Quantifying the dynamics of peak disruption in scientific careers

We examine the disruption of researchers with long-lived careers in Computer Science and Physics. Despite the epistemological differences between such disciplines, we consistently find that a researcher's most disruptive publication does not occur at random during their career, as it cannot be explained by a null model. Such publication is accompanied by a peak year in which researchers publish other work that exhibits a higher level of disruption than average. Through a series of linear models, we show that the disruption achieved by a researcher during their peak year is higher when it is preceded by a long period of focus and low productivity. These findings are in stark contrast with the dynamics of academic impact. In these dynamics, researchers are incentivized by the prevalent paradigms of scientific evaluation to pursue high productivity and incremental-less disruptive-work, as evidenced by extensive literature.

Twenty years of research on exercise-induced fatigue: A bibliometric analysis of hotspots, bursts, and research trends

Data from the Web of Science Core Collection (2004-2023) on "exercise-induced fatigue" were analyzed using bibliometric tools to explore research trends across countries, institutions, authors, journals, and keywords. The analysis was limited to "Article" and "Review" literature types. Among 4531 publications, the United States contributed the most articles (1005), followed by England (559) and China (516). The most influential institution was Universidade de São Paulo, while the most productive was Institut National de la Santé et de la Recherche Médicale with 103 papers. The European Journal of Applied Physiology ranked as the top journal with 233 articles. Millet Guillaume Y. emerged as the most prolific author, and Amann Markus was the most cited. Recent keyword trends showed a surge in terms like "physical activity" and "aerobic exercise," while "fatigue" and "exercise" remained dominant. Notable findings were observed in oncology, engineering, and multidisciplinary studies, indicating potential research trends. Oxidative stress was identified as the most commonly mentioned mechanism in exercise-induced fatigue studies. This bibliometric analysis highlights current research trends and gaps, suggesting that future studies should focus on understanding the mechanisms of exercise-induced fatigue, developing objective measurement criteria, and exploring strategies for its alleviation.

Citation manipulation through citation mills and pre-print servers

Citations are widely considered in scientists' evaluation. As such, scientists may be incentivized to inflate their citation counts. While previous literature has examined self-citations and citation cartels, it remains unclear whether scientists can purchase citations. Here, we compile a dataset of ~1.6 million profiles on Google Scholar to examine instances of citation fraud on the platform. We survey faculty at highly-ranked universities, and confirm that Google Scholar is widely used when evaluating scientists. We then engage with a citation-boosting service, and manage to purchase 50 citations while assuming the identity of a fictional author. Taken as a whole, our findings bring to light new forms of citation manipulation, and emphasize the need to look beyond citation counts.

Targeted Avoidance in Complex Networks

The study of spreading in networks presents a fascinating topic with a wide array of practical applications. Various strategies have been proposed to attack or immunize networks. However, it is often not feasible or necessary to consider the entire network in the context of real-world systems. Here, we focus on a certain group of target nodes with the aim of disconnecting them from the global network structure. For instance, it becomes possible to effectively prevent the transmission of the disease to vulnerable populations, such as infants and the elderly, by isolating some specific nodes such as their caretakers during the epidemic. From this perspective of targeted avoidance, we introduce a series of target centrality indicators and apply them to segment the target nodes from the giant component of the network. Additionally, we propose a more effective iterative graph-segmentation method for targeted immunization. Our experimental findings reveal that our proposed method can substantially reduce the number of nodes required for removal when compared with the methods based on target centrality, which implies a significant cost effectiveness in isolating target nodes from the rest of the network. Finally, we verify our method on a large mobility network in the scenario of the COVID-19 pandemic, and find that our method can effectively protect the elderly by immunizing or isolating a very small group of nodes.

Collective dynamics behind success

Understanding the collective dynamics behind the success of ideas, products, behaviors, and social actors is critical for decision-making across diverse contexts, including hiring, funding, career choices, and the design of interventions for social change. Methodological advances and the increasing availability of big data now allow for a broader and deeper understanding of the key facets of success. Recent studies unveil regularities beneath the collective dynamics of success, pinpoint underlying mechanisms, and even enable predictions of success across diverse domains, including science, technology, business, and the arts. However, this research also uncovers troubling biases that challenge meritocratic views of success. This review synthesizes the growing, cross-disciplinary literature on the collective dynamics behind success and calls for further research on cultural influences, the origins of inequalities, the role of algorithms in perpetuating them, and experimental methods to further probe causal mechanisms behind success. Ultimately, these efforts may help to better align success with desired societal values.

