Showing papers in "Scientometrics in 2020"
TL;DR: It is revealed that although both Web of Science and Scopus are increasingly used in academic papers, Scopus as a new-comer is really challenging the dominating role of WoS.
Abstract: Web of Science and Scopus are two world-leading and competing citation databases. By using the Science Citation Index Expanded and Social Sciences Citation Index, this paper conducts a comparative, dynamic, and empirical study focusing on the use of Web of Science (WoS) and Scopus in academic papers published during 2004 and 2018. This brief communication reveals that although both Web of Science and Scopus are increasingly used in academic papers, Scopus as a new-comer is really challenging the dominating role of WoS. Researchers from more and more countries/regions and knowledge domains are involved in the use of these two databases. Even though the main producers of related papers are developed economies, some developing economies such as China, Brazil and Iran also act important roles but with different patterns in the use of these two databases. Both two databases are widely used in meta-analysis related studies especially for researchers in China. Health/medical science related domains and the traditional Information Science and Library Science field stand out in the use of citation databases.
307 citations
TL;DR: The present paper characterise, quantify and measure the response of academia to international public health emergencies in a comparative bibliometric study of multiple outbreaks, and provides a preliminary review of the global research effort regarding the defeat of the COVID-19 pandemic.
Abstract: As of the middle of April 2020, the unprecedented COVID-19 pandemic has claimed more than 137,000 lives (https://coronavirus.jhu.edu/map.html). Because of its extremely fast spreading, the attention of the global scientific community is now focusing on slowing down, containing and finally stopping the spread of this disease. This requires the concerted action of researchers and practitioners of many related fields, raising, as always in such situations the question, of what kind of research has to be conducted, what are the priorities, how has research to be coordinated and who needs to be involved. In other words, what are the characteristics of the response of the global research community on the challenge? In the present paper, we attempt to characterise, quantify and measure the response of academia to international public health emergencies in a comparative bibliometric study of multiple outbreaks. In addition, we provide a preliminary review of the global research effort regarding the defeat of the COVID-19 pandemic. From our analysis of six infectious disease outbreaks since 2000, including COVID-19, we find that academia always responded quickly to public health emergencies with a sharp increase in the number of publications immediately following the declaration of an outbreak by the WHO. In general, countries/regions place emphasis on epidemics in their own region, but Europe and North America are also concerned with outbreaks in other, developed and less developed areas through conducting intensive collaborative research with the core countries/regions of the outbreak, such as in the case of Ebola in Africa. Researches in the fields of virology, infectious diseases and immunology are the most active, and we identified two characteristic patterns in global science distinguishing research in Europe and America that is more focused on public health from that conducted in China and Japan with more emphasis on biomedical research and clinical pharmacy, respectively. Universities contribute slightly less than half to the global research output, and the vast majority of research funding originates from the public sector. Our findings on how academia responds to emergencies could be beneficial to decision-makers in research and health policy in creating and adjusting anti-epidemic/-pandemic strategies.
110 citations
TL;DR: In this paper, a deep learning-based model and well-organized dataset for context-aware paper citation recommendation was proposed, which consists of a document encoder and a context encoder.
Abstract: With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the appropriate place in a sentence will be beneficial. In this perspective, the context-aware citation recommendation has been researched for around two decades. Many researchers have utilized the text data called the context sentence, which surrounds the citation tag, and the metadata of the target paper to find the appropriate cited research. However, the lack of well-organized benchmarking datasets, and no model that can attain high performance has made the research difficult. In this paper, we propose a deep learning-based model and well-organized dataset for context-aware paper citation recommendation. Our model comprises a document encoder and a context encoder. For this, we use graph convolutional networks layer, and bidirectional encoder representations from transformers, a pre-trained model of textual data. By modifying the related PeerRead dataset, we propose a new dataset called FullTextPeerRead containing context sentences to cited references and paper metadata. To the best of our knowledge, this dataset is the first well-organized dataset for a context-aware paper recommendation. The results indicate that the proposed model with the proposed datasets can attain state-of-the-art performance and achieve a more than 28% improvement in mean average precision and recall@k.
84 citations
TL;DR: The scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic and can learn from this on the go and adopt open science principles and a more mindful approach to CO VID-19-related data to accelerate the discovery of more efficient solutions.
Abstract: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.
81 citations
TL;DR: Despite this finding, both author co-citation and co-word analyses revealed the emergence of ‘integrated models of school leadership’ in which instructional leadership is enacted in concert with dimensions drawn from complementary leadership approaches.
