Search

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 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.

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.

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).

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.

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.

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

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.

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.

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.

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.

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

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.

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

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.

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 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.

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.

The paper presents the results of the study of international and national cooperation of Ukrainian scientists from different scientific fields using citation analysis data from Scopus in the period of 2011-2015. The results show that during the period under study, the number of documents of highly cited Ukrainian scientists that have received enough citations to be included to the top 1%, 5% and 10% most cited documents in the world, evidenced an increase and were significantly different in different subjects areas. Papers written by a group of co-authors predominate among highly cited documents of Ukrainian scientists. Consequently, international cooperation plays an important role in Ukrainian scientists research results visibility and impact. At the same time, up to 16%-27% highly cited articles of Ukrainian scientists have been written without partnering with foreign colleagues, that means a significant part of important scientific results in Ukraine are carried out by oneself. Therefore, for generating an optimal policy of science development in Ukraine it is important to provide a balanced view of the expectations of the results of the international cooperation of Ukrainian scientists and achieving a required balance between the inter-country and international collaboration.

Researchers typically need to filter several academic papers to find those relevant to their research. This filtering is cumbersome and time-consuming because the number of published academic papers is growing exponentially. Some researchers have focused on developing better recommender systems for academic papers by using citation analysis and content analysis. Most traditional content analysis is implemented using a keyword matching process, and thus it cannot consider the semantic contexts of items. Further, citation analysis-based techniques rely on the number of links directly citing or being cited in a single-level network. Consequently, it may be difficult to recommend the appropriate papers when the paper of interest does not have enough citation information. To address these problems, we propose a recommendation system for academic papers that combines citation analysis and network analysis. The proposed method is based on multilevel citation networks that compare all the indirectly linked papers to the paper of interest to inspect the structural and semantic relationships among them. Thus, the proposed method tends to recommend informative and useful papers related to both the research topic and the academic theory. The comparison results based on real data showed that the proposed method outperformed the Google Scholar and SCOPUS algorithms.

Purpose Citation analysis represents one of the best currently available methods for quantifying the impact of articles. Bibliometric studies list the ‘‘best sellers'' in a single field of interest. The purpose of the present study was to identify and analyze the most frequently cited papers in dry eye research that may be of high interest for researchers and clinicians. Methods We reviewed the database of the Institute for Scientific Information to identify articles published from 1900 to September 2016. All dry eye articles published in 59 ophthalmology journals were identified. The top 100 articles were selected for further analysis of authorship, source journal, number of citations, citation rate, geographic origin, article type, and level of evidence. Results The 100 most-cited articles were published between 1983 and 2011, with most of them in the 2000s. The number of citations per article ranged from 96 to 610, and was greatest for articles published in the 2000s. Each of these articles was published in one of 15 journals. Most articles represented Level-III evidence, followed by Levels II and I. Conclusions The present study focusing on dry eye research revealed that 55% of the most-cited articles came from the U.S. and 18% from Japan. Diagnostics and therapy were the areas of focus of most of the clinical articles; 13% of the most cited papers were review articles. This analysis provides researchers and clinicians with a detailed overview on the most cited dry eye papers over the past decades.

In our planned study, we shall empirically study the assessment of cited papers within the framework of the anchoring-and-adjustment heuristic. We are interested in the question whether citation decisions are (mainly) driven by the quality of cited references. The design of our study is oriented towards the study by Teplitskiy, Duede [10]. We shall undertake a survey of corresponding authors with an available email address in the Web of Science database. The authors are asked to assess the quality of papers that they cited in previous papers. Some authors will be assigned to three treatment groups that receive further information alongside the cited paper: citation information, information on the publishing journal (journal impact factor), or a numerical access code to enter the survey. The control group will not receive any further numerical information. In the statistical analyses, we estimate how (strongly) the quality assessments of the cited papers are adjusted by the respondents to the anchor value (citation, journal, or access code). Thus, we are interested in whether possible adjustments in the assessments can not only be produced by quality-related information (citation or journal), but also by numbers that are not related to quality, i.e. the access code. The results of the study may have important implications for quality assessments of papers by researchers and the role of numbers, citations, and journal metrics in assessment processes.

