Proximal advantage in knowledge diffusion: The time dimension

This paper intends to explore the impact of geographic proximity on the diffusion of knowledge in the form of publication citations, and argues that codified knowledge is transmitted faster in proximity and is subject to similar geographic constraints as tacit knowledge. The geographic proximity advantage would be particularly relevant in the early stage of dissemination. We collected three sets of research articles published in 1990, 2000 and 2010 and compared the longitudinal citations they received domestically and from abroad. The study found that domestic citations accumulate faster and reach their peak much earlier than foreign citations, and the difference is most evident in the first few years after publication. The result shows that geographic proximity does play a role in the speed of knowledge diffusion and points to the network effect for citations. Those located closer to the knowledge origin would be exposed and react to publications faster due to the additional opportunities of research exchange and network.     

An empirical approach based on quantile regression for estimating citation ageing

An aspect of citation behavior, which has received longstanding attention in research, is how articles’ received citations evolve as time passes since their publication (i.e., citation ageing). Citation ageing has been studied mainly by the formulation and fit of mathematical models of diverse complexity. Commonly, these models restrict the shape of citation ageing functions and explicitly take into account factors known to influence citation ageing. An alternative—and less studied—approach is to estimate citation ageing functions using data-driven strategies. However, research following the latter approach has not been consistent in taking into account those factors known to influence citation ageing. In this article, we propose a model-free approach for estimating citation ageing functions which combines quantile regression with a non-parametric specification able to capture citation inflation. The proposed strategy allows taking into account field of research effects, impact level effects, citation inflation effects and skewness in the distribution of cites effects. To test our methodology, we collected a large dataset consisting of more than five million citations to 59,707 research articles spanning 12 dissimilar fields of research and, with this data in hand, tested the proposed strategy.     

Overcitation and overrepresentation of review papers in the most cited papers

Review papers tend to be cited more frequently than regular research articles. This fact, together with the continuous increase of the share of reviews in scientific literature, can have important consequences for the measurement of individuals’ research output, usually based on citation analysis. However, studies evaluating the differences in citations of review papers compared to original research articles are almost non-existing in the literature. This paper presents a thorough analysis of the overcitation and overrepresentation of review papers in the most cited papers of the 35 largest subject categories in Science Citation Index-Expanded. Results indicate the average citations received by reviews depends largely on the research area considered, varying from 1.34 to 6.74 times the citations received by original research articles (average value is 2.95). Correlated with this overcitation, there is an important overrepresentation of reviews in the most cited papers, this overrepresentation being greater when the most highly cited papers are considered, i.e. 0.05% and 0.1% most cited papers, where the share of reviews have increased from 16 to 18% in 1990 to around 40% in 2010. Interestingly, the overcitation and overrepresentation in the most cited papers is more important in the areas with the lowest shares of reviews in total publications.     

Predicting future influence of papers,researchers, and venues in a dynamic academic network

Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors.     

A comparative study of cross-domain research output and citations: Research impact cubes and binary citation frequencies

Across the various scientific domains, significant differences occur with respect to research publishing formats, frequencies and citing practices, the nature and organisation of research and the number and impact of a given domain's academic journals. Consequently, differences occur in the citations and h-indices of the researchers. This paper attempts to identify cross-domain differences using quantitative and qualitative measures. The study focuses on the relationships among citations, most-cited papers and h-indices across domains and for research group sizes. The analysis is based on the research output of approximately 10,000 researchers in Slovenia, of which we focus on 6536 researchers working in 284 research group programmes in 2008–2012.As comparative measures of cross-domain research output, we propose the research impact cube (RIC) representation and the analysis of most-cited papers, highest impact factors and citation distribution graphs (Lorenz curves). The analysis of Lotka's model resulted in the proposal of a binary citation frequencies (BCF) distribution model that describes well publishing frequencies. The results may be used as a model to measure, compare and evaluate fields of science on the global, national and research community level to streamline research policies and evaluate progress over a definite time period.     

