Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany

Background

Research on Neglected Tropical Diseases (NTDs) has increased in recent decades, and significant need-gaps in diagnostic and treatment tools remain. Analysing bibliometric data from published research is a powerful method for revealing research efforts, partnerships and expertise. We aim to identify and map NTD research networks in Germany and their partners abroad to enable an informed and transparent evaluation of German contributions to NTD research.

Methodology/Principal Findings

A SCOPUS database search for articles with German author affiliations that were published between 2002 and 2012 was conducted for kinetoplastid and helminth diseases. Open-access tools were used for data cleaning and scientometrics (OpenRefine), geocoding (OpenStreetMaps) and to create (Table2Net), visualise and analyse co-authorship networks (Gephi). From 26,833 publications from around the world that addressed 11 diseases, we identified 1,187 (4.4%) with at least one German author affiliation, and we processed 972 publications for the five most published-about diseases. Of those, we extracted 4,007 individual authors and 863 research institutions to construct co-author networks. The majority of co-authors outside Germany were from high-income countries and Brazil. Collaborations with partners on the African continent remain scattered. NTD research within Germany was distributed among 220 research institutions. We identified strong performers on an individual level by using classic parameters (number of publications, h-index) and social network analysis parameters (betweenness centrality). The research network characteristics varied strongly between diseases.

Conclusions/Significance

The share of NTD publications with German affiliations is approximately half of its share in other fields of medical research. This finding underlines the need to identify barriers and expand Germany’s otherwise strong research activities towards NTDs. A geospatial analysis of research collaborations with partners abroad can support decisions to strengthen research capacity, particularly in low- and middle-income countries, which were less involved in collaborations than high-income countries. Identifying knowledge hubs within individual researcher networks complements traditional scientometric indicators that are used to identify opportunities for collaboration. Using free tools to analyse research processes and output could facilitate data-driven health policies. Our findings contribute to the prioritisation of efforts in German NTD research at a time of impending local and global policy decisions.     

Does Collocation Inform the Impact of Collaboration?

Background

It has been shown that large interdisciplinary teams working across geography are more likely to be impactful. We asked whether the physical proximity of collaborators remained a strong predictor of the scientific impact of their research as measured by citations of the resulting publications.

Methodology/Principal Findings

Articles published by Harvard investigators from 1993 to 2003 with at least two authors were identified in the domain of biomedical science. Each collaboration was geocoded to the precise three-dimensional location of its authors. Physical distances between any two coauthors were calculated and associated with corresponding citations. Relationship between distance of coauthors and citations for four author relationships (first-last, first-middle, last-middle, and middle-middle) were investigated at different spatial scales. At all sizes of collaborations (from two authors to dozens of authors), geographical proximity between first and last author is highly informative of impact at the microscale (i.e. within building) and beyond. The mean citation for first-last author relationship decreased as the distance between them increased in less than one km range as well as in the three categorized ranges (in the same building, same city, or different city). Such a trend was not seen in other three author relationships.

Conclusions/Significance

Despite the positive impact of emerging communication technologies on scientific research, our results provide striking evidence for the role of physical proximity as a predictor of the impact of collaborations.     

Predicting Co-Author Relationship in Medical Co-Authorship Networks

Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard''s Coefficient, Preferential Attachment, Katz_β, and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups.     

Canada's Neglected Tropical Disease Research Network: Who's in the Core—Who's on the Periphery?

Background

This study designed and applied accessible yet systematic methods to generate baseline information about the patterns and structure of Canada''s neglected tropical disease (NTD) research network; a network that, until recently, was formed and functioned on the periphery of strategic Canadian research funding.

Methodology

Multiple methods were used to conduct this study, including: (1) a systematic bibliometric procedure to capture archival NTD publications and co-authorship data; (2) a country-level “core-periphery” network analysis to measure and map the structure of Canada''s NTD co-authorship network including its size, density, cliques, and centralization; and (3) a statistical analysis to test the correlation between the position of countries in Canada''s NTD network (“k-core measure”) and the quantity and quality of research produced.