Introduction Although bibliometric analyses have been performed in the past on cancer and genomics, little is known about the most frequently cited articles specifically related to cancer epigenetics. Therefore, the purpose of this study is to use citation count to identify those papers in the scientific literature that have made key contributions in the field of cancer epigenetics and identify key driving forces behind future investigations. Materials and methods The Thomas Reuters Web of Science services was queried for the years 1980-2018 without language restrictions. Articles were sorted in descending order of the number of times they were cited in the Web of Science database by other studies, and all titles and abstracts were screened to identify the research areas of the top 100 articles. The number of citations per year was calculated. Results We identified the 100 most-cited articles on cancer epigenetics, which collectively had been cited 147,083 times at the time of this writing. The top-cited article was cited 7,124 times, with an average of 375 citations per year since publication. In the period 1980-2018, the most prolific years were the years 2006 and 2010, producing nine articles, respectively. Twenty-eight unique journals contributed to the 100 articles, with the Nature journal contributing most of the articles (n=22). The most common country of article origin was the United States of America (n=78), followed by Germany (n=4), Switzerland (n=4), Japan (n=3), Spain (n=2), and United Kingdom (n=2). Conclusions In this study, the 100 most-cited articles in cancer epigenetics were examined, and the contributions from various authors, specialties, and countries were identified. Cancer epigenetics is a rapidly growing scientific field impacting translational research in cancer screening, diagnosis, classification, prognosis, and targeted treatments. Recognition of important historical contributions to this field may guide future investigations.

The much-discussed ‘relevance gap’ (Starkey and Madan, 2001) between research and practice in management is a major source of concern for business schools, in terms of their legitimacy in the eyes of students, employers and funding bodies. In this paper, we frame the relevance gap as a knowledge-transfer issue, and we focus on the role of “bridging media” as one possible mechanism through which academic research is made accessible to practising managers. Specifically, we ask two questions: (1) which academic research is selected for dissemination by bridging media? And (2) how can we develop better ways of measuring this type of bridging activity? Through a preliminary empirical analysis (of the citation trails from 264 academic papers to bridge journals such as Sloan Management Review), we identify the key characteristics of papers that are picked up in bridging media, and we describe how our methodology could be extended to contribute to Aguinis et al’s (2014) pluralist conceptualization of scholarly impact

There is a literature gap regarding the period representativeness bias associated with sample selection in longitudinal bibliometric studies. The purpose of this paper is to analyse and compare, in terms of period representativeness, the common methods used for selecting a sample of the highly impactful papers in a field/ journal. Using 92 593 papers (Information Science & Library Science area, 1977–2016), we compared, in terms of the number of papers/year, samples of the 100 most impactful papers, obtained with different selection options. We repeated the analysis also for Top500, Top2000, and Top20000. This study shows that the frequently used metrics to compare the impact of papers and to select a sample of "most impactful papers" published in each year and each field may privilege specific periods while neglecting others. The main result of our study is that the percentile citation-based method reduces this "year of publication" representativeness bias. This paper draws attention to the importance of the sample selection, in bibliometric studies, and to the period representativeness bias associated with different choices to select the "most impactful papers".

