Abstract Purpose We present an analytical, open source and flexible natural language processing and text mining method for topic evolution, emerging topic detection and research trend forecasting for all kinds of data-tagged text. Design/methodology/approach We make full use of the functions provided by the open source VOSviewer and Microsoft Office, including a thesaurus for data clean-up and a LOOKUP function for comparative analysis. Findings Through application and verification in the domain of perovskite solar cells research, this method proves to be effective. Research limitations A certain amount of manual data processing and a specific research domain background are required for better, more illustrative analysis results. Adequate time for analysis is also necessary. Practical implications We try to set up an easy, useful, and flexible interdisciplinary text analyzing procedure for researchers, especially those without solid computer programming skills or who cannot easily access complex software. This procedure can also serve as a wonderful example for teaching information literacy. Originality/value This text analysis approach has not been reported before.
{"title":"Topic Evolution and Emerging Topic Analysis Based on Open Source Software","authors":"Xiang Shen, Li Wang","doi":"10.2478/jdis-2020-0033","DOIUrl":"https://doi.org/10.2478/jdis-2020-0033","url":null,"abstract":"Abstract Purpose We present an analytical, open source and flexible natural language processing and text mining method for topic evolution, emerging topic detection and research trend forecasting for all kinds of data-tagged text. Design/methodology/approach We make full use of the functions provided by the open source VOSviewer and Microsoft Office, including a thesaurus for data clean-up and a LOOKUP function for comparative analysis. Findings Through application and verification in the domain of perovskite solar cells research, this method proves to be effective. Research limitations A certain amount of manual data processing and a specific research domain background are required for better, more illustrative analysis results. Adequate time for analysis is also necessary. Practical implications We try to set up an easy, useful, and flexible interdisciplinary text analyzing procedure for researchers, especially those without solid computer programming skills or who cannot easily access complex software. This procedure can also serve as a wonderful example for teaching information literacy. Originality/value This text analysis approach has not been reported before.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"126 - 136"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44822114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research. Design/methodology/approach We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis. Findings The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups. Research limitations First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration. Practical implications The findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research. Originality/value This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.
{"title":"Global Collaboration in Artificial Intelligence: Bibliometrics and Network Analysis from 1985 to 2019","authors":"Haotian Hu, Dongbo Wang, Sanhong Deng","doi":"10.2478/jdis-2020-0027","DOIUrl":"https://doi.org/10.2478/jdis-2020-0027","url":null,"abstract":"Abstract Purpose This study aims to explore the trend and status of international collaboration in the field of artificial intelligence (AI) and to understand the hot topics, core groups, and major collaboration patterns in global AI research. Design/methodology/approach We selected 38,224 papers in the field of AI from 1985 to 2019 in the core collection database of Web of Science (WoS) and studied international collaboration from the perspectives of authors, institutions, and countries through bibliometric analysis and social network analysis. Findings The bibliometric results show that in the field of AI, the number of published papers is increasing every year, and 84.8% of them are cooperative papers. Collaboration with more than three authors, collaboration between two countries and collaboration within institutions are the three main levels of collaboration patterns. Through social network analysis, this study found that the US, the UK, France, and Spain led global collaboration research in the field of AI at the country level, while Vietnam, Saudi Arabia, and United Arab Emirates had a high degree of international participation. Collaboration at the institution level reflects obvious regional and economic characteristics. There are the Developing Countries Institution Collaboration Group led by Iran, China, and Vietnam, as well as the Developed Countries Institution Collaboration Group led by the US, Canada, the UK. Also, the Chinese Academy of Sciences (China) plays an important, pivotal role in connecting the these institutional collaboration groups. Research limitations First, participant contributions in international collaboration may have varied, but in our research they are viewed equally when building collaboration networks. Second, although the edge weight in the collaboration network is considered, it is only used to help reduce the network and does not reflect the strength of collaboration. Practical implications The findings fill the current shortage of research on international collaboration in AI. They will help inform scientists and policy makers about the future of AI research. Originality/value This work is the longest to date regarding international collaboration in the field of AI. This research explores the evolution, future trends, and major collaboration patterns of international collaboration in the field of AI over the past 35 years. It also reveals the leading countries, core groups, and characteristics of collaboration in the field of AI.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"86 - 115"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47767991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose We study the proportion of Web of Science (WoS) citation links that are represented in the Crossref Open Citation Index (COCI), with the possible aim of using COCI in research evaluation instead of the WoS, if the level of coverage was sufficient. Design/methodology/approach We calculate the proportion on citation links where both publications have a WoS accession number and a DOI simultaneously, and where the cited publications have had at least one author from our institution, the Czech Technical University in Prague. We attempt to look up each such citation link in COCI. Findings We find that 53.7% of WoS citation links are present in the COCI. The proportion varies largely by discipline. The total figures differ significantly from 40% in the large-scale study by Van Eck, Waltman, Larivière, and Sugimoto (blog 2018, https://www.cwts.nl/blog?article=n-r2s234). Research limitations The sample does not cover all science areas uniformly; it is heavily focused on Engineering and Technology, and only some disciplines of Natural Sciences are present. However, this reflects the real scientific orientation and publication profile of our institution. Practical implications The current level of coverage is not sufficient for the WoS to be replaced by COCI for research evaluation. Originality/value The present study illustrates a COCI vs WoS comparison on the scale of a larger technical university in Central Europe.
