Informetrics at the beginning of the 21st century—A review

International Publication Trends in Proteasome Inhibitors: From Tools for Cell Biologists to Anticancer Agents

Background:

There has been increased interest in the research of proteasome inhibitors for more than two decades. Hotspots in this field are constantly changing.

Objective:

This study aimed to investigate trends in proteasome inhibitors research from 1992 to 2018 and compare the contributions of such research from different countries and authors.

Methods:

We used Excel 2013 and VoSviewer to analyze bibliometric data on the subject of proteasome inhibitors, including the number of publications, citations frequency, H-index, and country contributions and hotspots (keywords of popular scientific fields).

Results:

A total of 3646 articles were included. The USA contributed the largest percentage of articles (1742), with the most citations (90666) and the highest H-index (139). The journal Blood had the most articles. Dana Farber Cancer Institute and Millennium Pharmaceuticals Incorporation were the most contributive institutions. Keywords could be divided into three clusters: Basic experiment, clinical research, and others.

Conclusion:

The number of proteasome inhibitors articles has been increasing for the past 27 years. The USA made the largest contribution in this field. Recent studies on the topic of “carfilzomib” are relatively new and should be closely followed in proteasome inhibitors research.

Intellectual Structure of CORINE Land Cover Research Applications in Web of Science: A Europe-Wide Review

The objective of this paper is to manifest the intellectual and cognitive structure of CORINE Land Cover (CLC) research applications. Data from the Web of Science (WoS) was used to delimit publication on CLC during the period from 1985 until 2019 (29th April), retrieving a total of 873 documents. Through author citations, the origins and the most influential papers were identified. The main lines of research were identified from word co-occurrences extracted from the titles, keywords, and abstracts of the papers. In the view of both structures, it can be concluded that CORINE land cover constitutes a relatively young set of scientific data, with a constant expansion and a strongly interdisciplinary structure. The development of this application is dependent on the knowledge of such research areas as geography, remote sensing, ecology, forestry, agriculture, engineering, optics, and/or computer science. We believe that this information could be very useful for CLC users, as it reflects a large-scale analysis of the research lines of CLC and illuminates how research has changed over time in diverse areas of applications. Moreover, this study is intended to offer a useful tool for the CLC scientific community, showcasing the main research lines and the most noteworthy papers. Finally, the methodology used in this study can be replicated in many other fields of science to explore its intellectual and cognitive structure.

Neural Networks in Big Data and Web Search

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

Landscape of Research Areas for Zeolites and Metal-Organic Frameworks Using Computational Classification Based on Citation Networks

The field of porous materials is widely spreading nowadays, and researchers need to read tremendous numbers of papers to obtain a "bird's eye" view of a given research area. However, it is difficult for researchers to obtain an objective database based on statistical data without any relation to subjective knowledge related to individual research interests. Here, citation network analysis was applied for a comparative analysis of the research areas for zeolites and metal-organic frameworks as examples for porous materials. The statistical and objective data contributed to the analysis of: (1) the computational screening of research areas; (2) classification of research stages to a certain domain; (3) "well-cited" research areas; and (4) research area preferences of specific countries. Moreover, we proposed a methodology to assist researchers to gain potential research ideas by reviewing related research areas, which is based on the detection of unfocused ideas in one area but focused in the other area by a bibliometric approach.

Bibliometrics and altmetrics literature review

Purpose

The purpose of this paper is to present the evolution in notions from bibliometrics to altmetrics and confront them taking into consideration specific criteria. The objective of this paper is to present the evolution of research, regarding the above fields, the study of metrics and indicators used, and the strength and weaknesses resulting from the current literature. Furthermore, the authors present the manipulation techniques for both fields as their main weakness, as well as further key points, analyzing the alternative options of bibliometrics and altmetrics.

Design/methodology/approach

First, the authors present the evolution of the literature, concerning the specific field and metrics used, following with a brief description of basic indicators related to the field of bibliometrics (journal impact factor (JIF), eigenfactor, article influence score and h-index) discussing their advantages and disadvantages. In the second part, the authors describe altmetrics and present the differences with bibliometrics.

Findings

Both bibliometrics and altmetrics remain weak indicators as fraught with disadvantages with manipulation being the greatest of all. Nevertheless, the combination of the two is proposed in order to export safer conclusions on assessing the impact. Regarding the manipulation there is yet not a clean technique to eliminate manipulation. In specific, regarding bibliometrics, the manipulation of indicators refers only to the human factor intervention. The theoretical implication of this study constitutes of collecting the relevant literature regarding scientific indicators.

Research limitations/implications

We must consider the study of new indicators, which combine metrics and methodologies used in both bibliometrics and altmetrics. The theoretical implication of this study constitutes of collecting the relevant literature regarding scientific indicators. Therefore, researchers are encouraged to test the proposed propositions further.

Practical implications

The practical contribution, on the other side, provides scholars with the knowledge of how making their work more accessible, increasing their impact.