A Review of: Lisée, Cynthia, Vincent Larivière and Eric Archambault. Conference Proceedings as a Source of Scientific Information: A Bibliometric Analysis.‛ Journal of the American Society for Information Science and Technology 59.11 (2008): 1776-84. Objective – To compare the impact and ageing of conference proceedings with that of scientific literature in general, as reflected in citation characteristics. Design – Citation analysis. Setting – Thomson’s Science Citation Index, Social Science Citation Index, and Arts and Humanities Citation Index (CD-ROM version). Subjects – Conference proceedings citations. Methods – The Thomson citation indexes were searched to identify all citations to conference proceedings in natural sciences and engineering (NSE) and social sciences and humanities (SSH) from 1980 to 2005. Keywords in English, Spanish, Italian and German, truncated terms (such as ‘bienn’), single letters (such as P), and numbers were combined to retrieve all possible citations. Additional filters to exclude citations to publications other than proceedings were applied to the P search results, which had accounted for 75% of the total results. The references remaining in the P search set were validated using Google Scholar and WorldCat. Finally, two random samples of 1,000 references were checked manually to determine the extent of false positives and false negatives in the results. Main Results – The study’s findings are presented for NSE and SSH separately, with 1.7% of NSE citations and 2.5% of SSH citations referring to conference proceedings. The total number of citations to proceedings has increased over the period 1980-2005, however, citations to proceedings in NSE and SSH as a proportion of all citations decreased during this time. A small increase in the average number of proceedings citations per paper was found for NSE and SSH. When this increase is compared to the overall increase in references per paper over this period, the share of proceedings citations per paper has decreased. Of all fields in NSE and SSH, only engineering has increased the proportion of proceedings citations, rising from 7% to 10% in the period studied. In 2005, the share of proceedings citations in NSE (excluding engineering) was below 3%, and for SSH it was below 1.5%. The share of proceedings citations varies across different fields within NSE and SSH. Engineering fields and computer science range from around 5% (general engineering) to 19.6% (computers) in the share of proceedings citations, with only five of the 109 NSE fields having 10% or more as a share of proceedings citations. SSH has only one field (ergonomics, 7.6%) with a share of proceedings citations over 5%. Transport studies has a share of proceedings just under 5%, followed by the field information science & library science with proceedings citations at 3.3%. In relation to the ageing characteristics of proceedings citations overall, the findings show a median age of 4.0 years compared with 6.1 years for citations to literature in general. The difference between the age of NSE cited proceedings and NSE cited literature in general had decreased during the period specified. In 1980, the median age of NSE cited proceedings was 6.3 years compared with 9.3 years for NSE citations to literature in general. In 2005, the median ages were 8.4 years and 10.1 years, respectively. The median age of SSH cited proceedings in 2005 was 10.3 years, compared with 14.2 years for all SSH cited literature. Cited literature in general is older for SSH (14.2 years) than NSE (10.1 years), but the age difference between proceedings cited in the two discipline areas is almost half this. A number of fields in NSE (such as physics, chemistry, and engineering) indicate a greater difference between the age of cited proceedings and literature in general, while for others (such as biology and biomedical research) the ageing characteristics are similar. In SSH, the difference between age of cited proceedings and literature in general is greater. Fine arts and psychology proceedings citations are 43% younger than citations to literature in general; literature cited proceedings are 42% younger, and social sciences 31% younger. Humanities are an exception, with cited proceedings only 11% younger than citations to literature in general. Conclusion – Only 2% of all citations are to conference proceedings in NSE and SSH combined; a proportion that has declined over the 25-year period studied. While there was an increase in the average number of (all) citations per paper during this time, proceedings citations per paper have seen only a very slight increase. These findings are true of all fields studied, with the exception of engineering-related fields which have enjoyed an increase of over 2% in proceedings citations in the period studied. The results also indicate the importance of proceedings in the field of computers. The authors speculate that in these fields, proceedings are regarded as ‚more than just prototypes, but rather as the final products of scientific research.‛ Due to the higher proportion of proceedings citations in engineering and computer science fields, they should be considered for analysis in bibliometric studies. Despite arriving at this conclusion, the authors suggest that computer scientists might consider publishing their papers through channels other than conference proceedings to ‚maximize their scientific impact [original italics].‛ They support this statement by noting that although proceedings citations in computer science represent 20% of total citations, a study of Australian computer science research output (Butler) found proceedings comprise over 60% of all publications in computer science. The authors suggest that the difference between the proportion of proceeding published and the proportion of proceedings cited indicate that their scientific impact does not seem to be all that important.‛ In all fields, proceedings are cited sooner after publication and they cease to be cited earlier than literature in general. These results indicate that proceedings deliver more current information and cutting edge research findings than literature in general. The differences between ageing of proceedings citations and of literature in general lead the authors to conclude that conference proceedings serve different functions and have different life cycles depending on the community they serve.‛

International audience; The use of scholarly publications that have not been formally published in e.g. journals is widespread in some fields. In the past they have been disseminated through various channels of informal communication. However, the Internet has enabled dissemination of these unpublished and often unrefereed publications to a much wider audience. This is particularly interesting seen in relation to the highly disputed open access advantage as the potential advantage for low visibility publications has not been given much attention in the literature. The present study examines the role of working papers in economics during a ten-year period (1996 to 2005). It shows that working papers are increasingly becoming visible in the field specific databases. The impact of working papers is relatively low; however, high impact working paper series have citation rate levels similar to the low impact journals in the field. There is no tendency to an increase in impact during the ten years which is the case for the high impact journals. Consequently, the result of this study does not provide evidence of an open access advantage for working papers in economics.