China and the U.S. produce more impactful AI research when collaborating together

Artificial Intelligence (AI) has become a disruptive technology, promising to grant a significant economic and strategic advantage to nations that harness its power. China, with its recent push towards AI adoption, is challenging the U.S.'s position as the global leader in this field. Given AI's massive potential, as well as the fierce geopolitical tensions between China and the U.S., several recent policies have been put in place to discourage AI scientists from migrating to, or collaborating with, the other nation. Nevertheless, the extent of talent migration and cross-border collaboration are not fully understood. Here, we analyze a dataset of over 350,000 AI scientists and 5,000,000 AI papers. We find that since 2000, China and the U.S. have led the field in terms of impact, novelty, productivity, and workforce. Most AI scientists who move to China come from the U.S., and most who move to the U.S. come from China, highlighting a notable bidirectional talent migration. Moreover, the vast majority of those moving in either direction have Asian ancestry. Upon moving, those scientists continue to collaborate frequently with those in the origin country. Although the number of collaborations between the two countries has increased since the dawn of the millennium, such collaborations continue to be relatively rare. A matching experiment reveals that the two countries have always been more impactful when collaborating than when each works without the other. These findings suggest that instead of suppressing cross-border migration and collaboration between the two nations, the science could benefit from promoting such activities.

Mid-career pitfall of consecutive success in science

The creativity of scientists often manifests as localized hot streaks of significant success. Understanding the underlying mechanisms of these influential phases can enhance the effectiveness of support systems and funding allocation, fostering groundbreaking discoveries worthy of accolades. Historically, analyses have suggested that hot streaks occur randomly over time. However, our research, through meticulous examination, reveals that these phases are not flatly distributed but are more frequent at the early and late stages of scientists' careers. Notably, both early and late hot streaks are marked by dense tie collaborations, with the former typically involving close partnerships with particular authors and the latter being characterized by involvement in large-scale projects compared with single-top or ordinary papers. This pattern indicates that mid-career researchers lack both intimate relations and resources to keep big projects, leading to "mid-career pitfall" of consecutive success. This insight holds profound implications for the development of policies and initiatives aimed at bolstering innovative research and discovery.

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.

Individual and team performance in cricket

Advancements in technology have recently allowed us to collect and analyse large-scale fine-grained data about human performance, drastically changing the way we approach sports. Here, we provide the first comprehensive analysis of individual and team performance in One-Day International cricket, one of the most popular sports in the world. We investigate temporal patterns of individual success by quantifying the location of the best performance of a player and find that they can happen at any time in their career, surrounded by a burst of comparable top performances. Our analysis shows that long-term performance can be predicted from early observations and that temporary exclusions of players from teams are often due to declining performances but are also associated with strong comebacks. By computing the duration of streaks of winning performances compared to random expectations, we demonstrate that teams win and lose matches consecutively. We define the contributions of specialists such as openers, all-rounders and wicket-keepers and show that a balanced performance from multiple individuals is required to ensure team success. Finally, we measure how transitioning to captaincy in the team improves the performance of batsmen, but not that of bowlers. Our work emphasizes how individual endeavours and team dynamics interconnect and influence collective outcomes in sports.

Trajectories of biomedical research leading to Nobel Prize-winning discoveries

Significant advancements in public health come from scientific discoveries, but more are needed to meet the ever-growing societal needs. Examining the best practices of outstanding scientists may help develop future researchers and lead to more discoveries. This study compared the comprehensive work of 49 Nobel laureates in Physiology or Medicine from 2000 to 2019 to a matched control of National Institutes of Health (NIH)-funded biomedical investigators. Our unique data set, comprising 11,737 publications, 571 US patents, and 1693 NIH research awards produced by pre-Nobel laureates, was compared to a similar data set of control researchers. Compared to control researchers, pre-Nobel laureates produce significantly more publications annually (median = 5.66; interquartile range [IQR] = 5.16); significantly fewer coauthors per publication (median = 3.32; IQR = 1.95); consistently higher Journal Impact Factor publications (median = 12.04; IQR = 6.83); and substantially more patents per researcher (median = 5; IQR = 14). Such differences arose from nearly identical cumulative NIH award budgets of pre-Nobel laureates (median $25.3 M) and control researchers. Nobel laureates are neither hyper-prolific (>72 papers per year) nor hyper-funded (>$100 M cumulative). An academic age-specific trajectory graph allows aspiring researchers to compare their productivity and collaboration patterns to those of pre-Nobel laureates.