Originality/value

The authors add to the originality by providing a framework of the relevant literature for bibliometrics and altmetrics for future researchers. The authors describe altmetrics and present the differences with bibliometrics. The authors conclude the research with the implications of the conducted analysis and the potential directions for future research. Regarding manipulation, the authors provide with the techniques so researchers are aware of the methods in order to protect their academic profile.

Science Mapping: A Systematic Review of the Literature

Purpose

We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes: First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.

Design/methodology/approach

We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain’s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.

Findings

The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages: conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.

Research limitations

The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.

Practical implications

The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.

Originality/value

Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.

Ruling out static latent homophily in citation networks

Citation and coauthor networks offer an insight into the dynamics of scientific progress. We can also view them as representations of a causal structure, a logical process captured in a graph. From a causal perspective, we can ask questions such as whether authors form groups primarily due to their prior shared interest, or if their favourite topics are ‘contagious’ and spread through co-authorship. Such networks have been widely studied by the artificial intelligence community, and recently a connection has been made to nonlocal correlations produced by entangled particles in quantum physics—the impact of latent hidden variables can be analyzed by the same algebraic geometric methodology that relies on a sequence of semidefinite programming (SDP) relaxations. Following this trail, we treat our sample coauthor network as a causal graph and, using SDP relaxations, rule out latent homophily as a manifestation of prior shared interest only, leading to the observed patternedness. By introducing algebraic geometry to citation studies, we add a new tool to existing methods for the analysis of content-related social influences.

Homophily in coauthorship networks of East European sociologists

We study to what degree and how homophily and network properties affect individual citation counts of researchers in the sociology departments of three East European countries, namely Poland, Romania, and Slovenia. We built first-order personal coauthorship networks out of the Web of Science publication records. Each sociologist is assigned as a focal node or ego, while her coauthors are alters. We analyze the data using structural measurements methods, hierarchical regression models, and we make visualizations based on the clustered graph technique. For all three populations, our results indicate that the mean score of the citations of alters substantially predicts the citation counts of egos. In particular, citation similarity increases the chances for coauthorship ties. Evidence for the impact of network properties on the citation levels of egos is mixed. For Poland, normalized ego-betweenness shows a negative effect on citation counts, while network density displays a positive one. For Romania and Slovenia, network characteristics have only a minor impact. Even if the visual summarization of the personal networks uncovers a wide palette of coauthorship patterns, homophily appears to be pervasive. These results are relevant for domestic policy makers who aim to improve the aggregated research performance in East European countries.

Forecasting natural hazards, performance of scientists, ethics, and the need for transparency

Landslides are one of several natural hazards. As other natural hazards, landslides are difficult to predict, and their forecasts are uncertain. The uncertainty depends on the poor understanding of the phenomena that control the slope failures, and on the inherent complexity and chaotic nature of the landslides. This is similar to other natural hazards, including hurricanes, earthquakes, volcanic eruptions, floods, and droughts. Due to the severe impact of landslides on the population, the environment, and the economy, forecasting landslides is of scientific interest and of societal relevance, and scientists attempting to forecast landslides face known and new problems intrinsic to the multifaceted interactions between science, decision-making, and the society. The problems include deciding on the authority and reliability of individual scientists and groups of scientists, and evaluating the performances of individual scientists, research teams, and their institutions. Related problems lay in the increasing subordination of research scientists to politics and decision-makers, and in the conceptual and operational models currently used to organize and pay for research, based on apparently objective criteria and metrics, considering science as any other human endeavor, and favoring science that produces results of direct and immediate application. The paper argues that the consequences of these problems have not been considered fully.

Influential journals in health research: a bibliometric study

Background

There is a wide range of intellectual work written about health research, which has been shaped by the evolution of diseases. This study aims to identify the leading journals over the last 25 years (1990–2014) according to a wide range of bibliometric indicators.

Methods

The study develops a bibliometric overview of all the journals that are currently indexed in Web of Science (WoS) database in any of the four categories connected to health research. The work classifies health research in nine subfields: Public Health, Environmental and Occupational Health, Health Management and Economics, Health Promotion and Health Behavior, Epidemiology, Health Policy and Services, Medicine, Health Informatics, Engineering and Technology, and Primary Care.

Results

The results indicate a wide dispersion between categories being the American Journal of Epidemiology, Environmental Health Perspectives, American Journal of Public Health, and Social Science & Medicine, the journals that have received the highest number of citations over the last 25 years. According to other indicators such as the h-index and the citations per paper, some other journals such as the Annual Review of Public Health and Medical Care, obtain better results which show the wide diversity and profiles of outlets available in the scientific community. The results are grouped and studied according to the nine subfields in order to identify the leading journals in each specific sub discipline of health.

Conclusions

The work identifies the leading journals in health research through a bibliometric approach. The analysis shows a deep overview of the results of health journals. It is worth noting that many journals have entered the WoS database during the last years, in many cases to fill some specific niche that has emerged in the literature, although the most popular ones have been in the database for a long time.

Electronic supplementary material

The online version of this article (doi:10.1186/s12992-016-0186-4) contains supplementary material, which is available to authorized users.

