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To provide a dynamic perspective on the evolution of Mendeley readership, this study conducts an 8‐year longitudinal analysis of approximately 3.4 million scholarly papers published in 2015. Mendeley readership data were collected annually from 2016 to 2023 for the sampled papers to analyze the temporal accumulation patterns of readership following publication. The results indicate that Mendeley readership exhibits a speed advantage compared to citations and a prevalence advantage compared to Twitter mentions, demonstrating both initial prevalence and sustained growth on a yearly basis. However, the patterns of accumulation vary across disciplines, with papers in Biomedical and Health Sciences showing the fastest accrual of extensive Mendeley readership data. Leveraging demographic data provided by Mendeley, this study further investigates how different user groups—categorized by academic status, disciplinary affiliation, and geographic location—engage with papers across various disciplines. The findings highlight Mendeley readership as a rapid and substantial altmetric, yet they also emphasize the need to interpret the nature of the attention captured by Mendeley readership with caution, considering its potential biases introduced by the varying engagement levels of different user groups across disciplines. [ABSTRACT FROM AUTHOR]

In exploring the intersection of scholarly research with technological advancement and societal impact, our analysis delves into nearly 40 million research papers spanning from 1950 to 2020 across all fields of study in science. Our scrutiny reveals an intriguing phenomenon: papers characterized by a higher CD index, often considered transformative, paradoxically exhibit a diminished propensity to influence technological and societal domains. This observation suggests a latent bias against the CD index, prompting a deeper inquiry into its implications. To unravel this trend, we introduce the concept of “disruptive citation,” a nuanced metric gauging the absolute disruptive impact of papers. Notably, papers drawing higher disruptive citations exhibit a significantly higher probability to influence both technological and societal spheres. Upon examining the heterogeneity across years and fields, we identify a bias against the CD index predominantly in the last two decades and within STEM fields. However, the positive effects of disruptive impact remain consistent across all years and fields. Our findings remain robust even when employing alternative measures of disruptive impact and controlling for total citations. By shedding light on these dynamics, our study seeks to enrich discussions regarding the recognition and role of disruptive scientific endeavors in shaping our world. [ABSTRACT FROM AUTHOR]

Since the introduction of OpenAI's ChatGPT‐3 in late 2022, conversational chatbots have gained significant popularity. These chatbots are designed to offer a user‐friendly interface for individuals to engage with technology using natural language in their daily interactions. However, these interactions raise user privacy concerns due to the data shared and the potential for misuse in these conversational information exchanges. Furthermore, there are no overarching laws and regulations governing such conversational interfaces in the United States. Thus, there is a need to investigate the user privacy concerns. To understand these concerns in the existing literature, this paper presents a literature review and analysis of 38 papers out of 894 retrieved papers that focus on user privacy concerns arising from interactions with text‐based conversational chatbots through the lens of social informatics. The review indicates that the primary user privacy concern that has consistently been addressed is self‐disclosure. This review contributes to the broader understanding of privacy concerns regarding chatbots the need for further exploration in this domain. As these chatbots continue to evolve, this paper acts as a foundation for future research endeavors and informs potential regulatory frameworks to safeguard user privacy in an increasingly digitized world. [ABSTRACT FROM AUTHOR]

This article provides a scoping review of 25 years of research on the notion of cognitive authority (CA), examining its conceptualization and empirical examination. The review follows the PRISMA statement and its extension for scoping reviews. Peer‐reviewed journal articles on CA were identified through database searching with the specific search term “cognitive authorit*” in the title or abstract and covering work published in 2022 or earlier. In total, 235 unique references were identified, and their abstracts and then selected full texts were screened according to predetermined exclusion criteria. In total, 40 articles were included in the review, extracted, and analyzed with qualitative content analysis focusing on the conceptualization of CA, the methodological approach taken to examine it, and the different spheres of knowledge and levels of activity the research addressed. Based on this analysis, four parallel lines of research were identified including studies conceptualizing CA: (1) as an indicator of information source quality, (2) as discursively constructed, (3) as situated in social mechanisms and settings, and (4) as institutional legitimacy of science and professions. This body of research has extended Wilson's (1983; Second‐hand knowledge: An inquiry into cognitive authority. Greenwood Press) original work contributing to our understanding of CA at individual, communal, and societal levels. [ABSTRACT FROM AUTHOR]