摘要目的研究交叉参考开放引文索引(COCI)中Web of Science (WoS)引文链接的比例，以期在覆盖水平足够的情况下，使用COCI代替WoS进行研究评价。设计/方法/方法我们计算引文链接的比例，其中两个出版物同时具有WoS登录号和DOI，并且被引用的出版物至少有一位作者来自我们的机构，布拉格的捷克技术大学。我们试图在COCI中查找每一个这样的引文链接。研究发现53.7%的WoS引文链接存在于COCI中。这一比例在很大程度上因学科而异。总的数字与Van Eck、Waltman、larivi和Sugimoto的大规模研究中的40%有很大不同(博客2018,https://www.cwts.nl/blog?article=n-r2s234)。研究局限样本并未均匀覆盖所有科学领域;它主要侧重于工程和技术，只有一些自然科学学科存在。然而，这反映了我们机构真正的科学取向和出版形象。实际影响目前的覆盖范围不足以让COCI取代WoS进行研究评价。原创性/价值本研究说明了中欧一所大型技术大学的COCI与WoS的比较。
{"title":"Can Crossref Citations Replace Web of Science for Research Evaluation? The Share of Open Citations","authors":"Tomás Chudlarský, J. Dvorák","doi":"10.2478/jdis-2020-0037","DOIUrl":"https://doi.org/10.2478/jdis-2020-0037","url":null,"abstract":"Abstract Purpose We study the proportion of Web of Science (WoS) citation links that are represented in the Crossref Open Citation Index (COCI), with the possible aim of using COCI in research evaluation instead of the WoS, if the level of coverage was sufficient. Design/methodology/approach We calculate the proportion on citation links where both publications have a WoS accession number and a DOI simultaneously, and where the cited publications have had at least one author from our institution, the Czech Technical University in Prague. We attempt to look up each such citation link in COCI. Findings We find that 53.7% of WoS citation links are present in the COCI. The proportion varies largely by discipline. The total figures differ significantly from 40% in the large-scale study by Van Eck, Waltman, Larivière, and Sugimoto (blog 2018, https://www.cwts.nl/blog?article=n-r2s234). Research limitations The sample does not cover all science areas uniformly; it is heavily focused on Engineering and Technology, and only some disciplines of Natural Sciences are present. However, this reflects the real scientific orientation and publication profile of our institution. Practical implications The current level of coverage is not sufficient for the WoS to be replaced by COCI for research evaluation. Originality/value The present study illustrates a COCI vs WoS comparison on the scale of a larger technical university in Central Europe.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"35 - 42"},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43546453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Sampaio, António Abreu, B. Ferreira, M. Barreto, Jesús P. Mena-Chalco
Abstract Purpose This paper aims to test the use of e-Lattes to map the Brazilian scientific output in a recent research health subject: Zika Virus. Design/methodology/approach From a set of Lattes CVs of Zika researchers registered on the Lattes Platform, we used the e-Lattes to map the Brazilian scientific response to the Zika crisis. Findings Brazilian science articulated quickly during the public health emergency of international concern (PHEIC) due to the creation of mechanisms to streamline funding of scientific research. Research limitations We did not assess any dimension of research quality, including the scientific impact and societal value. Practical implications e-Lattes can provide useful guidelines for different stakeholders in research groups from Lattes CVs of members. Originality/value The information included in Lattes CVs permits us to assess science from a broader perspective taking into account not only scientific research production but also the training of human resources and scientific collaboration.
