How to build up big team science: a practical guide for large-scale collaborations

The past decade has witnessed a proliferation of big team science (BTS), endeavours where a comparatively large number of researchers pool their intellectual and/or material resources in pursuit of a common goal. Despite this burgeoning interest, there exists little guidance on how to create, manage and participate in these collaborations. In this paper, we integrate insights from a multi-disciplinary set of BTS initiatives to provide a how-to guide for BTS. We first discuss initial considerations for launching a BTS project, such as building the team, identifying leadership, governance, tools and open science approaches. We then turn to issues related to running and completing a BTS project, such as study design, ethical approvals and issues related to data collection, management and analysis. Finally, we address topics that present special challenges for BTS, including authorship decisions, collaborative writing and team decision-making.

Win-win interactions: Results and implications of a user needs assessment of clinical and translational scientists

Introduction

This study describes a needs assessment of clinical and translational research (CTR) scientists at a large, distributed, School of Medicine within a public university and affiliated clinics.

Method

We performed an Exploratory Conversion Mixed-Methods analysis using a quantitative survey and qualitative interviews with CTR scientists across the training continuum, from early-career scholars, mid-career mentors, and senior administrators at the University of Wisconsin and Marshfield Clinics. Qualitative findings were confirmed using epistemic network analysis (ENA). A survey was distributed to CTR scientists in training.

Results

Analyses supported that early-career and senior-career scientists have unique needs. Scientists who identified as non-White or female reported needs that differed from White male scientists. Scientists expressed the needs for educational training in CTR, for institutional support of career development, and trainings for building stronger relationships with community stakeholders. The tension between meeting tenure clocks and building deep community connections was particularly meaningful for scholars who identified as under-represented, including based on race, gender, and discipline.

Conclusions

This study yielded clear differences in support needs between scientists based upon their years in research and diversity of identities. The validation of qualitative findings, through quantification with ENA, enables robust identification of unique needs of CTR investigators. It is critically important to the future of CTR that scientists are provided with supports throughout the career. Delivery of that support in efficient and timely ways improves scientific outcomes. Advocacy at the level of the institution for under-represented scientists is of utmost importance.

An integrative review and practical guide to team development interventions for translational science teams: One size does not fit all

As the need to tackle complex clinical and societal problems rises, researchers are increasingly taking on a translational approach. This approach, which seeks to integrate theories, methodologies, and frameworks from various disciplines across a team of researchers, places emphasis on translation of findings in order to offer practical solutions to real-world problems. While translational research leads to a number of positive outcomes, there are also a multitude of barriers to conducting effective team science, such as effective coordination and communication across the organizational, disciplinary, and even geographic boundaries of science teams. Given these barriers to success, there is a significant need to establish team interventions that increase science team effectiveness as translational research becomes the new face of science. This review is intended to provide translational scientists with an understanding of barriers to effective team science and equip them with the necessary tools to overcome such barriers. We provide an overview of translational science teams, discuss barriers to science team effectiveness, demonstrate the lacking state of current interventions, and present recommendations for improving interventions in science teams by applying best practices from the teams and groups literature across the four phases of transdisciplinary research.

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

Introduction

Federal grant funding to support infrastructure development of translational biomedical research centers is a form of public health intervention. Establishing rigorous methods for measuring center success and outcomes is essential to justify continued funding.

Methods

Bibliometric data compiled from a 5-year funding cycle of neurodegeneration and translational neuroscience research center were analyzed using the package bibliometrix for open-source software R and the NIH-developed research tool iCite.

Results

The research team and their collaborators (n = 485) produced 157 grant-citing publications from 2015-2020. The science was produced by small research teams clustered around three main communities of topics: Alzheimer's Disease, brain imaging, and neuropsychological testing in the elderly. Using the relative citation ratio, the publications produced by the research team were found to be influential when compared to other R01-funded publications.

Conclusion

Recent developments in bibliometric analysis expand beyond traditional measurement capabilities to better understand the characteristics, outcomes, and influences of research teams. These findings can be used to inform researchers and institutions about research team composition, productivity, and success. Measures of research influence may be used to justify return on investment to funders.

The teams of early-career investigators: A qualitative pilot study

Introduction: Early team experiences can influence the professional trajectories of early-career investigators profoundly, yet they remain underexplored in the team science literature, which has focused primarily on large, multisite teams led by established researchers. To better understand the unique challenges of teams led by early-career investigators, we conducted a qualitative pilot study. Methods: Interviews were conducted with the principal investigator and members of 5 teams led by KL2 and K12 scholars at the University of Pittsburgh. A code book was developed and thematic analysis was conducted. Results: Seven distinct themes emerged. Interview subjects reported a high level of trust and strong communication patterns on their teams; however, the data also suggested underlying tensions that have the potential to escalate into larger problems if unaddressed. Conclusions: This study yields a deeper understanding of teams led by early-career investigators, which can help us provide appropriately targeted training and support.

Assessing Research Collaboration through Co-authorship Network Analysis

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.

Breaking down silos: mapping growth of cross-disciplinary collaboration in a translational science initiative

The importance of transdisciplinary collaboration is growing, though not much is known about how to measure collaboration patterns. The purpose of this paper is to present multiple ways of mapping and evaluating the growth of cross-disciplinary partnerships over time. Social network analysis was used to examine the impact of a Clinical and Translational Science Award (CTSA) on collaboration patterns. Grant submissions from 2007 through 2010 and publications from 2007 through 2011 of Institute of Clinical and Translational Sciences (ICTS) members were examined. A Cohort Model examining the first-year ICTS members demonstrated an overall increase in collaborations on grants and publications, as well as an increase in cross-discipline collaboration as compared to within-discipline. A Growth Model that included additional members over time demonstrated the same pattern for grant submissions, but a decrease in cross-discipline collaboration as compared to within-discipline collaboration for publications. ICTS members generally became more cross-disciplinary in their collaborations during the CTSA. The exception of publications for the Growth Model may be due to the time lag between funding and publication, as well as pressure for younger scientists to publish in their own fields. Network analysis serves as a valuable tool for evaluating changes in scientific collaboration.