An exploratory social network analysis of academic research networks

For several decades, academics around the world have been collaborating with the view to support the development of their research domain. Having said that, the majority of scientific and technological policies try to encourage the creation of strong inter-related research groups in order to improve the efficiency of research outcomes and subsequently research funding allocation. In this paper, we attempt to highlight and thus, to demonstrate how these collaborative networks are developing in practice. To achieve this, we have developed an automated tool for extracting data about joint article publications and analyzing them from the perspective of social network analysis. In this case study, we have limited data from works published in 2010 by England academic and research institutions. The outcomes of this work can help policy makers in realising the current status of research collaborative networks in England

Quantifying the impact of weak, strong, and super ties in scientific careers

Scientists are frequently faced with the important decision to start or terminate a creative partnership. This process can be influenced by strategic motivations, as early career researchers are pursuers, whereas senior researchers are typically attractors, of new collaborative opportunities. Focusing on the longitudinal aspects of scientific collaboration, we analyzed 473 collaboration profiles using an ego-centric perspective which accounts for researcher-specific characteristics and provides insight into a range of topics, from career achievement and sustainability to team dynamics and efficiency. From more than 166,000 collaboration records, we quantify the frequency distributions of collaboration duration and tie-strength, showing that collaboration networks are dominated by weak ties characterized by high turnover rates. We use analytic extreme-value thresholds to identify a new class of indispensable `super ties', the strongest of which commonly exhibit >50% publication overlap with the central scientist. The prevalence of super ties suggests that they arise from career strategies based upon cost, risk, and reward sharing and complementary skill matching. We then use a combination of descriptive and panel regression methods to compare the subset of publications coauthored with a super tie to the subset without one, controlling for pertinent features such as career age, prestige, team size, and prior group experience. We find that super ties contribute to above-average productivity and a 17% citation increase per publication, thus identifying these partnerships - the analog of life partners - as a major factor in science career development.Comment: 13 pages, 5 figures, 1 Tabl

Gender Disparities in Science? Dropout, Productivity, Collaborations and Success of Male and Female Computer Scientists

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adapt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages

Co-authorship, Homophily, and Scholarly Influence in Information Systems Research

Information Systems (IS) researchers have increasingly focused attention on understanding the identity of our field (Hirschheim & Klein 2003; Lyytinen & King 2004). One facet of any discipline’s identity is the social aspect of how its scholars actually conduct their work (DeSanctis 2003), which is formally labeled as the study of sociology of science. Contributing to this tradition of work, we empirically examine scholarly influence (Acedo et al., 2006); scientific collaboration, including metrics that capture the prevalence of c-oauthored work; antecedents to co-authorship; and the effect of co-authorship on subsequent citations. Based on analyzing five leading IS journals for a period of seven years, we found that co-authored papers have become increasingly common in leading IS journals and that co-authoring continues to be more prevalent in journals published in North America compared to European journals. Moreover, we found significant effects of homophily related to gender, homophily/proximity, and geography. IS scholars worldwide exhibit a stronger preference for collaborating with co-authors of the same sex and those who attended the same PhD program than one would expect by chance. We also examined differences among journals and found some intriguing results for the effect of co-authorship on citations. Overall, we found evidence that the number of co-authors was positively related to citations although there was some variance across journals. These findings point to a need for more research to better understand both the processes of collaboration and the drivers and downstream benefits associated with it

The role of citation networks to explain academic promotions: an empirical analysis of the Italian national scientific qualification

The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Then, we apply logistic regression models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices

Gender Differences in Recognition of Coauthored Research: Evidence from the Italian Academia

I use data from Italian National Qualification evaluations to analyse whether women and men re-ceive differential credit for their coauthored work. National-level committees assess applicants' research quality, and a positive assessment is a requirement for promotion to associate and full professorship in Italian universities. I find that, conditional on the candidates’ individual characteristics and publications’ average qual-ity, the returns to an extra last- and middle-authored publication are, respectively, 35% and over 50% lower for women. On the other hand, I find no gender differences in the returns to single- and first-authored publications. The evidence is consistent with the possibility that women are evaluated differently from men in the presence of information asymmetries and stereotypes. Heterogeneity analysis reveals that gender differences in the attribution of credit for coauthored work emerge only in applications for associate professorship, where information asymmetries are larger. Moreover, stereotypes in science seem to penalise women when they undertake leadership roles as heads of labs, as women appear to suffer a last-authorship penalty in STEMM fields (sci-ence, technology, engineering, mathematics and medicine). Additionally, using data on all publications in the Italian academia from the past twenty years, I explore whether observed coauthorship patterns are consistent with the possibility that women anticipate a coauthorship disadvantage. I find some support for the hypothesis that women might strategically engage in coauthorship in the presence of potential information asymmetries and stereotypes. In fact, in smaller fields, there are no gender differences in the propensity to coauthor, whereas in larger ones, women have fewer coauthors than men. In STEMM fields where authors are listed alphabetically, the gender difference in the share of female coauthors is consistently larger than in STEMM fields where authors are listed according to contribution

