{"title":"Mapping open science research using a keyword bibliographic coupling analysis network","authors":"Jae Yun Lee, Eunkyung Chung","doi":"10.47989/irpaper949","DOIUrl":null,"url":null,"abstract":"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. 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. 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. 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.","PeriodicalId":47431,"journal":{"name":"Information Research-An International Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Research-An International Electronic Journal","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.47989/irpaper949","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 1
Abstract
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. 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. 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. 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.
自2010年代中期以来,开放科学运动迅速发展,在公共卫生、医学、教育和计算机科学等各个学科进行了研究。研究成果主要发表在信息科学、计算机科学及多学科期刊上。为了识别开放科学的智力结构,我们构建了一个关键词书目耦合分析网络。我们从Web of science上总共检查了1000篇关于开放科学的文章,提取并分析了4645个关键词。然后,通过构建关键词数据集和每个关键词的参考数据集,实现了关键词书目耦合网络的可视化。通过分析网络中的骨干关键词和集群,研究发现最突出的关键词是“开放获取”、“开放数据”和“可重复性”。该分析还揭示了开放科学研究的九个集群:开放获取、可重复性、数据共享、预注册和注册报告、研究数据、开放同行评审、可重复性研究的工具和平台、开放创新、科学政策和预印本。这些结果表明,开放科学研究的重点是透明度和可重复性。此外,值得注意的是,本研究对开放式创新和科学政策领域的关注相当大,而这些领域在以往的研究中没有得到太多的关注。研究结果有助于理解开放科学研究的格局,并可能指导科研资助机构和科研决策者制定政策,以改善开放科学学术环境。
期刊介绍:
Information Research, is an open access, international, peer-reviewed, scholarly journal, dedicated to making accessible the results of research across a wide range of information-related disciplines. It is published by the University of Borås, Sweden, with the financial support of an NOP-HS Scientific Journal Grant. It is edited by Professor T.D. Wilson, and is hosted, and given technical support, by Lund University Libraries, Sweden.