Search

In this short paper, we use a mix of scientometrics and DH methods to investigate a novel corpus of term papers from German students of the humanities, hoping to shed some light on undergrad citation practices.

Scientific publication metrics provide a way to measure the impact and influence of a publication. These metrics can be used to evaluate the quality and importance of a scientific publication, as well as the research productivity and success of a scientist. While citation is key, citation metrics are closely linked to number of papers. There is anocdetal evidence that some hyperprolific authors can publish a paper within 5 days. This paper examines these authors in detail using the latest top 2% scientists database. The study reveals that some authors can publish, on average, 100-200 papers per year, including single-authored papers. A closer look reveals the flaw of the database and that these authors from medical science are journalists. Some of these journalists were even ranked higher than Nobel laureattes. Further investigation exposes some close-connected scientists who publish more than 100 papers per year in environmental sciences and gain incredible number of highly cited paper records. It is concluded that the scientific community has the need to establish ethical guidelines and practices on number of publications to ensure that publishing a paper is conducted with integrity and that the pursuit of knowledge is not compromised by personal interests.

Up to 90% of the human genome is transcribed into Long-noncoding RNAs (lncRNAs) that longer than 200 nucleotides but do not code for proteins. LncRNAs play a vital role in a broad range of biological process, it’s dysregulations and mutations are linked to the development and progression of various complex human diseases. Given the dramatic changes and growing scientific outputs in lncRNAs field, using a quantitative measurement to analyze and characterize the existing studies has become imperative.Bibliometric analysis is a widely used tool to assess the academic influence of a publication or a country in a specific field. However, a bibliometric analysis of the top 100 most-cited papers in lncRNAs area has not been conducted. Thus, we executed a bibliometric study to identify the authors, journals, countries and institutions that contributed most to the top 100 lncRNAs list, characterize the key words and focus of top 100 most-cited papers, and detect the factors related to their successful citation. This study provides a comprehensive list of the most influential papers on lncRNAs research and demonstrates the important advances in this field, which might be benefit to researchers in their paper publication and scientific cooperation.

The study extends the conversation on the effect that co-authorship has on the citation impact of papers by analyzing the short-term advantage of co-authored papers. The results suggest that co-authored papers have a higher short-term impact than single-authored ones in all scientific domains. The study adds insights on the use of the immediacy index as an alternative indicator to evaluate the short-term competitive advantage of co-authored papers concerning the number of citations they attract. Furthermore, the study shows the efficacy of the immediacy index in comparing the short-term impact of different groups. Resumo O estudo estende a conversa sobre o efeito que a coautoria tem no impacto da citação de artigos, analisando a vantagem de curto prazo de artigos em coautoria. Os resultados sugerem que artigos em coautoria têm maior impacto em curto prazo do que artigos em autoria única em todos os domínios científicos. O estudo adiciona insights sobre o uso do índice de imediatismo como um indicador alternativo para avaliar a vantagem competitiva de curto prazo de artigos em coautoria quanto ao número de citações que eles atraem. Além disso, o estudo mostra a eficácia do índice de imediatismo para comparar o impacto de curto prazo de diferentes grupos.

This project contains the citations found in Google Scholar, Web of Science, and Scopus, to the sample of 2,515 highly-cited documents displayed in the 2017 edition of Google Scholar's Classic Papers. Additionally, it contains the tables resulting from the analysis carried out in the paper "Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories"

PurposeHow to provide the most useful papers for searchers is a key issue for academic search engines. A lot of research has been carried out to address this problem. However, when evaluating the effectiveness of an academic search engine, most of the previous investigations assume that the only concern of the user is the relevancy of the paper to the query. The authors believe that the usefulness of a paper is determined not only by its relevance to the query but also by other aspects including its publication age and impact in the research community. This is vital, especially when a large number of papers are relevant to the query.Design/methodology/approachThis paper proposes a group of metrics to measure the usefulness of a ranked list of papers. When defining these metrics, three factors, including relevance, publication age and impact, are considered at the same time. To accommodate this, the authors propose a framework to rank papers by a combination of their relevance, publication age and impact scores.FindingsThe framework is evaluated with the ACL (Association for Computational Linguistics Anthology Network) dataset. It demonstrates that the proposed ranking algorithm is effective for improving usefulness when two or three aspects of academic papers are considered at the same time, while the relevance of the retrieved papers is slightly down compared with the relevance-only retrieval.Originality/valueTo the best of the authors’ knowledge, the proposed multi-objective academic search framework is the first of its kind that is proposed and evaluated with a group of new evaluation metrics.

PurposeIn this article, the ideas and methods behind the “patent-paper citation” are scrutinised by following the intellectual and technical development of approaches and ideas in early work on patentometrics. The aim is to study how references from patents to papers came to play a crucial role in establishing a link between science and technology.Design/methodology/approachThe study comprises a conceptual history of the “patent paper citation” and its emergence as an important indicator of science and technology interaction. By tracing key references in the field, it analyses the overarching frameworks and ideas, the conceptual “hinterland”, in which the approach of studying patent references emerged.FindingsThe analysis explains how interest in patents – not only as legal and economic artefacts but also as scientific documents – became evident in the 1980s. The focus on patent citations was sparked by a need for relevant and objective indicators and by the greater availability of databases and methods. Yet, the development of patentometrics also relied on earlier research, and established theories, on the relation between science and technology.Originality/valueThis is the first attempt at situating patentometrics in a larger societal and scientific context. The paper offers a reflexive and nuanced analysis of the “patent-paper citation” as a theoretical and historical construct, and it calls for a broader and contextualised understanding of patent references, including their social, legal and rhetorical function.

