analyserts

Analyserts is a proposed term in data analysis referring to the discrete outputs produced by an analysis pipeline. An analysert, plural analyserts, is the unit of analysis result that can be stored, compared, or re-used across projects. The concept aims to standardize naming for outputs regardless of toolchain and is used mainly in discussions of reproducibility and cross-tool communication.

Etymology and scope: The word analysert blends analyze with artifact, signaling that these outputs are artifacts of analysis. The term is not widely standardized and remains primarily in use in theoretical discussions and certain workflow documentation. As a relatively new term, its precise meaning can vary between communities and projects.

Common types: Analyserts can include numerical scores (such as metrics or p-values), predictive outputs (probabilities, class labels), feature representations (embeddings, SHAP values), decision rules, and transformed data. They can be scalar, vector, or structured objects and may originate from data preprocessing, model training, evaluation, or interpretation steps. The concept is intended to cover outputs across stages of the analysis pipeline.

Applications: Using analyserts can help compare results across tools, track reproducibility, document analysis pipelines, and enable meta-analyses. They support cross-domain communication when different teams employ different software, and they can facilitate archival and re-use of analysis results.

Relation to other terms: Analyserts are analogous in spirit to an analyte in chemistry as a named output of a process, and are related to the ideas of artifacts, outputs, or features in data science. Limitations include the lack of a formal, universally adopted definition and potential confusion with existing terminology.

See also: artifact, feature, output, analyte.