Productive scientists are associated with lower disruption in scientific publishing

While scientific researchers often aim for high productivity, prioritizing the quantity of publications may come at the cost of time and effort dedicated to individual research. It is thus important to examine the relationship between productivity and disruption for individual researchers. Here, we show that with the increase in the number of published papers, the average citation per paper will be higher yet the mean disruption of papers will be lower. In addition, we find that the disruption of scientists' papers may decrease when they are highly productive in a given year. The disruption of papers in each year is not determined by the total number of papers published in the author's career, but rather by the productivity of that particular year. Besides, more productive authors also tend to give references to recent and high-impact research. Our findings highlight the potential risks of pursuing productivity and aim to encourage more thoughtful career planning among scientists.

Utilizing a composite citation index for evaluating clinical ophthalmology research: insights into gender, nationality, and self-citation among top ophthalmology researchers

OBJECTIVES

To compare the performance of a composite citation score (c-score) and its six constituent citation indices, including H-index, in predicting winners of the Weisenfeld Award in ophthalmologic research. Secondary objectives were to explore career and demographic characteristics of the most highly cited researchers in ophthalmology.

METHODS

A publicly available database was accessed to compile a set of top researchers in the field of clinical ophthalmology and optometry based on Scopus data from 1996 to 2021. Each citation index was used to construct a multivariable model adjusted for author demographic characteristics. Using area under the receiver operating curve (AUC) analysis, each index's model was evaluated for its ability to predict winners of the Weisenfeld Award in Ophthalmology, a research distinction presented by the Association for Research in Vision and Ophthalmology (ARVO). Secondary analyses investigated authors' self-citation rates, career length, gender, and country affiliation over time.

RESULTS

Approximately one thousand unique authors publishing primarily in clinical ophthalmology/optometry were analyzed. The c-score outperformed all other citation indices at predicting Weisenfeld Awardees, with an AUC of 0.99 (95% CI: 0.97-1.0). The H-index had an AUC of 0.89 (95% CI: 0.83-0.96). Authors with higher c-scores tended to have longer career lengths and similar self-citation rates compared to other authors. Sixteen percent of authors in the database were identified as female, and 64% were affiliated with the United States of America.

CONCLUSION

The c-score is an effective metric for assessing research impact in ophthalmology, as seen through its ability to predict Weisenfeld Awardees.

Understanding scholar-trajectories across scientific periodicals

Despite the rapid growth in the number of scientific publications, our understanding of author publication trajectories remains limited. Here we propose an embedding-based framework for tracking author trajectories in a geometric space that leverages the information encoded in the publication sequences, namely the list of the consecutive publication venues for each scholar. Using the publication histories of approximately 30,000 social media researchers, we obtain a knowledge space that broadly captures essential information about periodicals as well as complex (inter-)disciplinary structures of science. Based on this space, we study academic success through the prism of movement across scientific periodicals. We use a measure from human mobility, the radius of gyration, to characterize individual scholars' trajectories. Results show that author mobility across periodicals negatively correlates with citations, suggesting that successful scholars tend to publish in a relatively proximal range of periodicals. Overall, our framework discovers intricate structures in large-scale sequential data and provides new ways to explore mobility and trajectory patterns.

The top 100 highly cited articles on neck pain: A bibliometric analysis

Objective

Neck pain has emerged as a significant public health concern. This study is to unveil the present state of neck pain research, thereby furnishing invaluable insights for prospective research endeavours and clinical applications.

Methods

The study was initiated by searching the Web of Science Core Collection database, focusing on "neck pain". From the amassed results, the top 100 most cited references were imported into CiteSpace and VOSviewer, enabling a rigorous bibliometric analysis. To ensure precision, synonymous terms conveying similar meanings were harmonized. The bibliometric study encompassed countries, research institutions, authors, journals, and keyword analysis.

Results

The investigation centered on a curated compilation of 100 articles, disseminated across a diverse array of 36 scholarly journals. These seminal articles originated from 24 distinct countries, reflecting contributions from a wide spectrum of 188 research institutions. Impressively, a collaborative effort involving 385 authors emerged. Noteworthy core research countries included the United States and Australia, with the University of Queensland and the University of Toronto asserting notable influence. Prolific authors such as J. David Cassidy and Pierre Cote garnered attention. Present research endeavours pivot around the incidence of neck pain, the identification of risk factors, the efficacy evaluation of treatment modalities, and a pronounced focus on high-quality randomized controlled trials and systematic reviews.