High acknowledgement index: Characterizing research supporters with factors of acknowledgement affecting paper citation counts

The acknowledgments section of scientific papers is paramount in academic production, providing valuable insights into the individuals and organizations involved in the research process. Developing a standard index of acknowledgments would provide a significant metric for measuring institutional and researcher influence. However, the absence of standardized formats has impeded effective analysis of information in acknowledgement sections. This study develop an acknowledgement index utilizing automated text-mining techniques to address these limitations. In addition, we propose two methods to disambiguate research supporters in the acknowledgement section: entity scores based on specific keywords and research similarity scores calculated through co-references. Based on our investigation, we explore the characteristics of research supporters with high acknowledgement index scores and examine the correlation between each acknowledgement index and their performance. Notably, paper citation strongly correlates with research supporters' performance. Next, our analysis delves into the impact of acknowledgement and entity types on paper citations. Our findings reveal that acknowledgments with only person names exert the most significant impact on paper citation.     

A bibliometric analysis of academic publication and NIH funding

Academic productivity and research funding have been hot topics in biomedical research. While publications and their citations are popular indicators of academic productivity, there has been no rigorous way to quantify co-authors’ relative contributions. This has seriously compromised quantitative studies on the relationship between academic productivity and research funding. Here we apply an axiomatic approach and associated bibliometric measures to revisit a recent study by Ginther et al. (Ginther et al., 2011a, Ginther et al., 2011b) in which the probability of receiving a U.S. National Institutes of Health (NIH) R01 award was analyzed with respect to the applicant's race/ethnicity. Our results provide new insight and suggest that there is no significant racial bias in the NIH review process, in contrast to the conclusion from the study by D. K. Ginther et al. Our axiomatic approach has a potential to be widely used for scientific assessment and management.     

Do open access articles have greater citation impact?: A critical review of the literature

The last few years have seen the emergence of several open access options in scholarly communication which can broadly be grouped into two areas referred to as ‘gold’ and ‘green’ open access (OA). In this article we review the literature examining the relationship between OA status and citation counts of scholarly articles. Early studies showed a correlation between the free online availability or OA status of articles and higher citation counts, and implied causality without due consideration of potential confounding factors. More recent investigations have dissected the nature of the relationship between article OA status and citations. Three non-exclusive postulates have been proposed to account for the observed citation differences between OA and non-OA articles: an open access postulate, a selection bias postulate, and an early view postulate. The most rigorous study to date (in condensed matter physics) showed that, after controlling for the early view postulate, the remaining difference in citation counts between OA and non-OA articles is explained by the selection bias postulate. No evidence was found to support the OA postulate per se; i.e. article OA status alone has little or no effect on citations. Further studies using a similarly rigorous approach are required to determine the generality of this finding.     

科学数据分类研究述评

研究科学数据分类有利于完善科学数据的内容体系,有利于构建更完整有效的科学数据元数据框架体系,有利于揭示科学数据的特殊性与差异性,对科学数据的管理机构、研究机构、高校以及科学数据发布平台等开展科学数据的管理有直接的帮助。文章在文献调研基础上,从数据利用的角度出发,基于内容相关性、数据完整性、数据可靠性与数据权威性,对目前研究中科学数据分类方式和分类结果进行汇总整理,并且对当前科学数据分类体系进行述评。完善当前的科学数据分类体系,为科学数据的管理更加有序奠定基础。当前科学数据的分类大多从内容相关性、数据完整性等角度进行分类,对于从数据权威性角度对科学数据进行分类还没有引起大家的关注,对科学数据的引用研究还需要继续深入。     

Predicting citation counts based on deep neural network learning techniques

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.     

A quantitative measure to compare the disciplinary profiles of research systems and their evolution over time

By modeling research systems as complex systems we generalize similarity measures used in the literature during the last two decades. We propose to use the mathematical tools developed within the spin-glasses literature to evaluate similarity within systems and between systems in a unified manner. Our measure is based on the ‘overlap’ of disciplinary profiles of a set of research systems and can readily be integrated in the framework of traditional bibliometric profile analysis. The investigation of the distribution of the overlaps provides useful insights on the dynamics of the general system, that is whether it converges toward a unique disciplinary structure or to a differentiated pattern.We illustrate the usefulness of the approach by investigating the dynamics of disciplinary profiles of European countries from 1996 to 2011. We analyze several bibliometric indicators (including publications and citations) of European countries in the 27 Scopus subject categories. We compare the disciplinary profiles of European countries (i) among them; (ii) with respect to the European standard; and (iii) to the World reference.We find that there is a convergence toward a unique European disciplinary profile of the scientific production even if large differences in the scientific profiles still remain. The investigation of the dynamics by year shows that developing countries are converging toward the European model while some developed countries are departing from it.     

