Open reproducible scientometric research with Alexandria3k

Area of Science:

• Bibliometrics
• Scientometrics
• Scientific Information Management

Background:

• Scientific literature analysis relies on online databases, but faces challenges in repeatability, transparency, and technical limitations.
• Existing methods for synthesizing publication data often lack reproducibility.

Purpose of the Study:

• To introduce Alexandria3k, a Python software package and command-line tool for reproducible scientific publication analysis.
• To enable efficient and transparent processing and analysis of large publication metadata sets.
• To demonstrate the utility of Alexandria3k in visualizing research trends and calculating bibliometric indicators.

Main Methods:

• Developed Alexandria3k as a Python package and command-line tool.
• Integrated functionality to populate embedded relational databases with open publication metadata.
• Utilized versatile and performant queries for data processing and analysis.

Main Results:

• Demonstrated Alexandria3k's capability to visualize the evolution of scientific fields and inter-publication relationships.
• Outlined scientometric facts related to COVID-19 research using the tool.
• Successfully replicated common bibliometric measures for scientific productivity, impact, and disruption.

Conclusions:

• Alexandria3k provides a robust solution for reproducible and transparent analysis of scientific publications.
• The software facilitates deeper insights into research trends, scientific impact, and the dynamics of scientific fields.
• Offers a valuable tool for researchers, bibliometricians, and scientometricians.