Conclusion

The study shines a light on key research countries, influential institutions, prominent authors, and prevalent trends, effectively contributing to comprehending the knowledge landscape and research dynamics in the field of neck pain.

A network-based normalized impact measure reveals successful periods of scientific discovery across discipline

The impact of a scientific publication is often measured by the number of citations it receives from the scientific community. However, citation count is susceptible to well-documented variations in citation practices across time and discipline, limiting our ability to compare different scientific achievements. Previous efforts to account for citation variations often rely on a priori discipline labels of papers, assuming that all papers in a discipline are identical in their subject matter. Here, we propose a network-based methodology to quantify the impact of an article by comparing it with locally comparable research, thereby eliminating the discipline label requirement. We show that the developed measure is not susceptible to discipline bias and follows a universal distribution for all articles published in different years, offering an unbiased indicator for impact across time and discipline. We then use the indicator to identify science-wide high impact research in the past half century and quantify its temporal production dynamics across disciplines, helping us identifying breakthroughs from diverse, smaller disciplines, such as geosciences, radiology, and optics, as opposed to citation-rich biomedical sciences. Our work provides insights into the evolution of science and paves a way for fair comparisons of the impact of diverse contributions across many fields.

Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields

Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world's 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers' initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers' impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on.

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.

The evolution of scientific literature as metastable knowledge states

The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life cycle of scientific research, understand how knowledge is transferred between scientists and stakeholders, and explain how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. Through a novel approach combining natural language clustering and citation graph analysis, we predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.

SciSciNet: A large-scale open data lake for the science of science research

The science of science has attracted growing research interests, partly due to the increasing availability of large-scale datasets capturing the innerworkings of science. These datasets, and the numerous linkages among them, enable researchers to ask a range of fascinating questions about how science works and where innovation occurs. Yet as datasets grow, it becomes increasingly difficult to track available sources and linkages across datasets. Here we present SciSciNet, a large-scale open data lake for the science of science research, covering over 134M scientific publications and millions of external linkages to funding and public uses. We offer detailed documentation of pre-processing steps and analytical choices in constructing the data lake. We further supplement the data lake by computing frequently used measures in the literature, illustrating how researchers may contribute collectively to enriching the data lake. Overall, this data lake serves as an initial but useful resource for the field, by lowering the barrier to entry, reducing duplication of efforts in data processing and measurements, improving the robustness and replicability of empirical claims, and broadening the diversity and representation of ideas in the field.

Data, measurement and empirical methods in the science of science

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

Modeling a successful citation trajectory structure for scholar's impact evaluation in Israeli academia

One of the main concerns of researchers and institutions is how to assess the future performance of scholars and identify their potential to become successful scientists. In this study, we model scholarly success in terms of the probability of a scholar belonging to a group of highly impactful scholars as determined by their citation trajectory structures. To this end, we developed a new set of impact measures based on a scholar's citation trajectory structure (rather than on absolute citation or h-index rates), that show a stable trend and scale for highly impactful scholars, independent of their field of study, seniority and citation index. These measures were then incorporated as influence factors into the logistic regression models and used as features for probabilistic classifiers based on these models to identify the successful scholars in the heterogeneous corpus of 400 of most and least cited professors from two Israeli universities. From the practical point of view, the study may yield useful insights and serve as an aid in making promotion decisions by institutions, as well as a self-assessment tool for researchers who strive to increase their academic influence and become leaders in their field.

Quantifying the scientific revolution

The Scientific Revolution represents a turning point in the history of humanity. Yet it remains ill-understood, partly because of a lack of quantification. Here, we leverage large datasets of individual biographies (N = 22,943) and present the first estimates of scientific production during the late medieval and early modern period (1300-1850). Our data reveal striking differences across countries, with England and the United Provinces being much more creative than other countries, suggesting that economic development has been key in generating the Scientific Revolution. In line with recent results in behavioural sciences, we show that scientific creativity and economic development are associated with other kinds of creative activities in philosophy, literature, music and the arts, as well as with inclusive institutions and ascetic religiosity, suggesting a common underlying mindset associated with long-term orientation and exploration. Finally, we investigate the interplay between economic development and cultural transmission (the so-called 'Republic of Letters') using partially observed Markov models imported from population biology. Surprisingly, the role of horizontal transmission (from one country to another) seems to have been marginal. Beyond the case of science, our results suggest that economic development is an important factor in the evolution of aspects of human culture.

