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What are the landmark papers in scientific disciplines? Which papers are indispensable for scientific progress? These are typical questions which are of interest not only for researchers (who frequently know the answers – or guess to know them) but also for the interested general public. Citation counts can be used to identify very useful papers since they reflect the wisdom of the crowd – in this case, the scientists using published results for their research. In this study, we identified with recently developed methods for the program CRExplorer landmark publications in nearly all Web of Science subject categories (WoS-SCs). These are publications which belong more frequently than other publications during the citing years to the top-1‰ in their subject area. As examples, we show the results of five subject categories: 'Information Science & Library Science', 'Computer Science, Information Systems', 'Computer Science, Software Engineering', 'Psychology, Social' and, 'Chemistry, Physical'. The results of the other WoS-SCs can be found online at http://crexplorer.net. An analyst of the results should keep in mind that the identification of landmark papers depends on the used methods and data. Small differences in methods and/or data may lead to other results. [ABSTRACT FROM AUTHOR]

The emergence of new technologies such as AI, machine learning, and so on drives the discussion over human consciousness among researchers from various disciplines, particularly those from philosophy of mind and computer science. This is true since certain academic theories argue that new technologies like those are much more efficient at simulating human tasks. This allows the discussion concerning human consciousness to acquire attention among researchers interested in learning more about human consciousness. As a result, this study provides a bibliometric analysis of human consciousness research from 2000 to 2022 to provide researchers and academics worldwide with an overview of the outcomes and trends in human consciousness research. A textual query on two databases; Scopus (1413 articles), and PubMed (258 papers) using the term "human consciousness" was conducted on 16 August 2022 by collecting 1671 academic works from 2000 to 2022 relating to human consciousness research for in-depth study. Bibliometric analyses were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to show the current of human consciousness research. This bibliometric study examined the yearly scientific publishing growth, the most productive authors, most often term that has been utilised, most renowned journal name, and which nation has the maximum cooperation with other country. According to the findings of the study, there is a significant inconsistency in worldwide patterns in yearly scientific output, with the number of publications growing and declining. Among all nations, United States of America (USA) published the most papers around human consciousness study. According to the research, it has shown that the most relevant contributors is Jun'ichi Takeno (Scopus), and Brian L Edlow (PubMed). Moreover, the most famous journal in human consciousness research includes Journal of Consciousness Studies (Scopus), and Frontiers in Psychology (PubMed). The significance of this study may aid new researchers in this area by offering information on important publications and authors to refer while undertaking research on this issue. Furthermore, this research helps other researchers understand current trends in this field of study. As an outcome, the objective for conducting this study is to give bibliometric analysis and to fulfill research gaps in bibliometric studies of human consciousness by giving information in the form of an overview, and suggestions. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to offer a bibliometric and coding analysis of blockchain articles published in the accounting, auditing and accountability fields. Design/methodology/approach: The data were collected using the Scopus database and a bibliometric and qualitative coding analysis with the keywords "blockchain" and "accounting" or "auditing" or "accountability." Of the 514 initial sources, 93 peer-reviewed papers, book chapters and conference proceedings in the areas of business, management and accounting were finally selected. Nonscientific sources such as nonpeer-reviewed books and white papers were excluded. Findings: This study reveals a promising and multidisciplinary field of research dominated by scholars and less by practitioners. Qualitative research, especially discourse analysis, is the most used method among authors. This study gives some useful insights about blockchain's definition and characteristics, business models, processes involved, connection with other technologies and relationships with accounting theories. Among the most interesting insights, the results confirm that technology as an external force can create an intersection among several research areas: accounting, auditing, accountability, business, management, computer science and engineering fields. Finally, in terms of research themes, although blockchain has a clear effect on auditing accounting, the links with the area of accountability are less clear and validated. Originality/value: This study highlights the current state of the field, combining methodological approaches and providing valuable future research insights. Additionally, it is also a starting point for professionals to fully understand blockchain's characteristics and potential with a constructive and systemic approach. [ABSTRACT FROM AUTHOR]

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [ABSTRACT FROM AUTHOR]