Social network analysis to assess the impact of the CTSA on biomedical research grant collaboration

Social network analysis of a decade-long collaborative innovation network between hospitals and the biomedical industry in China

Collaborative innovation between hospitals and biomedical enterprises is crucial for ensuring breakthroughs in their development. This study explores the structural characteristics and examines the main roles of associated key actors of collaborative innovation between hospitals and biomedical enterprises in China. Using the jointly owned patent data within the country's healthcare industry, a decade-long collaborative innovation network between hospitals and biomedical enterprises in China was established and analyzed through social network analysis. The results revealed that the overall levels of collaborative innovation network density, collaborative frequency, and network connectivity were significantly low, especially in less-developed regions. In terms of actors with higher degree centrality, hospitals accounted for the majority, whereas a biomedical enterprise in Shenzhen had the highest degree centrality. Organizations in underdeveloped and northwest regions and small players were more likely to implement collaborative innovation. In conclusion, a collaborative innovation network between hospitals and biomedical enterprises in China demonstrated high dispersion and poor development levels. Stimulating organizations' initiatives for collaborative innovation may enhance quality and quantity of such innovation. Policy support and economic investments, strategic collaborative help, and resource and partnership optimization, especially for small players and in less-developed and northwest regions, should be encouraged to enhance collaborative innovation between hospitals and the biomedical industry in China and other similar countries or regions.

Collaboration and growth in a large research cooperative: A network analytic approach

Research networks encourage team science and facilitate collaboration within and across research teams. While many analyses have examined the output of these collaborative networks (e.g., authorship networks, publications, grant applications), less attention has been paid to the formative phases of these initiatives. This article presents analyses of a whole-network survey of investigators participating in a new research initiative, and examines the development of collaborative ties over the network's first year. In particular, we examine the influence of research center affiliation, seniority, and prior network experience on the number and structure of collaborative ties, including participants' bridging and broker roles. Such analyses can inform the overall management of the project in purposefully promoting new collaboration opportunities, and may ultimately predict the number of collaborative products generated by the network members.

Bibliometric and social network analysis of a Clinical and Translational Resource awardee: An Oklahoma experience 2014-2021

Background

Social Network Analysis is a method of analyzing coauthorship networks or relationships through graph theory. Institutional Development Award (IDeA) Networks for Clinical and Translational Research (IDeA-CTR) was designed to expand the capability for clinical and translational research to enhance National Institutes of Health funding.

Methods

All publications from a cohort of clinical and translational scientists in Oklahoma were collected through a PubMed search for 2014 through 2021 in October 2022. For this study's bibliometric portion, we pulled the citations from iCite in November of 2022.

Results

There were 2,391 articles published in 1,019 journals. The number of papers published by year increased from 56 in 2014 to 448 in 2021. The network had an average of 6.4 authors per paper, with this increasing by year from 5.3 in 2014 to 6.9 in 2021. The average journal impact factor for the overall network was 7.19, with a range from 0.08 to 202.73. The Oklahoma Shared Clinical and Translational Resources (OSCTR) network is a small world network with relatively weak ties.

Conclusions

This study provides an overview of coauthorship in an IDeA-CTR collaboration. We show the growth and structure of coauthorship in OSCTR, highlighting the importance of understanding and fostering collaboration within research networks.

Evaluating the impact of a CTSA program from 2008 to 2021 through bibliometrics, social network analysis, and altmetrics

Introduction

We evaluate a CTSA program hub by applying bibliometrics, social network analysis (SNA), and altmetrics and examine the changes in research productivity, citation impact, research collaboration, and CTSA-supported research topics since our pilot study in 2017.

Methods

The sampled data included North Carolina Translational and Clinical Science Institute (NC TraCS)-supported publications produced between September 2008 and March 2021. We applied measures and metrics from bibliometrics, SNA, and altmetrics to the dataset. In addition, we analyzed research topics and correlations between different metrics.