Objective To perform bibliometric analysis of the top cited articles in urology as a guide for journal club article selection. Methods Bibliometric citation analysis was performed using Scopus. Tables illustrating the top cited clinical, basic science, and guidelines/position statements papers were constructed. Linear regression was used to determine association between h-index and number of citations. Results A total of 3,188,861 publications from 1788 to 2020 were analyzed. The top 100 cited clinical papers from 1788 to 2020, top 100 cited contemporary clinical papers from 2000 to 2020, top 25 cited basic science papers, and the top 25 cited guidelines/position statement papers were reported. Median number of citations in the top 100 cited clinical papers was 1463 (interquartile range 1186-1821). Memorial Sloan Kettering Cancer Center (12), Johns Hopkins University (6), and Harvard University (6) contributed the most top cited clinical papers in urology. Urologic oncology was the most represented subspecialty in both clinical (75%) and basic science (96%) papers. First author and last author h-index were found to correlate with the number paper citations in the top 100 cited clinical papers from 1788 to 2020 (first author β:5.3, P= .003, last author β:4.5, P= .03). Only 7% of the most cited clinical papers in urology were from female first authors, which was not statistically significantly different from those reported in prior publications published in 2009 and 2013. Conclusion Contemporary citation analysis of indexed manuscripts in urology may serve as a valuable educational tool for urologists and trainees.

Background: The main basis for the public recognition of the merits of scientists has always been the system of scientific publications and citations. Our goal is to identify and analyze the most cited articles in the Public, Environmental & Occupational Health category. (2) Methods: We searched the Web of Science for all articles published in the “Public, Environmental & Occupational Health” category up to March 2022 and selected the 100 most cited articles. We recorded the number of citations, the journal, the year of publication, quartile, impact factor, institution, country, authors, topic, type of publication and collaborations. (3) Results: 926,665 documents were analyzed. The top 100 had 401,620 citations. The journal with the most articles was the Journal of Clinical Epidemiology and the one with the highest number of citations was Medical Care. The year with the highest number of articles in the top 100 was 1998. The country with the highest percentage of publications was the USA and the most productive institution was Harvard. The most frequent keywords were bias, quality, and extension. The largest collaboration node was between the USA, Canada, Germany, Spain, Australia, France, and Sweden. (4) Conclusions: This bibliometric study on Public, Environmental & Occupational Health provides valuable information not only to identify topics of interest in the analyzed category, but also to identify the differences in the topics they study. This research was funded by Human Movement Research Group: SGR-Cat 2021 grant number SGR 1463. Generalitat de Catalunya.

The effectiveness of foreign aid in stimulating economic development is a topic of intense debate in the scientific community and among policy analysts. Numerous empirical studies are devoted to investigating the impact of foreign aid on the economic growth/development of recipient countries. This study reviews the literature relevant to this debate using the bibliometric data of scholarly papers in the Scopus database. Our intention is to identify the trends of publications, their geographical distribution and the most influential journals, authors and articles in this field of research.

Scaphoid fractures are commonly seen following a fall on an outstretched hand and often missed on initial presentation. An untreated scaphoid fracture may result in avascular necrosis of the scaphoid due to its retrograde blood supply. Published articles guide our investigation and management of these injuries. A citation analysis was performed on the top 30 articles relating to scaphoid fractures ranked by citation number. The 30 articles have been cited a total of 4595 times originating from 9 different countries. The leading article was cited 443 times with an average of 12.66 citations/year. Although this may not directly correlate with study quality, it does provide an insight to the influence which a paper has had on the scientific community. This list may prove invaluable to clinicians involved in the treatment of patients with scaphoid fractures and those actively furthering the development of the field.

Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem by recommending relevant papers to scholars. However, previous paper recommendations calculate paper similarity based on hand-engineered features which are inflexible. To address this problem, we develop a scientific paper recommendation system, namely VOPRec, by vector representation learning of paper in citation networks. VOPRec takes advantages of recent research in both text and network representation learning for unsupervised feature design. In VOPRec, the text information is represented with word embedding to find papers of similar research interest. Then, the structural identity is converted into vectors to find papers of similar network topology. After bridging text information and structural identity with the citation network, vector representation of paper can be learned with network embedding. Finally, top- $Q$ Q recommendation list is generated based on the similarity calculated with paper vectors. Through the APS data set, we show that VOPRec outperforms state-of-the-art paper recommendation baselines measured by precision, recall, F1, and NDCG.

We study the citation dynamics of the papers published in three scientific disciplines (Physics, Economics, and Mathematics) and four broad scientific categories (Medical, Natural, Social Sciences, and Arts & Humanities). We measure the uncitedness ratio, namely, the fraction of uncited papers in these data sets and its dependence on the time following publication. These measurements are compared with a model of citation dynamics that considers acquiring citations as an inhomogeneous Poisson process. The model captures the fraction of uncited papers in our collections fairly well, suggesting that uncitedness is an inevitable consequence of the Poisson statistics.

Reseachers have investigated numerous factors influencing citation counts of cited papers. One factor investigated has been the number of gained citations, as this could increase the visibility of cited papers and subsequently induce further citations. In this paper, aiming to identify a particular kind of citation that could trigger a rapid growth in the citation counts of cited papers, a concept of "citation promoter" was proposed. We defined citation promoters based on the annual citation rates of the cited papers and the co-citation counts received by the pair of cited and citing papers. The comparative results showed that papers would obtain a sharp rise in citation counts shortly after they were cited by citation promoters. Papers that received citation promoters at an early age outperformed other papers in long-term citation counts. In addition, we developed a classification model for predicting whether a citing paper would be a citation promoter for its cited paper. Since it was a class imbalanced problem (4% positive instances), and there was a lack of content and author features in our dataset, our preliminary models achieved moderate performance with an $F_1$ score slightly higher than 0.5, while the $F_1$ score obtained by random guessing was 0.07. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its Science of Research, Innovation and Enterprise programme (SRIE Award No. NRF2014-NRF-SRIE001-019).