Abstract: In the 1980s when research on effective schools surfaced the importance of ‘instructional leadership’ in the United States, skeptics wondered if this would be just another educational fad. Yet, 40 years later, the expectation for school principals to be ‘instructional leaders’ has become ubiquitous throughout much of the world. This systematic review of research used science mapping to gain insights into the growth and geographic distribution of this literature, as well as to identify key documents, authors, and topics. The authors used a variety of quantitative bibliometric analyses to examine 1206 Scopus-indexed journal articles on instructional leadership published between 1940 and 2018. The results affirm that the knowledge base on instructional leadership has not only increased in size, but also geographic scope. Contrary to expectations during the 1980s, instructional leadership has demonstrated remarkable staying power, growing into one of the most powerful models guiding research, policy and practice in school leadership. Despite this finding, both author co-citation and co-word analyses revealed the emergence of ‘integrated models of school leadership’ in which instructional leadership is enacted in concert with dimensions drawn from complementary leadership approaches. Key themes in the recent literature include studies of leadership effects on teachers and students, contexts for leadership practice, and means of developing instructional leaders.
72 citations
TL;DR: The capacity of researchers to generate scientific knowledge about a health crisis emergency, and their global capacity to collaborate among them in a global emergency is explored and the proportion of international collaboration is growing in all countries in 2019–2020, which contrasts with the situation of the last two decades.
Abstract: The COVID-19 pandemic is creating a global health emergency Mapping this health emergency in scientific publications demands multiple approaches to obtain a picture as complete as possible To progress in the knowledge of this pandemic and to control its effects, international collaborations between researchers are essentials, as well as having open and immediate access to scientific publications, what we called "coopetition" Our main objectives are to identify the most productive countries in coronavirus publications, to analyse the international scientific collaboration on this topic, and to study the proportion and typology of open accessibility to these publications We have analyzed 18,875 articles indexed in Web of Science We performed the descriptive statistical analysis in order to explore the performance of the more prolific countries and organizations, as well as paying attention to the last 2 years Registers have been analyzed separately via the VOSviewer software, drawing a network of links among countries and organizations to identify the starred countries and organizations, and the strongest links of the net We have explored the capacity of researchers to generate scientific knowledge about a health crisis emergency, and their global capacity to collaborate among them in a global emergency We consider that science is moving rapidly to find solutions to international health problems but access to this knowledge by society is not so quick due to several limitations (open access policies, corporate interests, etc) We have observed that papers from China in the last 3 months (from January 2020 to March 2020) have a strong impact compared with papers published in years before The United States and China are the major producers of documents of our sample, followed by all European countries, especially the United Kingdom, Germany, the Netherlands, and France At the same time, the leading role of Saudi Arabia, Canada or South Korea should be noted, with a significant number of documents submitted but very different dynamics of international collaboration The proportion of international collaboration is growing in all countries in 2019-2020, which contrasts with the situation of the last two decades The organizations providing the most documents to the sample are mostly Chinese The percentage of open access articles on coronavirus for the period 2001-2020 is 592% but if we focus in 2020 the figures increase up to 914%, due to the commitment of commercial publishers with the emergency
67 citations
TL;DR: Based on a large-scale academic survey, some new predictors of international research collaboration were identified by multivariate analyses and have global policy implications for resource-poor science systems “playing catch-up” in terms of academic careers, productivity patterns, and research internationalization policies.
Abstract: The principal distinction drawn in this study is between research “internationalists” and “locals.” The former are scientists involved in international research collaboration while the latter group are not. These two distinct types of scientist compete for academic prestige, research funding, and international recognition. International research collaboration proves to be a powerful stratifying force. As a clearly defined subgroup, internationalists are a different academic species, accounting for 51.4% of Polish scientists; predominantly male and older, they have longer academic experience and higher academic degrees and occupy higher academic positions. Across all academic clusters, internationalists consistently produce more than 90% of internationally co-authored publications, representing 2320% of locals’ productivity for peer-reviewed articles and 1600% for peer-reviewed article equivalents. Internationalists tend to spend less time than locals on teaching-related activities, more time on research, and more time on administrative duties. Based on a large-scale academic survey (N = 3704), some new predictors of international research collaboration were identified by multivariate analyses. The findings have global policy implications for resource-poor science systems “playing catch-up” in terms of academic careers, productivity patterns, and research internationalization policies.
64 citations
TL;DR: The main aim of this article is to analyze the structure and endogenous processes of experimental physics to explain and generalize the properties of the evolution of applied sciences in the phase of continuous expansion of the universe of science.