{"title":"Scientometric Analysis of Research Output from Brazil in Response to the Zika Crisis Using e-Lattes","authors":"R. Sampaio, António Abreu, B. Ferreira, M. Barreto, Jesús P. Mena-Chalco","doi":"10.2478/jdis-2020-0038","DOIUrl":"https://doi.org/10.2478/jdis-2020-0038","url":null,"abstract":"Abstract Purpose This paper aims to test the use of e-Lattes to map the Brazilian scientific output in a recent research health subject: Zika Virus. Design/methodology/approach From a set of Lattes CVs of Zika researchers registered on the Lattes Platform, we used the e-Lattes to map the Brazilian scientific response to the Zika crisis. Findings Brazilian science articulated quickly during the public health emergency of international concern (PHEIC) due to the creation of mechanisms to streamline funding of scientific research. Research limitations We did not assess any dimension of research quality, including the scientific impact and societal value. Practical implications e-Lattes can provide useful guidelines for different stakeholders in research groups from Lattes CVs of members. Originality/value The information included in Lattes CVs permits us to assess science from a broader perspective taking into account not only scientific research production but also the training of human resources and scientific collaboration.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"137 - 146"},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42912574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Catalano, C. Daraio, J. Leta, H. Moed, G. Ruocco, Xiaolin Zhang
This volume (Vol. 5, No. 3) of the Journal of Data and Information Science (JDIS) is the Part I of the Special Issue on ISSI 2019, the 17th International Conference on Scientometrics and Informetrics (ISSI2019) held in Rome, on 2–5 September 2019 and includes the first part of the selected posters presented during the conference and extended by the authors afterward. The goal of ISSI 2019 was to bring together scholars and practitioners in the area of informetrics, bibliometrics, scientometrics, webometrics and altmetrics to discuss new research directions, methods and theories, and to highlight the best research in this area. The 13 selected papers included in this issue relate the general topic of novel approaches to the development and application of informetric and scientometric tools and have been grouped in four themes:
{"title":"Novel Approaches to the Development and Application of Informetric and Scientometric Tools","authors":"G. Catalano, C. Daraio, J. Leta, H. Moed, G. Ruocco, Xiaolin Zhang","doi":"10.2478/jdis-2020-0022","DOIUrl":"https://doi.org/10.2478/jdis-2020-0022","url":null,"abstract":"This volume (Vol. 5, No. 3) of the Journal of Data and Information Science (JDIS) is the Part I of the Special Issue on ISSI 2019, the 17th International Conference on Scientometrics and Informetrics (ISSI2019) held in Rome, on 2–5 September 2019 and includes the first part of the selected posters presented during the conference and extended by the authors afterward. The goal of ISSI 2019 was to bring together scholars and practitioners in the area of informetrics, bibliometrics, scientometrics, webometrics and altmetrics to discuss new research directions, methods and theories, and to highlight the best research in this area. The 13 selected papers included in this issue relate the general topic of novel approaches to the development and application of informetric and scientometric tools and have been grouped in four themes:","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"1 - 4"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43348632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Research dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored. Design/methodology/approach In this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining. Findings For highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence. Research limitations This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research. Practical implications This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence. Originality The main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton's publications and the topic dynamics of forward chaining.
{"title":"A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic Analysis","authors":"Xiaoli Chen, T. Han","doi":"10.2478/jdis-2020-0034","DOIUrl":"https://doi.org/10.2478/jdis-2020-0034","url":null,"abstract":"Abstract Purpose Research dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored. Design/methodology/approach In this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining. Findings For highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence. Research limitations This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research. Practical implications This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence. Originality The main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton's publications and the topic dynamics of forward chaining.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"19 - 34"},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49228151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank. Design/methodology/approach We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers. Findings We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences. Research limitations We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations. Practical implications Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy. Originality/value We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.
{"title":"Classification of Paper Values Based on Citation Rank and PageRank","authors":"W. Souma, I. Vodenska, Lubomir T. Chitkushev","doi":"10.2478/jdis-2020-0031","DOIUrl":"https://doi.org/10.2478/jdis-2020-0031","url":null,"abstract":"Abstract Purpose The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank. Design/methodology/approach We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers. Findings We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences. Research limitations We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations. Practical implications Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy. Originality/value We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"57 - 70"},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42121497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education. Design/methodology/approach The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation. Findings Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe. Research limitations This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans. Practical implications To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments. Originality/value The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.