SCiMet: Stable,sCalable and reliable Metric-based framework for quality assessment in collaborative content generation systems

In collaborative content generation (CCG), such as publishing scientific articles, a group of contributors collaboratively generates artifacts available through a venue. The main concern in such systems is the quality. A remarkable range of research considers quality metrics partially when dealing with the quality of artifacts, contributors, and venues. However, such approaches have several drawbacks. One of the most notable ones is that they are not comprehensive in terms of the metrics to evaluate all entities, including artifacts, contributors, and venues. Also, they are vulnerable to potential attacks.In this paper, we propose a novel iterative definition in which the quality of artifacts, collaborators, and venues are defined interconnectedly. In our framework, the quality of an artifact is defined based on the quality of its contributors, venue, references, and citations. The quality of a contributor is defined based on the quality of his artifacts, collaborators, and the venues. Quality of a venue is defined based on both quality of artifacts and contributors. We propose a data model, formulations, and an algorithm for the proposed approach. We also compare the robustness of our approach against malicious manipulations with two well-known related approaches. The comparison results show the superiority of our method over other related approaches.     

Practical Engagements and Co-Created Research

In this essay we foreground the value of engaging meaningfully with practitioners in our work. We review research by scholars whose work cuts across topics and contexts to gain insight into the power and practice of human communication as it shapes the world in which we live-highlighting work that is at its best because of its co-creation with practitioners. We propose five key features that provide a simple yet robust framework for developing and assessing the quality and value of engaged scholarship. Our thesis is that the best engaged scholarship may or may not be widely disseminated, but it is widely owned by co-creators who believe in its purpose and product.     

Unpacking the Matthew effect in citations

One problem confronting the use of citation-based metrics in science studies and research evaluations is the Matthew effect. This paper reviews the role of citations in science and decomposes the Matthew effect in citations into three components: networking, prestige, and appropriateness. The networking and prestige effects challenge the validity of citation-based metrics, but the appropriateness effect does not. Using panel data of 1279 solo-authored papers’ citation histories and fixed effects models, we test these three effects controlling for unobserved paper characteristics. We find no evidence of retroactive networking effect and only weak evidence of prestige effect (very small and not always significant), which provides some support for the use of citation-based metrics in science studies and evaluation practices. In addition, adding the appropriateness effect reduces the size of the prestige effect considerably, suggesting that previous studies controlling for paper quality but not appropriateness may have overestimated the prestige effect.     

The decisions and processes involved in a systematic search strategy: a hierarchical framework

Objective:The decisions and processes that may compose a systematic search strategy have not been formally identified and categorized. This study aimed to (1) identify all decisions that could be made and processes that could be used in a systematic search strategy and (2) create a hierarchical framework of those decisions and processes.Methods:The literature was searched for documents or guides on conducting a literature search for a systematic review or other evidence synthesis. The decisions or processes for locating studies were extracted from eligible documents and categorized into a structured hierarchical framework. Feedback from experts was sought to revise the framework. The framework was revised iteratively and tested using recently published literature on systematic searching.Results:Guidance documents were identified from expert organizations and a search of the literature and Internet. Data were extracted from 74 eligible documents to form the initial framework. The framework was revised based on feedback from 9 search experts and further review and testing by the authors. The hierarchical framework consists of 119 decisions or processes sorted into 17 categories and arranged under 5 topics. These topics are “Skill of the searcher,” “Selecting information to identify,” “Searching the literature electronically,” “Other ways to identify studies,” and “Updating the systematic review.”Conclusions:The work identifies and classifies the decisions and processes used in systematic searching. Future work can now focus on assessing and prioritizing research on the best methods for successfully identifying all eligible studies for a systematic review.     