Abstract: How do scientific disciplines evolve? This is one of the fundamental problems of the dynamics of science. This study confronts this problem here by investigating the evolution of experimental physics, which plays a vital role for the progress of science in society. In particular, the main aim of this article is to analyze the structure and endogenous processes of experimental physics to explain and generalize, whenever possible, the properties of the evolution of applied sciences in the phase of continuous expansion of the universe of science. Empirical analysis here suggests the following properties of the dynamics of science: (a) scientific fission, the evolution of scientific disciplines generates a process of division into two or more research fields that evolve as autonomous entities, creating new disciplines of scientific specialization; (b) ambidextrous drivers of science, the evolution of scientific disciplines by scientific fission is due to scientific discoveries or new technologies; (c) higher growth rates of the scientific production are in new research fields of a scientific discipline rather than old ones; (d) average duration of the growth phase of scientific production in research fields is about 80 years, almost the period of one generation of scholars. Overall, then, this study explains, whenever possible, the relationships that support scientific change of disciplines to develop comprehensive properties of the evolution of science directed to economic, technological and social progress.
63 citations
TL;DR: This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies.
Abstract: Deep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies. First, a sample data set was constructed using Gartner’s hype cycle and multiple patent features. Second, a generative adversarial network was used to generate many synthetic samples (data augmentation) to expand the scale of the sample data set. Finally, a deep neural network classifier was trained with the augmented data set to forecast emerging technologies, and it could predict up to 77% of the emerging technologies in a given year with high precision. This approach was used to forecast emerging technologies in Gartner’s hype cycles for 2017 based on patent data from 2000 to 2016. Four out of six of the emerging technologies were forecasted correctly, showing the accuracy and precision of the proposed approach. This approach enables deep learning to forecast emerging technologies with limited training samples.
62 citations
TL;DR: Through this letter, the authors aimed to show an evaluation of the retracted COVID-19 articles, to show the accuracy and quality of published articles in this pandemic era.
Abstract: In early 2020, coronavirus disease 2019 (COVID-19) caused a pandemic, affecting the entire globe. Initially, the disease was relatively unknown. However, as COVID-19 became more widespread, our knowledge regarding the virus and its resultant disease increased. The healthcare system and policymakers in different countries relied on articles published on the subject, from epidemiological features to useful drugs and at-risk populations, for their decisions. Thousands of research articles have since been published on COVID-19 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers were and are keen to publish their COVID-19-related research as quickly as possible in this hot race to publication. In this atmosphere, the accuracy and quality of published articles might cause some concerns. Although pre-publication peer-review systems are undeniably robust and traditionally employed in scientific publications, post-publication review is flourishing during this pandemic era. Researchers and scholars have critically scrutinized the COVID19-related scientific output of medical journals, leading to several corrections and retractions. Moreover, some authors later noticed some inconsistencies in their published work, which forced them to withdraw their articles. Through this letter, the authors aimed to show an evaluation of the retracted COVID-19 articles. The database of retraction watch was searched on June 18, 2020, using “COVID-19”, “coronavirus disease 2019”, “coronavirus 2019”, “SARS-COV-2”, and “2019-nCov” as keywords. Additionally, the special webpage in retraction watch was analyzed for retracted or withdrawn articles (Marcus 2020). The search retrieved a total of 26 articles from the retraction watch database. Of these articles, two were temporarily retracted (Cai et al. 2020; Yin 2020), two received an expression of concern (Gautret et al. 2020; Wang et al. 2020a, b), and two have been currently corrected (Sriwijitalai and Wiwanitkit 2020; Tang et al. 2020). One was a withdrawn preprint as it was submitted to the preprint server without the full consent of all the authors. This preprint is now published in the Cell Host and Microbes journal (Chen et al. 2020). Moreover, a reliable access was not obtained to one
58 citations
TL;DR: Scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major coronavirus diseases, i.e., Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratories Syndrome (MERS), and interesting recurring patterns across all three literatures were discovered.
