Flat teams drive scientific innovation

Seven years of time-tracking data capturing collaboration and failure dynamics: the Gryzzly dataset

We introduce the Gryzzly time-tracking dataset: a longitudinal, high-resolution collection of 4.4 million interactions recorded between 12,447 users and 173,323 tasks across 50,759 projects, spanning from 2017 to 2024. Compiled from real-world usage data of the Gryzzly software, the dataset encompasses projects from diverse industries such as marketing, finance, and banking. It provides a detailed view of daily activities contributing to project completion, including information about the users involved, the tasks they worked on, and the planned versus actual costs of each project. To validate the published data, we analyzed the underlying temporal collaboration network, revealing expected patterns such as circadian user activity, power-law characteristics in degree distributions, and heterogeneously distributed inter-declaration times. Additionally, we observed well-documented failure dynamics, including a heavy-tailed distribution of failure streak lengths and diverging performance improvement trends between successful and failed projects. These features make the Gryzzly dataset a key resource for studying productivity, team dynamics, and project failure.

Academic mentees thrive in big groups, but survive in small groups

Mentoring is a key component of scientific achievements, contributing to overall measures of career success for mentees and mentors. Within the scientific community, possessing a large research group is often perceived as an indicator of exceptional mentorship and high-quality research. However, such large, competitive groups may also escalate dropout rates, particularly among early-career researchers. Overly high dropout rates of young researchers may lead to severe postdoc shortage and loss of top-tier academics in contemporary academia. In this context, we collect longitudinal genealogical data on mentor-mentee relations and their publications, and analyse the influence of a mentor's group size on the future academic longevity and performance of their mentees. Our findings indicate that mentees trained in larger groups tend to exhibit superior academic performance compared with those from smaller groups, provided they remain in academia post graduation. However, we also observe two surprising patterns: academic survival rate is significantly lower for (1) mentees from larger groups and for (2) mentees with more productive mentors. The trend is verified in institutions of different prestige levels. These findings highlight a negative correlation between a mentor's success and the academic survival rate of their mentees, prompting a rethinking of effective mentorship and offering actionable insights for career advancement.

Corruption and hierarchy: a replication of studies 1c and 6 of Fath & Kay 2018

Corruption represents a complex problem firmly embedded within our societal structures, governments, and organizations. The current study aimed to build a clearer consensus on the extent to which perceptions of organizational corruption are associated with organizational hierarchy. Two high-powered close replications of studies 1c and 6 by Fath and Kay provide further evidence for the claim that taller organizational structures are associated with greater perceived potential for corruption, and that these perceptions may compromise subsequent trust-related outcomes. Our results reinforce the importance of organizational design and aim to inspire future works to consider the ways in which researchers and organizations can minimize corruption. Preregistration, data and materials can be found on the OSF: https://osf.io/zb5j2.

Science of science: A multidisciplinary field studying science

Science and knowledge are studied by researchers across many disciplines, examining how they are developed, what their current boundaries are and how we can advance them. By integrating evidence across disparate disciplines, the holistic field of science of science can address these foundational questions. This field illustrates how science is shaped by many interconnected factors: the cognitive processes of scientists, the historical evolution of science, economic incentives, institutional influences, computational approaches, statistical, mathematical and instrumental foundations of scientific inference, scientometric measures, philosophical and ethical dimensions of scientific concepts, among other influences. Achieving a comprehensive overview of a multifaceted field like the science of science requires pulling together evidence from the many sub-fields studying science across the natural and social sciences and humanities. This enables developing an interdisciplinary perspective of scientific practice, a more holistic understanding of scientific processes and outcomes, and more nuanced perspectives to how scientific research is conducted, influenced and evolves. It enables leveraging the strengths of various disciplines to create a holistic view of the foundations of science. Different researchers study science from their own disciplinary perspective and use their own methods, and there is a large divide between quantitative and qualitative researchers as they commonly do not read or cite research using other methodological approaches. A broader, synthesizing paper employing a qualitative approach can however help provide a bridge between disciplines by pulling together aspects of science (economic, scientometric, psychological, philosophical etc.). Such an approach enables identifying, across the range of fields, the powerful role of our scientific methods and instruments in shaping most aspects of our knowledge and science, whereas economic, social and historical influences help shape what knowledge we pursue. A unifying theory is then outlined for science of science - the new-methods-drive-science theory.