Quantifying human performance in chess

From sports to science, the recent availability of large-scale data has allowed to gain insights on the drivers of human innovation and success in a variety of domains. Here we quantify human performance in the popular game of chess by leveraging a very large dataset comprising of over 120 million games between almost 1 million players. We find that individuals encounter hot streaks of repeated success, longer for beginners than for expert players, and even longer cold streaks of unsatisfying performance. Skilled players can be distinguished from the others based on their gaming behaviour. Differences appear from the very first moves of the game, with experts tending to specialize and repeat the same openings while beginners explore and diversify more. However, experts experience a broader response repertoire, and display a deeper understanding of different variations within the same line. Over time, the opening diversity of a player tends to decrease, hinting at the development of individual playing styles. Nevertheless, we find that players are often not able to recognize their most successful openings. Overall, our work contributes to quantifying human performance in competitive settings, providing a first large-scale quantitative analysis of individual careers in chess, helping unveil the determinants separating elite from beginner performance.

Practical operation and theoretical basis of difference-in-difference regression in science of science: The comparative trial on the scientific performance of Nobel laureates versus their coauthors

Purpose

In recent decades, with the availability of large-scale scientific corpus datasets, difference-in-difference (DID) is increasingly used in the science of science and bibliometrics studies. DID method outputs the unbiased estimation on condition that several hypotheses hold, especially the common trend assumption. In this paper, we gave a systematic demonstration of DID in the science of science, and the potential ways to improve the accuracy of DID method.

Design/methodology/approach

At first, we reviewed the statistical assumptions, the model specification, and the application procedures of DID method. Second, to improve the necessary assumptions before conducting DID regression and the accuracy of estimation, we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention. Lastly, we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates, by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.

Findings

We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results. As a case study, we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.

Research limitations

This study ignored the rigorous mathematical deduction parts of DID, while focused on the practical parts.

Practical implications

This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.

Originality/value

This study gains insights into the usage of econometric tools in science of science.

Evaluating individual scientific output normalized to publication age and academic field through the Scientometrics.org project

When evaluating the publication performance of a scientist one has to consider not only the difference in publication norms in different scientific fields, but also the length of the academic career of the investigated researcher. Here, our goal was to establish a database suitable as a reference for the ranking of scientific performance by normalizing the researchers output to those with the same academic career length and active in same scientific field. By using the complete publication and citation data of 17,072 Hungarian researchers, we established a framework enabling the quick assessment of a researcher’s scientific output by comparing four parameters (h-index, yearly independent citations received, number of publications, and number of high impact publications), to the age-matched values of all other researchers active in the same scientific discipline. The established online tool available at www.scientometrics.org could be an invaluable help for faster and more evidence-based grant review processes.

Analyzing and predicting success of professional musicians

The emergence of streaming services, e.g., Spotify, has changed the way people listen to music and the way professional musicians achieve fame and success. Classical music has been the backbone of Western media for a long time, but Spotify has introduced the public to a much wider variety of music, also opening a new venue for professional musicians to gain exposure. In this paper, we use open-source data from Spotify and Musicbrainz databases to construct collaboration-based and genre-based networks. We call genres defined in these databases primary genres. Our goal is to find the correlation between various features of each professional musician, the current stage of their career, and the level of their success in the music field. We build regression models using XGBoost to first analyze correlation between features provided by Spotify. We then analyze the correlation between the digital music world of Spotify and the more traditional world of Billboard charts. We find that within certain bounds, machine learning techniques such as decision tree classifiers and Q-based models perform quite well on predicting success of professional musicians from the data on their early careers. We also find features that are highly predictive of their success. The most prominent among them are the musicians' collaboration counts and the span of their career. Our findings also show that classical musicians are still very centrally placed in the general, genre-agnostic network of musicians. Using these models and success metrics, aspiring professional musicians can check if their chances for career success could be improved by increasing their specific success measures in both Spotify and Billboard charts.