Abstract: During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on its respective topic. The spike in research publications following the 2019 Novel Coronavirus (Covid-19) outbreak, however, has been like no other. The global crisis caused by the Covid-19 pandemic has mobilised scientific efforts at an unprecedented scale. In less than 5 months, more than 12,000 research items and in less than seven months, more than 30,000 items were indexed, while it is projected that the number could exceed 80,000 by the end of 2020, should the current trend continues. With the health crisis affecting all aspects of life, research on Covid-19 seems to have become a focal point of interest across many academic disciplines. Here, scientometric aspects of the Covid-19 literature are analysed and contrasted with those of the two previous major coronavirus diseases, i.e., Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). The focus is on the co-occurrence of key-terms, bibliographic coupling and citation relations of journals and collaborations between countries. Interesting recurring patterns across all three literatures were discovered. All three outbreaks have commonly generated three distinct cohorts of studies: (i) studies linked to public health response and epidemic control, (ii) studies on chemical constitution of the virus; and (iii) studies related to treatment, vaccine and clinical care. While studies affiliated with category (i) seem to have been relatively earliest to emerge, they have overall received relatively smaller number of citations compared to publications the two other categories. Covid-19 studies seem to have been disseminated across a broader variety of journals and across a more diverse range of subject areas. Clear links are observed between the geographical origins of each outbreak as well as the local geographical severity of each outbreak and the magnitude of research originated from regions. Covid-19 studies also display the involvement of authors from a broader variety of countries compared to SARS and MERS. Considering the speed at which the Covid-19-related literature is accumulating, an interesting dimension that warrants further exploration could be to assess if the quality and rigour of these publications have been affected.
TL;DR: This paper discovers several major research areas of blockchain, including internet of things (IoT), healthcare, energy industry, voting, insurance and supply chain management, and analyzes the research hotspots, as well as the development trajectories of blockchain in the areas of IoT, healthcare and supply network management by using the key-route main path analysis.
Abstract: Blockchain technology, as a disruptive technology, has received widespread attention in the past few years from all over the world, leading to rapid growth in research outputs. This paper adopts a quantitative method, the main path analysis, to comprehensively and systematically investigate the development trajectories of blockchain. Four different main paths, the global main path, the forward local main path, the backward local main path and the key-route main path are conducted simultaneously. By analyzing these various paths, on the one hand, this paper finds that papers on paths focus on two aspects, cryptocurrencies and blockchain-based applications. On the other hand, this paper discovers several major research areas of blockchain, including internet of things (IoT), healthcare, energy industry, voting, insurance and supply chain management. At the same time, this paper further analyzes the research hotspots, as well as the development trajectories of blockchain in the areas of IoT, healthcare and supply chain management by using the key-route main path analysis. This paper is conductive for both the new and experienced researchers to identify some influential papers and grasp the knowledge diffusion paths in these domains.
TL;DR: The results show significant disciplinary and platform specific differences in the OA advantage, with articles in OA journals within for instance veterinary sciences, social and economic geography and psychology receiving more citations and attention on social media platforms, while the opposite was found for articles within medicine and health sciences.
Abstract: Scientific articles available in Open Access (OA) have been found to attract more citations and online attention to the extent that it has become common to speak about OA Altmetrics Advantage. This research investigates how the OA Altmetrics Advantage holds for a specific case of research articles, namely the research outputs from universities in Finland. Furthermore, this research examines disciplinary and platform specific differences in that (dis)advantage. The new methodological approaches developed in this research focus on relative visibility, i.e. how often articles in OA journals receive at least one mention on the investigated online platforms, and relative receptivity, i.e. how frequently articles in OA journals gain mentions in comparison to articles in subscription-based journals. The results show significant disciplinary and platform specific differences in the OA advantage, with articles in OA journals within for instance veterinary sciences, social and economic geography and psychology receiving more citations and attention on social media platforms, while the opposite was found for articles in OA journals within medicine and health sciences. The results strongly support field- and platform-specific considerations when assessing the influence of journal OA status on altmetrics. The new methodological approaches used in this research will serve future comparative research into OA advantage of scientific articles over time and between countries.
TL;DR: This study provides the first comprehensive investigation of OPR adoption, its early adopters and the implementation approaches used, and suggests publishers of optional OPR journals should add metric data in their annual status reports.
Abstract: Open peer review (OPR), where review reports and reviewers’ identities are published alongside the articles, represents one of the last aspects of the open science movement to be widely embraced, although its adoption has been growing since the turn of the century. This study provides the first comprehensive investigation of OPR adoption, its early adopters and the implementation approaches used. Current bibliographic databases do not systematically index OPR journals, nor do the OPR journals clearly state their policies on open identities and open reports. Using various methods, we identified 617 OPR journals that published at least one article with open identities or open reports as of 2019 and analyzed their wide-ranging implementations to derive emerging OPR practices. The findings suggest that: (1) there has been a steady growth in OPR adoption since 2001, when 38 journals initially adopted OPR, with more rapid growth since 2017; (2) OPR adoption is most prevalent in medical and scientific disciplines (79.9%); (3) five publishers are responsible for 81% of the identified OPR journals; (4) early adopter publishers have implemented OPR in different ways, resulting in different levels of transparency. Across the variations in OPR implementations, two important factors define the degree of transparency: open identities and open reports. Open identities may include reviewer names and affiliation as well as credentials; open reports may include timestamped review histories consisting of referee reports and author rebuttals or a letter from the editor integrating reviewers’ comments. When and where open reports can be accessed are also important factors indicating the OPR transparency level. Publishers of optional OPR journals should add metric data in their annual status reports.
