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This purpose of this study is to examine future fears and reassurances about the nature of the recent artificial intelligence (AI) language model-based application, ChatGPT, use in the fields of scientific research and academic works and assignments. This study aims at exploring the positive and negative aspects of the use of ChatGPT by researchers and students. This paper recommends some practical academic steps and suggestions that help the researchers and publishers curtail the percentage of spread of unethical works such as plagiarism.

The emergence of OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3) has recently sparked controversy and heated debate among academics worldwide about its use and application. The concern of experts and researchers about the GPT-3 platform entails how it would be of much support to the researchers and academic staff and how it might be used and misused to negatively affect academic and scholarly works. This research explored future fears and reassurances about the nature of Chat GPT-3 use at academic and scientific levels. The data for this research was obtained through the qualitative interviews of seven experts in AI, scientific research and academic works. The findings of the study showed that ChatGPT-3 has significant potential and is helpful if used wisely and ethically at scientific and academic levels. On the other hand, the results reported the experts' fears of the frequent use of ChatGPT including the misuse of ChatGPT as a tool to plagiarize and make the researchers dependent, not self-reliant and lazy. The widespread concern of many scholars is that ChatGPT would lead to an increase in the possibility of plagiarism and provide less control over research and writing ethics. This study proposed some stages and suggested that AI language model programs, including ChatGPT, should be integrated with widespread publishers and academic platforms to curtail the percentage of plagiarism and organize the process of publishing and writing scientific research and academic works to save the rights of researchers and writers.

The findings of the research presented that ChatGPT can act as a potential and useful tool if used wisely and ethically at scientific and academic fields. On contrast, the results also reported the negative aspects of the extensive ChatGPT's that leads to the spread of plagiarism and making the researchers and the students machine-dependent, not self-reliant and lazy. This study proposed some stages and suggested that AI language model programs, including ChatGPT, should be integrated with widespread publishers and academic platforms to curtail the percentage of plagiarism and organize the process of publishing and writing scientific research and academic works to save rights of researchers and writers.

To the best of the authors’ knowledge, this paper is the first of its kind to highlight the relationship between using ChatGPT and the spread of both positive and negative aspects of its extensive use in scientific research and academic work. The importance of this study lies in the fact that it presents the concerns and future fears of people in academia as they cope with and deal with the inevitable reality of AI language models such as ChatGPT.

This chapter provides an overview of the findings and chapters of a thematic volume in the International Perspectives on Education and Society (IPES) series. It describes the common dataset and methods used by an international research team.

The chapter synthesizes the results of a series of country-level case studies and cross-national and regional comparisons on the growth of scientific research from 1900 until 2011. Additionally, the chapter provides a quantitative analysis of global trends in scientific, peer-reviewed publishing over the same period.

The introduction identifies common themes that emerged across the case studies examined in-depth during the multi-year research project Science Productivity, Higher Education, Research and Development and the Knowledge Society (SPHERE). First, universities have long been and are increasingly the primary organizations in science production around the globe. Second, the chapters describe in-country and cross-country patterns of competition and collaboration in scientific publications. Third, the chapters describe the national policy environments and institutionalized organizational forms that foster scientific research.

The introduction reviews selected findings and limitations of previous bibliometric studies and explains that the chapters in the volume address these limitations by applying neo-institutional theoretical frameworks to analyze bibliometric data over an extensive period.

The purpose of this paper is to describe the development and validation of an instrument for measuring intellectual capital in the academic research context. The current research context describes a new paradigm of scientific production characterized by interdisciplinarity, heterogeneity and the intensification of the relations between the generators of knowledge. In this scenario, traditional measures of intellectual capital do not capture all the variables that make up the environment in which the research activities are carried out. This transformation of research processes suggests the need to bring theories of organizational behavior, more appropriate to an organizational context, to the study of scientific context. Thus, the paper contextualizes the intellectual capital approach, thereby explaining how the different attributes that build it influence scientific productivity and providing a measurement instrument to evaluate relative levels of intellectual capital in an academic research context.

The scale was designed through a double qualitative–quantitative scale development process. The literature on intellectual capital does not provide strong theoretical support for the definition of a specific set of items to be applied in the specific academic research context. Consequently, the scale constructs and observable variables were initially conceptualized through a Delphi panel. This initial set of indicators was empirically validated through a second quantitative stage to a sample of 1,798 Spanish academics. Given that no prior published studies have examined the construct validity of the proposed scale, and the proposed scale is not based on other previously validated scales, the authors used exploratory and confirmatory factor analysis to assess the internal consistency, using Cronbach’s α to determine reliability.

Drawing on the evidence obtained from a double qualitative–quantitative process, a scale consisting of 47 items was proposed to measure the three dimensions of intellectual capital, namely, the researcher’s human capital, as well as the nature of the social capital and organizational capital of the team in which the scholar is integrated. The process of identifying and validating indicators of intellectual capital allowed the authors to identify certain intangible elements that are key in the research process and that, therefore, determine scientific productivity. Thus, the proposed scale contributes by conceptualizing new variables that could be used to deepen and broaden the study of the determinants of research performance. The contextualization of intellectual capital approach can also help to assess the value of intangibles, offering an external reporting tool and making universities’ social contributions more visible to public and private stakeholders, justifying the efforts made by societies in the generation of academic knowledge.