Principal Findings

Over the past sixty years (1950–2010), Canadian researchers have contributed to 1,079 NTD publications, specializing in Leishmania, African sleeping sickness, and leprosy. Of this work, 70% of all first authors and co-authors (n = 4,145) have been Canadian. Since the 1990s, however, a network of international co-authorship activity has been emerging, with representation of researchers from 62 different countries; largely researchers from OECD countries (e.g. United States and United Kingdom) and some non-OECD countries (e.g. Brazil and Iran). Canada has a core-periphery NTD international research structure, with a densely connected group of OECD countries and some African nations, such as Uganda and Kenya. Sitting predominantly on the periphery of this research network is a cluster of 16 non-OECD nations that fall within the lowest GDP percentile of the network.

Conclusion/Significance

The publication specialties, composition, and position of NTD researchers within Canada''s NTD country network provide evidence that while Canadian researchers currently remain the overall gatekeepers of the NTD research they generate; there is opportunity to leverage existing research collaborations and help advance regions and NTD areas that are currently under-developed.     

Scientometric sorting by importance for literatures on life cycle assessments and some related methodological discussions

Purpose

This paper aims to sort the literatures on life cycle assessments (LCA) by their respective importance through citation and co-citation analysis and to further discuss the strengths and weaknesses of these kinds of scientometric methods in the case of LCA research.

Methods

CiteSpace II was used to generate document co-citation networks based on 3,824 articles retrieved from the ISI Web of Science database on this topic.

Results

Table 1 provides the top 50 highest cited documents in the LCA field. Here, we use two indicators, i.e., citation frequency in citation analysis and betweenness centrality metric in co-citation analysis, to measure the importance of these LCA literatures.

Conclusions

Citation and co-citation analysis are useful for environmental scientists and engineers to get a better understanding of the inner structure of LCA research. However, like all other research methods, this kind of analysis has some limitations. On the one hand, Scientometric studies and related software are very dependent on ISI Web of Science database, but considering the ISI Web of Science only began to track the LCA field fairly recently, the Scopus database would probably give a fuller picture. On the other hand, since the essence of scientometrics analysis is outsiders commenting insiders, so with only citation and co-citation analysis, to our understanding of the past, present, and future of LCA field, is insufficient.     

A Principal Component Analysis of 39 Scientific Impact Measures

Background

The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact.

Methodology

We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data.

Conclusions

Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.     

A network-based approach on elucidating the multi-faceted nature of chronological aging in S. cerevisiae

Background

Cellular mechanisms leading to aging and therefore increasing susceptibility to age-related diseases are a central topic of research since aging is the ultimate, yet not understood mechanism of the fate of a cell. Studies with model organisms have been conducted to ellucidate these mechanisms, and chronological aging of yeast has been extensively used as a model for oxidative stress and aging of postmitotic tissues in higher eukaryotes.

Methodology/Principal Findings

The chronological aging network of yeast was reconstructed by integrating protein-protein interaction data with gene ontology terms. The reconstructed network was then statistically “tuned” based on the betweenness centrality values of the nodes to compensate for the computer automated method. Both the originally reconstructed and tuned networks were subjected to topological and modular analyses. Finally, an ultimate “heart” network was obtained via pooling the step specific key proteins, which resulted from the decomposition of the linear paths depicting several signaling routes in the tuned network.

Conclusions/Significance

The reconstructed networks are of scale-free and hierarchical nature, following a power law model with γ  =  1.49. The results of modular and topological analyses verified that the tuning method was successful. The significantly enriched gene ontology terms of the modular analysis confirmed also that the multifactorial nature of chronological aging was captured by the tuned network. The interplay between various signaling pathways such as TOR, Akt/PKB and cAMP/Protein kinase A was summarized in the “heart” network originated from linear path analysis. The deletion of four genes, TCB3, SNA3, PST2 and YGR130C, was found to increase the chronological life span of yeast. The reconstructed networks can also give insight about the effect of other cellular machineries on chronological aging by targeting different signaling pathways in the linear path analysis, along with unraveling of novel proteins playing part in these pathways.     