TL;DR: This paper considers self-referencing and self-citing, describes the typical shape of self-Citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantifies the variance in the distribution ofSelf-citation rates within and between all 21 Essential Science Indicators’ fields.
Abstract: Citations can be an indicator of publication significance, utility, attention, visibility or short-term impact but analysts need to confirm whether a high citation count for an individual is a genuine reflection of influence or a consequence of extraordinary, even excessive, self-citation. It has recently been suggested there may be increasing misrepresentation of research performance by individuals who self-cite inordinately to achieve scores and win rewards. In this paper we consider self-referencing and self-citing, describe the typical shape of self-citation patterns for carefully curated publication sets authored by 3517 Highly Cited Researchers and quantify the variance in the distribution of self-citation rates within and between all 21 Essential Science Indicators’ fields. We describe both a generic level of median self-referencing rates, common to most fields, and a graphical, distribution-driven assessment of excessive self-citation that demarcates a threshold not dependent on statistical tests or percentiles (since for some fields all values are within a central ‘normal’ range). We describe this graphical procedure for identifying exceptional self-citation rates but emphasize the necessity for expert interpretation of the citation profiles of specific individuals, particularly in fields with atypical self-citation patterns.
TL;DR: Investigation of the data accumulation velocity of 12 Altmetric.com data sources finds that most altmetric data sources show higher velocity values in the fields of Physical Sciences and Engineering and Life and Earth Sciences, and there also exist some research topics that attract social attention faster than others.
Abstract: This paper investigates the data accumulation velocity of 12 Altmetric.com data sources. DOI created date recorded by Crossref and altmetric event posted date tracked by Altmetric.com are combined to reflect the altmetric data accumulation patterns over time and to compare the data accumulation velocity of various data sources through three proposed indicators, including Velocity Index, altmetric half-life, and altmetric time delay. Results show that altmetric data sources exhibit different data accumulation velocity. Some altmetric data sources have data accumulated very fast within the first few days after publication, such as Reddit, Twitter, News, Facebook, Google+, and Blogs. On the opposite spectrum, research outputs are at relatively slow pace in accruing data on some data sources, like Policy documents, Peer review, Q&A, Wikipedia, Video, and F1000Prime. Most altmetric data sources’ velocity degree also changes by document types, subject fields, and research topics. The type Review is slower in receiving altmetric mentions than Article, while Editorial Material and Letter are typically faster. In general, most altmetric data sources show higher velocity values in the fields of Physical Sciences and Engineering and Life and Earth Sciences. Within each field, there also exist some research topics that attract social attention faster than others.
TL;DR: Compared to the H1N1 pandemic, the majority of early publications on COVID-19 does not provide new information, possibly diluting the original data published on this disease and consequently slowing down the development of a valid knowledge base onThis disease.
Abstract: The COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles. The aim of this study is to assess the type of articles published during the first 3 months of the COVID-19 pandemic and to compare them with articles published during 2009 H1N1 swine influenza pandemic. Two operators independently extracted and assessed all articles on COVID-19 and on H1N1 swine influenza that had an abstract and were indexed in PubMed during the first 3 months of these pandemics. Of the 2482 articles retrieved on COVID-19, 1165 were included. Over half of them were secondary articles (590, 50.6%). Common primary articles were: human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations. In a similar timeframe, there were 223 articles published on the H1N1 pandemic in 2009. During the COVID-19 pandemic there was a higher prevalence of reviews and guidance articles and a lower prevalence of in vitro and animal research studies compared with the H1N1 pandemic. In conclusions, compared to the H1N1 pandemic, the majority of early publications on COVID-19 does not provide new information, possibly diluting the original data published on this disease and consequently slowing down the development of a valid knowledge base on this disease. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings. Researchers, peer reviewers, and editors should take action to flatten the curve of secondary articles.