Beijing's central role in global artificial intelligence research

Nations worldwide are mobilizing to harness the power of Artificial Intelligence (AI) given its massive potential to shape global competitiveness over the coming decades. Using a dataset of 2.2 million AI papers, we study inter-city citations, collaborations, and talent migrations to uncover dependencies between Eastern and Western cities worldwide. Beijing emerges as a clear outlier, as it has been the most impactful city since 2007, the most productive since 2002, and the one housing the largest number of AI scientists since 1995. Our analysis also reveals that Western cities cite each other far more frequently than expected by chance, East-East collaborations are far more common than East-West or West-West collaborations, and migration of AI scientists mostly takes place from one Eastern city to another. We then propose a measure that quantifies each city's role in bridging East and West. Beijing's role surpasses that of all other cities combined, making it the central gateway through which knowledge and talent flow from one side to the other. We also track the center of mass of AI research by weighing each city's geographic location by its impact, productivity, and AI workforce. The center of mass has moved thousands of kilometers eastward over the past three decades, with Beijing's pull increasing each year. These findings highlight the eastward shift in the tides of global AI research, and the growing role of the Chinese capital as a hub connecting researchers across the globe.

Internal and External Causal Explanations of Happiness

The present study extends previous research on the folk concept of happiness by investigating people's causal attributions toward the things that make them happy. Six hundred ten Italian adults (18–55 years old) took part in a questionnaire study. Respondents were asked to report five happiness sources and to provide ratings for both the attainment of these sources and the internal and external factors potentially causing them (self, other people, luck, chance). We also measured the participants’ levels of psychological well-being. Results showed that the participants’ happiness conceptions incorporated 27 categories of happiness sources referring to four semantic domains: relational life, personal life, hedonic psychological sources, and eudaimonic psychological sources. Multilevel analyses showed that internal attributions exceeded external attributions across all these domains; moreover, internal attributions positively predicted happiness attainment, whereas the latter was negatively associated with attributions to other people. Findings were interpreted in the Italian cultural and linguistic context.

Evolving collaboration, dependencies, and use in the Rust Open Source Software ecosystem

Open Source Software (OSS) is widely spread in industry, research, and government. OSS represents an effective development model because it harnesses the decentralized efforts of many developers in a way that scales. As OSS developers work independently on interdependent modules, they create a larger cohesive whole in the form of an ecosystem, leaving traces of their contributions and collaborations. Data harvested from these traces enable the study of large-scale decentralized collaborative work. We present curated data on the activity of tens of thousands of developers in the Rust ecosystem and the evolving dependencies between their libraries. The data covers eight years of developer contributions to Rust libraries and can be used to reconstruct the ecosystem's development history, such as growing developer collaboration networks or dependency networks. These are complemented by data on downloads and popularity, tracking dynamics of use, visibility, and success over time. Altogether the data give a comprehensive view of several dimensions of the ecosystem.

Getting funded in a highly fluctuating environment: Shifting from excellence to luck and timing

Recent data highlights the presence of luck in research grant allocations, where most vulnerable are early-career researchers. The national research funding contributes typically the greatest share of total research funding in a given country, fulfilling simultaneously the roles of promoting excellence in science, and most importantly, development of the careers of young generation of scientists. Yet, there is limited supply of studies that have investigated how do early-career researchers stand compared to advanced-career level researchers in case of a national research grant system. We analyzed the Estonian national highly competitive research grant funding across different fields of research for a ten-year-period between 2013-2022, including all the awarded grants for this period (845 grants, 658 individual principal investigators, PI). The analysis was conducted separately for early-career and advanced-career researchers. We aimed to investigate how the age, scientific productivity and the previous grant success of the PI vary across a national research system, by comparing early- and advanced-career researchers. The annual grant success rates varied between 14% and 28%, and within the discipline the success rate fluctuated across years even between 0-67%. The year-to-year fluctuations in grant success were stronger for early-career researchers. The study highlights how the seniority does not automatically deliver better research performance, at some fields, younger PIs outperform older cohorts. Also, as the size of the available annual grants fluctuates remarkably, early-career researchers are most vulnerable as they can apply for the starting grant only within a limited "time window".

Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian

The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works. In addition, the equal odds baseline implies that the percentage of high-quality works and total number of works are uncorrelated. The tilted funnel hypothesis proposes that the linear regression implied by the equal odds baseline is heteroscedastic with residual variance in the quality of work increasing as a function of quantity. The aim of the current research is to leverage Bayesian statistical modeling of the equal odds baseline. Previous work has examined the tilted funnel by means of frequentist quantile regression, but Bayesian quantile regression based on the asymmetric Laplace model allows for only one conditional quantile at a time. Hence, we propose additional Bayesian methods, including Poisson modeling to study conditional variance as a function of quantity. We use a classical small sample of eminent neurosurgeons, as well as the brms Bayesian R package, to accomplish this work. In addition, we provide open code and data to allow interested researchers to extend our work and utilize the proposed modeling alternatives.

The Use of Fi-Index Tool to Assess Per-manuscript Self-citations

Bibliometric parameters are now increasingly used in the evaluation of scientific research and researchers/authors. Over the years, different indices have been taken into consideration with the aim of “quantifying” different authors. A new index was recently defined, the Fi-index, with the aim of evaluate how much the h-index of a given author is influenced by his self-citations. The purpose of this work is to apply the Fi-index, not to the entire career of the author, as normally happens, but to the single paper in course of publication, so as to verify or certify that a specific manuscript does not affect the h-index or citations from the single author or authors. Fi-index tool score measure the impact of a paper on author career and it is obtained by a simple calculation that could be made with an online tool (www.fident.eu/fidentresearch/fiindextool). The use of fi-index tool could be useful as a guarantee parameter on a specific manuscript, obviously provided that a particular author could have a scientific research trend. It is hoped that this index will be used on a large scale for scientific publications affected by bibliometric parameters.

Fund behavioral science like the frameworks we endorse: the case for increased funding of preliminary studies by the National Institutes of Health

Innovative, groundbreaking science relies upon preliminary studies (aka pilot, feasibility, proof-of-concept). In the behavioral sciences, almost every large-scale intervention is supported by a series of one or more rigorously conducted preliminary studies. The importance of preliminary studies was established by the National Institutes of Health (NIH) in 2014/2015 in two translational science frameworks (NIH Stage and ORBIT models). These frameworks outline the essential role preliminary studies play in developing the next generation of evidence-based behavioral prevention and treatment interventions. Data produced from preliminary studies are essential to secure funding from the NIH's most widely used grant mechanism for large-scale clinical trials, namely the R01. Yet, despite their unquestionable importance, the resources available for behavioral scientists to conduct rigorous preliminary studies are limited. In this commentary, we discuss ways the existing funding structure at the NIH, despite its clear reliance upon high-quality preliminary studies, inadvertently discourages and disincentivizes their pursuit by systematically underfunding them. We outline how multiple complementary and pragmatic steps via a small reinvestment of funds from larger trials could result in a large increase in funding for smaller preliminary studies. We make the case such a reinvestment has the potential to increase innovative science, increase the number of investigators currently funded, and would yield lasting benefits for behavioral science and scientists alike.

Does early publishing in top journals really predict long-term scientific success in the business field?

The soaring number of researchers has led to increasingly intense competition in academia. Early identification of scientists’ potential is a practical but difficult issue currently attracting escalating attention. This study takes the business field as an example and explores whether early publishing in top journals is an effective yardstick to recognise scientists who will have better academic performance in their careers. We extract the career records of publication and citations for 1933 business scientists with stable and continuous publication records from the combination of the ORCID and Scopus databases. Through regression analysis and various checks, we find that researchers publishing in top journals early in their careers indeed perform better subsequently compared to peers with similar early career profiles but no top journal publications. Our research sheds light on a new perspective for early identification of potential star scientists, especially in the business field, and justifies encouraging junior researchers to devote themselves to publishing in top-ranked peer-reviewed journals.

Posts on central websites need less originality to be noticed

Information has major consequences for democracy and society. It is important to understand what factors favor its diffusion. The impact of the content of a message on its likelihood of going viral is poorly understood. Some studies say originality is important for a message not to be overlooked. Others give more relevance to paratextual elements—network centrality, timing, human cognitive limits. Here we propose that originality and centrality interact in a nontrivial way, which might explain why originality by itself is not a good predictor of success. We collected data from Reddit on users sharing hyperlinks. We estimated the originality of each post title and the centrality of the website hosting the shared link. We show that the interaction effect exists: if users share content from a central website, originality no longer increases the odds of receiving at least one upvote. The same is not true for the odds of becoming one of the top 10% scoring posts. We show that originality is concentrated in the domain network: domains in the core of the network produce more original content. Our results imply that research on online information virality needs to take into account the nontrivial interaction between originality and prominence.