Results

1154 NC TraCS-supported publications generated over 53,560 citation counts by April 2021. The average cites per year and the relative citation ratio (RCR) mean of these publications improved from 33 and 2.26 in 2017 to 48 and 2.58 in 2021. The number of involved UNC units in the most published authors' collaboration network increased from 7 (2017) to 10 (2021). NC TraCS-supported co-authorship involved 61 NC organizations. PlumX metrics identified articles with the highest altmetrics scores. About 96% NC TraCS-supported publications have above the average SciVal Topic Prominence Percentile; the average approximate potential to translate of the included publication was 54.2%; and 177 publications addressed health disparity issues. Bibliometric measures (e.g., citation counts, RCR) and PlumX metrics (i.e., Citations, Captures, and Social-Media) are positively correlated (p < .05).

Conclusion

Bibliometrics, SNA, and altmetrics offer distinctive but related perspectives to examine CTSA research performance and longitudinal growth, especially at the individual program hub level. These perspectives can help CTSAs build program foci.

Relationships between changing communication networks and changing perceptions of psychological safety in a team science setting: Analysis with actor-oriented social network models

A growing evidence base suggests that complex healthcare problems are optimally tackled through cross-disciplinary collaboration that draws upon the expertise of diverse researchers. Yet, the influences and processes underlying effective teamwork among independent researchers are not well-understood, making it difficult to fully optimize the collaborative process. To address this gap in knowledge, we used the annual NIH mHealth Training Institutes as a testbed to develop stochastic actor-oriented models that explore the communicative interactions and psychological changes of its disciplinarily and geographically diverse participants. The models help investigate social influence and social selection effects to understand whether and how social network interactions influence perceptions of team psychological safety during the institute and how they may sway communications between participants. We found a degree of social selection effects: in particular years, scholars were likely to choose to communicate with those who had more dissimilar levels of psychological safety. We found evidence of social influence, in particular, from scholars with lower psychological safety levels and from scholars with reciprocated communications, although the sizes and directions of the social influences somewhat varied across years. The current study demonstrated the utility of stochastic actor-oriented models in understanding the team science process which can inform team science initiatives. The study results can contribute to theory-building about team science which acknowledges the importance of social influence and selection.

Evaluating the Evolution of Social Networks: A Ten-Year Longitudinal Analysis of an Agricultural, Fishing and Forestry Occupational Health Research Center

As part of our evaluation of the NIOSH-funded Northeast Center for Occupational Health and Safety: Agriculture, Forestry and Fishing (NEC), we present methodology, findings and the potential implications of a sequential social network analysis (SNA) conducted over ten years. Assessing the effectiveness of the center's scientific projects was our overarching evaluation goal. The evaluation design employed SNA to (a) look at changes to the center's network over time by visualizing relationships between center collaborators annually, (b) document collaborative ties and (c) identify particularly strong or weak areas of the network. Transdisciplinary social network criteria were applied to the SNA to examine the collaboration between center personnel, their partners and the industry groups they serve. SNA participants' perspectives on the utility of the SNA were also summarized to assess their interest in ongoing SNA measures. Annual installments of the SNA (2011-2020) showed an expansion of the network with a 30% increase in membership from baseline, as well as an increase in total relational ties (any type of contact). SNA measures also indicated significant increases in co-publication, cross-sector and transdisciplinary ties. Overall, SNA is an effective tool in visualizing and sustaining an occupational safety and health research and outreach network. Its utility is limited by how ties are characterized, grant cycle timeframes and how SNA metrics relate to productivity.

Evaluating the impact of a health hackathon on collaborative team science: a Social Network Analysis (SNA)

The Mount Sinai Health Hackathon is designed to provide a novel forum to foster experiential team science training. Utilizing a Social Network Analysis survey, we studied the impact of the Mount Sinai Health Hackathon on the nature of collaborative relationships of hackathon participants. After the event, the number of links between participants from different disciplines increased and network density overall increased, suggesting a more interconnected network with greater interdisciplinary communication. This social network approach may be a useful addition to the evaluation strategies for team science education initiatives.