TL;DR: This paper uses network analysis, citation context analysis, and retraction status visibility analysis to illustrate the potential for extended propagation of misinformation over a citation network, updating and extending a case study of the first 6 years of post-retraction citation.
Abstract: This paper presents a case study of long-term post-retraction citation to falsified clinical trial data (Matsuyama et al. in Chest 128(6):3817–3827, 2005. https://doi.org/10.1378/chest.128.6.3817
), demonstrating problems with how the current digital library environment communicates retraction status. Eleven years after its retraction, the paper continues to be cited positively and uncritically to support a medical nutrition intervention, without mention of its 2008 retraction for falsifying data. To date no high quality clinical trials reporting on the efficacy of omega-3 fatty acids on reducing inflammatory markers have been published. Our paper uses network analysis, citation context analysis, and retraction status visibility analysis to illustrate the potential for extended propagation of misinformation over a citation network, updating and extending a case study of the first 6 years of post-retraction citation (Fulton et al. in Publications 3(1):7–26, 2015. https://doi.org/10.3390/publications3010017
). The current study covers 148 direct citations from 2006 through 2019 and their 2542 second-generation citations and assesses retraction status visibility of the case study paper and its retraction notice on 12 digital platforms as of 2020. The retraction is not mentioned in 96% (107/112) of direct post-retraction citations for which we were able to conduct citation context analysis. Over 41% (44/107) of direct post-retraction citations that do not mention the retraction describe the case study paper in detail, giving a risk of diffusing misinformation from the case paper. We analyze 152 second-generation citations to the most recent 35 direct citations (2010–2019) that do not mention the retraction but do mention methods or results of the case paper, finding 23 possible diffusions of misinformation from these non-direct citations to the case paper. Link resolving errors from databases show a significant challenge in a reader reaching the retraction notice via a database search. Only 1/8 databases (and 1/9 database records) consistently resolved the retraction notice to its full-text correctly in our tests. Although limited to evaluation of a single case (N = 1), this work demonstrates how retracted research can continue to spread and how the current information environment contributes to this problem.
TL;DR: This new attempt crystallizes out key findings and valuable information of PPPs research, which can consolidate and broaden the bibliometric findings of previous P PPs literature studies and act as a guidance for analyzing the knowledge base of other research fields.
Abstract: Studies around public–private partnerships (PPPs) have shaped a complex and colorful research field. A series of review studies have been performed to explore the knowledge base of this field. Despite their significant contributions, bibliometric research on the PPPs literature is still needed to capture more comprehensive, diverse and detailed information in this area from a holistic perspective, for reducing subjectivity and one-sidedness. Under this situation, this paper continues the bibliometric journey by conducting a comprehensive metrological and content analysis of the PPPs research field. By applying a newly developed Bibliometrix R-package tool, the overview of PPPs research is presented via metrological analysis using a series of indexes. The intellectual structure of this domain is then explored via content analysis using the methods of keywords analysis and citation analysis from both static and dynamic perspectives. Consequently, the panoramic view including the overview, pivotal points of topics, thematic evolution and research focuses of this domain are depicted visually and intuitively via a set of science maps. This new attempt crystallizes out key findings and valuable information of PPPs research, which can consolidate and broaden the bibliometric findings of previous PPPs literature studies and act as a guidance for analyzing the knowledge base of other research fields.
TL;DR: This review is conducted to identify the information and methods used for recommendations recently, and introduces definitions of the task, recommending factors along with the corresponding problems and some application platforms.
Abstract: Citation recommendation systems play an important role to alleviate the dilemma that scholar users spend a lot of time and experiences for literature survey. With the burgeoning computational models and open data movement, scientific repository can provide more evidence in support of recommendation. On the one hand, recommenders are applying better algorithms to understand the text of user queries and candidate citations. On the other hand, more types of data such as citation network and co-author relationship are aggregated to enrich the citation contextual information. The available data used for recommendation has been extended from textual content to enriched context. This review is conducted to identify the information and methods used for recommendations recently. We begin by introducing definitions of the task, recommending factors along with the corresponding problems and some application platforms. Then, we classify existing recommenders according to user query types and review representative methods for each type. We also elaborate on different strategies applied in three main stages of citation recommendation. Finally, a few open issues for future investigations are proposed.
TL;DR: It is argued that FA text (FT) alone no longer seems an appropriate field to retrieve and analyze funding information, since a substantial number of documents only report funding agency or grant number information in respective fields.
