Novelpy: A Python package to measure novelty and disruptiveness of bibliometric and patent data

TL;DR

Novelpy is an open-source Python package that centralizes and compares various bibliometric indicators of novelty and disruptiveness, aiding reproducibility and analysis in scientometrics.

Contribution

This paper introduces Novelpy, a comprehensive Python toolkit for measuring and comparing bibliometric indicators of novelty and disruptiveness in scientific and patent data.

Findings

Demonstrated Novelpy's capabilities on 1.5 million PubMed articles.

Provided formal descriptions and graphical representations of indicators.

Highlighted benefits and limitations of different measures.

Abstract

Novelpy (v1.2) is an open-source Python package designed to compute bibliometrics indicators. The package aims to provide a tool to the scientometrics community that centralizes different measures of novelty and disruptiveness, enables their comparison and fosters reproducibility. This paper offers a comprehensive review of the different indicators available in Novelpy by formally describing these measures (both mathematically and graphically) and presenting their benefits and limitations. We then compare the different measures on a random sample of 1.5M articles drawn from Pubmed Knowledge Graph to demonstrate the module's capabilities. We encourage anyone interested to participate in the development of future versions.