Networks of Collaboration among Scientists in a Center for Diabetes Translation Research.
ABSTRACT: Transdisciplinary collaboration is essential in addressing the translation gap between scientific discovery and delivery of evidence-based interventions to prevent and treat diabetes. We examined patterns of collaboration among scientists at the Washington University Center for Diabetes Translation Research.Members (n = 56) of the Washington University Center for Diabetes Translation Research were surveyed about collaboration overall and on publications, presentations, and grants; 87.5% responded (n = 49). We used traditional and network descriptive statistics and visualization to examine the networks and exponential random graph modeling to identify predictors of collaboration.The 56 network members represented nine disciplines. On average, network members had been affiliated with the center for 3.86 years (s.d. = 1.41). The director was by far the most central in all networks. The overall and publication networks were the densest, while the overall and grant networks were the most centralized. The grant network was the most transdisciplinary. The presentation network was the least dense, least centralized, and least transdisciplinary. For every year of center affiliation, network members were 10% more likely to collaborate (OR: 1.10; 95% CI: 1.00-1.21) and 13% more likely to write a paper together (OR: 1.13; 95% CI: 1.02-1.25). Network members in the same discipline were over twice as likely to collaborate in the overall network (OR: 2.10; 95% CI: 1.40-3.15); however, discipline was not associated with collaboration in the other networks. Rank was not associated with collaboration in any network.As transdisciplinary centers become more common, it is important to identify structural features, such as a central leader and ongoing collaboration over time, associated with scholarly productivity and, ultimately, with advancing science and practice.
Project description:Academic collaboration is critical to knowledge production, especially as teams dominate scientific endeavors. Typical predictors of collaboration include individual characteristics such as academic rank or institution, and network characteristics such as a central position in a publication network. The role of disciplinary affiliation in the initiation of an academic collaboration between two investigators deserves more attention. Here, we examine the influence of disciplinary patterns on collaboration formation with control of known predictors using an inferential network model. The study group included all researchers in the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis. Longitudinal data were collected on co-authorships in grants and publications before and after ICTS establishment. Exponential-family random graph models were used to build the network models. The results show that disciplinary affiliation independently predicted collaboration in grant and publication networks, particularly in the later years. Overall collaboration increased in the post-ICTS networks, with cross-discipline ties occurring more often than within-discipline ties in grants, but not publications. This research may inform better evaluation models of university-based collaboration, and offer a roadmap to improve cross-disciplinary collaboration with discipline-informed network interventions.
Project description:<h4>Background</h4>Large cross-disciplinary scientific teams are becoming increasingly prominent in the conduct of research.<h4>Purpose</h4>This paper reports on a quasi-experimental longitudinal study conducted to compare bibliometric indicators of scientific collaboration, productivity, and impact of center-based transdisciplinary team science initiatives and traditional investigator-initiated grants in the same field.<h4>Methods</h4>All grants began between 1994 and 2004 and up to 10 years of publication data were collected for each grant. Publication information was compiled and analyzed during the spring and summer of 2010.<h4>Results</h4>Following an initial lag period, the transdisciplinary research center grants had higher overall publication rates than the investigator-initiated R01 (NIH Research Project Grant Program) grants. There were relatively uniform publication rates across the research center grants compared to dramatically dispersed publication rates among the R01 grants. On average, publications produced by the research center grants had greater numbers of coauthors but similar journal impact factors compared with publications produced by the R01 grants.<h4>Conclusions</h4>The lag in productivity among the transdisciplinary center grants was offset by their overall higher publication rates and average number of coauthors per publication, relative to investigator-initiated grants, over the 10-year comparison period. The findings suggest that transdisciplinary center grants create benefits for both scientific productivity and collaboration.
Project description:BACKGROUND:The emergence and spread of multidrug resistant microorganisms is a serious threat to transnational public health. Therefore, it is vital that cross-border outbreak response systems are constantly prepared for fast, rigorous, and efficient response. This research aims to improve transnational collaboration by identifying, visualizing, and exploring two cross-border response networks that are likely to unfold during outbreaks involving the Netherlands and Germany. METHODS:Quantitative methods were used to explore response networks during a cross-border outbreak of carbapenem resistant Enterobacteriaceae in healthcare settings. Eighty-six Dutch and German health professionals reflected on a fictive but realistic outbreak scenario (response rate ? 70%). Data were collected regarding collaborative relationships between stakeholders during outbreak response, prior working relationships, and trust in the networks. Network analysis techniques were used to analyze the networks on the network level (density, centralization, clique structures, and similarity of tie constellations between two networks) and node level (brokerage measures and degree centrality). RESULTS:Although stakeholders mainly collaborate with stakeholders belonging to the same country, transnational collaboration is present in a centralized manner. Integration of the network is reached, since several actors are beneficially positioned to coordinate transnational collaboration. However, levels of trust are moderately low and prior-existing cross-border working relationships are sparse. CONCLUSION:Given the explored network characteristics, we conclude that the system has a promising basis to achieve effective coordination. However, future research has to determine what kind of network governance form might be most effective and efficient in coordinating the necessary cross-border response activity. Furthermore, networks identified in this study are not only crucial in times of outbreak containment, but should also be fostered in times of non-crisis.
Project description:Interdisciplinary 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 inter-programmatic 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.
Project description:A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.
