Open Reproducible Publication Research

TL;DR

This paper introduces an open-source Python tool that enables reproducible, in-depth analysis of scientific publication data by utilizing open datasets, addressing transparency issues inherent in traditional database searches.

Contribution

It presents a new software package and command-line tool for reproducible bibliometric analysis using open data sources like Crossref and ORCID.

Findings

Analyzed publication trends across scientific fields.

Visualized relationships among publications and authors.

Replicated common bibliometric measures.

Abstract

Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications. Its results end up in a paper's "background" section or in standalone articles, which include meta-analyses and systematic literature reviews. The required research is aided through the use of online scientific publication databases and search engines, such as Web of Science, Scopus, and Google Scholar. However, use of online databases suffers from a lack of repeatability and transparency, as well as from technical restrictions. Thankfully, open data, powerful personal computers, and open source software now make it possible to run sophisticated publication studies on the desktop in a self-contained environment that peers can readily reproduce. Here we report a Python software package and an associated command-line tool that can populate embedded relational databases with slices from the complete set of Crossref publication metadata, ORCID author records, and other open data sets, for in-depth processing through performant queries. We demonstrate the software's utility by analyzing the underlying dataset's contents, by visualizing the evolution of publications in diverse scientific fields and relationships among them, by outlining scientometric facts associated with COVID-19 research, and by replicating commonly-used bibliometric measures of productivity, impact, and disruption.