This study provides a systematic analysis of review studies in selected hospitality and tourism journals published to date. Although a number of review studies published within the hospitality and tourism literature have examined a diverse/wide range of topics, no systematic overview of the trends and impacts of such review studies has been provided. This study, hence, presents a comprehensive classification of 171 review studies published in the leading hospitality and tourism journals listed in the Web of Science, and examines the impacts that the review studies have made in the literature. Based on its constructive overview of the review studies published to date, this study contributes to the hospitality and tourism literature by providing a table of reference for future researchers. Taking a look at the past and how far we have come as a discipline should reveal unexplored research avenues for the future.

A single highly cited article can give a big but temporary lift in its host journal's impact factor evidenced by the striking example of "A short history of SHELX" published in Acta Crystallographica Section A. By using Journal Citation Reports and Web of Science's citation analysis tool, we find a more general and continuous form of this phenomenon in the Particle Physics field. The highly-cited "Review of Particle Physics" series have been published in one of the major Particle Physics journals biennially. This study analyses the effect of these articles on the Impact Factor (IF) of the host journals. The results show that the publication of Review of Particle Physics articles has a direct effect of lifting the IF of its host journal. However the effect on the IF varies according to whether the host journal already has a relatively high or low IF, and the number of articles that it publishes. The impact of these highly cited articles clearly demonstrates the limitations of journal impact factor, and endorses the need to use it more wisely when deciding where to publish and how to evaluate the relative impact of a journal.

The Geological Society of America Bulletin was an early home for quantitative geomorphology research. Although geomorphology papers are not uniformly the highest cited papers in the Bulletin, many show ‘delayed recognition’—they garner only few citations directly after publication, before suddenly being widely and numerously cited (sometimes decades after publication). I focus here on (1) algorithmically detecting cases of delayed recognition in geomorphology literature from the Bulletin and (2) providing insight into why delayed recognition occurred for these papers.

It is inevitable that the ´publish or perish´ paradigm has implications for the quality of research published because this leads to scientific output being evaluated based on quantity and not preferably on quality. The pressure to continually publish results in the creation of predatory journals acting without quality peer review. Moreover the citation records of papers do not reflect their scientific quality but merely increase the impact of their quantity. The growth of sophisticated ´push -button´ technologies allows for easier preparation of publications while facilitating ready-to-publish data. Articles can thus be compiled merely through combining various measurements, usually without thought to their significance and to what purpose they may serve. Moreover any deep-rooted theory which contravenes mainstream assumptions is not welcomed because it challenges often long-established practice. The driving force for the production of an ever growing number of scientific papers is the need for authors to be recognised in order to be seriously considered when seeking financial support. Funding and fame are distributed to scientists according to their publication and citation scores. While the number of publications is clearly a quantitative criterion, much hope has been placed on citation analysis, which promised to serve as an adequate measure of genuine scientific value, i.e. of the quality of the scientific work.

The purposes of this study were to examine the extent to which literature was used to discuss findings in selected papers from Zimbabwe Journal, and to compare the quality of reviews in this journal with other international journals. The study was largely qualitative in nature and used convenient sampling. In the study, the Zimbabwe Journal was chosen because of easy access. Three papers were conveniently selected based on personal interest and areas previously studied. Content analysis was used to compare the quality of discussion of literature in the sampled papers. Findings suggest that authors cite relevant literature extensively in the background to the study but use the same literature sparingly in the discussion of their results. Further, in the discussion of findings, the use of literature was limited to confirming what was already known, and does not show how the new studies reported contribute to knowledge. The study concluded that the journal studied was failing to attract authors who write high quality papers. Perhaps the journal should broaden its brief and target an international audience, because at present as evident in the three cases cited, the journal can only reach out to practitioners within (Southern) Africa. Key words: Literature review, information and communications technology (ICT), mentoring, science, technology, engineering, and mathematics (STEM).

The citation analysis methods, based on knowledge atlas and social network, which have been already applied in the academic research fields are getting more and more widespread. This paper combines the method of content analysis with the one of information visualization, and uses the analysis method based on concurrence matrix knowledge atlas and social network through analyzing the research directions, the academic community relationships, the foreign cooperated relationships, etc. on the academic papers in Zhejiang Open University system in recent 10 years. And then this paper also finds out the problems in the scientific research by multi-dimensional scaling analysis and clustering results. Therefore, the author gives some suggestions on how Zhejiang Open University focuses its scientific research orientation in future.