20多年来，数字素养及其相关领域一直受到学者和实践者的关注;然而，学术界需要系统地回顾这些领域是如何发展的。本研究旨在探讨自2000年以来数位素养及相关概念的研究趋势，特别是在教育领域。本研究通过科学计量学方法分析了数字素养领域的关键词、合著者和被引出版物。期刊文章通过“数字素养”、“信息通信技术素养”、“信息素养”和“媒体素养”四个关键词从WoS (Web of Science)上检索。此外，关键词，出版物和合著者被检查并进一步分类为更深入的调查群集。数字素养是一个多学科领域，广泛包括识字、信息通信技术、互联网、计算机技能熟练程度、科学、护理、健康和语言教育。数字素养研究的参与者或研究对象范围从小学生到专业人士，共同作者集群在美国和欧洲国家各有特色。本文通过同时搜索所有四个关键字来分析数据集的一个固定块。进一步的研究将检索来自不同学科的数据，并将按时间跨度追踪主要研究主题的变化。为了阐明研究结果，使用定制的数字素养课程和技术对于不同年龄的学习者根据自己的学习目标培养数字素养至关重要。他们需要培养他们对利用技术和计算思维的社会影响的理解。为了提高数字扫盲相关研究的原创性，来自不同国家和文化的研究人员可以合作调查更广泛的数字扫盲环境。本研究采用科学计量学方法，从关键词、期刊名称、合著者和被引出版物等多维角度分析了数字素养及相关领域的研究趋势。
{"title":"A Scientometric Study of Digital Literacy, ICT Literacy, Information Literacy, and Media Literacy","authors":"H. Park, Hansol Kim, H. Park","doi":"10.2478/jdis-2021-0001","DOIUrl":"https://doi.org/10.2478/jdis-2021-0001","url":null,"abstract":"Abstract Purpose Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education. Design/methodology/approach The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation. Findings Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe. Research limitations This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans. Practical implications To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments. Originality/value The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"54 13","pages":"116 - 138"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41265068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose This paper proposes a discrimination index method based on the Jain's fairness index to distinguish researchers with the same H-index. Design/methodology/approach A validity test is used to measure the correlation of D-offset with the parameters, i.e. H-index, the number of cited papers, the total number of citations, the number of indexed papers, and the number of uncited papers. The correlation test is based on the Saphiro-Wilk method and Pearson's product-moment correlation. Findings The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset (D-offset), with a range of D-offset from 0.00 to 0.99. The result of the correlation value between the D-offset and the number of uncited papers is 0.35, D-offset with the number of indexed papers is 0.24, and the number of cited papers is 0.27. The test provides the result that it is very unlikely that there exists no relationship between the parameters. Practical implications For this reason, D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index. The H-index for researchers can be written with the format of “H-index: D-offset”. Originality/value D-offset is worthy to be considered as a complement value to add the H-index value. If the D-offset is added in the H-index value, the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.
{"title":"A Discrimination Index Based on Jain's Fairness Index to Differentiate Researchers with Identical H-index Values","authors":"Adian Fatchur Rochim, Abdul Muis, R. F. Sari","doi":"10.2478/jdis-2020-0026","DOIUrl":"https://doi.org/10.2478/jdis-2020-0026","url":null,"abstract":"Abstract Purpose This paper proposes a discrimination index method based on the Jain's fairness index to distinguish researchers with the same H-index. Design/methodology/approach A validity test is used to measure the correlation of D-offset with the parameters, i.e. H-index, the number of cited papers, the total number of citations, the number of indexed papers, and the number of uncited papers. The correlation test is based on the Saphiro-Wilk method and Pearson's product-moment correlation. Findings The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset (D-offset), with a range of D-offset from 0.00 to 0.99. The result of the correlation value between the D-offset and the number of uncited papers is 0.35, D-offset with the number of indexed papers is 0.24, and the number of cited papers is 0.27. The test provides the result that it is very unlikely that there exists no relationship between the parameters. Practical implications For this reason, D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index. The H-index for researchers can be written with the format of “H-index: D-offset”. Originality/value D-offset is worthy to be considered as a complement value to add the H-index value. If the D-offset is added in the H-index value, the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"5 - 18"},"PeriodicalIF":0.0,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46340395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Our work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration (IRC). Design/methodology/approch We propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets. Findings Our evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC. Research limitations Though our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time. Practical implications The new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph. Originality This is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.
{"title":"A Novel Method for Resolving and Completing Authors’ Country Affiliation Data in Bibliographic Records","authors":"B. Nguyen, J. Dinneen, Markus Luczak-Rösch","doi":"10.2478/jdis-2020-0020","DOIUrl":"https://doi.org/10.2478/jdis-2020-0020","url":null,"abstract":"Abstract Purpose Our work seeks to overcome data quality issues related to incomplete author affiliation data in bibliographic records in order to support accurate and reliable measurement of international research collaboration (IRC). Design/methodology/approch We propose, implement, and evaluate a method that leverages the Web-based knowledge graph Wikidata to resolve publication affiliation data to particular countries. The method is tested with general and domain-specific data sets. Findings Our evaluation covers the magnitude of improvement, accuracy, and consistency. Results suggest the method is beneficial, reliable, and consistent, and thus a viable and improved approach to measuring IRC. Research limitations Though our evaluation suggests the method works with both general and domain-specific bibliographic data sets, it may perform differently with data sets not tested here. Further limitations stem from the use of the R programming language and R libraries for country identification as well as imbalanced data coverage and quality in Wikidata that may also change over time. Practical implications The new method helps to increase the accuracy in IRC studies and provides a basis for further development into a general tool that enriches bibliographic data using the Wikidata knowledge graph. Originality This is the first attempt to enrich bibliographic data using a peer-produced, Web-based knowledge graph like Wikidata.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"5 1","pages":"115 - 97"},"PeriodicalIF":0.0,"publicationDate":"2020-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43735062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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