Purpose To analyze the 50 most-cited articles pertaining to “Bankart lesions,” also known as anteroinferior labral tears, by means of citation analysis as well as to provide analysis and summary of the origins and trends of research on Bankart lesions. Methods Scopus was used to query the literature on Bankart lesions. Included articles were related to Bankart lesions and the indications, risk factors, techniques, and outcomes of arthroscopic and open Bankart repair. The 50 most-cited articles were analyzed in the following areas: year of publication, citations in the most recent year, total citation count, contributing authors, institutions, countries, and journals, article classifications, and level of evidence. Results Years of publication ranged from 1938 to 2013. There were 608 total citations in the most recent year. Total citation count was 12,441. Regarding country, journal, and authorship, United States, R. A. Arciero, and Arthroscopy were the highest respective contributors. Rush University had the greatest number of publications. The most common article classification was clinical outcomes. Of 49 clinical articles, the most frequent Level of Evidence was IV. The majority of the top 50 Bankart literature consisted of case series and retrospective studies performed in the United States. Conclusions Our findings are consistent with the hypothesis that the 50 most-cited articles about Bankart lesions are predominantly U.S.-based, produced by academic orthopaedic groups, clinical outcomes articles, and of Level IV and V evidence. This list of articles should serve as a reference tool for any orthopaedist looking to review Bankart literature.

The study analyses papers published in DESIDOC Journal of Library and Information Technology (DJLIT) using bibliometric techniques for the period of 1992-2019 (28 years) and citations received by these papers until 20th March 2020as reflected by Google Scholar. The study examined the pattern of growth, geographical distribution of the articles; identified the prolific authors & institutions, and their output; and the pattern of citations of the papers and identified most cited authors. The findings indicate that the highest number of articles was published during 2012-2015 followed by 2016-2019. The distribution of output by countries indicates that 39 countries contributed 1,698 articles, including India. Indian authors published the highest percentage (86.1 %) of articles followed by USA and had the highest value of CPP and RCI. Authors affiliated to different institutions of Delhi contributed the most (30.7 %) followed by Karnataka (13.1 %) and Maharashtra (10.5 %). Among the institutions, DRDO-DESIDOC and CSIR-NISTADS topped the list. Among the 26 most prolific authors, B.M. Gupta (CSIR-NISTADS) published the maximum number of articles. However, B.R. Babu (University of Madras, Chennai) had the highest value of CPP and RCI. During the studied period, 1,698 papers obtained 15,538 citations, of which 248 (14.6 %) articles did not receive any citation.

The volume of research articles in digital repositories is increasing. This spectacular growth of repositories makes it rather difficult for researchers to obtain related research papers in response to their queries. The problem becomes worse when a researcher with insufficient knowledge of searching research articles uses these repositories. In the traditional recommendation approaches, the results of the query miss many high-quality papers, in the related work section, which are either published recently or have low citation count. To overcome this problem, there needs to be a solution which considers not only structural relationships between the papers but also inspects the quality of authors publishing those articles. Many research paper recommendation approaches have been implemented which includes collaborative filtering-based, content-based, and citation analysis-based techniques. The collaborative filtering-based approaches primarily use paper-citation matrix for recommendations, whereas the content-based approaches only consider the content of the paper. The citation analysis considers the structure of the network and focuses on papers citing or cited by the paper of interest. It is therefore very difficult for a recommender system to recommend high-quality papers without a hybrid approach that incorporates multiple features, such as citation information and author information. The proposed method creates a multilevel citation and relationship network of authors in which the citation network uses the structural relationship between the papers to extract significant papers, and authors' collaboration network finds key authors from those papers. The papers selected by this hybrid approach are then recommended to the user. The results have shown that our proposed method performs exceedingly well as compared with the state-of-the-art existing systems, such as Google scholar and multilevel simultaneous citation network.

Purpose Unicompartmental knee arthroplasty (UKA) is a treatment option for anteromedial osteoarthritis of the knee. The number of UKA has been increasing constantly worldwide in recent decades. The aim of this study was to determine the most frequently cited scientific articles addressing this subject and to establish a ranking of the 50 most influential papers. Methods The 50 most cited articles related to UKA were searched in Web of Science® (Clarivate Analytics, Penn., USA) by the use of defined search terms. All types of scientific papers with reference to this topic were ranked according to the absolute number of citations and analyzed for the following characteristics: journal title, year of publication, number of citations, citation density, geographic origin, article type, and level of evidence. Results The 50 most cited articles had up to 453 citations. Most papers were published in the Journal of Bone and Joint Surgery (British volume). More than half of the articles were published in the 2000s and 2010s (n = 30). Ten countries contributed to the top 50 list, with most contributions from the UK (n = 17). Most articles could be attributed to the category of Clinical Science (n = 33), and most reported level IV studies. Conclusion Most of the frequently cited articles in UKA are clinical studies that have a low level of evidence. Few basic scientific studies could be identified, which suggests that most product development is done by commercial companies.

Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact on systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., approximately 8.3%. For CS, 56% of retracted papers, provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also a remarkable disparity between different publishers, a tendency for multiple versions of a retracted paper over and above the Version of Record (VoR), and for new citations long after a paper is officially retracted. Conclusions: Unfortunately retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies., Comment: This is a preprint of the paper submitted to PLOS ONE

Purpose This paper aims to assess the predictive power of authorship properties determined at the time of publishing conference papers on future citations of conference papers in computer and information science. Design/methodology/approach This study examined 21 factors – related to all authors and to the first author – as potentially predictive of citation counts. Specifically, the study assessed properties of author's academic performance, degree of collaboration and topological properties of their research collaboration networks. Findings The results of comparing all authors with first authors indicate that the all author-related factors have a significantly higher power for explaining conference paper citation counts than the first author-related factors. Moreover, among the all author-related factors, the degree centrality before the target paper made the largest contribution. Originality/value This is one of the first attempts to focus on the relationship of author characteristics to conference papers. This is also one of only a few studies to expand prior research, which limited its bibliometric foci to journal articles, to conference papers.

Účel: Tento příspěvek přináší výsledky případové studie zabývající se historickými kořeny východoevropských počítačových vědců. Design/metodologie/přístup: Studie je založena na analýze citovaných referencí pocházejících ze souboru přibližně 80 000 odborných informatických článků východoevropských výzkumníků publikovaných od roku 1989 do roku 2014. Využitím metody nazvané „referenční spektroskopie podle roku vydání (RPYS)“ pro historické analýzy založené na bibliometrických datech analyzujeme celkem 800 000 referencí citovaných v těchto publikacích. Identifikujeme nejvýznamnější roky zahrnující nejčastěji citovaná díla ( z let 1952, 1965 a 1975) a zaměřujeme se na tyto v oboru natolik vynikající publikační výstupy. Ukazujeme, jak se tyto vlivné publikace citovaly ve východní Evropě a obecně a na jaké vědní obory měly největší dopad. Výsledky: Pozoruhodnou publikací, jež měla podle všeho enormní vliv na východoevropskou počítačovou vědu, je Zadehův článek „Fuzzy sets“ v časopise „Information and Control“ z roku 1965. Demonstrujeme, že informatici z východní Evropy jsou v citačním chování konzervativnější než jejich protějšky ze západní Evropy a mají sklon citovat a starší a zavedenější výzkum. Originalita/hodnota: Jaké jsou historické kořeny vědců pracujících v určitém oboru nebo na konkrétním tématu? Existují konkrétní publikace – přelomová díla – která jsou důležitá pro jejich výzkum? Domníváme se, že tyto otázky zajímají výzkumníky mnoha vědních disciplín. Purpose: The current article presents the results of a case study dealing with the historical roots of Eastern European researchers in computer science. Design/methodology/approach: The study is based on an analysis of cited references stemming from a collection of around 80,000 computer science papers by Eastern European researchers published from 1989 to 2014. By using a method called “reference publication year spectroscopy” (RPYS) for historical analyses based on bibliometric data, we analyze around 800,000 references cited in those papers. We identify the peak years including most frequently cited publications (from 1952, 1965, and 1975) and focus on these outstanding works for the field. We show how these influential papers were cited in Eastern Europe and in general and on which scientific fields they have the most impact. Findings: A noteworthy publication that seems to have a tremendous effect on Eastern European computer science is Zadeh’s “Fuzzy sets” article which appeared in Information and Control in 1965. We demonstrate that computer scientists from Eastern Europe are more conservative in their citation behavior and tend to refer to older and more established research than their counterparts from the West. Originality/value: Which are the historical roots of researchers working in a particular field or on a specific topic? Are there certain publications – landmark papers – which are important for their research? We guess that these are questions bothering researchers in many fields.

Background: Neuropathic pain research has grown impressively in the past two decades, as evidenced by improvements in research quality and increments in the number of research papers. In views of this situation, the use of quantitative measurements to analyze and characterize existing research has become imperative. The aim of this research is to identify and analyze the 100 most-cited papers in neuropathic pain research.Methods: Neuropathic pain-related articles published between 2000 and 2020 were screened from Web of Science (WOS) by using the following subject terms: TI = (Neuralgia$ OR Neurodynia$ OR “Neuropathic pain” OR sciatica OR “Nerve pain$”). The publications were ranked in a descending order on the basis of citation counts, and the top 100 most-cited neuropathic pain papers were determined. Subsequently, we conducted a bibliometric study to determine the authors, journals, countries, and institutions that contributed the most to the top 100 neuropathic pain lists; describe the keywords and hotspots of the top 100 most-cited papers; and explore the factors associated with successful citations.Results: The top 100 most-cited papers were published from 2000 to 2017, and 2003 had the largest number of published papers (n = 16). The mean number of citations per paper was 480.72, with a range of 262–1,569. Forty-four kinds of journals contributed to the top 100 most-cited papers, which were predominantly published in “Pain” (n = 23). The USA was determined to be the leader of neuropathic pain research in terms of quality and quantity.Conclusion: This study provides a comprehensive list of the most influential papers on neuropathic pain and demonstrates the important advances in this field to help understand academic concerns and the directions of technological innovations in neuropathic pain worldwide.

