Network-based statistical comparison of citation topology of bibliographic databases

Applications of Bacteriocins of Lactic Acid Bacteria in Biotechnology and Food Preservation: A Bibliometric Review

Introduction:

Due to the growing prevalence of antibiotic resistance in microorganisms and the demand for safe food, there is increasing interest in using natural bioproducts such as the antimicrobial peptides bacteriocins to extend the shelf-life of foods. This is because of their spectrum of activity, ease of synthesis and applicability. This study reports on the global trends in lactic acid bacteria (LAB) bacteriocins based research publications in the Web of Science core collections within the last 20 years (2000-2019), with specific focus to their applications in biotechnology and food science.

Methods:

Data analysis was undertaken using VOSviewer and HistCite software to evaluate relationships between articles and visualise research linkages amongst authors, institutions and countries.

Results:

In the 20 years under review, a total of 1741 bacteriocin related articles were published, with the most cited publication examining the anti-infective activity of Lactobacillus salivarius. The highest research output was recorded by the United States, followed by Spain and China. However, Europe as a continent had the highest research output with a higher inter-institution collaboration network and stronger food safety legislations.

Discussion:

The bibliometric analysis gave insights into the research areas, cooperation network of authors, co-citation maps and co-occurrence of keywords utilized in the research field and indicates that bacteriocin-based research is highly multidisciplinary with a global reach.

Conclusion:

Key focus is on the control of foodborne disease pathogens, search for new producer organisms and approaches to improve bacteriocin yield and application. This class of antimicrobial peptides has the potential to replace chemical food preservatives in the future.

Academic Publication of Neurodegenerative Diseases From a Bibliographic Perspective: A Comparative Scientometric Analysis

Background: For measuring the impact in clinical and scientific research, the citation count of the articles is used in the bibliometric analysis, although there is no comprehensive summary of neurodegenerative disease research. This study intends to provide the neuroscientists and investigators with a practical reference guide to appraise the most important and influential articles written on this subject through a macroscopic view of the research activities on neurodegenerative diseases.

Materials and Methods: The Clarivate Analytics Web of Science was searched in July 2020. To ensure the breadth of the search scope, the search terms were confirmed as “multiple sclerosis” (MS) or “amyotrophic lateral sclerosis” (ALS) or “Parkinson's” or “Alzheimer's” or “Huntington's” or “neurodegenerative.” After excluding completely unrelated articles, the top-cited articles were collected and evaluated from special characteristics. The data analysis was performed using SPSS 18.0. The articles were characterized by citation number, publication year, topic, study type, authorship, journal, country, and institute of responding author and foundation.

Results: The query identified 593,050 articles. A total of 45% of the top-cited articles were published during 2000–2009, followed by 30 articles from 1990–1999. Diagnosis and pathology were the main research categories (n = 62). Alzheimer's disease (AD) was the main study topic (n = 43). Meanwhile, the United States confirmed the tremendous impact on the field of neurodegenerative diseases. Notably, 69 of 100 articles were studied in the United States, and the National Institutes of Health sponsored 49 articles. There were only 22 articles that can be divided by evidence level. No article was categorized as level 1 evidence. In the journal list with multiple articles, seven of 15 were general journals. The 58 authors, who contributed to more than one article, have been identified by VOSviewer, and the clusters of authors reveal the evolution of research focus in neurodegenerative diseases.

Conclusions: This study analyzed the bibliometric characteristics and connections of 100 top-cited articles in the field of neurodegenerative diseases in the Web of Science. Their main outcomes were as follows: First, the pathology and diagnostic researches took a major role in top-cited articles while the therapy articles are relatively less. Second, the United States confirmed the tremendous impact on the field of neurodegenerative diseases. Third, researchers also submitted their researches to general journals, not just focused on specialty journals.