Simple autonomous agents can enhance creative semantic discovery by human groups

Innovation is challenging, and theory and experiments indicate that groups may be better able to identify and preserve innovations than individuals. But innovation within groups faces its own challenges, including groupthink and truncated diffusion. We performed experiments involving a game in which people search for ideas in various conditions: alone, in networked social groups, or in networked groups featuring autonomous agents (bots). The objective was to search a semantic space of 20,000 nouns with defined similarities for an arbitrary noun with the highest point value. Participants (N = 1875) were embedded in networks (n = 125) of 15 nodes to which we sometimes added 2 bots. The bots had 3 possible strategies: they shared a random noun generated by their immediate neighbors, or a noun most similar from among those identified, or a noun least similar. We first confirm that groups are better able to explore a semantic space than isolated individuals. Then we show that when bots that share the most similar noun operate in groups facing a semantic space that is relatively easy to navigate, group performance is superior. Simple autonomous agents with interpretable behavior can affect the capacity for creative discovery of human groups.

The Impact of Career Plateaus on Job Performance: The Roles of Organizational Justice and Positive Psychological Capital

Previous studies suggest that career plateaus have detrimental effects on employees' satisfaction and performance. Psychological distress generated by career plateaus hinders organizations from achieving the United Nations' Sustainable Development Goals (UNSDGs) of 'health and well-being at work' (SDG-3) and 'decent work' (SDG-8). How to mitigate the negative impact of career plateaus becomes the key to enhancing sustainable well-being at work. However, the influencing mechanisms of career plateaus have not been fully discussed, especially regarding employees' psychological processes. Drawing on the equity theory and the conservation of resource theory, this study examines the influence mechanism of career plateaus on employee job performance via organizational justice, with positive psychological capital moderating the process. Mplus and the Process macro for SPSS are adopted to conduct confirmatory factor analysis and regression analyses. Building on 368 supervisor-employee paired questionnaires with an average of eight employees per supervisor, empirical results indicate that employees who encounter career plateaus reduce their perceived organizational justice to discourage them from performing well in their jobs. Positive psychological capital, however, mitigates the negative effects of career plateaus on perceived organizational justice and the indirect effects of career plateaus on job performance through organizational justice. Theoretically, this study advances our understanding of the influence mechanism of career plateaus on employees' job performance. Practical implications are also drawn for organizations to alleviate the negative impact of career plateaus to promote sustainable well-being at work.

Beyond kindness: a proposal for the flourishing of science and scientists alike

We argue that many of the crises currently afflicting science can be associated with a present failure of science to sufficiently embody its own values. Here, we propose a response beyond mere crisis resolution based on the observation that an ethical framework of flourishing derived from the Buddhist tradition aligns surprisingly well with the values of science itself. This alignment, we argue, suggests a recasting of science from a competitively managed activity of knowledge production to a collaboratively organized moral practice that puts kindness and sharing at its core. We end by examining how Flourishing Science could be embodied in academic practice, from individual to organizational levels, and how that could help to arrive at a flourishing of scientists and science alike.

Remote collaboration fuses fewer breakthrough ideas

Theories of innovation emphasize the role of social networks and teams as facilitators of breakthrough discoveries1-4. Around the world, scientists and inventors are more plentiful and interconnected today than ever before4. However, although there are more people making discoveries, and more ideas that can be reconfigured in new ways, research suggests that new ideas are getting harder to find5,6-contradicting recombinant growth theory7,8. Here we shed light on this apparent puzzle. Analysing 20 million research articles and 4 million patent applications from across the globe over the past half-century, we begin by documenting the rise of remote collaboration across cities, underlining the growing interconnectedness of scientists and inventors globally. We further show that across all fields, periods and team sizes, researchers in these remote teams are consistently less likely to make breakthrough discoveries relative to their on-site counterparts. Creating a dataset that allows us to explore the division of labour in knowledge production within teams and across space, we find that among distributed team members, collaboration centres on late-stage, technical tasks involving more codified knowledge. Yet they are less likely to join forces in conceptual tasks-such as conceiving new ideas and designing research-when knowledge is tacit9. We conclude that despite striking improvements in digital technology in recent years, remote teams are less likely to integrate the knowledge of their members to produce new, disruptive ideas.

Quantifying hierarchy and prestige in US ballet academies as social predictors of career success

In the recent decade, we have seen major progress in quantifying the behaviors and the impact of scientists, resulting in a quantitative toolset capable of monitoring and predicting the career patterns of the profession. It is unclear, however, if this toolset applies to other creative domains beyond the sciences. In particular, while performance in the arts has long been difficult to quantify objectively, research suggests that professional networks and prestige of affiliations play a similar role to those observed in science, hence they can reveal patterns underlying successful careers. To test this hypothesis, here we focus on ballet, as it allows us to investigate in a quantitative fashion the interplay of individual performance, institutional prestige, and network effects. We analyze data on competition outcomes from 6363 ballet students affiliated with 1603 schools in the United States, who participated in the Youth America Grand Prix (YAGP) between 2000 and 2021. Through multiple logit models and matching experiments, we provide evidence that schools' strategic network position bridging between communities captures social prestige and predicts the placement of students into jobs in ballet companies. This work reveals the importance of institutional prestige on career success in ballet and showcases the potential of network science approaches to provide quantitative viewpoints for the professional development of careers beyond science.