Research on how people look for and interact with information has a long history in the information field. The current literature has been repeatedly reviewed in earlier volumes of Annual Review of Information Science and Technology. In this review, we offer an overview of the research published in this area in the years 2016–2022 with a focus on the trends that have emerged in this period. We use the term “information behavior” as an umbrella for the research area interested in how people become informed and engage with information in diverse manners acknowledging that different researchers and subfields prefer other terms and frameworks, including information practices, information experience, and health information seeking, to name a few. We reviewed 1270 articles in the field published in the years 2016–2022 and identified seven emerging trends: The CoVID‐19 Pandemic, Diversity and Inclusion, Embodiment, Misinformation and Trust, Social Q&A Websites, Collaboration, and Information Creation. The reviewed literature and trends are discussed in relation to their significance for information, earlier review of information behavior research, and the long‐debated issue of theory‐driven versus atheoretical research in the field. [ABSTRACT FROM AUTHOR]

Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research. [ABSTRACT FROM AUTHOR]

The gig economy and gig work have grown quickly in recent years and have drawn much attention from researchers in different fields. Because the platform mediated gig economy is a relatively new phenomenon, studies have produced a range of interesting findings; of interest here are the socio‐technical issues that this work has surfaced. This systematic literature review (SLR) provides a snapshot of a range of socio‐technical issues raised in the last 12 years of literature focused on the platform mediated gig economy. Based on a sample of 515 papers gathered from nine databases in multiple disciplines, 132 were coded that specifically studied the gig economy, gig work, and gig workers. Three main socio‐technical themes were identified: (1) the digital workplace, which includes information infrastructure and digital labor that are related to the nature of gig work and the user agency; (2) algorithmic management, which includes platform governance, performance management, information asymmetry, power asymmetry, and system manipulation, relying on a diverse set of technological tools including algorithms and big data analytics; (3) ethical design, as a relevant value set that gig workers expect from the platform, which includes trust, fairness, equality, privacy, and transparency. A social informatics perspective is used to rethink the relationship between gig workers and platforms, extract the socio‐technical issues noted in prior research, and discuss the underexplored aspects of the platform mediated gig economy. The results draw attention to understudied yet critically important socio‐technical issues in the gig economy that suggest short‐ and long‐term opportunities for future research directions. [ABSTRACT FROM AUTHOR]

While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon‐based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon‐based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon‐based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon‐based classification systems. [ABSTRACT FROM AUTHOR]

Death is an inevitable part of life and highly relevant to information management: its approach often requires preparation, and its occurrence often demands a response. Many works in information science have acknowledged so much, and yet death is rarely a focused topic, appearing instead sporadically and disconnected across research. As a result there is no introduction to, overview of, or synthesis across studies on death and information. We therefore conducted an extensive literature search and reviewed nearly 300 scholarly publications at the intersection of death and information (and data) management. Covering seven topics in total, we review two groups of work directly engaging information management in relation to death (digital possessions, inheritance, and legacy; information behavior, needs, and practices around death), three engaging death and technology that require information and its management (death and the Internet, thanatosensitive design and technology‐augmented death practices, and the digital afterlife and digital immortality), and two reflecting the ethical and legal dimensions unique to death and information. We then integrate the collective findings to summarize the landscape of death‐related information research, outline remaining challenges for individuals, families, institutions, and society, and identify promising directions for future information science research. [ABSTRACT FROM AUTHOR]

