Journal of Data and Information Science最新文献

{"title":"The Unique citing documents Journal Impact Factor (Uniq-JIF) as a supplement for the standard Journal Impact Factor","authors":"Zhesi Shen, Li Li, Yu Liao","doi":"10.2478/jdis-2024-0024","DOIUrl":"https://doi.org/10.2478/jdis-2024-0024","url":null,"abstract":"","PeriodicalId":515322,"journal":{"name":"Journal of Data and Information Science","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829959","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}

{"title":"Ranking academic institutions based on the productivity, impact, and quality of institutional scholars","authors":"Amir Faghri, Theodore L. Bergman","doi":"10.2478/jdis-2024-0017","DOIUrl":"https://doi.org/10.2478/jdis-2024-0017","url":null,"abstract":"\u0000 \u0000 \u0000 The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity, impact, and quality.\u0000 \u0000 \u0000 \u0000 The institutional ranking process developed here considers all institutions in all countries and regions, thereby including those that are established, as well as those that are emerging in scholarly prowess. Rankings of individual scholars worldwide are first generated using the recently introduced, fully indexed ScholarGPS database. The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor, in 14 individual Fields, in 177 Disciplines, and in approximately 350,000 unique Specialties. Rankings associated with five specific Fields (Medicine, Engineering & Computer Science, Life Sciences, Physical Sciences & Mathematics, and Social Sciences), and in two Disciplines (Chemistry, and Electrical & Computer Engineering) are presented as examples, and changes in the rankings over time are discussed.\u0000 \u0000 \u0000 \u0000 For the Fields considered here, the Top 20 institutional rankings in Medicine have undergone the least change (lifetime versus last five years), while the rankings in Engineering & Computer Science have exhibited significant change. The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions, although this emergence is shown to be highly Field- and Discipline-dependent.\u0000 \u0000 \u0000 \u0000 The ScholarGPS database used here ranks institutions in the categories of: (i) all Fields, (ii) in 14 individual Fields, (iii) in 177 Disciplines, and (iv) in approximately 350,000 unique Specialties. A comprehensive investigation covering all categories is not practical.\u0000 \u0000 \u0000 \u0000 Existing rankings of academic institutions have: (i) often been restricted to pre-selected institutions, clouding the potential discovery of scholarly activity in emerging institutions and countries; (ii) considered only broad areas of research, limiting the ability of university leadership to act on the assessments in a concrete manner, or in contrast; (iii) have considered only a narrow area of research for comparison, diminishing the broader applicability and impact of the assessment. In general, existing institutional rankings depend on which institutions are included in the ranking process, which areas of research are considered, the breadth (or granularity) of the research areas of interest, and the methodologies used to define and quantify research performance. In contrast, the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall (all Fields) level, to the level of the Specialty. The methods may also assist identification of the root causes of shifts in institution ","PeriodicalId":515322,"journal":{"name":"Journal of Data and Information Science","volume":" 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829778","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}

{"title":"A quantitative study of disruptive technology policy texts: An example of China’s artificial intelligence policy","authors":"Ying Zhou, Linzhi Yan, Xiao Liu","doi":"10.2478/jdis-2024-0016","DOIUrl":"https://doi.org/10.2478/jdis-2024-0016","url":null,"abstract":"Abstract Purpose The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention. This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence (AI) disruptive technology policy, and to put forward suggestions for optimizing China’s AI disruptive technology policy. Design/methodology/approach Develop a three-dimensional analytical framework for “policy tools-policy actors-policy themes” and apply policy tools, social network analysis, and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools, cooperative relationships among policy actors, and the trends in policy theme settings within China’s innovative AI technology policy. Findings We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close. Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects, forming a “center-periphery” network structure. Policy tool usage is predominantly focused on supply and environmental types, with a severe inadequacy in demand-side policy tool utilization. Policy themes are diverse, encompassing topics such as “Intelligent Services” “Talent Cultivation” “Information Security” and “Technological Innovation”, which will remain focal points. Under the themes of “Intelligent Services” and “Intelligent Governance”, policy tool usage is relatively balanced, with close collaboration among policy entities. However, the theme of “AI Theoretical System” lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities. Research limitations The data sources and experimental scope are subject to certain limitations, potentially introducing biases and imperfections into the research results, necessitating further validation and refinement. Practical implications The study introduces a three-dimensional analysis framework for disruptive technology policy texts, which is significant for formulating and enhancing disruptive technology policies. Originality/value This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts. It systematically evaluates China’s AI policies quantitatively, focusing on policy tools, policy actors, policy themes. The study uncovers the characteristics and deficiencies of current AI policies, offering recommendations for formulating and enhancing disruptive technology policies.","PeriodicalId":515322,"journal":{"name":"Journal of Data and Information Science","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363331","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}

{"title":"A comparative study on characteristics of retracted publications across different open access levels","authors":"Er-Te Zheng, Hui-Zhen Fu","doi":"10.2478/jdis-2024-0010","DOIUrl":"https://doi.org/10.2478/jdis-2024-0010","url":null,"abstract":"\u0000 \u0000 \u0000 Recently, global science has shown an increasing open trend, however, the characteristics of research integrity of open access (OA) publications have rarely been studied. The aim of this study is to compare the characteristics of retracted articles across different OA levels and discover whether OA level influences the characteristics of retracted articles.\u0000 \u0000 \u0000 \u0000 The research conducted an analysis of 6,005 retracted publications between 2001 and 2020 from the Web of Science and Retraction Watch databases. These publications were categorized based on their OA levels, including Gold OA, Green OA, and non-OA. The study explored retraction rates, time lags and reasons within these categories.\u0000 \u0000 \u0000 \u0000 The findings of this research revealed distinct patterns in retraction rates among different OA levels. Publications with Gold OA demonstrated the highest retraction rate, followed by Green OA and non-OA. A comparison of retraction reasons between Gold OA and non-OA categories indicated similar proportions, while Green OA exhibited a higher proportion due to falsification and manipulation issues, along with a lower occurrence of plagiarism and authorship issues. The retraction time lag was shortest for Gold OA, followed by non-OA, and longest for Green OA. The prolonged retraction time for Green OA could be attributed to an atypical distribution of retraction reasons.\u0000 \u0000 \u0000 \u0000 There is no exploration of a wider range of OA levels, such as Hybrid OA and Bronze OA.\u0000 \u0000 \u0000 \u0000 The outcomes of this study suggest the need for increased attention to research integrity within the OA publications. The occurrences of falsification, manipulation, and ethical concerns within Green OA publications warrant attention from the scientific community.\u0000 \u0000 \u0000 \u0000 This study contributes to the understanding of research integrity in the realm of OA publications, shedding light on retraction patterns and reasons across different OA levels.\u0000","PeriodicalId":515322,"journal":{"name":"Journal of Data and Information Science","volume":"18 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368371","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}