Effective Identification of Conserved Pathways in Biological Networks Using Hidden Markov Models

Background

The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems.

Methodology/Principal Findings

In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs). Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions.

Conclusions/Significance

Based on several protein-protein interaction (PPI) networks obtained from the Database of Interacting Proteins (DIP) and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors.     

The Evolution of Your Success Lies at the Centre of Your Co-Authorship Network

Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars’ publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship.     

Statistical Parsimony Networks and Species Assemblages in Cephalotrichid Nemerteans (Nemertea)

Background

It has been suggested that statistical parsimony network analysis could be used to get an indication of species represented in a set of nucleotide data, and the approach has been used to discuss species boundaries in some taxa.

Methodology/Principal Findings

Based on 635 base pairs of the mitochondrial protein-coding gene cytochrome c oxidase I (COI), we analyzed 152 nemertean specimens using statistical parsimony network analysis with the connection probability set to 95%. The analysis revealed 15 distinct networks together with seven singletons. Statistical parsimony yielded three networks supporting the species status of Cephalothrix rufifrons, C. major and C. spiralis as they currently have been delineated by morphological characters and geographical location. Many other networks contained haplotypes from nearby geographical locations. Cladistic structure by maximum likelihood analysis overall supported the network analysis, but indicated a false positive result where subnetworks should have been connected into one network/species. This probably is caused by undersampling of the intraspecific haplotype diversity.

Conclusions/Significance

Statistical parsimony network analysis provides a rapid and useful tool for detecting possible undescribed/cryptic species among cephalotrichid nemerteans based on COI gene. It should be combined with phylogenetic analysis to get indications of false positive results, i.e., subnetworks that would have been connected with more extensive haplotype sampling.     

Evolution of Cooperation Patterns in Psoriasis Research: Co-Authorship Network Analysis of Papers in Medline (1942–2013)

Background

Although researchers have worked in collaboration since the origins of modern science and the publication of the first scientific journals in the eighteenth century, this phenomenon has acquired exceptional importance in the last several decades. Since the mid-twentieth century, new knowledge has been generated from within an ever-growing network of investigators, working cooperatively in research groups across countries and institutions. Cooperation is a crucial determinant of academic success.

Objective

The aim of the present paper is to analyze the evolution of scientific collaboration at the micro level, with regard to the scientific production generated on psoriasis research.

Methods

A bibliographic search in the Medline database containing the MeSH terms “psoriasis” or “psoriatic arthritis” was carried out. The search results were limited to articles, reviews and letters. After identifying the co-authorships of documents on psoriasis indexed in the Medline database (1942–2013), various bibliometric indicators were obtained, including the average number of authors per document and degree of multi-authorship over time. In addition, we performed a network analysis to study the evolution of certain features of the co-authorship network as a whole: average degree, size of the largest component, clustering coefficient, density and average distance. We also analyzed the evolution of the giant component to characterize the changing research patterns in the field, and we calculated social network indicators for the nodes, namely betweenness and closeness.

Results

The main active research clusters in the area were identified, along with their authors of reference. Our analysis of 28,670 documents sheds light on different aspects related to the evolution of scientific collaboration in the field, including the progressive increase in the mean number of co-authors (which stood at 5.17 in the 2004–2013 decade), and the rise in multi-authored papers signed by many different authors (in the same decade, 25.77% of the documents had between 6 and 9 co-authors, and 10.28% had 10 or more). With regard to the network indicators, the average degree gradually increased up to 10.97 in the study period. The percentage of authors pertaining to the largest component also rose to 73.02% of the authors. The clustering coefficient, on the other hand, remained stable throughout the entire 70-year period, with values hovering around 0.9. Finally, the average distance peaked in the decades 1974–1983 (8.29) and 1984–2003 (8.12) then fell over the next two decades, down to 5.25 in 2004–2013. The construction of the co-authorship network (threshold of collaboration ≥ 10 co-authored works) revealed a giant component of 161 researchers, containing 6 highly cohesive sub-components.