Bibliometrics approach to evaluating the research impact of CTSAs: A pilot study

INTRODUCTION

To enhance the performance evaluation of Clinical and Translational Science Award (CTSA) hubs, we examined the utility of advanced bibliometric measures that go beyond simple publication counts to demonstrate the impact of translational research output.

METHODS

The sampled data included North Carolina Translational and Clinical Science Institute (NC TraCS)-supported publications produced between September 2008 and March 2017. We adopted advanced bibliometric measures and a state-of-the-art bibliometric network analysis tool to assess research productivity, citation impact, the scope of research collaboration, and the clusters of research topics.

RESULTS

Totally, 754 NC TraCS-supported publications generated over 24,000 citation counts by April 2017 with an average of 33 cites per article. NC TraCS-supported research papers received more than twice as many cites per year as the average National Institute of Health-funded research publications from the same field and time. We identified the top productive researchers and their networks within the CTSA hub. Findings demonstrated the impact of NC TraCS in facilitating interdisciplinary collaborations within the CTSA hub and across the CTSA consortium and connecting researchers with right peers and organizations.

CONCLUSION

Both improved bibliometrics measures and bibliometric network analysis can bring new perspectives to CTSA evaluation via citation influence and the scope of research collaborations.

Approaches to Measuring Trends in Interdisciplinary Research Publications at One Academic Medical Center

PURPOSE

To determine if interdisciplinary research has increased between 2005 and 2015, based on an analysis of journal articles containing at least 1 author from Johns Hopkins University, and to compare different methods for determining the disciplinarity of research articles.

METHOD

In 2017-2018, 100 peer-reviewed biomedical science articles were randomly selected from years 2005, 2010, and 2015 and classified as unidisciplinary or interdisciplinary based on Scopus author affiliation data (method 1). The corresponding authors of the 2010 and 2015 articles were sent a survey asking them to describe the disciplines involved in their research (method 2) and to define their research as unidisciplinary or interdisciplinary based on provided definitions (method 3).

RESULTS

There was a statistically significant increase in the proportion of interdisciplinary articles in 2015 compared with both 2005 and 2010 (P = .02). Comparison of the 3 methods indicated that 45% of the articles were classified as interdisciplinary based on author affiliation data (method 1), 40% based on the corresponding author's description of the disciplines involved in their research (method 2), and 71% based on the corresponding author's definition of their article's disciplinarity (method 3). There was a statistically significant difference in the proportion of articles classified as interdisciplinary between methods 1 and 3 (P < .001) and between methods 2 and 3 (P < .001).

CONCLUSIONS

This study found that interdisciplinary research increased at Johns Hopkins University over the past decade and highlights the difference between corresponding authors' views of their own research and other methods for determining interdisciplinarity.

Evaluating a Center for Interprofessional Education via Social Network Analysis

Centers and institutes are created to support interdisciplinary collaboration. However, all centers and institutes face the challenge of how best to evaluate their impact since traditional counts of productivity may not fully capture the interdisciplinary nature of this work. The authors applied techniques from social network analysis (SNA) to evaluate the impact of a center for interprofessional education (IPE), a growing area for centers because of the global emphasis on IPE.The authors created networks based on the connections between faculty involved in programs supported by an IPE center at Virginia Commonwealth University from 2014 to 2017. They used mathematical techniques to describe these networks and the change in the networks over time. The results of these analyses demonstrated that, while the number of programs and involved faculty grew, the faculty maintained a similar amount of connection between members. Additional faculty clusters emerged, and certain key faculty were important connectors between clusters. The analysis also confirmed the interprofessional nature of faculty collaboration within the network.SNA added important evaluation data beyond typical metrics such as counts of learners or faculty. This approach demonstrated how a center was evolving and what strategies might be needed to support further growth. With further development of benchmarks, SNA could be used to evaluate the effectiveness of centers and institutes relative to each other. SNA should guide strategic decisions about the future of centers and institutes as they strive to meet their overarching goal of tackling a social challenge through interdisciplinary collaboration.