Project description:We study scientific collaboration at the level of universities. The scope of this study is to answer two fundamental questions: (i) can one indicate a category (i.e., a scientific discipline) that has the greatest impact on the rank of the university and (ii) do the best universities collaborate with the best ones only? Restricting ourselves to the 100 best universities from year 2009 we show how the number of publications in certain categories correlates with the university rank. Strikingly, the expected negative trend is not observed in all cases - for some categories even positive values are obtained. After applying Principal Component Analysis we observe clear categorical separation of scientific disciplines, dividing the papers into almost separate clusters connected to natural sciences, medicine and arts and humanities. Moreover, using complex networks analysis, we give hints that the scientific collaboration is still embedded in the physical space and the number of common papers decays with the geographical distance between them.
Project description:Medical researchers have called for new forms of translational science that can solve complex medical problems. Mainstream science has made complementary calls for heterogeneous teams of collaborators who conduct transdisciplinary research so as to solve complex social problems. Is transdisciplinary translational science what the medical community needs? What challenges must the medical community overcome to successfully implement this new form of translational science? This article makes several contributions. First, it clarifies the concept of transdisciplinary research and distinguishes it from other forms of collaboration. Second, it presents an example of a complex medical problem and a concrete effort to solve it through transdisciplinary collaboration: for example, the problem of preterm birth and the March of Dimes effort to form a transdisciplinary research center that synthesizes knowledge on it. The presentation of this example grounds discussion on new medical research models and reveals potential means by which they can be judged and evaluated. Third, this article identifies the challenges to forming transdisciplines and the practices that overcome them. Departments, universities and disciplines tend to form intellectual silos and adopt reductionist approaches. Forming a more integrated (or 'constructionist'), problem-based science reflective of transdisciplinary research requires the adoption of novel practices to overcome these obstacles.
Project description:Modelling and simulation (M&S) techniques are frequently used in Operations Research (OR) to aid decision-making. With growing complexity of systems to be modelled, an increasing number of studies now apply multiple M&S techniques or hybrid simulation (HS) to represent the underlying system of interest. A parallel but related theme of research is extending the HS approach to include the development of hybrid models (HM). HM extends the M&S discipline by combining theories, methods and tools from across disciplines and applying multidisciplinary, interdisciplinary and transdisciplinary solutions to practice. In the broader OR literature, there are numerous examples of cross-disciplinary approaches in model development. However, within M&S, there is limited evidence of the application of conjoined methods for building HM. Where a stream of such research does exist, the integration of approaches is mostly at a technical level. In this paper, we argue that HM requires cross-disciplinary research engagement and a conceptual framework. The framework will enable the synthesis of discipline-specific methods and techniques, further cross-disciplinary research within the M&S community, and will serve as a transcending framework for the transdisciplinary alignment of M&S research with domain knowledge, hypotheses and theories from diverse disciplines. The framework will support the development of new composable HM methods, tools and applications. Although our framework is built around M&S literature, it is generally applicable to other disciplines, especially those with a computational element. The objective is to motivate a transdisciplinarity-enabling framework that supports the collaboration of research efforts from multiple disciplines, allowing them to grow into transdisciplinary research.
Project description:PURPOSE:The purpose of this article is to provide an overview of strategies to build and sustain a career as a nurse scientist. This article examines how to integrate technologies and precision approaches into clinical practice, research, and education of the next generation of nursing scholars. DESIGN:This article presents information for shaping a sustainable transdisciplinary career. Programs of research that utilize self-management to improve quality of life are discussed throughout the article. The ongoing National Institute of Nursing Research-funded (R01 grant) iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology (iHEART) study is the first prospective, randomized controlled trial to evaluate whether electrocardiographic monitoring with the AliveCor™ device in the real-world setting will improve the time to detection and treatment of recurrent atrial fibrillation over a 6-month period as compared to usual cardiac care. METHODS:Opportunities to sustain a career as a nurse scientist and build programs of transdisciplinary research are identified. These opportunities are focused within the area of research and precision medicine. FINDINGS:Nurse scientists have the potential and ability to shape their careers and become essential members of transdisciplinary partnerships. Exposure to clinical research, expert mentorship, and diverse training opportunities in different areas are essential to ensure that contributions to nursing science are visible through publications and presentations as well as through securing grant funding to develop and maintain programs of research. CONCLUSIONS:Transcending boundaries and different disciplines, nurses are essential members of many diverse teams. CLINICAL RELEVANCE:Nurse scientists are strengthening research approaches, clinical care, and communication and improving health outcomes while also building and shaping the next generation of nurse scientists.
Project description:The purpose of this study is to identify and characterize the structure and dynamics of global R&D collaboration networks in ICT by analyzing cross-country co-patents, with a special focus on the role of China. We employ a Social Network Analysis (SNA) perspective, using information on more than 77 thousand co-patents from 2001-2015. These co-patents are disaggregated by three time periods and four ICT subsectors. Global measures for the network as a whole, as well as local measures on the positioning of countries in the networks are interpreted. The empirical results are highly interesting. First, international R&D collaboration networks in ICT show a dynamic transformation in becoming larger in magnitude (more countries but also more inter-linkages), less centralized and more densely connected, though with varying degrees across ICT subsectors. Second, the powerful position of the US weakens relatively compared to other, increasingly connected countries, in particular China. While China has already surpassed the US in total patenting in ICT in 2015, China is now also catching up from a network perspective shown by its growing central position over the observed time period.