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Sense‐making, sensemaking, and sense making are terms used in different disciplines. Similarities of usage seem unclear. (1) to examine the concepts used in different approaches to sense‐making/sensemaking/sense making; (2) to identify, classify and synthesize recent studies relevant to information science, as well as similar group on sensemaking in education research; (3) to reflect on future directions for sense‐making/sensemaking methodology in information science. The objectives were to retrieve, examine, classify and perform meta‐synthesis on sense‐making/sensemaking studies in both information science and education research. The review used systematic review principles, with selection criteria for case studies for examination in both information science and education sets. The final meta‐synthesis used a meta‐ethnographic approach, together with findings of recent overviews on organizational sensemaking, and other information science reviews. Qualitative sense‐making studies in information science often used Dervin's SMM (sense‐making methodology) and studies in organizations and education frequently used Weick's organizational sensemaking. Different mixed methods approaches were identified. Sense‐making is actively used in research and practice in information science and knowledge management. Using a coherent sense‐making methodology helps and dialogic principles are useful in planning, data collection and analysis. Individual and collective sense‐making are important to information science. [ABSTRACT FROM AUTHOR]

The data paper is an emerging academic genre that focuses on the description of research data objects. However, there is a lack of empirical knowledge about this rising genre in quantitative science studies, particularly from the perspective of its linguistic features. To fill this gap, this research aims to offer a first quantitative examination of which rhetorical moves—rhetorical units performing a coherent narrative function—are used in data paper abstracts, as well as how these moves are used. To this end, we developed a new classification scheme for rhetorical moves in data paper abstracts by expanding a well‐received system that focuses on English‐language research article abstracts. We used this expanded scheme to classify and analyze rhetorical moves used in two flagship data journals, Scientific Data and Data in Brief. We found that data papers exhibit a combination of introduction, method, results, and discussion‐ and data‐oriented moves and that the usage differences between the journals can be largely explained by journal policies concerning abstract and paper structure. This research offers a novel examination of how the data paper, a data‐oriented knowledge representation, is composed, which greatly contributes to a deeper understanding of research data and its publication in the scholarly communication system. [ABSTRACT FROM AUTHOR]

In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow‐up retractions. No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2‐fold. First, we designed a detector that combs the scientific literature for grammar‐based computer‐generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen‐papers from 19 publishers. We estimate the prevalence of SCIgen‐papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34). Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references. It stresses the need to screen papers for nonsense before peer‐review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish. [ABSTRACT FROM AUTHOR]

To more reasonably allocate a paper's credit, this article argues that both a paper's authors and references contribute to a given paper. Accordingly, we quantitatively represent the proportion of contributions from each author and reference to a paper. A paper's credit can be allocated among its authors and references based on their contributions. All papers carry innate credit because of publication. If cited, they also carry external credit from the citing papers. The proportion of a paper's credit allocated to references can be regarded as a credit output and serves as an input for these references. In this scenario, only the credit assigned to a paper's authors remains as the paper's deserved credit. The credit of papers can be transferred in a direction opposite that of knowledge diffusion. Via this method, the estimate of an individual reference's contribution incorporates content‐based citation analysis, a promising method to differentiate different citations. A paper's deserved credit represents the contribution of the paper's authors to the scientific community via the new knowledge they provide in the paper. Therefore, it is rational to evaluate papers according to their deserved credit, not the credit they carry. [ABSTRACT FROM AUTHOR]

During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta‐research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID‐19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data. [ABSTRACT FROM AUTHOR]

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories. [ABSTRACT FROM AUTHOR]

To what extent is the destiny of a scientific paper shaped by the cocitation network in which it is involved? What are the social contexts that can explain these structuring? Using bibliometric data, interviews with researchers, and social network analysis, this article proposes a typology based on egocentric cocitation networks that displays a quadruple structuring (before and after publication): polarization, clusterization, atomization, and attrition. It shows that the academic capital of the authors and the intellectual resources of their research are key factors of these destinies, as are the social relations between the authors concerned. The circumstances of the publishing are also correlated with the structuring of the egocentric cocitation networks, showing how socially embedded they are. Finally, the article discusses the contribution of these original networks to the analyze of scientific production and its dynamics. [ABSTRACT FROM AUTHOR]

