Appendix C

High-Functioning Science Teams

What does a high-functioning, effective science team look like? According to Hackman (1987), team effectiveness is not just the product of collaboration; it is also a function of the social processes within a group and the personal satisfaction of its members. While the success of science teams is often measured through metrics such as research impact, successful grant applications, accepted journal publications, filed patents, and community connections, these outcomes alone do not reveal the processes that teams undergo to achieve these goals. In other words, they do not capture how the team functions on a day-to-day basis, such as team member behavior or their psychological states. It is crucial to understand both how teams function and what indicates that they are functioning well. For instance, if one attempts to improve a team based solely on outcome-based metrics, the only guidance they can provide is to enhance these metrics (e.g., secure more grants, file more patents).

Building on the foundation laid by the 2015 report Enhancing the Effectiveness of Team Science (National Research Council [NRC], 2015), this appendix aims to equip readers with an understanding of the mechanisms that underpin successful team science by illuminating the processes that contribute to the evolution of high-functioning science teams. First, the text explores how teams operate by offering two perspectives: a systems perspective and a temporal perspective. These perspectives will give the reader an understanding that teams neither operate in a vacuum, nor do they remain static over time. Subsequently, this appendix will highlight key indicators of successful scientific teamwork, providing a practical guide for identifying the components of effective scientific collaboration.

Before presenting these perspectives and indicators, it is necessary to first establish a shared understanding of terminology. Formally, a science team consists of two or more scientists working collaboratively toward a shared goal (NRC, 2015). In practice, science teams often include a diverse array of members, such as principal and co-investigators, research associates, postdoctoral fellows, graduate and undergraduate students, financial and administrative staff, and community members. The roles within science teams are often functionally complementary, contributing both directly (e.g., idea generation, experiment execution) and indirectly (e.g., processing annual reports, hiring postdoctoral fellows, purchasing research equipment) to the generation of new scientific knowledge.

Larger science teams may constitute a scientific “multiteam system”—in other words, interdependent networks of multiple component teams working toward broader objectives while maintaining their respective goals and responsibilities (Carter et al., 2019; Zaccaro et al., 2020). As an organizational form, the multiteam system structure is becoming increasingly prevalent, particularly in scientific endeavors, as it allows for exploring and investigating more complex challenges. Thus, science teams can be viewed as existing within a system composed of individuals, teams, multiteam systems, and institutes. This recognition of teams existing in a system is the first perspective to be discussed.

Importantly, teams are not static entities; they change and evolve over time. Although a team is technically formed once individuals are assigned to it, teams need time to develop and integrate psychologically before members fully consider themselves part of a cohesive unit. Therefore, it is crucial to understand that time is a key factor in how teams function (Cronin et al., 2011; Mohammed et al., 2008). Time not only influences the way teams operate but also provides valuable insights that can inform team science practices. For example, measuring trust immediately after a team is formed would be premature, as trust between team members develops over time (Grossman & Feitosa, 2018). Assessing a team too early would not yield an accurate understanding of trust within the team. Additionally, there are specific stages in a team’s evolution where certain behaviors become particularly important. For instance, while communication is critical for success throughout the team’s life cycle, clarifying roles and responsibilities is especially vital during the initial formation of the team (Kozlowski & Bell, 2007); see Chapter 3 in this report for a discussion of relevant best practices. Understanding these temporal dynamics is essential for fostering effective teamwork.

Much like the systems perspective, there are many ways in which time can be incorporated into understanding team functioning. One could take a team evolution or team development approach, which examines the stages or phases that teams go through as they get to know each other

and work toward achieving their goals. For instance, Tuckman’s five-stage model (Tuckman, 1965) outlines the stages of forming, storming, norming, performing, and adjourning, where teams move from initial formation and conflict to effective collaboration and eventual disbandment. This model highlights how team dynamics evolve over time, with each stage building on the previous one. Another classic model is the punctuated equilibrium model (Gersick, 1988, 1989), which suggests that teams experience periods of stability punctuated by significant shifts in behavior, often occurring midway through a project. These shifts lead to a more focused and productive second phase of work, emphasizing the impact of critical moments in team development. A last example is Kozlowski & Bell’s (2007) process model of team compilation, which not only describes the stages teams go through (team formation, team compilation, role compilation, team compilation) but also where these processes occur (individual level, dyadic level, team level).

