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Contemporary science has been characterized by an exponential growth in publications and a rise of team science. At the same time, there has been an increase in the number of awarded PhD degrees, which has not been accompanied by a similar expansion in the number of academic positions. In such a competitive environment, an important measure of academic success is the ability to maintain a long active career in science. In this paper, we study workforce trends in three scientific disciplines over half a century. We find dramatic shortening of careers of scientists across all three disciplines. The time over which half of the cohort has left the field has shortened from 35 y in the 1960s to only 5 y in the 2010s. In addition, we find a rapid rise (from 25 to 60% since the 1960s) of a group of scientists who spend their entire career only as supporting authors without having led a publication. Altogether, the fraction of entering researchers who achieve full careers has diminished, while the class of temporary scientists has escalated. We provide an interpretation of our empirical results in terms of a survival model from which we infer potential factors of success in scientific career survivability. Cohort attrition can be successfully modeled by a relatively simple hazard probability function. Although we find statistically significant trends between survivability and an author’s early productivity, neither productivity nor the citation impact of early work or the level of initial collaboration can serve as a reliable predictor of ultimate survivability.

Experience plays a critical role in crafting high-impact scientific work. This is particularly evident in top multidisciplinary journals, where a scientist is unlikely to appear as senior author if he or she has not previously published within the same journal. Here, we develop a quantitative understanding of author order by quantifying this “chaperone effect,” capturing how scientists transition into senior status within a particular publication venue. We illustrate that the chaperone effect has a different magnitude for journals in different branches of science, being more pronounced in medical and biological sciences and weaker in natural sciences. Finally, we show that in the case of high-impact venues, the chaperone effect has significant implications, specifically resulting in a higher average impact relative to papers authored by new principal investigators (PIs). Our findings shed light on the role played by experience in publishing within specific scientific journals, on the paths toward acquiring the necessary experience and expertise, and on the skills required to publish in prestigious venues.

Silicon-based photodetectors operating in the near-infrared (NIR) wavelength range ( λ = 700–1,100 nm) are essential for applications such as light detection and ranging, facial recognition, and eye-tracking. However, silicon’s low absorption coefficient in this range limits photodetection efficiency. While recent advances in nano-diffraction structures have improved photo-absorption by increasing the effective absorption path, optimizing carrier dynamics remains challenging. In the NIR regime, photons penetrate deeply into the silicon substrate, making it critical to align the spatial distribution of photo-generated carriers with the charge collection regions. However, the angular and spatial behavior of carrier generation (CG) and collection under nano-diffraction structures remain underexplored. This study presents an analytical model that visualizes CG pathways and corresponding collection probabilities induced by plasmonic diffraction structures, providing insight into diffraction-driven CG in silicon. The model is experimentally validated through photocurrent responses in non-illuminated neighboring pixels, directly revealing plasmonic diffraction effects. The results show that diffraction enhances light absorption and enables visualization of the CG and collection pathways based on the diffraction angle. This approach enables the spatial overlap of CG and collection pathways, efficiently guiding incident photons to photosensitive regions. This framework offers a new strategy to enhance NIR photodetector performance through diffraction-guided light propagation and device-specific modeling.

Rapid research progress in science and technology (S&T) and continuously shifting workforce needs exert pressure on each other and on the educational and training systems that link them. Higher education institutions aim to equip new generations of students with skills and expertise relevant to workforce participation for decades to come, but their offerings sometimes misalign with commercial needs and new techniques forged at the frontiers of research. Here, we analyze and visualize the dynamic skill (mis-) alignment between academic push, industry pull, and educational offerings, paying special attention to the rapidly emerging areas of data science and data engineering (DS/DE). The visualizations and computational models presented here can help key decision makers understand the evolving structure of skills so that they can craft educational programs that serve workforce needs. Our study uses millions of publications, course syllabi, and job advertisements published between 2010 and 2016. We show how courses mediate between research and jobs. We also discover responsiveness in the academic, educational, and industrial system in how skill demands from industry are as likely to drive skill attention in research as the converse. Finally, we reveal the increasing importance of uniquely human skills, such as communication, negotiation, and persuasion. These skills are currently underexamined in research and undersupplied through education for the labor market. In an increasingly data-driven economy, the demand for “soft” social skills, like teamwork and communication, increase with greater demand for “hard” technical skills and tools.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry-, occupation-, and location-specific knowledge carried by workers from one establishment to the next, using a dataset summarizing the individual work history for an entire country. We study pioneer firms—firms operating in an industry that was not present in a region—because the success of pioneers is the basic unit of regional economic diversification. We find that the growth and survival of pioneers increase significantly when their first hires are workers with experience in a related industry and with work experience in the same location, but not with past experience in a related occupation. We compare these results with new firms that are not pioneers and find that industry-specific knowledge is significantly more important for pioneer than for nonpioneer firms. To address endogeneity we use Bartik instruments, which leverage national fluctuations in the demand for an activity as shocks for local labor supply. The instrumental variable estimates support the finding that industry-specific knowledge is a predictor of the survival and growth of pioneer firms. These findings expand our understanding of the micromechanisms underlying regional economic diversification.