This article discusses OpenAI's ChatGPT, a generative pre‐trained transformer, which uses natural language processing to fulfill text‐based user requests (i.e., a "chatbot"). The history and principles behind ChatGPT and similar models are discussed. This technology is then discussed in relation to its potential impact on academia and scholarly research and publishing. ChatGPT is seen as a potential model for the automated preparation of essays and other types of scholarly manuscripts. Potential ethical issues that could arise with the emergence of large language models like GPT‐3, the underlying technology behind ChatGPT, and its usage by academics and researchers, are discussed and situated within the context of broader advancements in artificial intelligence, machine learning, and natural language processing for research and scholarly publishing. [ABSTRACT FROM AUTHOR]

Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure. [ABSTRACT FROM AUTHOR]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users' online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers' early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers' early attention from both sources appears to be larger than these three traditional factors. [ABSTRACT FROM AUTHOR]

The data paper, an emerging scholarly genre, describes research data sets and is intended to bridge the gap between the publication of research data and scientific articles. Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this article addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility. Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research articles and they describe data‐centric research processes in a through way. This article also discusses how our results could inform a better data publication ecosystem in the future. [ABSTRACT FROM AUTHOR]

Users of research databases, such as CiteSeerX, Google Scholar, and Microsoft Academic, often search for papers using a set of keywords. Unfortunately, many authors avoid listing sufficient keywords for their papers. As such, these applications may need to automatically associate good descriptive keywords with papers. When the full text of the paper is available this problem has been thoroughly studied. In many cases, however, due to copyright limitations, research databases do not have access to the full text. On the other hand, such databases typically maintain metadata, such as the title and abstract and the citation network of each paper. In this paper we study the problem of predicting which keywords are appropriate for a research paper, using different methods based on the citation network and available metadata. Our main goal is in providing search engines with the ability to extract keywords from the available metadata. However, our system can also be used for other applications, such as for recommending keywords for the authors of new papers. We create a data set of research papers, and their citation network, keywords, and other metadata, containing over 470 K papers with and more than 2 million keywords. We compare our methods with predicting keywords using the title and abstract, in offline experiments and in a user study, concluding that the citation network provides much better predictions. [ABSTRACT FROM AUTHOR]

The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform. [ABSTRACT FROM AUTHOR]

As a follow-up to the highly cited authors list published by Thomson Reuters in June 2014, we analyzed the top 1% most frequently cited papers published between 2002 and 2012 included in the Web of Science ( WoS) subject category 'Information Science & Library Science.' In all, 798 authors contributed to 305 top 1% publications; these authors were employed at 275 institutions. The authors at Harvard University contributed the largest number of papers, when the addresses are whole-number counted. However, Leiden University leads the ranking if fractional counting is used. Twenty-three of the 798 authors were also listed as most highly cited authors by Thomson Reuters in June 2014 (). Twelve of these 23 authors were involved in publishing 4 or more of the 305 papers under study. Analysis of coauthorship relations among the 798 highly cited scientists shows that coauthorships are based on common interests in a specific topic. Three topics were important between 2002 and 2012: (a) collection and exploitation of information in clinical practices; (b) use of the Internet in public communication and commerce; and (c) scientometrics. [ABSTRACT FROM AUTHOR]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

Compared to previous studies that generally detect scientific breakthroughs based on citation patterns, this article proposes a knowledge entity‐based disruption indicator by quantifying the change of knowledge directly created and inspired by scientific breakthroughs to their evolutionary trajectories. Two groups of analytic units, including MeSH terms and their co‐occurrences, are employed independently by the indicator to measure the change of knowledge. The effectiveness of the proposed indicators was evaluated against the four datasets of scientific breakthroughs derived from four recognition trials. In terms of identifying scientific breakthroughs, the proposed disruption indicator based on MeSH co‐occurrences outperforms that based on MeSH terms and three earlier citation‐based disruption indicators. It is also shown that in our indicator, measuring the change of knowledge inspired by the focal paper in its evolutionary trajectory is a larger contributor than measuring the change created by the focal paper. Our study not only offers empirical insights into conceptual understanding of scientific breakthroughs but also provides practical disruption indicator for scientists and science management agencies searching for valuable research. [ABSTRACT FROM AUTHOR]