Performance episodes offer another way to apply a temporal lens to team dynamics. These episodes refer to distinct periods during which a team works toward specific objectives, often engaging in multiple episodes simultaneously, each with a different focus and duration (Marks et al., 2001; Weingart, 1997; Zaheer et al., 1999). These episodes can range from short-term cycles lasting a few hours or days to long-term cycles extending over months or even years. Short-term episodes might involve daily operations, problem-solving sessions, or completing specific sections of a project, while long-term episodes could encompass strategic planning, the development of complex initiatives, or the overall progression of a major project. The input-mediator-output-input (IMOI) model (Ilgen et al., 2005) is often applied to understanding performance episodes. This model describes team development as a cyclical process where inputs (e.g., team composition, resources) influence mediators (e.g., team processes, emergent states) that, in turn, affect outputs (e.g., team performance, member satisfaction). These outputs then become inputs for the next cycle, influencing subsequent team interactions and development. The IMOI model highlights the dynamic and iterative nature of team functioning, emphasizing that teams continually evolve through feedback loops and ongoing interactions.

In summary, understanding what makes a science team high functioning requires more than just measuring their outcomes. By exploring both systems and temporal perspectives, we can gain insights into what makes these teams effective and how effective teams operate. Whether building, developing, or evaluating science teams, understanding the context in which they exist (i.e., a systems approach) and how they evolve over time (i.e., a temporal approach) is crucial for achieving successful outcomes. In the following section, the systems approach will serve as the organizing framework, focusing on the key constructs and competencies at the individual,

team, and multiteam system levels. Critically, as noted in Chapter 2, mid- to large-scale research on science teams has been limited in recent years. Therefore, this section draws on both small-scale research from the science of team science to more extensive studies from organizational sciences to highlight the most important constructs and competencies of high-functioning science teams.

FACTORS OF SUCCESSFUL SCIENCE TEAMS

It is crucial to acknowledge that a wide array of factors contribute to team success across various contexts. Extensive reviews and meta-analyses within the organizational sciences have identified numerous individual-, team-, and multiteam-level factors that predict team effectiveness. The discussion that follows will concentrate on the team- and multiteam-level factors most pertinent to the success of science teams. Discussion of individual-level factors can be found in Chapter 3.

Science Team-Level Factors

The effectiveness of a team is not solely determined by the individual characteristics of its members but also by the dynamics that occur at the team level. Two critical categories of team-level factors that significantly influence team success are team processes and team emergent states. Team processes refer to the specific behaviors and interactions that teams engage in as they work together, such as communication, coordination, and conflict (Mathieu et al., 2008). These processes are essential for ensuring that team members collaborate effectively and efficiently toward achieving their goals. On the other hand, team emergent states are conditions that develop over time within the team, such as trust, psychological safety, cohesion, and shared mental models (Mathieu et al., 2008). These emergent states are crucial for fostering an environment where team members feel safe to share ideas, take risks, and support one another. The following discussion will focus on the team processes and emergent states that are most vital to the success of science teams.

Team Learning Behaviors

Another important team behavioral process is team learning behaviors. Team learning behaviors refer to collaborative actions that contribute to either altering or reinforcing the team’s shared understanding (Wiese & Burke, 2019) and are critically important to the success of science teams. This is particularly true in scientific contexts, where teams constantly integrate new information, adapt to evolving research findings, and synthesize

different expertise. Science teams often operate in highly complex and dynamic environments, requiring them to collectively learn from both successes and failures to remain innovative and effective. These behaviors foster the team’s ability to process and apply new knowledge, ensuring that the team is continually advancing its understanding and improving its approach to solving problems.