Multidisciplinary learning through collective performance favors decentralization

Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through "exploring" searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through "exploiting" searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.

North-south scientific collaborations on research datasets: a longitudinal analysis of the division of labor on genomic datasets (1992-2021)

Collaborations between scientists from the global north and global south (N-S collaborations) are a key driver of the "fourth paradigm of science" and have proven crucial to addressing global crises like COVID-19 and climate change. However, despite their critical role, N-S collaborations on datasets are not well understood. Science of science studies tend to rely on publications and patents to examine N-S collaboration patterns. To this end, the rise of global crises requiring N-S collaborations to produce and share data presents an urgent need to understand the prevalence, dynamics, and political economy of N-S collaborations on research datasets. In this paper, we employ a mixed methods case study research approach to analyze the frequency of and division of labor in N-S collaborations on datasets submitted to GenBank over 29 years (1992-2021). We find: (1) there is a low representation of N-S collaborations over the 29-year period. When they do occur, N-S collaborations display "burstiness" patterns, suggesting that N-S collaborations on datasets are formed and maintained reactively in the wake of global health crises such as infectious disease outbreaks; (2) The division of labor between datasets and publications is disproportionate to the global south in the early years, but becomes more overlapping after 2003. An exception in the case of countries with lower S&T capacity but high income, where these countries have a higher prevalence on datasets (e.g., United Arab Emirates). We qualitatively inspect a sample of N-S dataset collaborations to identify leadership patterns in dataset and publication authorship. The findings lead us to argue there is a need to include N-S dataset collaborations in measures of research outputs to nuance the current models and assessment tools of equity in N-S collaborations. The paper contributes to the SGDs objectives to develop data-driven metrics that can inform scientific collaborations on research datasets.

Continued collaboration shortens the transition period of scientists who move to another institution

Scientific collaboration plays a significant role in scientists' research performance. When scientists move from one institution to another and leave the team they belong to or lead, they may continue collaborating with the former team because engaging in or building a new team takes time. In this study, we collected data from the Open Researcher and Contributor ID (ORCID) website on 2,922 scientists who published first-tier journal papers defined by the Chinese Academy of Science (CAS) before they moved to a new institution. By applying a Poisson regression model to the dataset, we explored the correlation between continued collaboration and the transition period after scientists moved, which is defined as the time span between the year of the move and the year when they published their first top-tier journal paper after moving. Our findings indicated that: (1) continued collaboration significantly shortens the transition period by 27.2%; (2) continued collaboration significantly shortens the transition period of senior scientists to a larger extent than that of junior scientists; (3) continued collaboration significantly shortens the transition period of social scientists to a larger extent than that of natural scientists; (4) the transition period is shorter after moves for scientists with higher inherent potential; and (5) there is no evidence that the transition period is associated with culture-related differences between the origin country and the destination country after the move, or whether they had lived in the destination country before.

The credibility crisis and democratic governance: how to reform university governance to be compatible with the nature of science

To address the credibility crisis facing many disciplines, change is needed at the institutional level. Science will only function optimally if the culture by which it is governed becomes aligned with the way of thinking required in science itself. The paper suggests a series of graduated reforms to university governance, to radically reform the operation of universities. The reforms are based on existing established open democratic practices. The aim is for universities to become consistent with the flourishing of science and research more generally.

The network science of collective intelligence

In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts.

Organizational Development as Generative Entrenchment

A critical task for organizations is how to best structure themselves to efficiently allocate information and resources to individuals tasked with solving sub-components of the organization's central problems. Despite this criticality, the processes by which organizational structures form remain largely opaque within organizational theory, with most approaches focused on how structure is influenced by individual managerial heuristics, normative cultural perceptions, and trial-and-error. Here, we propose that a broad understanding of organizational formation can be aided by appealing to generative entrenchment, a theory from developmental biology that helps explain why phylogenetically diverse animals appear similar as embryos. Drawing inferences from generative entrenchment and applying it to organizational differentiation, we argue that the reason many organizations appear structurally similar is due to core informational restraints on individual actors beginning at the top and descending to the bottom of these informational hierarchies, which reinforces these structures via feedback between separate levels. We further argue that such processes can lead to the emergence of a variety of group-level traits, an important but undertheorized class of phenomena in cultural evolution.