Measuring team science: Associations between a clinical-translational science institute and investigator ego networks

The National Institutes of Health's Clinical and Translational Science Awards (CTSA) institutes have been created, in part, to have a positive impact on collaboration and team science. This study is the first to examine the associations between a CTSA hub, the Michigan Institute for Clinical and Health Research (MICHR), and investigators' ego networks. We ran cross-sectional and panel models of the associations between consulting with MICHR and the ego network measure of two-step reach (TSR) - that is, colleagues of colleagues reachable in two steps - from a network of 2161 investigators who had co-submitted a grant proposal to an external sponsor in 2006. Our analyses covered the period 2004-2012, although some model specifications covered the shorter time period 2006-2010. Consulting with MICHR had positive associations with the size of and changes in an investigator's TSR across and over time, even controlling for research productivity and organizational affiliation. For example, over the period 2006-2010 an investigator who consulted with MICHR reached 44 more individuals than a non-consulting investigator. This study expands our understanding of the indirect impacts that clinical and translational science institutes have on investigators' scientific networks. This network-based approach might be useful in quantifying the impact of team science initiatives at the university level.

Network Abstractions of Prescription Patterns in a Medicaid Population

Understanding prescription patterns have relied largely on aggregate statistical measures. Evidence of doctor- shopping, inappropriate prescribing, drug diversion and patient seeking prescription drugs across multiple prescribers demand understanding the concerted working of prescribers and prescriber communities as opposed to treating them as independent entities. We model potential associations between prescribers as prescriber-prescriber network (PPN) and subsequently investigate its properties across Schedule II, III, IV drugs in a single month in a Medicaid population. Community structure detection algorithms and geo-spatial layouts revealed characteristic patterns in PPN markedly different from their random graph surrogate counterparts rejecting them as potential generative mechanism. Outlier detection with recommended thresholds also revealed a subset of prescriber specialties to be constitutively flagged across Schedule II, III, IV drugs. Presence of prescriber communities may assist in targeted monitoring and their deviation from random graphs may serve as a metric in assessing PPN evolution temporally and pre-/post- interventions.

Network-based assessment of collaborative research in neuroscience

INTRODUCTION

The 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.

METHODS

Social 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).

RESULTS

Exponential 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.

DISCUSSION

Assessment of team science efforts is critical to evaluating and developing appropriate support structures that facilitate successful team science efforts in translational neuroscience.

Charting the Publication and Citation Impact of the NIH Clinical and Translational Science Awards (CTSA) Program From 2006 Through 2016

PURPOSE

The authors evaluated publication and citation patterns for articles supported by Clinical and Translational Science Awards (CTSA) hub investment over the first decade of the CTSA program. The aim was to elucidate a pivotal step in the translational process by providing an account of how time, hub maturity, and hub attributes were related to productivity and influence in the academic literature.

METHOD

In 2017, the authors collected bibliometric data from PubMed, Web of Science InCites, and National Institutes of Health (NIH) iCite for articles citing any CTSA hub grants published from hub inception through 2016. They compiled data on publication and citation rates and indices of relative citation impact aggregated by hub funding year cohort. They compared hub-level bibliometric activity by multi- versus single-institution structure and total monetary award sums, compiled from NIH RePORTER.

RESULTS

From 2006-2016, CTSA hubs supported over 66,000 publications, with publication rates accelerating as hubs matured. These publications accumulated over 1.2 million citations, with some articles cited over 1,000 times. Indices of relative citation impact indicated CTSA-supported publications were cited more than twice as often as expected for articles of their publication years and disciplines. Multi-institutional hubs and those awarded higher grant sums exhibited significantly higher publication and citation activity.