Stories of mega-jams that last tens of hours or even days appear not only in fiction but also in reality. In this context, it is important to characterize the collapse of the network, defined as the transition from a characteristic travel time to orders of magnitude longer for the same distance traveled. In this multicity study, we unravel this complex phenomenon under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time τ that takes a representative group of commuters to arrive at their destinations once their maximum density has been reached. While this time differs from city to city, it can be explained by Γ, defined as the ratio of the vehicle miles traveled to the total vehicle distance the road network can support per hour. Modifying 􀀀 can improve τ and directly inform planning and infrastructure interventions. In this study we focus on measuring the vulnerability of the system by increasing the volume of cars in the network, keeping the road capacity and the empirical spatial dynamics from origins to destinations unchanged. We identify three states of urban traffic, separated by two distinctive transitions. The first one describes the appearance of the first bottlenecks and the second one the collapse of the system. This collapse is marked by a given number of commuters in each city and it is formally characterized by a nonequilibrium phase transition.

Scientific prizes confer credibility to persons, ideas, and disciplines, provide financial incentives, and promote community-building celebrations. We examine the growth dynamics and interlocking relationships found in the worldwide scientific prize network. We focus on understanding how the knowledge linkages among prizes and scientists’ propensities for prizewinning relate to knowledge pathways between disciplines and stratification within disciplines. Our data cover more than 3,000 different scientific prizes in diverse disciplines and the career histories of 10,455 prizewinners worldwide for over 100 years. We find several key links between prizes and scientific advances. First, despite an explosive proliferation of prizes over time and across the globe, prizes are more concentrated within a relatively small group of scientific elites, and ties among elites are highly clustered, suggesting that a relatively constrained number of ideas and scholars push the boundaries of science. For example, 64.1% of prizewinners have won two prizes and 13.7% have won five or more prizes. Second, certain prizes strongly interlock disciplines and subdisciplines, creating key pathways by which knowledge spreads and is recognized across science. Third, genealogical and coauthorship networks predict who wins multiple prizes, which helps to explain the interconnectedness among celebrated scientists and their pathbreaking ideas.

PurposeIn order to further understand the research status and prospect, the purpose of this paper is to adopt a novel method in the research field of institutional investor to depict the knowledge structure and the evolution path over the past three decades.Design/methodology/approachBased on the 4,194 records retrieved from Web of Science, Citespace combined with VOSviewer are employed to perform visualized analysis.FindingsThe results reveal that the number of published articles of research on institutional investor has an exponential growth. Although the United States is the most significant contributor with more publications compared with other countries, Malaysia and Nigeria show higher centrality in the research network worldwide. Furthermore, “shareholder activism”, “corporate governance”, “global convergence”, “corporate reporting regulation” and “individual investor” are the largest five knowledge clusters. “Media coverage”, “corporate social responsibility” and “stock price crash risk” are the latest three knowledge clusters. Moreover, “governance worldwide”, “institutional character”, “dynamic information environment”, “investment patterns” and “sustainable development” are the potential extended research fields in the future.Originality/valueThis research helps the scholars and participants to capture the knowledge structure of research on institutional investors and to develop a reference to future opportunities.

Human survival depends on our ability to predict future outcomes so that professionals can make informed decisions. Human cognition and perception are optimized for local, short-term decision-making, such as deciding when to fight or flight, whom to mate, or what to eat. In the 21st century, computational models and visualizations of model results inform much of humans decision-making: near real-time weather forecasts help us decide when to take an umbrella, plant, or harvest; where to ground airplanes; or when to evacuate inhabitants in the path of a hurricane, tornado, or flood. Here, Borner et al look at the tends and development of forecasting innovations in science, technology, and education.

This study investigated the effects of reading a science text illustrated by either a labeled or unlabeled picture. Both the online process of reading the text and the offline conceptual learning from the text were examined. Eye-tracking methodology was used to trace text and picture processing through indexes of first-and second-pass reading or inspection. Fifty-six sixth graders were randomly assigned to one of three reading conditions (text with a labeled illustration, text with an unlabeled illustration, or text only) in a pretest, immediate posttest, and delayed posttest design. Results showed no differences for factual knowledge as a function of reading condition. However, for the transfer of knowledge at both posttests, readers of the text with the labeled illustration outperformed readers in the other two conditions, who did not differentiate from each other. Eye-fixation data showed that the labeled illustration promoted more integrative processing of the learning material, as revealed by the time spent refixating text segments while reinspecting the illustration. In addition, relations emerged between the indexes of integration of text and picture during online processing and the offline measures of factual knowledge and transfer of knowledge. Overall, in accordance with the theoretical assumptions of the multimedia principle, the study underlines the crucial role of integrative processing of words and graphics to sustain learning from illustrated text. Moreover, the study indicates that this integrative processing can be effectively supported by appropriate visual signaling.