Applying findings from a systematic review of workplace‐based e‐learning: implications for health information professionals

Objectives: To systematically review the UK published literature on e‐learning in the health workplace and to apply the findings to one of the most prolific UK e‐learning initiatives in the health sector—the National Library for Health Facilitated Online Learning Interactive Opportunity (FOLIO) Programme. Methods: Sensitive searches were conducted across assia , Australian Education Index, British Education Index, cinahl , CSA Abstracts, Dissertation Abstracts, Emerald, eric , ibss , Index to Theses, lisa , medline , Psyc Info and Social Science Citation Index. Additional citations were identified from reference lists of included studies and of relevant reviews; citation tracking and contact with experts. Twenty‐nine studies met the inclusion criteria and were coded and analysed using thematic analysis as described by Miles & Huberman (Qualitative Data Analysis: A Sourcebook of New Methods. Newbury Park, CA: Sage, 1984). Results: Five broad themes were identified from the 29 included studies: (i) peer communication; (ii) flexibility; (iii) support; (iv) knowledge validation; and (v) course presentation and design. These broad themes were supported by a total of eleven sub‐themes. Components from the FOLIO Programme were analysed and existing and proposed developments were mapped against each sub‐theme. This provides a valuable framework for ongoing course development. Conclusion: Librarians involved in delivering and supporting e‐learning can benefit from applying the findings from the systematic review to existing programmes, exemplified by the FOLIO Programme. The resultant framework can also be used in developing new e‐learning programmes.     

国外撤销论文研究综述

以国外撤销论文的相关研究为基础,从定性与定量研究、医学与非医学数据库研究、撤销原因、撤销论文的引用情况等几个方面对前人研究成果进行梳理总结,概述撤销论文的研究现状,以期对我国科研管理、期刊管理、科研评价以及遏制学术不端行为等方面政策的制订提供参考。     

Main path analysis considering citation structure and content: Case studies in different domains

Main path analysis (MPA) is an effective method widely accepted in science and technology for extracting knowledge diffusion paths. Traditional citation analysis assumes that all citations are treated equally. In contrast, this paper proposes a new MPA framework from the perspective of citation structure and content. Three indicators are considered to adjust edge weight: (1) Structural similarity, (2) Topic similarity and (3) Sentiment analysis. This study takes the bullwhip effect and the Internet of Things domain as examples to verify the reliability and feasibility of improved MPA. The results show that the improved main path uncovers the knowledge trajectories appropriately, which has an ability to distinguish citations and detect important papers. This research enriches MPA theory and provides future research directions from perspective of citation structure and content.     

Citation differences across research funding and access modalities

This research provides insight into the complex relationship between open access, funding, and citation advantage. It presents an analysis of research articles and their citations in the Scopus database across 40 subject categories. The sample includes 12 categories from Health Sciences, 7 from Life Sciences, 10 from Physical Sciences & Engineering, and 11 from Social Sciences & Humanities. Specifically, the analysis focuses on articles published in 2016 and the citations they received from 2016 to 2020. Our findings show that open access articles published in hybrid journals receive considerably more citations than those published in gold open access journals. Articles under the hybrid gold modality are cited on average twice as much as those in the gold modality, regardless of funding. Furthermore, we found that funded articles generally obtain 50 % more citations than unfunded ones within the same publication modality. Open access repositories significantly increase citations, particularly for articles without funding. Thus, articles in open access repositories receive 50 % more citations than paywalled ones.     

The citation disadvantage of clinical research

Biomedical research encompasses diverse types of activities, from basic science (“bench”) to clinical medicine (“bedside”) to bench-to-bedside translational research. It, however, remains unclear whether different types of research receive citations at varying rates. Here we aim to answer this question by using a newly proposed paper-level indicator that quantifies the extent to which a paper is basic science or clinical medicine. Applying this measure to 5 million biomedical papers, we find a systematic citation disadvantage of clinical oriented papers; they tend to garner far fewer citations and are less likely to be hit works than papers oriented towards basic science. At the same time, clinical research has a higher variance in its citation. We also find that the citation difference between basic and clinical research decreases, yet still persists, if longer citation-window is used. Given the increasing adoption of short-term, citation-based bibliometric indicators in funding decisions, the under-cited effect of clinical research may provide disincentives for bio-researchers to venture into the translation of basic scientific discoveries into clinical applications, thus providing explanations of reasons behind the existence of the gap between basic and clinical research that is commented as “valley of death” and the commentary of “extinction” risk of translational researchers. Our work may provide insights to policy-makers on how to evaluate different types of biomedical research.