Application of graph theory in the library domain—Building a faceted framework based on a literature review

Based on a literature review, we present a framework for structuring the application of graph theory in the library domain. Our goal is to provide both researchers and libraries with a standard tool to classify scientific work, at the same time allowing for the identification of previously underrepresented areas where future research might be productive. To achieve this, we compile graph theoretical approaches from the literature to consolidate the components of our framework on a solid basis. The extendable framework consists of multiple facets grouped into five categories whose elements can be arbitrarily combined. Libraries can benefit from these facets by using them as a point of reference for the (meta)data they offer. Further work on formally defining the framework’s categories as well as on integration of other graph-related research areas not discussed in this article (e.g. knowledge graphs) would be desirable and helpful in the future.

The 100 most cited articles in the diagnosis and management of peripheral artery disease

OBJECTIVE

Peripheral artery disease (PAD) is a highly prevalent disease that places major lifestyle limitations and mortality risk on affected individuals. As the understanding of the disease has grown in the medical community, it is unknown which literature has made the greatest impact on the knowledge of PAD. We performed a bibliometric analysis using the number of citations as an indication of impact to analyze the top 100 most influential articles on PAD management.

METHODS

A retrospective search of the Web of Science (Thomson Reuters, New York, NY) database for English-only publications was conducted in November 2020. We identified initial references from the database using the search terms "Peripheral Arterial Disease," "Peripheral Vascular Disease," "Claudication," "Critical Limb Ischemia," "Chronic Limb Threatening Ischemia," "Rest Pain," "Ischemic Ulcer," "Toe Gangrene," "Ankle Brachial Index," and "Leg Ischemia" in Web of Science Core Collections. Articles were ranked based on the number of citations and then analyzed based on citation count and average number of citations per year. Additional metrics included the overall average number of publications per year, the journals, journal discipline, author (including degree and gender), institution, country, topic area, and the level of evidence.

RESULTS

The most popular articles were published between 1959 and 2017, with 46,716 citations in total (average 27.26 citations/y). The most popular article had 2225 citations in total and was Rutherford's "Recommended standards for reports dealing with lower extremity ischemia: Revised version." Peak years of citations were 2016, 2014, and 2018 (2753, 2674, and 2639 citations, respectively). Top journals for the most cited publications were Circulation, Journal of Vascular Surgery, and the Lancet with 21, 13, and 7 articles, respectively. A majority of articles originated from the United States (58 articles), followed by the United Kingdom (15 articles) and Germany (13 articles). Major topic areas of interest and trends in the progressive understanding of PAD were noted. Top areas of focus included surgical interventions (29%), therapeutic angiogenesis (15%), epidemiological studies in PAD (14%), and diagnosis and evaluation (13%). In the top cited literature, 48% (14/29) of surgical articles investigated endovascular interventions for PAD.

CONCLUSIONS

Overall, PAD research has evolved from basic epidemiological studies to advanced management with continued investigation toward future, improved treatments for PAD.

Evaluating the impact of citations of articles based on knowledge flow patterns hidden in the citations

The effective evaluation of the impact of a scholarly article is a significant endeavor; for this reason, it has garnered attention. From the perspective of knowledge flow, this paper extracted various knowledge flow patterns concealed in articles citation counts to describe the citation impact of the articles. First, the intensity characteristic of knowledge flow was investigated to distinguish the different citation vitality of articles. Second, the knowledge diffusion capacity was examined to differentiate the size of the scope of articles’ influences on the academic environment. Finally, the knowledge transfer capacity was discussed to investigate the support degree of articles on the follow-up research. Experimental results show that articles got more citations recently have a higher knowledge flow intensity. The articles have various impacts on the academic environment and have different supporting effects on the follow-up research, representing the differences in their knowledge diffusion and knowledge transfer capabilities. Compared with the single quantitative index of citation frequency, these knowledge flow patterns can carefully explore the citation value of articles. By integrating the three knowledge flow patterns to examine the total citation impact of articles, we found that the articles exhibit distinct value of citation impact even if they were published in the same field, in the same year, and with similar citation frequencies.