The empirical analysis was carried out with an initial sample of 1,798 Spanish scholars. The validation of the scale should therefore be confirmed in different national contexts, with larger data sets. Likewise, the use of longitudinal data sets could help to study the effects of intellectual capital in academic research, thereby contributing to the ongoing debate on the determinants of research performance.

From a practical perspective, the instrument could be considered both as a management and an external reporting tool, providing a self-assessment instrument of the levels of intellectual capital. As a management tool, a specific measure of intellectual capital in an academic context could help to identify training needs, the implementation of practices that encourage the capability for building research networks and the development of reports with intellectual capital-related inputs for the justification of the resources received. At an institutional level, the proposed set of indicators also identifies the attributes of scholars linked to higher scientific performance, and the scale could be used as an instrument for selection processes in academic institutions, to develop practices related to the distribution of workload or the publication of intellectual capital indicators of its researchers in a healthy exercise of transparency.

This study aims to investigate the factors affecting the motivation of graduate students of information science in scientific and research activities and science production. This research is applied in terms of purpose and descriptive in terms of type and method.

The study's statistical population includes all postgraduate information science students studying in public universities. The random sampling method was simple. The data collection tool was a questionnaire. Descriptive statistics and one-sample t-test, independent t-test, and ANOVA were used to analyze the collected data by SPSS software.

Findings showed that all the main variables (internal motivations, external motivations, self-empowerment, and intellectual and specialized interactions) affect the participation of postgraduate students in research and scientific activities. Among the identified components, the creation of opportunities, research facilities, innovation, and formal relations between students and professors, has the most significant impact on students' motivation to participate in scientific and research activities and science production. There was no significant difference between education and age. From the results obtained from the present study, it can be said that the above variables, which were divided into four categories, with the intensity of the participation of graduate students of universities that in the present study examined the field of librarianship and information, with power and Or weakness are influential. This means that the students at the beginning of the research path, in other words, will be future researchers, should be constantly monitored as a source in the production of science.

This research is one of the few types of research that examines the influential variables in increasing the motivation to participate in the study, considering the population of postgraduate students of universities and scientific institutions as drivers of science production.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.

First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.

The scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.

As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.

The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.

This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

In recent years, a large number of data repositories have been built and used. However, the extent to which scientific data are re-used in academic publications is still unknown. The purpose of this paper is to explore the functions of re-used scientific data in scholarly publication in different fields.

To address these questions, the authors identified 827 publications citing resources in the Dryad Digital Repository indexed by Scopus from 2010 to 2015.

The results show that: the number of citations to scientific data increases sharply over the years, but mainly from data-intensive disciplines, such as agricultural, biology science, environment science and medicine; the majority of citations are from the originating articles; and researchers tend to reuse data produced by their own research groups.

Dryad data may be re-used without being formally cited.

The conservatism in data sharing suggests that more should be done to encourage researchers to re-use other’s data.

This paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.

Scientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.

This paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.

The proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.

This paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

Although interdisciplinary research is an increasing trend in scientific funding projects, they are suffering from a lower probability of being funded. The purpose of this paper is to analyze the current situation on successful case of funding application and provides suggestions on how libraries can expand services to help scientific funding application.

This paper utilizes the co-occurrences of disciplinary application codes to construct an interdisciplinary knowledge flow network. Based on 193517 sponsored projects of the National Natural Science Foundation of China, the authors study the interdisciplinary flow of knowledge and investigate the evolution of network structure using social network analysis.

Results show that the interdisciplinary knowledge flow network is not only a small-world network but also a scale-free network. Two main knowledge flow paths across scientific departments exist, showing the heterogeneity of knowledge distributions across scientific disciplines. The authors also find that if two disciplines in the same scientific department both have a wide influence to other disciplines, they are more prone to link together and create a knowledge chain.

Funding consultation currently has not occupied an advisory role either in library services or in the research team. This paper conducts a co-occurrences network analysis of interdisciplinary knowledge flow in scientific funding projects. Considering the complexity of funding application and the advantage of traditional library services on information collection, integration, and utilization, the authors conclude the possibility and necessity of embedding funding consultation in traditional library services.

Growth in scientific production and productivity over the 20th century resulted significantly from three major countries in European science – France, Germany, and the United Kingdom. Charting the development of universities and research institutes that bolster Europe’s key position in global science, we uncover both stable and dynamic patterns of productivity in the fields of STEM, including health, over the 20th century. Ongoing internationalization of higher education and science has been accompanied by increasing competition and collaboration. Despite policy goals to foster innovation and expand research capacity, policies cannot fully account for the differential growth of scientific productivity we chart from 1975 to 2010.

Our sociological neo-institutional framework facilitates explanation of differences in institutional settings, organizational forms, and organizations that produce the most European research. We measure growth of published peer-reviewed articles indexed in Thomson Reuters’ Science Citation Index Expanded (SCIE).

Organizational forms vary in their contributions, with universities accounting for nearly half but rising in France; ultrastable in Germany at four-fifths, and growing at around two-thirds in the United Kingdom. Differing institutionalization pathways created the conditions necessary for continuous, but varying growth in scientific production and productivity in the European center of global science. The research university is key in all three countries, and we identify organizations leading in research output.

Few studies explicitly compare across time, space, and different levels of analysis. We show how important European science has been to overall global science production and productivity. In-depth comparisons, especially the organizational fields and forms in which science is produced, are crucial if policy is to support research and development.