Conclusions

Our study reveals the existence of a growing research community in which collaboration is increasingly important. We can highlight an essential feature associated with scientific collaboration: multi-authored papers, with growing numbers of collaborators contributing to them, are becoming more and more common, therefore the formation of research groups of increasing depth (specialization) and breadth (multidisciplinarity) is now a cornerstone of research success.     

The h’-Index,Effectively Improving the h-Index Based on the Citation Distribution

Background

Although being a simple and effective index that has been widely used to evaluate academic output of scientists, the h-index suffers from drawbacks. One critical disadvantage is that only h-squared citations can be inferred from the h-index, which completely ignores excess and h-tail citations, leading to unfair and inaccurate evaluations in many cases.

Methodology /Principal Findings

To solve this problem, I propose the h’-index, in which h-squared, excess and h-tail citations are all considered. Based on the citation data of the 100 most prolific economists, comparing to h-index, the h’-index shows better correlation with indices of total-citation number and citations per publication, which, although relatively reliable and widely used, do not carry the information of the citation distribution. In contrast, the h’-index possesses the ability to discriminate the shapes of citation distributions, thus leading to more accurate evaluation.

Conclusions /Significance

The h’-index improves the h-index, as well as indices of total-citation number and citations per publication, by possessing the ability to discriminate shapes of citation distribution, thus making the h’-index a better single-number index for evaluating scientific output in a way that is fairer and more reasonable.     

Co-authorship Network Analysis: A Powerful Tool for Strategic Planning of Research,Development and Capacity Building Programs on Neglected Diseases

Background

New approaches and tools were needed to support the strategic planning, implementation and management of a Program launched by the Brazilian Government to fund research, development and capacity building on neglected tropical diseases with strong focus on the North, Northeast and Center-West regions of the country where these diseases are prevalent.

Methodology/Principal Findings

Based on demographic, epidemiological and burden of disease data, seven diseases were selected by the Ministry of Health as targets of the initiative. Publications on these diseases by Brazilian researchers were retrieved from international databases, analyzed and processed with text-mining tools in order to standardize author- and institution''s names and addresses. Co-authorship networks based on these publications were assembled, visualized and analyzed with social network analysis software packages. Network visualization and analysis generated new information, allowing better design and strategic planning of the Program, enabling decision makers to characterize network components by area of work, identify institutions as well as authors playing major roles as central hubs or located at critical network cut-points and readily detect authors or institutions participating in large international scientific collaborating networks.

Conclusions/Significance

Traditional criteria used to monitor and evaluate research proposals or R&D Programs, such as researchers'' productivity and impact factor of scientific publications, are of limited value when addressing research areas of low productivity or involving institutions from endemic regions where human resources are limited. Network analysis was found to generate new and valuable information relevant to the strategic planning, implementation and monitoring of the Program. It afforded a more proactive role of the funding agencies in relation to public health and equity goals, to scientific capacity building objectives and a more consistent engagement of institutions and authors from endemic regions based on innovative criteria and parameters anchored on objective scientific data.     

Evaluating research and impact: a bibliometric analysis of research by the NIH/NIAID HIV/AIDS clinical trials networks