Bradford's law: identification of the core journals for neurosurgery and its subspecialties

OBJECTIVE

Bradford's law describes the scatter of citations for a given subject or field. It can be used to identify the most highly cited journals for a field or subject. The objective of this study was to use currently accepted formulations of Bradford's law to identify core journals of neurosurgery and neurosurgical subspecialties.

METHODS

All original research publications from 2009 to 2013 were analyzed for the top 25 North American academic neurosurgeons from each subspecialty. The top 25 were chosen from a ranked career h-index list identified from previous studies. Egghe's formulation and the verbal formulation of Bradford's law were applied to create specific citation density zones and identify the core journals for each subspecialty. The databases were then combined to identify the core journals for all of academic neurosurgery.

RESULTS

Using Bradford's verbal law with 4 zone models, the authors were able to identify the core journals of neurosurgery and its subspecialties. The journals found in the most highly cited first zone are presented here as the core journals. For neurosurgery as a whole, the core included the following journals: Journal of Neurosurgery, Neurosurgery, Spine, Stroke, Neurology, American Journal of Neuroradiology, International Journal of Radiation Oncology Biology Physics, and New England Journal of Medicine. The core journals for each subspecialty are presented in the manuscript.

CONCLUSIONS

Bradford's law can be used to identify the core journals of neurosurgery and its subspecialties. The core journals vary for each neurosurgical subspecialty, but Journal of Neurosurgery and Neurosurgery are among the core journals for each neurosurgical subspecialty.

The bibliometric analysis of scholarly production: How great is the impact?

Bibliometric methods or "analysis" are now firmly established as scientific specialties and are an integral part of research evaluation methodology especially within the scientific and applied fields. The methods are used increasingly when studying various aspects of science and also in the way institutions and universities are ranked worldwide. A sufficient number of studies have been completed, and with the resulting literature, it is now possible to analyse the bibliometric method by using its own methodology. The bibliometric literature in this study, which was extracted from Web of Science, is divided into two parts using a method comparable to the method of Jonkers et al. (Characteristics of bibliometrics articles in library and information sciences (LIS) and other journals, pp. 449-551, 2012: The publications either lie within the Information and Library Science (ILS) category or within the non-ILS category which includes more applied, "subject" based studies. The impact in the different groupings is judged by means of citation analysis using normalized data and an almost linear increase can be observed from 1994 onwards in the non-ILS category. The implication for the dissemination and use of the bibliometric methods in the different contexts is discussed. A keyword analysis identifies the most popular subjects covered by bibliometric analysis, and multidisciplinary articles are shown to have the highest impact. A noticeable shift is observed in those countries which contribute to the pool of bibliometric analysis, as well as a self-perpetuating effect in giving and taking references.

A descriptive and historical review of bibliometrics with applications to medical sciences

The discipline of bibliometrics involves the application of mathematical and statistical methods to scholarly publications. The first attempts at systematic data collection were provided by Alfred Lotka and Samuel Bradford, who subsequently established the foundational laws of bibliometrics. Eugene Garfield ushered in the modern era of bibliometrics with the routine use of citation analysis and systematized processing. Key elements of bibliometric analysis include database coverage, consistency and accuracy of the data, data fields, search options, and analysis and use of metrics. A number of bibliometric applications are currently being used in medical science and health care. Bibliometric parameters and indexes may be increasingly used by grant funding sources as measures of research success. Universities may build benchmarking standards from bibliometric data to determine academic achievement through promotion and tenure guidelines in the future. This article reviews the history, definition, laws, and elements of bibliometric principles and provides examples of bibliometric applications to the broader health care community. To accomplish this, the Medline (1966-2014) and Web of Science (1945-2014) databases were searched to identify relevant articles; select articles were also cross-referenced. Articles selected were those that provided background, history, descriptive analysis, and application of bibliometric principles and metrics to medical science and health care. No attempt was made to cover all areas exhaustively; rather, key articles were chosen that illustrate bibliometric concepts and enhance the reader's knowledge. It is important that faculty and researchers understand the limitations and appropriate uses of bibliometric data. Bibliometrics has considerable potential as a research area for health care scientists and practitioners that can be used to discover new information about academic trends, pharmacotherapy, disease, and broader health sciences trends.

Hyperlinks as inter-university collaboration indicators

Collaboration is essential for some types of research, and some agencies include collaboration among the requirements for funding research projects. This makes it important to analyse collaborative research ties. Traditional methods to indicate the extent of collaboration between organizations use co-authorship data in citation databases. Publication data from these databases are not publicly available and can be expensive to access and so hyperlink data has been proposed as an alternative. This paper investigates whether using machine learning methods to filter page types can improve the extent to which hyperlink data can be used to indicate the extent of collaboration between universities. Structured information about research projects extracted from UK and EU funding agency websites, co-authored publications and academic links between universities were analysed to identify if there is any association between the number of hyperlinks connecting two universities, with and without machine learning filtering, and the number of publications they co-authored. An increased correlation was found between the number of inlinks to a university’s website and the extent to which it collaborates with other universities when machine learning techniques were used to filter out apparently irrelevant inlinks.