Abstract: Despite the limitations of funding acknowledgment (FA) data in Web of Science (WoS), studies using FA information have increased rapidly over the last several years. Considering this WoS’ recent practice of updating funding data, this paper further investigates the characteristics and distribution of FA data in four WoS journal citation indexes. The research reveals that FA information coverage variances persist cross all four citation indexes by time coverage, language and document type. Our evidence suggests an improvement in FA information collection in humanity and social science research. Departing from previous studies, we argue that FA text (FT) alone no longer seems an appropriate field to retrieve and analyze funding information, since a substantial number of documents only report funding agency or grant number information in respective fields. Articles written in Chinese have a higher FA presence rate than other non-English WoS publications. This updated study concludes with a discussion of new findings and practical guidance for the future retrieval and analysis of funded research.
TL;DR: This work proposes an approach that relies on an external source of information for selecting and validating clusters of publications identified through an unsupervised author name disambiguation method and shows encouraging results.
Abstract: The disambiguation of author names is an important and challenging task in bibliometrics. We propose an approach that relies on an external source of information for selecting and validating clusters of publications identified through an unsupervised author name disambiguation method. The application of the proposed approach to a random sample of Italian scholars shows encouraging results, with an overall precision, recall, and F-measure of over 96%. The proposed approach can serve as a starting point for large-scale census of publication portfolios for bibliometric analyses at the level of individual researchers.
TL;DR: Results indicate that library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005 and bibliometrics, especially citation analysis, is highly stable across periods.
Abstract: This study investigated the evolution of library and information science (LIS) by analyzing research topics in LIS journal articles. The analysis is divided into five periods covering the years 1996–2019. Latent Dirichlet allocation modeling was used to identify underlying topics based on 14,035 documents. An improved data-selection method was devised in order to generate a dynamic journal list that included influential journals for each period. Results indicate that (a) library science has become less prevalent over time, as there are no top topic clusters relevant to library issues since the period 2000–2005; (b) bibliometrics, especially citation analysis, is highly stable across periods, as reflected by the stable subclusters and consistent keywords; and (c) information retrieval has consistently been the dominant domain with interests gradually shifting to model-based text processing. Information seeking and behavior is also a stable field that tends to be dispersed among various topics rather than presented as its own subject. Information systems and organizational activities have been continuously discussed and have developed a closer relationship with e-commerce. Topics that occurred only once have undergone a change of technological context from the networks and Internet to social media and mobile applications.
TL;DR: A method for extracting embedded feature vectors by applying a neural embedding approach for text features in patent documents and automatically clustering the embedding features by utilizing a deep embedding clustering method is proposed.
Abstract: The analysis of scientific and technical documents is crucial in the process of establishing science and technology strategies. One popular method for such analysis is for field experts to manually classify each scientific or technical document into one of several predefined technical categories. However, not only is manual classification error-prone and expensive, but it also requires extended efforts to handle frequent data updates. In contrast, machine learning and text mining techniques enable cheaper and faster operations, and can alleviate the burden on human resources. In this paper, we propose a method for extracting embedded feature vectors by applying a neural embedding approach for text features in patent documents and automatically clustering the embedding features by utilizing a deep embedding clustering method.
TL;DR: Experimental results show that the proposed novel patent information extraction framework out-performs the traditional one in terms of automation and accuracy, and is capable of extracting fine-grained structured information from patent texts.
Abstract: The text-based patent analysis is grounded in information extraction technique. However, such technique suffers from obvious defects such as low degree of automation and unsatisfactory extraction accuracy. To deal with these problems, after an information schema is pre-defined, which contains 17 types of entities and 15 types of semantic relations, a dataset of 1010 patent abstracts is annotated and opened freely to the research community. Then, a novel patent information extraction framework is proposed, in which two deep-learning models, BiLSTM-CRF and BiGRU-HAN, are respectively used for entity identification and semantic relation extraction. Finally, to demonstrate the advantages of the new framework, extensive experiments are conducted, and the SAO method and PCNNs model are taken as respective baselines on the framework and module levels. Experimental results show that our framework out-performs the traditional one in terms of automation and accuracy, and is capable of extracting fine-grained structured information from patent texts.
TL;DR: Research performance during the doctoral education has a positive effect on attaining excellence in the early career and there is an interaction between publication volume and excellence during doctoral education suggesting that a combination of quantity and quality in doctoral students’ performance is indicative of future excellence.
Abstract: Publishing in peer-reviewed journals as a part of the doctoral education is common practice in many countries. The publication output of doctoral students is increasingly used in selection processe ...
TL;DR: A new measure of originality for individual scientific papers building on the network betweenness centrality concept is operationalised, which finds that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality.