Peer review is essential for advancing scientific research, but there are long‐standing concerns that authors' prestige or other characteristics can bias reviewers. Double‐blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed. Here, we examine the effects of double‐blind peer review by analyzing the review files of 5,027 papers submitted to a top computer science conference that changed its reviewing format from single‐ to double‐blind in 2018. First, we find that the scores given to the most prestigious authors significantly decreased after switching to double‐blind review. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly. Second, the inter‐reviewer disagreement increased significantly in the double‐blind format. Third, papers rejected in the single‐blind format are cited more than those rejected under double‐blind, suggesting that double‐blind review better excludes poorer quality papers. Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers' acceptance was affected. These results support the effectiveness of double‐blind review in reducing biases, while opening new research directions on the impact of peer‐review formats. [ABSTRACT FROM AUTHOR]

Scientific progress is driven by important, infrequent discoveries that cannot be readily identified and quantified, which makes research assessment very difficult. Bibliometric indicators of important discoveries have been formulated using an empirical approach, based on the mathematical properties of the high-citation tail of the citation distribution. To investigate the theoretical basis of such formulations this study compares the US/ European research performance ratios expressed in terms of highly cited papers and Nobel Prize-winning discoveries. The research performance ratio in terms of papers was studied from the citation distributions in the fields of chemistry, physics, and biochemistry and molecular biology. It varied as a function of the citation level. Selecting an appropriate high citation level, the ratios in terms of highly cited papers were compared with the corresponding ratios for Nobel Prize-winning discoveries in Chemistry, Physics, and Physiology or Medicine. Research performance ratios expressed in terms of highly cited papers and Nobel Prize-winning discoveries are reasonably similar, and suggest that the research success of the United States is almost 3 times that of Europe. A similar conclusion was obtained using articles published in Nature and Science. [ABSTRACT FROM AUTHOR]

This study compares the 45 “Best Paper” award articles with nonaward articles published in the Journal of Association for Information Science and Technology (JASIST) to observe the differences in citations. The results show that, in most cases, the citations of the award articles are more numerous than the median, belonging to the Top‐50% stratum. Only 15.6% of the award articles have the status of being the most‐cited article of the year in which the article was published; 24.4% belong to the Top‐5% stratum of the publication year; 44.4% belong to the Top‐10% stratum of the publication year; and 73.3% belong to the Top‐25% stratum of the publication year. Surprisingly, from 2000 to 2012, none of the award articles made it to the Top‐10% stratum, apart from the year 2004; the least‐cited award article received only three citations during a 5‐year period. The results show a wide range of citations among the Best JASIST Papers. This study also observes that the number of articles changed little from 1969 to 1995 but grew rapidly from 1996 to 2012. Suggestions for possible ways to better meet the challenges of the journal's growth in size and scope in selecting award articles are provided. [ABSTRACT FROM AUTHOR]

In recent years, the relationship of collaboration among scientists and the citation impact of papers have been frequently investigated. Most of the studies show that the two variables are closely related: An increasing collaboration activity (measured in terms of number of authors, number of affiliations, and number of countries) is associated with an increased citation impact. However, it is not clear whether the increased citation impact is based on the higher quality of papers that profit from more than one scientist giving expert input or other (citation-specific) factors. Thus, the current study addresses this question by using two comprehensive data sets with publications (in the biomedical area) including quality assessments by experts (F1000Prime member scores) and citation data for the publications. The study is based on more than 15,000 papers. Robust regression models are used to investigate the relationship between number of authors, number of affiliations, and number of countries, respectively, and citation impact-controlling for the papers' quality (measured by F1000Prime expert ratings). The results point out that the effect of collaboration activities on impact is largely independent of the papers' quality. The citation advantage is apparently not quality related; citation-specific factors (e.g., self-citations) seem to be important here. [ABSTRACT FROM AUTHOR]

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First‐time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first‐time collaboration increased after the outbreak of COVID‐19, and international collaboration witnessed a sudden decrease. During COVID‐19, papers with more first‐time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. [ABSTRACT FROM AUTHOR]

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important. [ABSTRACT FROM AUTHOR]