Adherence to reporting guidelines increases the number of citations: the argument for including a methodologist in the editorial process and peer-review

BACKGROUND

From 2005 to 2010, we conducted 2 randomized studies on a journal (Medicina Clínica), where we took manuscripts received for publication and randomly assigned them to either the standard editorial process or to additional processes. Both studies were based on the use of methodological reviewers and reporting guidelines (RG). Those interventions slightly improved the items reported on the Manuscript Quality Assessment Instrument (MQAI), which assesses the quality of the research report. However, masked evaluators were able to guess the allocated group in 62% (56/90) of the papers, thus presenting a risk of detection bias. In this post-hoc study, we analyse whether those interventions that were originally designed for improving the completeness of manuscript reporting may have had an effect on the number of citations, which is the measured outcome that we used.

METHODS

Masked to the intervention group, one of us used the Web of Science (WoS) to quantify the number of citations that the participating manuscripts received up December 2016. We calculated the mean citation ratio between intervention arms and then quantified the uncertainty of it by means of the Jackknife method, which avoids assumptions about the distribution shape.

RESULTS

Our study included 191 articles (99 and 92, respectively) from the two previous studies, which all together received 1336 citations. In both studies, the groups subjected to additional processes showed higher averages, standard deviations and annual rates. The intervention effect was similar in both studies, with a combined estimate of a 43% (95% CI: 3 to 98%) increase in the number of citations.

CONCLUSIONS

We interpret that those effects are driven mainly by introducing into the editorial process a senior methodologist to find missing RG items. Those results are promising, but not definitive due to the exploratory nature of the study and some important caveats such as: the limitations of using the number of citations as a measure of scientific impact; and the fact that our study is based on a single journal. We invite journals to perform their own studies to ascertain whether or not scientific repercussion is increased by adhering to reporting guidelines and further involving statisticians in the editorial process.

On entropy research analysis: cross-disciplinary knowledge transfer

Our aim is to illustrate how the thermodynamics-based concept of entropy has spread across different areas of knowledge by analyzing the distribution of papers, citations and the use of words related to entropy in the predefined Scopus categories. To achieve this, we analyze the Scopus papers database related to entropy research during the last 20 years, collecting 750 K research papers which directly contain or mention the word entropy. First, some well-recognized works which introduced novel entropy-related definitions are monitored. Then we compare the hierarchical structure which emerges for the different cases of association, which can be in terms of citations among papers, classification of papers in categories or key words in abstracts and titles. Our study allowed us to evaluate, to some extent, the utility and versatility of concepts such as entropy to permeate in different areas of science. Furthermore, the use of specific terms (key words) in titles and abstracts provided a useful way to account for the interaction between areas in the category research space.

Detecting trends in academic research from a citation network using network representation learning

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

Citing a Data Repository: A Case Study of the Protein Data Bank

The Protein Data Bank (PDB) is the worldwide repository of 3D structures of proteins, nucleic acids and complex assemblies. The PDB’s large corpus of data (> 100,000 structures) and related citations provide a well-organized and extensive test set for developing and understanding data citation and access metrics. In this paper, we present a systematic investigation of how authors cite PDB as a data repository. We describe a novel metric based on information cascade constructed by exploring the citation network to measure influence between competing works and apply that to analyze different data citation practices to PDB. Based on this new metric, we found that the original publication of RCSB PDB in the year 2000 continues to attract most citations though many follow-up updates were published. None of these follow-up publications by members of the wwPDB organization can compete with the original publication in terms of citations and influence. Meanwhile, authors increasingly choose to use URLs of PDB in the text instead of citing PDB papers, leading to disruption of the growth of the literature citations. A comparison of data usage statistics and paper citations shows that PDB Web access is highly correlated with URL mentions in the text. The results reveal the trend of how authors cite a biomedical data repository and may provide useful insight of how to measure the impact of a data repository.

Quantifying the consistency of scientific databases

Science is a social process with far-reaching impact on our modern society. In recent years, for the first time we are able to scientifically study the science itself. This is enabled by massive amounts of data on scientific publications that is increasingly becoming available. The data is contained in several databases such as Web of Science or PubMed, maintained by various public and private entities. Unfortunately, these databases are not always consistent, which considerably hinders this study. Relying on the powerful framework of complex networks, we conduct a systematic analysis of the consistency among six major scientific databases. We found that identifying a single "best" database is far from easy. Nevertheless, our results indicate appreciable differences in mutual consistency of different databases, which we interpret as recipes for future bibliometric studies.