Assessing Research Collaboration through Co-authorship Network Analysis

This is the final version. Available from Society of Research Administrators International via the link in this recordMaterial used with permission from Society of Research Administrators InternationalInterdisciplinary research collaboration is needed to perform transformative science and accelerate innovation. The Science of Team Science strives to investigate, evaluate, and foster team science, including institutional policies that may promote or hinder collaborative interdisciplinary research and the resources and infrastructure needed to promote team science within and across institutions. Social network analysis (SNA) has emerged as a useful method to measure interdisciplinary science through the evaluation of several types of collaboration networks, including co-authorship networks. Likewise, research administrators are responsible for conducting rigorous evaluation of policies and initiatives. Within this paper, we present a case study using SNA to evaluate interprogrammatic collaboration (evidenced by co-authoring scientific papers) from 2007-2014 among scientists who are members of four formal research programs at an NCI-designated Cancer Center, the Markey Cancer Center (MCC) at the University of Kentucky. We evaluate change in network descriptives over time and implement separable temporal exponential-family random graph models (STERGMs) to estimate the effect of author and network variables on the tendency to form a co-authorship tie. We measure the diversity of the articles published over time (Blau's Index) to understand whether the changes in the co-authorship network are reflected in the diversity of articles published by research members. Over the 8-year period, we found increased inter-programmatic collaboration among research members as evidenced by co-authorship of published scientific papers. Over time, MCC Members collaborated more with others outside of their research program and outside their initial dense co-authorship groups, however tie formation continues to be driven by co-authoring with individuals of the same research program and academic department. Papers increased in diversity over time on all measures with the exception of author gender. This inter-programmatic research was fostered by policy changes in cancer center administration encouraging interdisciplinary research through both informal (e.g., annual retreats, seminar series) and formal (e.g., requiring investigators from more than two research programs on applications for pilot funding) means. Within this cancer center, interdisciplinary co-authorship increased over time as policies encouraging this collaboration were implemented. Yet, there is room for improvement in creating more interdisciplinary and diverse ties between research program members.This research was supported by the Research Communications Office as well as the Biostatistics and Bioinformatics and the Cancer Research Informatics Shared Resources of the University of Kentucky Markey Cancer Center, funded by the National Cancer Institute Cancer Center Support Grant (P30CA177558). Dr. Eddens’ contribution was supported in part by a Building Interdisciplinary Research Careers in Women’s Health grant (#K12 DA035150) from the Office of Women’s Health Research, administered by the Department of Obstetrics and Gynecology of the College of Medicine, University of Kentucky. Dr. Vanderford is supported by the University of Kentucky’s Cancer Center Support Grant (NCI P30CA177558) and the Center for Cancer and Metabolism (NIGMS P20GM121327)

How to Combine Research Guarantor and Collaboration Patterns to Measure Scientific Performance of Countries in Scientific Fields: Nanoscience and Nanotechnology as a Case Study

This paper presents a comparative benchmarking of scientometric indicators to characterize the patterns of publication and research performance at the country level, in a specific field (nanoscience and nanotechnology) during the period 2003–2013. The aim was to assess how decisive collaboration may be in attaining a sound level of scientific performance, and how important leadership is for publication. To this end, we used a new methodological approach that contributes to the debate about scientific autonomy or dependency of countries in their scientific performance, and which may serve as an aid in decision-making with regard to research management. The results reveal that in terms of output, USA and China are the main producers; and due to the huge increase in their publications, Iran, India, and Australia can be considered emerging countries. The results highlight USA, Ireland, and Singapore as the countries with the highest levels of normalized citation impact, scientific excellence, and good management of leadership, all of which suggest strong scientific development and scientific autonomy. Also worth mentioning is the high visibility and scientific consolidation of China and Australia, despite the meager growth of their output. Moreover, the performance results indicate that in most cases the countries whose pattern of publication is more international tend to have greater visibility. Yet, a high degree of leadership does not always translate as a high performance level; the contrary is often true. Due to the limitations of the sample and characteristics of the field, we propose that future studies evaluate the generation of new knowledge in this field and refine the approach presented here, so as to better measure scientific performance.Projects I + D + I, State Programme of Research, Development and Innovation oriented to the Challenges of the Society: NANOMETRICS (Ref. CSO2014-57770-R) supported by Ministerio de Economía y Competitividad of Spain.Peer reviewedPeer Reviewe

Inequality and cumulative advantage in science careers: a case study of high-impact journals

Analyzing a large data set of publications drawn from the most competitive journals in the natural and social sciences we show that research careers exhibit the broad distributions of individual achievement characteristic of systems in which cumulative advantage plays a key role. While most researchers are personally aware of the competition implicit in the publication process, little is known about the levels of inequality at the level of individual researchers. Here we analyzed both productivity and impact measures for a large set of researchers publishing in high-impact journals, accounting for censoring biases in the publication data by using distinct researcher cohorts defined over non-overlapping time periods. For each researcher cohort we calculated Gini inequality coefficients, with average Gini values around 0.48 for total publications and 0.73 for total citations. For perspective, these observed values are well in excess of the inequality levels observed for personal income in developing countries. Investigating possible sources of this inequality, we identify two potential mechanisms that act at the level of the individual that may play defining roles in the emergence of the broad productivity and impact distributions found in science. First, we show that the average time interval between a researcher’s successive publications in top journals decreases with each subsequent publication. Second, after controlling for the time dependent features of citation distributions, we compare the citation impact of subsequent publications within a researcher’s publication record. We find that as researchers continue to publish in top journals, there is more likely to be a decreasing trend in the relative citation impact with each subsequent publication. This pattern highlights the difficulty of repeatedly producing research findings in the highest citation-impact echelon, as well as the role played by finite career and knowledge life-cycles, and the intriguing possibility that confirmation bias plays a role in the evaluation of scientific careers