Much like communication, there are various forms of team learning behaviors. These are often categorized by where the learning occurs—either internal to the team, such as within discussions or task processes, or external to the team, such as gathering insights from outside sources. Additionally, team learning behaviors can be classified as exploitative, where the focus is on integrating existing knowledge and refining processes, or explorative, where the emphasis is on generating new ideas or gaining novel insights (Harvey et al., 2022). Regardless of the type, team learning behaviors are significantly related not only to team performance but also to the development of critical emergent states, such as psychological safety. This dynamic creates a positive feedback loop, as learning behaviors strengthen trust and cohesion, which, in turn, enhance the team’s ability to continue learning and innovating.

Leadership

Leadership literature has evolved over the past few decades, indicating a shift from viewing leadership as an inherent trait of a single individual to understanding it as a set of behaviors or styles that can be exhibited by anyone on the team (Pearce & Conger, 2003; Pearce et al., 2008). This shift is especially relevant for science teams, where different expertise and perspectives are essential. In such teams, members with specialized knowledge may need to step up and demonstrate leadership at different points in time, depending on the demands of the project and the challenges faced. The dynamic nature of scientific work requires a flexible leadership approach that leverages the unique strengths of each team member.

While there are many different theories on leadership, the concept of shared leadership best captures the view of leadership as something that can be demonstrated by any team member. Shared leadership happens over time, and leadership roles are spread throughout the team. This concept can be understood through five key dimensions (D’Innocenzo et al., 2016). The first is the locus of leadership, which distinguishes between leadership that originates from outside the team (external) and leadership that comes from within the team (internal). The second dimension is the formality of leadership, which differentiates between leadership exercised by someone in a formal leadership role (e.g., a designated team leader) and informal leadership, where influence is exerted by individuals not explicitly tasked with leading.

A third dimension involves the distribution of leadership, which refers to how equally leadership roles are shared within the team. Some individuals view leadership as a collective process, where leadership responsibilities are shared equally by all members, while others see leadership as being distributed among members in varying degrees, with different individuals influencing the team in unique ways. Another important aspect of shared leadership is its temporal nature. Leadership within teams is not static; it shifts over time, with different members assuming leadership roles depending on the team’s evolving needs and challenges. Finally, it is essential to recognize that leadership is not confined to a single set of behaviors. Leadership can range from traditional behaviors, such as decision-making and direction setting, to more task-oriented actions, such as organizing resources, facilitating collaboration, or providing support that helps the team achieve its goals. As mentioned earlier, the concept of shared leadership is particularly well suited for science teams, which typically consist of a group of experts with different expertise who contribute to the team’s goals over an extended period of time. This variety creates opportunities for individuals to demonstrate their expertise and take on leadership roles at different stages of the team’s life cycle. Shared leadership also has a demonstrated relationship with team performance.

Faultlines

One potential issue that can arise within science teams is the development of faultlines. Faultlines are divisions within a team that arise from differences between members, such as differences in expertise, discipline, or background (Jehn & Bezrukova, 2010; Thatcher & Patel, 2012; Thatcher et al., 2024). In science teams, faultlines are particularly important to consider due to the high likelihood of variety in expertise and functional roles. Science teams often consist of members from various disciplines, such as biologists, engineers, and social scientists, working together on complex problems. This variety, while essential for innovation and problem-solving, also increases the likelihood that faultlines will emerge, as differences in perspectives, knowledge, and approaches can create perceptual divisions within the team.

The impact of faultlines on team dynamics depends on whether they remain dormant or become activated. Dormant faultlines refer to the potential for division based on team composition, where members may recognize differences but do not yet act on them. In contrast, activated faultlines occur when these divisions become operational and influence team interactions, leading to subgroup formation and reduced cohesion. Faultlines typically become activated when the team faces challenges or stressors, such as conflicting goals, communication breakdowns, or perceived inequality in

contributions, which exacerbate underlying differences and cause these divisions to surface.