We present the results of running 4 different papers through the automated filtering system used by the open access preprint server 'arXiv' to classify papers and implement quality control barriers. The exercise was carried out in order to assess whether these highly sophisticated, state-of-the-art filters can distinguish between papers that are controversial or have gone past their 'sell-by date,' and otherwise normal papers. We conclude that not even the arXiv filters, which are otherwise successful in filtering fringe-topic papers, can fully acquire 'Domain-Specific Discrimination' and thus distinguish technical papers that are taken seriously by an expert community from those that are not. Finally, we discuss the implications this has for citizen and policy-maker engagement with the Primary Source Knowledge of a technical domain. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53. [ABSTRACT FROM AUTHOR]

The BRICS countries ( Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US. [ABSTRACT FROM AUTHOR]

The question of which type of computer science (CS) publication-0conference or journal-0is likely to result in more citations for a published paper is addressed. A series of data sets are examined and joined in order to analyze the citations of over 195,000 conference papers and 108,000 journal papers. Two means of evaluating the citations of journals and conferences are explored: h5 and average citations per paper; it was found that h5 has certain biases that make it a difficult measure to use (despite it being the main measure used by Google Scholar). Results from the analysis show that CS, as a discipline, values conferences as a publication venue more highly than any other academic field of study. The analysis also shows that a small number of elite CS conferences have the highest average paper citation rate of any publication type, although overall, citation rates in conferences are no higher than in journals. It is also shown that the length of a paper is correlated with citation rate. [ABSTRACT FROM AUTHOR]

The emphasis on research evaluation has brought scrutiny to the role of self‐citations in the scholarly communication process. While author self‐citations have been studied at length, little is known on national‐level self‐references (SRs). This paper analyses the citation context of national SRs, using the full‐text of 184,859 papers published in PLOS journals. It investigates the differences between national SRs and nonself‐references (NSRs) in terms of their in‐text mention, presence in enumerations, and location features. For all countries, national SRs exhibit a higher level of engagement than NSRs. NSRs are more often found in enumerative citances than SRs, which suggests that researchers pay more attention to domestic than foreign studies. There are more mentions of national research in the methods section, which provides evidence that methodologies developed in a nation are more likely to be used by other researchers from the same nation. Publications from the United States are cited at a higher rate in each of the sections, indicating that the country still maintains a dominant position in science. On the whole, this paper contributes to a better understanding of the role of national SRs in the scholarly communication system, and how it varies across countries and over time. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Citation data for institutes are generally provided as numbers of citations or as relative citation rates (as, for example, in the Leiden Ranking). These numbers can then be compared between the institutes. This study aims to present a new approach for the evaluation of citation data at the institutional level, based on regression models. As example data, the study includes all articles and reviews from the Web of Science for the publication year 2003 ( n = 886,416 papers). The study is based on an in-house database of the Max Planck Society. The study investigates how much the expected number of citations for a publication changes if it contains the address of an institute. The calculation of the expected values allows, on the one hand, investigating how the citation impact of the papers of an institute appears in comparison with the total of all papers. On the other hand, the expected values for several institutes can be compared with one another or with a set of randomly selected publications. Besides the institutes, the regression models include factors which can be assumed to have a general influence on citation counts (e.g., the number of authors). [ABSTRACT FROM AUTHOR]

This article provides the first historical analysis of the relationship between collaboration and scientific impact using three indicators of collaboration (number of authors, number of addresses, and number of countries) derived from articles published between 1900 and 2011. The results demonstrate that an increase in the number of authors leads to an increase in impact, from the beginning of the last century onward, and that this is not due simply to self-citations. A similar trend is also observed for the number of addresses and number of countries represented in the byline of an article. However, the constant inflation of collaboration since 1900 has resulted in diminishing citation returns: Larger and more diverse (in terms of institutional and country affiliation) teams are necessary to realize higher impact. The article concludes with a discussion of the potential causes of the impact gain in citations of collaborative papers. [ABSTRACT FROM AUTHOR]

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, Mutual Rank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. Mutual Rank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that Mutual Rank greatly outperforms the state-of-the-art competitors, including Page Rank, HITS, Co Rank, Future Rank, and P- Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by Mutual Rank are also quite reasonable. [ABSTRACT FROM AUTHOR]