Network‐based assessment of collaborative research in neuroscience

Abstract

IntroductionThe purpose of this study was to describe collaborative research in neuroscience within the context of the Center for Neurodegeneration and Translational Neuroscience (CNTN), a Center of Biomedical Research Excellence supported by the National Institute of General Medical Science. Drawing upon research on the science of team science, this study investigated the way that interactions around research emerged over the course of establishing a new research center. The objectives were to document changes in research activity and describe how human research support infrastructure functioned to support the production of science.MethodsSocial network analyses were used to model coauthorship relationships based on publication histories from baseline (2014) through the current grant year (2017) for key personnel (n = 12), as well as survey data on collaborative engagement among CNTN members (n = 59).ResultsExponential random graph models indicated that over time, CNTN members were increasingly likely to form coauthorship relationships. Community detection algorithms and brokerage analyses suggested that the CNTN was functioning as intended to support scientific development.DiscussionAssessment of team science efforts is critical to evaluating and developing appropriate support structures that facilitate successful team science efforts in translational neuroscience.

A large National Institute for Health Research (NIHR) Biomedical Research Centre facilitates impactful cross-disciplinary and collaborative translational research publications and research collaboration networks: a bibliometric evaluation study

“…In this study, we retrospectively evaluated different bibliometric measures for the NIHR Oxford BRC for its second 5-year funding period from 1st April 2012 to 31st March 2017. The studied three metrics i.e., research publications as a measure of research productivity [ 17 , 31 ], co-authorship networks as a measure of research collaboration [ 32 , 33 ], and publication citations as an indicator of the quality and impact of research [ 34 , 35 ]. These bibliometrics are important in measuring and assessing the success of a large-scale research effort and have been used for the evaluation of research and impact in the context of biomedical research [ 55 ] including translational research [ 35 , 56 ].…”

“…This study included three main outcome measures i.e. research productivity measured by research publications [ 17 , 31 ], research collaboration mapped through co-authorship networks [ 32 , 33 ], and the quality and impact of research gauged from publication citations [ 34 , 35 ].…”

Using bibliometrics to evaluate outcomes and influence of translational biomedical research centers

“…Network density is the proportion of present edges from all possible edges in the network and is a measure of how well-connected a network is (24). Transitivity measures small communities of nodes tied together in groups of three (triads) and is calculated as the number of observed transitive triads divided by the number of potential transitive triads (25). Finally, a path is defined as the edges taken when going from one node to another.…”

“…Networks are commonly reported using descriptive measures such as nodes, edges, density, transitivity, and path length [23][24][25]. Nodes represent each occurrence of the chosen criteria, and edges are ties between nodes.…”

Data Management Tools to Measure the Impact of Core Facilities

“…Application of software for analyzing synergy between programmatic components and investigator success Social network analysis can be used to map collaboration and produce evidence for effective team science. 9 We applied social network analysis methods to analyze the outputs of research from the participant user base. Data were retrieved for a 5-y period from MyNCBI, resulting in 96 publications that cited one of our programmatic grants, the COBRE in Matrix Biology (P20GM109095).…”