CONCLUSIONS

The CTSA program is yielding a robust and growing body of influential research findings with consistently high indices of relative citation impact. Preliminary evidence suggests multi-institutional collaborations and more monetary resources are associated with elevated bibliometric activity and, therefore, may be worth their investment.

Toward a science of translational science

Translational research as a discipline has experienced explosive growth over the last decade as evidenced by significant federal investment and the exponential increase in related publications. However, narrow project-focused or process-based measurement approaches have resulted in insufficient techniques to measure the translational progress of institutions or large-scale networks. A shift from traditional industrial engineering approaches to systematic investigation using the techniques of scientometrics and network science will be required to assess the impact of investments in translational research.

Detecting and analyzing research communities in longitudinal scientific networks

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.

Social Networking in an Agricultural Research Center: Using Data to Enhance Outcomes

The purpose of this article is to present a case study of one midwestern Agricultural Center (Ag Center) that used social network analysis (SNA) to (1) evaluate its collaborations with extramural stakeholders and (2) strategically plan for extending outreach for goal achievement. An evaluation team developed a data collection instrument based on SNA principles. It was administered to the Ag Center's intramural stakeholders (N = 9), who were asked to identify the key extramural stakeholders with whom they had collaborated within the previous 12 months. Additional questions about each extramural stakeholder helped to categorize them according to SNA network measures for degree of centrality, betweenness centrality, and closeness centrality. Findings showed the Ag Center had N = 305 extramural stakeholders. Most of these were other researchers and did not represent the diverse group of stakeholders that the Ag Center had targeted for engagement. Only a few of the intramural stakeholders had national or international connections. Findings were used to improve and diversify connections in order to leverage the Ag Center's expertise and ability to translate research into new best practices and policies. The SNA case study has implications for other evaluators and project directors looking for methodologies that can monitor networks in large science consortia and help leaders plan for translating research into practice and policies by networking with those who can influence such change.

Effect of a Clinical and Translational Science Award institute on grant funding in a major research university

INTRODUCTION

Previous studies have examined the impact of Clinical and Translational Science Awards programs on other outcomes, but not on grant seeking. The authors examined the effects on grant seeking of the Michigan Institute for Clinical & Health Research (MICHR), a Clinical and Translational Science Awards institute at the University of Michigan.

METHODS

We assessed over 63,000 grant proposals submitted at the University of Michigan in the years 2002-2012 using data from the university and MICHR's Tracking Metrics and Reporting System. We used a retrospective, observational study of the dynamics of grant-seeking success and award funding. Heckman selection models were run to assess MICHR's relationship with a proposal's success (selection), and subsequently the award's size (outcome). Models were run for all proposals and for clinical and translational research (CTR) proposals alone. Other covariates included proposal classification, type of grant award, academic unit, and year.

RESULTS

MICHR had a positive and statistically significant relationship with success for both proposal types. For all grants, MICHR was associated with a 29.6% increase in award size. For CTR grants, MICHR had a statistically nonsignificant relationship with award size.

CONCLUSIONS

MICHR's infrastructure, created to enable and enhance CTR, has also created positive spillovers for a broader spectrum of research and grant seeking.

Scientific retreats with 'speed dating': networking to stimulate new interdisciplinary translational research collaborations and team science

To stimulate the formation of new interdisciplinary translational research teams and innovative pilot projects, the South Carolina Clinical and Translational Research (SCTR) Institute (South Carolina Clinical and Translational Science Award, CTSA) initiated biannual scientific retreats with 'speed dating' networking sessions. Retreat themes were prioritized based on the following criteria; cross-cutting topic, unmet medical need, generation of novel technologies and methodologies. Each retreat begins with an external keynote speaker followed by a series of brief research presentations by local researchers focused on the retreat theme, articulating potential areas for new collaborations. After each session of presentations, there is a 30 min scientific 'speed dating' period during which the presenters meet with interested attendees to exchange ideas and discuss collaborations. Retreat attendees are eligible to compete for pilot project funds on the topic of the retreat theme. The 10 retreats held have had a total of 1004 participants, resulted in 61 pilot projects with new interdisciplinary teams, and 14 funded projects. The retreat format has been a successful mechanism to stimulate novel interdisciplinary research teams and innovative translational research projects. Future retreats will continue to target topics of cross-cutting importance to biomedical and public health research.