Research has shown that activated faultlines are more detrimental to team functioning than dormant faultlines. While dormant faultlines may influence team conflict by creating an underlying tension, activated faultlines have far more significant consequences. They lead to increased conflict, reduced information sharing, lower team satisfaction, and ultimately, diminished team performance (Thatcher et al., 2024). In science teams, where collaboration and integration of different knowledge is critical, activated faultlines can severely hinder the team’s ability to achieve its goals. Therefore, managing faultlines effectively is essential for maintaining team cohesion and ensuring the success of scientific collaborations.

Team Cognition

An essential indicator of a high-functioning science team is team cognition. Team cognition refers to the shared knowledge structures and patterns among team members that enable them to anticipate each other’s needs and coordinate their actions efficiently (Mohammed et al., 2021). As team cognition involves the alignment and integration of individual cognitive states, it is classified as an emergent state, meaning that it evolves and strengthens over time through interaction and collaboration. There are two primary types of team cognition: shared mental models and transactive memory systems. Shared mental models refer to the common understandings that team members have about key aspects of their tasks, roles, and the environment in which they are working. Shared mental models enable team members to develop similar expectations, allowing for smooth coordination without the need for explicit communication at every turn (Van den Bossche et al., 2011). Transactive memory systems, on the other hand, refer to the division of knowledge within the team, where individuals not only know their specific areas of expertise but also understand who within the team holds particular knowledge (Mohammed et al., 2021). This system enables members to effectively locate and retrieve information by relying on the specialized knowledge of others.

For science teams, team cognition is particularly important, as these teams often consist of members with different disciplinary backgrounds and specialized expertise. The complexity of scientific work means that team members not only bring their knowledge to the table but also integrate and apply the knowledge of others. Shared mental models can help science teams align on project goals, methodologies, and expected outcomes, ensuring that all members are working toward a unified vision. Meanwhile, transactive memory systems allow team members to efficiently leverage the expertise distributed across the team, enhancing problem-solving and innovation.

However, achieving strong team cognition in science teams can be especially challenging due to the different perspectives and specialized knowledge each member brings. The process of building shared understanding and trust across different disciplines can be time-consuming, but it is critical for the team’s overall success. Without effective team cognition, science teams may struggle with miscommunication, fragmented efforts, and missed opportunities for interdisciplinary collaboration. Therefore, fostering the development of shared mental models and transactive memory systems is essential to ensure that science teams can function cohesively and achieve their complex objectives.

Psychological Safety

Science teams are more likely to thrive when they foster a strong sense of psychological safety. Psychological safety is the collective perception within a team that reflects an interpersonal environment where members feel safe to take risks, share ideas, and engage in open dialogue without fear of negative repercussions (Edmondson & Lei, 2014; Frazier et al., 2017). In teams where psychological safety is high, individuals are more willing to voice concerns, offer unique perspectives, and admit mistakes, knowing that these behaviors will be met with support rather than judgment. This climate of openness and trust is particularly critical for science teams, where innovation, complex problem-solving, and continuous learning are essential.

In teams with high psychological safety, members engage in candid communication and are unafraid to challenge ideas or offer constructive criticism. They are more likely to embrace feedback and engage in productive conflict, which fosters greater learning and collaboration. For science teams where interdisciplinary work often requires the integration of different knowledge sets and expertise, psychological safety allows team members to propose new approaches and solutions without the fear of embarrassment, a condition which is necessary for bringing together people from different thought worlds. In this environment, team members feel empowered to push boundaries and provide their unique perspectives, knowing that their contributions will be valued even if they challenge the status quo.

Research has consistently demonstrated the positive impact of psychological safety on team dynamics and outcomes. Studies have shown that psychological safety enhances team learning behaviors, promotes knowledge sharing, and encourages creative problem-solving (Edmondson & Bransby, 2023). Additionally, psychological safety mitigates potential negative effects of team differences by fostering greater cohesion and collaboration among members with differing perspectives. In the context of science teams, psychological safety serves as the foundation for effective

collaboration, ensuring that teams can fully leverage their collective knowledge to achieve their scientific goals.