Background Citation analysis is a commonly used method for appraising the impact of academic publications within a particular field of study. A gap exists in the citation analysis literature with regard to the topic of direct anterior approach (DAA) hip arthroplasty. The purpose of this study is to identify the 50 most frequently cited publications related to this topic. Methods The Clarivate Analytics Web of Knowledge database was utilized to search for publications relating to DAA hip arthroplasty. The top 50 most cited articles that met inclusion criteria were recorded and reviewed for various metrics. Results The top 50 publications were cited a total of 3521 times, with an average of 86.3 total citations per year between 1980 and 2019. 47 of the 50 articles identified had been published since the year 2000. Cohort designs were the most common study type. Conclusions This analysis provides insight into factors that characterize highly cited articles on the specific topic of DAA hip arthroplasty. These factors include higher levels of evidence, recent publication, and origin in the United States. Citations of DAA hip arthroplasty papers appear to be on the rise. The curation and analysis of this set of 50 articles will provide orthopaedic surgery clinicians, researchers, and residency program directors a guide for quickly isolating influential articles on the topic of DAA hip arthroplasty. This may serve as a quick reference for clinical decision-making, foundation for further research, and curriculum on DAA hip arthroplasty.

The scientific publishing market suffers from injustice in several regards. Interestingly, traces of injustice can be found in both open-access (OA) and closed-access (CA) publishing-models. The OA-model makes scientific literature freely accessible for all scholars - but, the costs of publishing have to be assumed by somebody (e.g. the authors via APCs). This, however, may not be possible for authors who do not benefit from financial support for OA-publishing. In succession, such authors cannot publish in the OA-model (instead they must turn to the CA-model) and cannot profit from the citation advantage that was shown for many OA-articles [1]. Researchers (at institutions) who are not able to maintain subscriptions to CA-publications suffer from the CA-model as it builds a barrier to access the required resources. This may result in falling behind the state of the art in the field which along with it may negatively affect future publication success in outlets of high reputation. This study (conducted with data from Scopus and Unpaywall) will research the effect on researchers’ scientific success grounded in the unfairness of both publishing models. We aim to answer three questions. First, is there any limitation for a group of researchers (e.g. from different countries, career stages, career positions) to cite the CA-papers? Second, is there any limitation for researchers to publish in the OA-model and receiving citations? To answer these questions, we try to find the relationship between the groups that researchers belong to and the tendency to cite/publish in the OA- or CA-model. Third, to what extent do these limitations affect the scientific success of scholars? We assess scientific success by comparing the receiving citations for each group. We expect that the tendency to cite OA- and publish CA-articles affects the scientific success of scholars negatively. [1] Matheka et al. https://doi.org/10.1186/1744-8603-10-26

The objective of this study is to show the dynamics of the evolution on research of the generations by means of analysis of journal publications from the category of management and business from Clarivate Analytics Web of Science. Bibliometric analysis is used to unveil the authors, journals, institutions, and the most influential countries on the subject from 2001 to date. The most pertinent studies are presented that show the main characteristics of the five generations that can currently be found in the workplace, namely the Silent Generation, Baby Boomers, Generation X, Generation Y and Generation Z. The results highlight that the studies focus on countries and regions with more developed economies. Studies are scarcer from countries and regions with emerging economies such as Latin America and the Caribbean, Africa, Eastern Europe and the Middle East. Likewise, there is evidence of the lack of qualitative studies on Generation Z, which will be of greater importance in the labour force or employment market and with higher purchasing power. The results add knowledge to academics to define their research strategies. For the practitioners it contributes information on the most pertinent academics on the study of generations, which facilitates potential candidates to contract or hire in case they need to study the characteristics of their workforce to define recruitment policies, career planning or motivation.

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.

Digital libraries suffer from the problem of information overload due to immense proliferation of research papers in journals and conference papers. This makes it challenging for researchers to access the relevant research papers. Fortunately, research paper recommendation systems offer a solution to this dilemma by filtering all the available information and delivering what is most relevant to the user. Researchers have proposed numerous approaches for research paper recommendation which are based on metadata, content, citation analysis, collaborative filtering, etc. Approaches based on citation analysis, including co-citation and bibliographic coupling, have proven to be significant. Researchers have extended the co-citation approach to include content analysis and citation proximity analysis and this has led to improvement in the accuracy of recommendations. However, in co-citation analysis, similarity between papers is discovered based on the frequency of co-cited papers in different research papers that can belong to different areas. Bibliographic coupling, on the other hand, determines the relevance between two papers based on their common references. Therefore, bibliographic coupling has inherited the benefits of recommending relevant papers; however, traditional bibliographic coupling does not consider the citing patterns of common references in different logical sections of the citing papers. Since the use of citation proximity analysis in co-citation has improved the accuracy of paper recommendation, this paper proposes a paper recommendation approach that extends the traditional bibliographic coupling by exploiting the distribution of citations in logical sections in bibliographically coupled papers. Comprehensive automated evaluation utilizing Jensen Shannon Divergence was conducted to evaluate the proposed approach. The results showed significant improvement over traditional bibliographic coupling and content-based research paper recommendation.

This paper aims to determine the factors significantly predicting the future citation rates of conference papers. Whereas a large body of bibliometric studies has investigated the multiple factors predicting future citation rates, the attention has been paid mainly on journal articles. This study analyzes 43,463 papers from 81 conference series in the ‘Information Science’ and ‘Computer Science’ fields and examines the contributions of conference-related factors to the citation rates of the conference papers. More specifically, this paper assesses the following conference related factors as being potentially predictive factors of citation rates: longevity and names of the conference series, the number of presented papers at individual conferences, acceptance rates, the seasons of conferences, the content similarity of the presented papers at a conference, the degree of the authors’ international collaborations and the records of the best paper awards at conferences. The regression results illustrate that all of the factors were significantly predictive to the future citations of the conference papers. The factors that contributed the most to explain the citations of the conference papers include: the degree of the authors’ international collaborations at individual conferences, the records of best paper awards and the acceptance rates of individual conferences.

Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available., Projekt DEAL

The breakthrough potentials of research papers can be explained by their boundary-spanning qualities. Here, for the first time, we apply the structural variation analysis (SVA) model and its affiliated metrics to investigate the extent to which such qualities characterize a group of Nobel Prize winning papers. We find that these papers share remarkable boundary-spanning traits, marked by exceptional abilities to connect disparate and topically-diverse clusters of research papers. Further, their publications exert structural variations on the scale that significantly alters the betweenness centrality distributions of existing intellectual space. Overall, SVA not only provides a set of leading indicators for describing future Nobel Prize winning papers, but also broadens our understanding of the similar prize-winning properties that may have been overlooked among other regular publications., 27 pages, 8 figures, 9 tables. Submitted to Frontiers in Research Metrics and Analytics

This paper provides results of the investigation of arrays of Russian publications in 252 specified thematic areas for 2010–2017 according to the Web of Science Core Collection (WoS CC). This period is characterized by an increase in the Russian document flow. In 87 scientific areas, Russia reached the level of the top 10 countries for the number of publications submitted to WoS CC. However, in most scientific areas, the citation of Russian publications lags far behind the global average. The average level of citation for the study period matched or exceeded the global average only in 15 areas. The most successful research area for Russia, both in terms of the share component in the number of publications in the world and in the level of their citation, is Nuclear Physics. Other successful areas are those for which Russia reached the top 3% of countries regarding the number of publications: Spectroscopy; Mathematics; Physics, Mathematical; Chemistry, Inorganic & Nuclear; Geochemistry & Geophysics; Mining & Mineral Processing; and Engineering, Petroleum.

In the field of technology management, methods have been developed to detect technologies that are important in industry by analyzing massive numbers of documents. Such methods have been applied to the field of the Internet of Things as well to investigate technological trends. The Internet of Things consists of several conventional concepts, such as radio frequency identification, near field communication, and sensor networks. However, no research compares these technologies by analyzing massive quantities of papers and patents. Thus, in this study, we explored the research areas of technologies related to the Internet of Things, for which there are opportunities for commercialization in the near future. We also discuss potential applications of these technologies in diverse systems.

Review papers provide a foundation for knowledge development in information systems (IS) as well as in any other scientific discipline. While some of the prominent reviews in information systems are cited more than twice a day on average, others take years to accumulate single digit citations. The magnitude of these differences and the proliferation of review papers in recent years prompt us to empirically analyze what distinguishes those reviews that have proven to be integral to scientific progress from those that might not be considered impactful. Our results demonstrate that the attributes explaining scientific impact are unique for the different types of reviews: reviews for describing, understanding, explaining, and theory testing. Transparency of the applied methodology is important for reviews that target theory testing, understanding, or explaining; similarly, reviews for describing, understanding or explaining achieve a higher impact when they develop a research agenda. By providing nuanced insights into the attributes of review papers that are valued by subsequent research, our study contributes to the vibrant discourse on literature reviews in IS. We thereby inform the different stakeholders involved in the development and publication of review papers in the IS field.

This study identifies the most impactful papers on scapholunate reconstruction and provides a quantitative assessment of the impact of these papers in order to inform future clinical practice, education, and research of this condition. The Scopus database was used in May 2020 to identify the 50 most cited clinical articles pertaining solely to chronic scapholunate reconstruction. Citation number and density, publication variables, and Altmetric Attention Scores (AASs) were collected and analyzed. The top 50 articles on chronic scapholunate reconstruction produced 1,868 total citations, with an average of 37.36 ± 39.90 citations per article (range 7–196) and an average citation density of 2.44 ± 2.27. US-based publications (n = 20) and articles published in Journal of Hand Surgery (n = 24) were associated with significantly higher citation number and density (p

Ascertaining the impact of research is significant for the research community and academia of all disciplines. The only prevalent measure associated with the quantification of research quality is the citation-count. Although a number of citations play a significant role in academic research, sometimes citations can be biased or made to discuss only the weaknesses and shortcomings of the research. By considering the sentiment of citations and recognizing patterns in text can aid in understanding the opinion of the peer research community and will also help in quantifying the quality of research articles. Efficient feature representation combined with machine learning classifiers has yielded significant improvement in text classification. However, the effectiveness of such combinations has not been analyzed for citation sentiment analysis. This study aims to investigate pattern recognition using machine learning models in combination with frequency-based and prediction-based feature representation techniques with and without using Synthetic Minority Oversampling Technique (SMOTE) on publicly available citation sentiment dataset. Sentiment of citation instances are classified into positive, negative or neutral. Results indicate that the Extra tree classifier in combination with Term Frequency-Inverse Document Frequency achieved 98.26% accuracy on the SMOTE-balanced dataset.

The exponential increase in the number of scientific publications raises the question of whether the sciences are expanding into a fractured structure, making cross-field communication difficult. On the other hand, scientists may be motivated to learn extensively across fields to enhance their innovative capacity, and this may offset the negative effects of fragmentation. Through an investigation of the distances within and clustering of cross-sectional citation networks, this study presents evidence that fields of science become more integrated over time. The average citation distance between papers published in the same year decreased from approximately 5.33 to 3.18 steps between 1950 and 2018. This observation is attributed to the growth of cross-field communication throughout the entire period as well as the growing importance of high impact papers to bridge networks in the same year. Three empirical findings support this conclusion. First, distances decreased between almost all disciplines throughout the time period. Second, inequality in the number of citations received by papers increased, and as a consequence the shortest paths in the network depend more on high impact papers later in the period. Third, the dispersion of connections between fields increased continually. Moreover, these changes did not entail a lower level of clustering of citations. Both within- and cross-field citations show a similar rate of slowly growing clustering values in all years. The latter findings suggest that domain spanning scholarly communication is partly enabled by new fields that connect disciplines.