Forging a link between mentoring and collaboration: a new training model for implementation science

Background

Training investigators for the rapidly developing field of implementation science requires both mentoring and scientific collaboration. Using social network descriptive analyses, visualization, and modeling, this paper presents results of an evaluation of the mentoring and collaborations fostered over time through the National Institute of Mental Health (NIMH) supported by Implementation Research Institute (IRI).

Methods

Data were comprised of IRI participant self-reported collaborations and mentoring relationships, measured in three annual surveys from 2012 to 2014. Network descriptive statistics, visualizations, and network statistical modeling were conducted to examine patterns of mentoring and collaboration among IRI participants and to model the relationship between mentoring and subsequent collaboration.

Results

Findings suggest that IRI is successful in forming mentoring relationships among its participants, and that these mentoring relationships are related to future scientific collaborations. Exponential random graph network models demonstrated that mentoring received in 2012 was positively and significantly related to the likelihood of having a scientific collaboration 2 years later in 2014 (p = 0.001). More specifically, mentoring was significantly related to future collaborations focusing on new research (p = 0.009), grant submissions (p = 0.003), and publications (p = 0.017). Predictions based on the network model suggest that for every additional mentoring relationships established in 2012, the likelihood of a scientific collaboration 2 years later is increased by almost 7 %.

Conclusions

These results support the importance of mentoring in implementation science specifically and team science more generally. Mentoring relationships were established quickly and early by the IRI core faculty. IRI fellows reported increasing scientific collaboration of all types over time, including starting new research, submitting new grants, presenting research results, and publishing peer-reviewed papers. Statistical network models demonstrated that mentoring was strongly and significantly related to subsequent scientific collaboration, which supported a core design principle of the IRI. Future work should establish the link between mentoring and scientific productivity. These results may be of interest to team science, as they suggest the importance of mentoring for future team collaborations, as well as illustrate the utility of network analysis for studying team characteristics and activities.

Electronic supplementary material

The online version of this article (doi:10.1186/s13012-016-0499-y) contains supplementary material, which is available to authorized users.

Improving clinical and translational research training: a qualitative evaluation of the Atlanta Clinical and Translational Science Institute KL2-mentored research scholars program

A major impediment to improving the health of communities is the lack of qualified clinical and translational research (CTR) investigators. To address this workforce shortage, the National Institutes of Health (NIH) developed mechanisms to enhance the career development of CTR physician, PhD, and other doctoral junior faculty scientists including the CTR-focused K12 program and, subsequently, the KL2-mentored CTR career development program supported through the Clinical and Translational Science Awards (CTSAs). Our evaluation explores the impact of the K12/KL2 program embedded within the Atlanta Clinical and Translational Science Institute (ACTSI), a consortium linking Emory University, Morehouse School of Medicine and the Georgia Institute of Technology. We conducted qualitative interviews with program participants to evaluate the impact of the program on career development and collected data on traditional metrics (number of grants, publications). 46 combined K12/KL2 scholars were supported between 2002 and 2016. 30 (65%) of the 46 K12/KL2 scholars are women; 24 (52%) of the trainees are minorities, including 10 (22%) scholars who are members of an underrepresented minority group. Scholars reported increased research skills, strong mentorship experiences, and positive impact on their career trajectory. Among the 43 scholars who have completed the program, 39 (91%) remain engaged in CTR and received over $89 000 000 as principal investigators on federally funded awards. The K12/KL2 funding provided the training and protected time for successful career development of CTR scientists. These data highlight the need for continued support for CTR training programs for junior faculty.

Academic Cross-Pollination: The Role of Disciplinary Affiliation in Research Collaboration

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.