Mapping open science research using a keyword bibliographic coupling analysis network

Abstract

Introduction: The open science movement has grown rapidly since the mid-2010s, and research has been conducted in various disciplines such as public health, medicine, education, and computer science. Research results have mainly been published in the journals of information science, computer science, and multidisciplinary fields. Method: To identify the intellectual structure of open science, we constructed a keyword bibliographic coupling analysis network. We examined a total of 1,000 articles on open science from the Web of Science, extracting and analysing 4,645 keywords. Then, we implemented and visualised the keyword bibliographic coupling network by constructing a keyword dataset and a reference dataset for each keyword. Results: By analysing the backbone keywords and clusters in the network, the study revealed that the most prominent keywords were open access, open data, and reproducibility. The analysis also uncovered nine clusters in open science research: open access, reproducibility, data sharing, preregistrations and registered reports, research data, open peer review, tools and platforms for reproducible research, open innovation, science policy, and preprints. These results indicated that open science research focuses on transparency and reproducibility. Additionally, it is noteworthy that this study revealed a considerable focus on the open innovation and science policy areas, which have not received much attention in previous studies. Conclusions: The findings can help to understand the landscape of open science research and may guide research funding institutes and research policymakers to design their policies to improve the open science scholarly environment.