The effectiveness of science teams is not determined solely by individual characteristics but also by vital team-level factors. The behaviors these teams engage in (i.e., team processes) and the dynamics that manifest along the way (i.e., emergent states) are indicative of high-functioning science teams. Team processes, such as communication, coordination, and conflict management, represent the behaviors and interactions that enable teams to work together efficiently. These go together with the emergent states, including trust, psychological safety, and shared mental models that evolve over time, creating an environment where team members feel safe to share ideas and collaborate effectively. The processes and emergent states mentioned here are particularly important for science teams given their interdisciplinary nature and the complexity of their goals. There are many other processes and emergent states that contribute to successful teamwork across various domains. Ultimately, fostering both strong team processes and positive emergent states is crucial for ensuring that science teams can collaborate effectively and achieve their collective goals.

Multiteam System Factors

Much of what has been discussed so far has been well established since the publication of the previous report in 2015. Concepts such as team cognition, psychological safety, and the importance of shared leadership have long been recognized as critical components of high-functioning science teams. These factors contribute to the effective coordination of diverse expertise, the integration of knowledge, and the overall success of teams working toward complex goals. However, one area of research that has advanced significantly since the last report is the study of multiteam systems. Multiteam systems, which involve multiple interdependent teams working together to achieve a shared superordinate goal, have become increasingly relevant in the realm of scientific research (Zaccaro et al., 2020). A complementary development is the exponential growth in the use of multiteam systems within the scientific community, as evidenced by the rise of large-scale initiatives such as Science and Technology Centers (STCs) and Engineering Research Centers (ERCs).¹ These centers are built on the principle of cross-disciplinary collaboration, bringing teams together from various fields and institutions to tackle complex scientific and technological challenges. Given this shift, it is crucial to examine the specific attributes of multiteam systems that contribute to the success of their scientific endeavors. Understanding

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¹ For more information on STCs and ERCs, see https://www.nsf.gov/od/oia/ia/stc and https://www.nsf.gov/eng/engineering-research-centers

these factors—such as boundary status, component team distance, and the type of superordinate goals—will allow better assessment of what drives high performance in science multiteam systems and how these teams can be supported and structured to maximize their impact on research and innovation.

Multiteam System Attributes

Much like the previous section, which focused on individual- and team-level factors associated with high-functioning teams, the first aspect of a multiteam system that will be discussed are the key characteristics of the multiteam system itself. These characteristics are critical because they shape how teams within the system interact, coordinate, and ultimately achieve their shared objectives. In the context of science multiteam systems—multiteam systems composed of science teams—understanding these characteristics is important for ascertaining the most critical processes to focus on when developing and evaluating a science multiteam system. The three primary characteristics of multiteam systems that will be discussed are boundary status, component team distance, and superordinate goal type. Each attribute plays a distinct role in influencing the effectiveness of the scientific multiteam system.

Boundary status refers to whether the teams within the multiteam system come from within a single organization (internal boundary status) or across multiple organizations (external boundary status). Internal multiteam systems tend to operate under a shared set of norms, communication protocols, and authority structures, which can facilitate coordination and collaboration. On the other hand, external multiteam systems, composed of teams from different organizations or institutions, often face more challenges in aligning their goals, managing resources, and navigating different organizational cultures. For example, Kotarba et al. (2023) found that in a multi-institutional cross-disciplinary translational team focused on long COVID-19 research, teams from different universities faced challenges in coordinating efforts due to their external boundary status. Each institution had different operational norms and patient populations, which complicated collaboration and goal alignment across the team. For science multiteam systems, which often involve teams from multiple institutions (e.g., universities, research centers), understanding the boundary status is crucial, as it helps to anticipate challenges related to communication, trust-building, and the harmonization of different institutional priorities, all of which are vital for the success of large-scale interdisciplinary projects (Zaccaro et al., 2020).