Abstract: Originality has self-evident importance for science, but objectively measuring originality poses a formidable challenge. We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. Accordingly, we operationalise a new measure of originality for individual scientific papers building on the network betweenness centrality concept. Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality. We also find that originality can be reliably measured with only a small number of subsequent citing papers, which lowers computational cost and contributes to practical utility. The measure also predicts future citations, further confirming its validity. We further characterise the measure to guide its future use.
TL;DR: The study shows that contradicting citations are very uncommon and that retracted or corrected articles are not more contradicted in scholarly articles than those that are neither retracted nor corrected but they do generate more comments on Pubpeer, presumably because of the possibility for contributors to remain anonymous.
Abstract: This study investigates whether negative citations in articles and comments posted on post-publication peer review platforms are both equally contributing to the correction of science. These 2 types of written evidence of disputes are compared by analyzing their occurrence in relation to articles that have already been retracted or corrected. We identified retracted or corrected articles in a corpus of 72,069 articles coming from the Engineering field, from 3 journals (Science, Tumor Biology, Cancer Research) and from 3 authors with many retractions to their credit (Sarkar, Schon, Voinnet). We used Scite to retrieve contradicting citations and PubPeer to retrieve the number of comments for each article, and then we considered them as traces left by scientists to contest published results. Our study shows that contradicting citations are very uncommon and that retracted or corrected articles are not more contradicted in scholarly articles than those that are neither retracted nor corrected but they do generate more comments on Pubpeer, presumably because of the possibility for contributors to remain anonymous. Moreover, post-publication peer review platforms, although external to the scientific publication process contribute more to the correction of science than negative citations. Consequently, post-publication peer review venues, and more specifically the comments found on it, although not contributing to the scientific literature, are a mechanism for correcting science. Lastly, we introduced the idea of strengthening the role of contradicting citations to rehabilitate the clear expression of judgment in scientific papers.
TL;DR: It is found that samples of 1000 papers provide a good guide to relative (but not absolute) institutional citation performance, which is driven by the abundance of high performing individuals, but such samples may be perturbed by scarce ‘highly cited’ papers in smaller or less research-intensive units.
Abstract: While bibliometric analysis is normally able to rely on complete publication sets this is not universally the case. For example, Australia (in ERA) and the UK (in the RAE/REF) use institutional research assessment that may rely on small or fractional parts of researcher output. Using the Category Normalised Citation Impact (CNCI) for the publications of ten universities with similar output (21,000–28,000 articles and reviews) indexed in the Web of Science for 2014–2018, we explore the extent to which a ‘sample’ of institutional data can accurately represent the averages and/or the correct relative status of the population CNCIs. Starting with full institutional data, we find a high variance in average CNCI across 10,000 institutional samples of fewer than 200 papers, which we suggest may be an analytical minimum although smaller samples may be acceptable for qualitative review. When considering the ‘top’ CNCI paper in researcher sets represented by DAIS-ID clusters, we find that samples of 1000 papers provide a good guide to relative (but not absolute) institutional citation performance, which is driven by the abundance of high performing individuals. However, such samples may be perturbed by scarce ‘highly cited’ papers in smaller or less research-intensive units. We draw attention to the significance of this for assessment processes and the further evidence that university rankings are innately unstable and generally unreliable.
TL;DR: Five major clusters of corporate university emerged: (1) corporate university as a source of competitive advantage, (2) corporateUniversity as a layered concept, (3) reimagining corporate university through technology, (4) corporate universities: paradigms and models and (5)porate university: performance metrics.
Abstract: Corporate university is a unique form of educational arrangement to accomplish an organization’s goals by building the intellectual capital of its employees. In the academia, it is a relatively new concept that gained foothold during the 1990s. While it is a fertile area of research with several works unravelling its diverse aspects, only a few studies have delved into building the knowledge blocks of the concept of corporate university. Therefore, this study aims at investigating the major clusters of corporate universities obtained from the extant literature. In this study, 207 pertinent articles were retrieved from Scopus, an online electronic database. Post this, co-citation and citation analysis was performed on the 207 articles and a co-citation matrix was formulated to enlist the clusters of corporate universities. Subsequently, multi-dimensional scaling and cluster analysis techniques were used to extract the essential clusters of corporate university. Finally, five major clusters emerged: (1) corporate university as a source of competitive advantage, (2) corporate university as a layered concept, (3) reimagining corporate university through technology, (4) corporate universities: paradigms and models and (5) corporate university: performance metrics. Results of this study can act as leading insights for future studies and practitioners.