Evaluative bibliometrics uses advanced techniques to assess the impact of scholarly work in the context of other scientific work and usually compares the relative scientific contributions of research groups or institutions. Using publications from the National Institute of Allergy and Infectious Diseases (NIAID) HIV/AIDS extramural clinical trials networks, we assessed the presence, performance, and impact of papers published in 2006-2008. Through this approach, we sought to expand traditional bibliometric analyses beyond citation counts to include normative comparisons across journals and fields, visualization of co-authorship across the networks, and assess the inclusion of publications in reviews and syntheses. Specifically, we examined the research output of the networks in terms of the a) presence of papers in the scientific journal hierarchy ranked on the basis of journal influence measures, b) performance of publications on traditional bibliometric measures, and c) impact of publications in comparisons with similar publications worldwide, adjusted for journals and fields. We also examined collaboration and interdisciplinarity across the initiative, through network analysis and modeling of co-authorship patterns. Finally, we explored the uptake of network produced publications in research reviews and syntheses. Overall, the results suggest the networks are producing highly recognized work, engaging in extensive interdisciplinary collaborations, and having an impact across several areas of HIV-related science. The strengths and limitations of the approach for evaluation and monitoring research initiatives are discussed.     

Citation Analysis May Severely Underestimate the Impact of Clinical Research as Compared to Basic Research

Background

Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research.

Methodology

A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact.

Results

Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields: Cardiac & cardiovascular systems, Clinical neurology, and Surgery. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research.

Conclusions

Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research.     

Association between the Pro12Ala Polymorphism of the Peroxisome Proliferator-Activated Receptor Gamma Gene and Strength Athlete Status

Background

The 12Ala allele of the Peroxisome Proliferator-Activated Receptor gamma gene (PPARG) Pro12Ala polymorphism produces a decreased binding affinity of the PPARγ2 protein, resulting in low activation of the target genes. The 12Ala allele carriers display a significantly improved insulin sensitivity that may result in better glucose utilisation in working skeletal muscles. We hypothesise that the PPARG 12Ala allele could be associated with strength athlete status in Polish athletes.

Methodology

The genotype distribution of PPARG Pro12Ala was examined in 660 Polish athletes. The athletes were stratified into four subgroups: endurance, strength-endurance, sprint-strength and strength. Control samples were prepared from 684 unrelated sedentary volunteers. A χ² test was used to compare the PPARG Pro12Ala allele and genotype frequencies between the different groups of athletes and control subjects. Bonferroni’s correction for multiple testing was applied.

Results

A statistically significant higher frequency of PPARG 12Ala alleles was observed in the subgroup of strength athletes performing short-term and very intense exertion characterised by predominant anaerobic energy production (13.2% vs. 7.5% in controls; P = 0.0007).

Conclusion

The PPARG 12Ala allele may be a relevant genetic factor favouring strength abilities in professional athletes, especially in terms of insulin-dependent metabolism, a shift of the energy balance towards glucose utilisation and the development of a favourable weight-to-strength ratio.     

Matching–centrality decomposition and the forecasting of new links in networks

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, whereas the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteristics are known, we show how the matching–centrality decomposition can be related to this external information. Consequently, it offers us a simple and versatile tool to explore how node characteristics explain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network.     

A simple index for the high-citation tail of citation distribution to quantify research performance in countries and institutions

Background

Conventional scientometric predictors of research performance such as the number of papers, citations, and papers in the top 1% of highly cited papers cannot be validated in terms of the number of Nobel Prize achievements across countries and institutions. The purpose of this paper is to find a bibliometric indicator that correlates with the number of Nobel Prize achievements.

Methodology/Principal Findings

This study assumes that the high-citation tail of citation distribution holds most of the information about high scientific performance. Here I propose the x-index, which is calculated from the number of national articles in the top 1% and 0.1% of highly cited papers and has a subtractive term to discount highly cited papers that are not scientific breakthroughs. The x-index, the number of Nobel Prize achievements, and the number of national articles in Nature or Science are highly correlated. The high correlations among these independent parameters demonstrate that they are good measures of high scientific performance because scientific excellence is their only common characteristic. However, the x-index has superior features as compared to the other two parameters. Nobel Prize achievements are low frequency events and their number is an imprecise indicator, which in addition is zero in most institutions; the evaluation of research making use of the number of publications in prestigious journals is not advised.

Conclusion

The x-index is a simple and precise indicator for high research performance.