. There are also privacy concerns when converting sensitive attributes to numerical form, and careful governance is needed to manage data quality and ethics.

Etymology and usage: the term blends numerus with -ised to describe the result of numerisation. It is used informally in white papers, dashboards, and academic writing to discuss data preparation, but it is not a formal standard or widely codified terminology.