The second attribute, component team distance (CTD), refers to the geographical, cultural, functional, or disciplinary distance between the teams that make up the multiteam system. In high CTD systems, teams are more

likely to face challenges in coordinating efforts due to physical separation, differences in disciplinary languages, or contrasting cultural norms. For scientific multiteam systems, CTD can be particularly relevant because science teams often work across institutions, regions, and even countries, introducing significant logistical and communication challenges. High CTD can also exacerbate misunderstandings or delays, slowing research progress or hindering collaboration. For example, Ingersoll et al. (2024) demonstrated the complexities of conducting a multisite clinical trial using virtual multiteam systems, where geographically dispersed teams had to navigate communication barriers, time zone differences, and coordination issues to maintain fidelity and progress in data collection. However, understanding CTD can also help teams develop strategies for bridging these divides, such as paying special attention to developing communication norms, creating cross-team coordination roles, or holding regular in-person meetings to strengthen relationships and align goals.

The final attribute to consider is the superordinate goal type, which refers to the overarching goal all teams within the multiteam system are working toward. Superordinate goals in multiteam systems can generally be classified as either intellectual or physical. Intellectual goals involve generating knowledge, such as developing new theories or conducting scientific research, while physical goals may include building a tangible product or implementing a solution. In multiteam systems, it is crucial that teams not only focus on their individual or team-specific goals but also recognize how their contributions align with and support the broader system-level objective. As Carter et al. (2019) emphasize, one of the key features of multiteam systems is the hierarchical structure of goals, where component teams have both local (subordinate) goals and shared (superordinate) goals. Aligning these goals is essential for the system’s success, as misalignment can lead to internal tensions, competition, or even conflict between teams, which can undermine the overall performance of the multiteam system. Ensuring that teams remain focused on the broader objective while pursuing their local goals is a balancing act that requires careful management (Shuffler et al., 2015).

Crucial Multiteam System Indicators

Beyond the structural components of multiteam systems, behavioral processes and emergent states play a critical role in determining their overall effectiveness. Many of these factors have been discussed in the previous section, such as communication, team cognition, and psychological safety, which are essential for team success at the multiteam level (Shuffler & Carter, 2018; Zaccaro et al., 2020). However, these elements take on added complexity at the multiteam system level, where coordination and

collaboration occur not just within teams but also across multiple teams. While many of the core team-level components remain relevant, there are unique considerations specific to multiteam systems, especially within the scientific context. In the following section, three critical factors that are indicative of science multiteam system effectiveness are highlighted: boundary spanners, who facilitate cross-team communication and bridge organizational divides; inter-team coordination, which ensures the alignment and integration of outputs across teams; and balancing countervailing forces, which helps maintain system cohesion while allowing individual teams to achieve their specific objectives.

A crucial component of high-functioning, effective science multiteam systems is boundary spanners. Boundary spanners are individuals who facilitate communication and coordination across the boundaries between different teams, especially when those teams come from different disciplines, organizations, or geographic locations. They are particularly important in complex multiteam system structures, such as those with high CTD or external boundary status, where teams face greater challenges in aligning goals, managing cultural differences, and coordinating tasks. This can be seen in the challenges and recommendations provided by Kotarba et al. (2023). Boundary spanners help bridge gaps between teams by facilitating information exchange, trust-building, and alignment of objectives, which are crucial for maintaining system-wide cohesion (Carter et al., 2019; Zaccaro et al., 2020). Importantly, boundary spanners operate at both the component team level and the multiteam system level—coming not only from leadership but also from team members who help manage interdependencies across the system.

The boundary spanner role is critical for the success of science multiteam systems because it ensures that teams can collaborate effectively despite differences in expertise, institutional/organizational norms, and goals. Boundary spanners help manage the complex dynamics inherent in interdisciplinary and multi-institutional projects, ensuring that communication and information flow freely and that teams are aligned toward the multiteam system’s superordinate goals. For instance, boundary spanners likely play important roles in large-scale, federally funded projects such as STCs and ERCs, where teams from various institutions collaborate to solve complex, multidisciplinary challenges. In these projects, boundary spanners facilitate collaboration between laboratories working on different aspects of the center’s overarching goal, such as combining expertise in artificial intelligence with domain-specific knowledge like climate science or engineering. They help ensure that research findings from one team are effectively integrated into the work of other teams and that all teams remain aligned with the overarching center objectives. In science multiteam systems, where research teams from different fields or institutions collaborate to address

complex problems, boundary spanners ensure that these collaborations are productive and not hindered by miscommunication or misalignment.