This study is a systematic survey of literature on the energy-growth nexus, which has been carried out with a view to identifying the leading sources of knowledge in the forms of the most influential journals, authors, and papers. This study not only recognizes and classifies the well-known methodologies used in the energy-growth nexus analysis but also reveals intriguing content-based findings, with quantitative measures for the top 50 papers ranked according to the highest average citations per year. This survey is unique in that the process of selecting articles is entirely objective, allowing the research community's opinions to take the lead in the process rather than any subjective judgments of the authors. In this way, we examine 1041 peer-reviewed articles that specifically focused on the energy-growth nexus. We found that, as of the end of 2017, with 200 articles, Energy Policy is the leading journal publishing on this area while Energy Economics, with a total of 25,352 citation counts, holds the highest impact on this field of research. In addition, the most frequently cited article by the scholastic community in terms of average citations per year has been a literature survey conducted by Ozturk (2010) . Our study's main conclusion, based on a thorough content analysis, is that the nexus results of previous studies are generally inconclusive, with conflicting policy implications. This is not helpful and to a large extent is due to a lack of an appropriate theory. This, we contend, is essentially a methodological weakness and could be addressed by incorporating an appropriate testable economic/environmental theory.

The amount of academic articles in digital libraries is increasing exponentially. This growth of scientific papers’ growth made it difficult for researchers to obtain related papers from their queries. Recommendation systems can help them resolve the problem of information overload. However, existing paper recommender methods generally rely on the simple citation network, which ignores the semantic of papers and has the problem of cold start. In this paper, a hybrid paper recommendation approach AMHG is proposed which is based on a multi-level citation heterogeneous graph. Unlike existing works which only use the reference relationship, we consider the same or similar authors’ papers to alleviate the cold start problem of zero-citation and newly published papers. Besides, the metadata information of papers is also incorporated into a representation model to generate better recommender results to alleviate the cold start problem. We use the authors’ influence factors to reorder the candidate list outputting by MLP to obtain high-quality articles. Through experiments, we compare our model with several methods on the DBLP-REC dataset to demonstrate that AMHG outperforms state-of-the-art performance and the effectiveness of recommender.

Citation analysis does not tell the whole story about the innovativeness of scientific papers. Works by prominent authors tend to receive disproportionately many citations, while publications by less well-known researchers covering the same topics may not attract as much attention. In this paper we address the shortcomings of traditional scientometric approaches by proposing a novel method that utilizes a classifier for predicting publication years based on latent topic distributions. We then calculate real-number innovation scores used to identify potential breakthrough papers and turnaround years. The proposed approach can complement existing citation-based measures of article importance and author contribution analysis; it opens as well novel research direction for time-based, innovation-centered research scientific output evaluation. In our experiments, we focus on two corpora of research papers published over several decades at two well-established conferences: The World Wide Web Conference (WWW) and the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), containing around 3500 documents in total. We indicate significant years and demonstrate examples of highly-ranked papers, thus providing a novel insight on the evolution of the two conferences. Finally, we compare our results to citation analysis and discuss how our approach may complement traditional scientometrics.

The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their interest. Hence, approaches to effectively identify papers of high impact have attracted great attention in the past. In this work, we present a method that seeks to rank papers based on their estimated short-term impact, as measured by the number of citations received in the near future. Similar to previous work, our method models a researcher as she explores the paper citation network. The key aspect is that we incorporate an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to read papers which received a lot of attention recently. A detailed experimental evaluation on four real citation datasets across disciplines, shows that our approach is more effective than previous work in ranking papers based on their short-term impact.

The International Conference on Agile Software Development (XP) was established almost sixteen years ago. Based on data from Scopus database, a total of 789 papers have been published in between years of 2002 and 2018. We employed bibliometrics analysis and topic modeling with R/RStudio to analyze these published papers from various dimensions, including the most active authors, collaboration of authorship, most cited papers, used keywords and trends of probable topics from the titles and abstracts of those papers. The results show that the first five years of XP conference cover nearly 40% of the papers published until now and almost 62% of the XP papers have been cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues and problems faced by the practitioners in the industry. The results highlight the most influential researchers and institutions, and the collaboration between the authors in the conference papers. The approach applied in this study can be extended to other software engineering venues and can be applied to large-scale studies.

The International Conference on Agile Software Development (XP) was established almost sixteen years ago. Based on data from Scopus database, a total of 789 papers have been published in between years of 2002 and 2018. We employed bibliometrics analysis and topic modeling with R/RStudio to analyze these published papers from various dimensions, including the most active authors, collaboration of authorship, most cited papers, used keywords and trends of probable topics from the titles and abstracts of those papers. The results show that the first five years of XP conference cover nearly 40% of the papers published until now and almost 62% of the XP papers have been cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues and problems faced by the practitioners in the industry. The results highlight the most influential researchers and institutions, and the collaboration between the authors in the conference papers. The approach applied in this study can be extended to other software engineering venues and can be applied to large-scale studies.