Relatedly, another component of high-functioning science multiteam systems is their ability to coordinate each component team within their system. Inter-team coordination refers to the processes by which teams manage interdependencies and synchronize their activities to achieve both their own goals and the system’s superordinate goals (Ziegert et al., 2022). High-functioning multiteam systems require coordination both within and between teams, which may involve shifting focuses over time as projects evolve (Zaccaro et al., 2020). Early on, multiteam systems may prioritize task allocation and communication protocols, but as time progresses, coordination shifts toward aligning outputs and integrating efforts across teams. Ensuring that there is alignment between superordinate and subordinate goals facilitates effective inter-team coordination, as teams can see how their efforts contribute to the broader objectives.

Inter-team coordination is critical for the success of science multiteam systems because it enables teams to work in parallel while ensuring that their outputs are integrated into a cohesive whole. Returning to the example of STCs and ERCs, in these large, federally funded initiatives, teams with specialized expertise often address different facets of a complex problem. For instance, one team might focus on developing computational models that simulate neural circuits responsible for visual processing, while another team conducts experimental studies on how the brain processes social intelligence. Without effective inter-team coordination, these efforts could remain siloed, preventing the full integration of computational insights with biological data. However, when coordination mechanisms are in place, the contributions of each team—whether focused on machine learning, neuroscience, cognitive science, or any other scientific field—are aligned and complement one another, leading to scientific progress aligned with the STC’s or ERC’s superordinate goals.

Lastly, a unique element of effective science multiteam systems is balancing countervailing forces. High-functioning multiteam systems pay special attention to the unique needs of both the component teams and the overall multiteam system. Something that may be good for a single team, such as increasing cohesion or prioritizing its own goals, can sometimes detract from the performance of the entire multiteam system (Carter et al., 2019). For example, promoting strong cohesion within one team might reduce its willingness to collaborate with other teams, leading to silos and reduced system-level performance. Similarly, focusing too much on local team goals can result in a misalignment with the superordinate goals of the multiteam system, undermining the overall success.

Balancing these forces is critical for the success of science multiteam systems because it ensures that the system can leverage the strengths of

each team while maintaining a focus on system-wide objectives. In the case of large, interdisciplinary initiatives like Clinical and Translational Science Awards, where teams often come from different fields with distinct goals and priorities, balancing these forces is essential for integrating different perspectives and achieving the broader research objectives.² For example, one team may focus on clinical trial design while another works on community engagement. Without careful management, these distinct priorities could lead to misalignment or competition for resources. However, by balancing the needs of individual teams with the overall goals of the multiteam system, the system can capitalize on the unique expertise of each team while ensuring progress toward the shared mission.

High-functioning science multiteam systems can take many forms, shaped by factors such as boundary status, component team distance, and the nature of the superordinate goal. These elements play a critical role in determining how teams interact, navigate logistical and disciplinary differences, and align their goals to drive the collective success of the system. Equally important are the processes and emergent states that support these interactions, such as boundary spanning, inter-team coordination, and balancing competing priorities, which together define a high-functioning science multiteam system. Despite the importance of these attributes and processes, empirical research specifically focused on scientific multiteam systems remains relatively limited, with a few notable exceptions (e.g., Kotarba et al., 2023). This gap is unfortunate given the increasing prevalence of large-scale scientific initiatives, such as STCs and ERCs, which have immense potential to advance scientific discovery and innovation.

REFERENCES

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² For more information about Clinical and Translational Science Awards, see https://ncats.nih.gov/research/research-activities/ctsa.