The overabundance of academic papers makes it difficult for researchers to find relevant or interested papers. To address the problem, existing studies have developed many approaches to recommend academic papers effectively. Most of them mainly utilize content-based filtering or citation analysis to measure similarity or relatedness of two papers and recommend relevant papers to the given query. However, these recommended papers are usually discrete from each other, i.e., the relationship between recommended papers are omitted, which disables researchers from having an sight into the time-oriented development of the topic they are interested in. To overcome the drawbacks of existing work, we propose a novel academic paper recommendation method called PAPR (Path-based Academic Paper Recommendation). Our method aims to recommend an ordered path of relevant papers, which are of great benefit in helping researchers understand the development of a specific topic. During process, we take both content and network structure into account to learn the representation of a paper. Next, the similarity between papers are measured based on the representation. The experimental results based on real data show that the proposed method outperforms the state-of-art methods.

A systematic review of literature is a crucial aspect in pursuing any research problem. With the galloping growth in the number of researchers and scientific publications available online in digital libraries, there is information overload which makes the search of relevant papers cumbersome. An efficient academic paper recommendation process could be a promising maneuver for this purpose. The state-of-the-art methods in this domain primarily employ link-based approaches on citation network and do not consider the semantics (context) of papers being recommended, although the later one largely accounts for the relevance of recommendations. Also achieving online response time (scalability) is a perennial desire for any recommendation system. In this context we propose a hybrid approach “RecCite” that blends the popularity of papers with semantic similarity to acquire relevance. Further, the approach follows a top-down methodology for filtering papers from a more generic and larger network to a community pertaining to a SIG (Special Interest Group) that inherently addresses the scalability aspect. The proposed approach has been systematically evaluated with state-of-the-art baseline method using the publicly available Arnet-Miner data set. Satisfyingly, “RecCite” outperforms the baseline method with up to 37.66%, 20.14%, and 97.24% improvement in precision, recall, and response time, respectively.

Purpose The purpose of this paper is to review and explore the primary characteristics of the best papers, which have been selected by the Toulon-Verona Conference (TVC) Excellence in Services scientific committee for the period 2008-2016. Design/methodology/approach The study includes a review of the best papers (n = 51) selected by the TVC’s scientific committee from 2008 to 2016 and obtained through the TVC’s archives. The primary highlights of each paper include number of authors, authorship profile (i.e. lead author affiliation, gender, and geographic location), co-authors’ geographic locations, classification of the papers, research topics, varieties of research methods used, number of electronic downloads (as of June 2017), number of citations, and subject keywords. Coding sheet developed to ensure standardization, consistency and to ensure the relevant data were collected for the content analysis. Mixed methods analysis (analyze quantitative data qualitative and and vice versa) was used to provide descriptive statistics. Findings In total, 51 best papers have been selected by the TVC scientific committee and reflected 145 authors. Lead authors were from a total of 50 HEIs worldwide and one consultant (private sector). Six papers were self-authored while the 45 were multi-authored. The majority of the papers written by Italian authors (58.8 percent); followed by France, Greece, and Sweden (5.9 percent each); Australia, Portugal, Spain, and UK (4 percent each); Austria, Norway, Slovenia, and Vietnam (1.9 percent each). In total, 37 papers were classified as a research paper, 10 case studies, and 4 theoretical. Practical implications The analysis for the first time define methodological trends for the TVC’s best papers and suggest possible future research topic and methodological approach for research in the management field. Originality/value The findings are unique for the TVC conference, for the TQM’s special issue, which provide practical implications for researchers and conference organizers and contribute to the literature of analyzing published papers.

Introduction: The cites received by a paper is the most common method used to determine its impact in a particular scientifi c fi eld. Plantar fasciitis is one of the fi ve most common skeletal muscle injuries in runners. With a multi-factor aetiology, this degenerative process of the plantar fascia occurs when it is repetitively exposed to tensile overload. This research aims to analyse the characteristics of the 25 most-cited papers on plantar fasciitis in runners. Material and methods: To fi nd the top-cited articles, the Web of Science database was used. Data was analysed regarding the number of citations, year of publication, publishing journal, research area, authorship, institution, country, and bibliographic references. Results: The 25 most cited articles were published between 1983 and 2009. The 2000s is the most productive decade, paperwise. 84 % of these articles are from the US. They were published in 11 journals. On average, they had 78.5 citations per article. The main research areas in which the papers were developed were Orthopaedics and Sport Science. Conclusion: The results of this research facilitate the identifi cation of the most relevant research papers contributing to the development of scientifi c knowledge in the aetiology, diagnosis, prevention and treatment of plantar fasciitis in runners, and help to determine how it has evolved over time. It is also a way of acknowledging authors and institutions with outstanding contributions and leadership in this research area, as well as their most infl uential authors.

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018. We collected data from Scopus database, finding 789 XP papers. We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data. The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues. The results also highlight the most influential researchers and institutions. The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.

We analyze the impact of open-access (OA) articles published by China and the USA by using Web of Science (WoS) data covering a period of 5 years (2011–2015), five indexes (citation and four altmetric indexes), five disciplines, and three types of articles. With regard to article type, Type I papers are those wherein the authors are all from China or the USA. Type II are those in which the first author is from China or the USA. Type III includes those in which the first author is not from China or the USA. We found that the proportion of OA papers in WoS has been growing in recent years. In terms of citations and altmetric indexes, the mean value of the USA is larger than that of China in general; Type II articles possess the highest value among all papers in the USA, whereas Type III has the highest value in China. Compared with the scenario in citations, social sciences and humanities possess larger altmetric values in China and the USA. The correlation among indicators is similar for the OA papers from China and the USA. Generally, citations cannot effectively represent the altmetric indexes. The gap between China and the USA is the largest in the altmetric attention score and Type I, and the smallest in citations and Type III. Measuring the international impact of OA papers using only citations underestimates the gap between China and the USA.