Quantifying the impact of weak, strong, and super ties in scientific careers

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.

Medical researchers' perceptions regarding research evaluation: a web-based survey in Japan

OBJECTIVES

Japanese medical academia continues to depend on quantitative indicators, contrary to the general trend in research evaluation. To understand this situation better and facilitate discussion, this study aimed to examine how Japanese medical researchers perceive quantitative indicators and qualitative factors of research evaluation and their differences by the researchers' characteristics.

DESIGN

We employed a web-based cross-sectional survey and distributed the self-administered questionnaire to academic society members via the Japanese Association of Medical Sciences.

PARTICIPANTS

We received 3139 valid responses representing Japanese medical researchers in any medical research field (basic, clinical and social medicine).

OUTCOMES

The subjective importance of quantitative indicators and qualitative factors in evaluating researchers (eg, the journal impact factor (IF) or the originality of the research topic) was assessed on a four-point scale, with 1 indicating 'especially important' and 4 indicating 'not important'. The attitude towards various opinions in quantitative and qualitative research evaluation (eg, the possibility of research misconduct or susceptibility to unconscious bias) was also evaluated on a four-point scale, ranging from 1, 'strongly agree', to 4, 'completely disagree'.

RESULTS

Notably, 67.4% of the medical researchers, particularly men, younger and basic medicine researchers, responded that the journal IF was important in researcher evaluation. Most researchers (88.8%) agreed that some important studies do not get properly evaluated in research evaluation using quantitative indicators. The respondents perceived quantitative indicators as possibly leading to misconduct, especially in basic medicine (strongly agree-basic, 22.7%; clinical, 11.7%; and social, 16.1%). According to the research fields, researchers consider different qualitative factors, such as the originality of the research topic (especially important-basic, 46.2%; social, 39.1%; and clinical, 32.0%) and the contribution to solving clinical and social problems (especially important-basic, 30.4%; clinical, 41.0%; and social, 52.0%), as important. Older researchers tended to believe that qualitative research evaluation was unaffected by unconscious bias.

CONCLUSION

Despite recommendations from the Declaration on Research Assessment and the Leiden Manifesto to de-emphasise quantitative indicators, this study found that Japanese medical researchers have actually tended to prioritise the journal IF and other quantitative indicators based on English-language publications in their research evaluation. Therefore, constantly reviewing the research evaluation methods while respecting the viewpoints of researchers from different research fields, generations and genders is crucial.

Collaboration and topic switches in science

Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that scientists choose future research directions via the intertwined mechanisms of selection and social influence. Here we thoroughly investigate the interplay between collaboration and topic switches. We find that the probability for a scholar to start working on a new topic increases with the number of previous collaborators, with a pattern showing that the effects of individual collaborators are not independent. The higher the productivity and the impact of authors, the more likely their coworkers will start working on new topics. The average number of coauthors per paper is also inversely related to the topic switch probability, suggesting a dilution of this effect as the number of collaborators increases.

The power of partnership: Exploring collaboration dynamics in U.S. transplant research

INTRODUCTION

Collaboration is one of the hallmarks of academic research. This study analyzes collaboration patterns in U.S. transplant research, examining publication trends, productive institutions, co-authorship networks, and citation patterns in high-impact transplant journals.

METHODS

4,265 articles published between 2012 and 2021 were analyzed using scientometric tools, logistic regression, VantagePoint software, and Gephi software for network visualization.

RESULTS

16,003 authors from 1,011 institutions and 59 countries were identified, with Harvard, Johns Hopkins, and University of Pennsylvania contributing the most papers. Odds of international collaboration significantly increased over time (OR 1.03; p ​= ​0.040), while odds of citation in single-institution collaborations decreased (OR 0.99; p ​= ​0.016). Five major scientific communities and central institutions (Harvard University and University of Pittsburgh) connecting them were identified, revealing interconnected research clusters.

CONCLUSIONS

Collaboration enhances knowledge exchange and research productivity, with an increasing trend of institutional and international collaboration in U.S. transplant research. Understanding this community is essential for promoting research impact and forming strategic partnerships.

Quantifying the impact of strong ties in international scientific research collaboration

Tie strength has been examined as an antecedent of creativity. Although it has been discovered that international collaboration affects scientific performance, the effect of tie strength in the international collaboration network has been largely neglected. Based on international publications of 72 countries/regions published from 1993 to 2013, we combine descriptive and panel regression methods to examine how the bonding of strong collaboration ties contributes to countries' international scientific performance. Strong ties occur at an average rate of 1 in 4 collaborators, whereas countries/regions share on average 84% of articles with their strong-tie collaborators. Our quantitative results provide an explanation for this phenomenon in international collaboration: the establishment of a strong tie relationship contributes to above-average productivity and citation frequency for countries/regions. To further explore which types of strong ties tend to have stronger citation impact, we analyse the relationship between persistent and stable collaboration and publication citation impact. Experimental results show that international collaborations with greater persistence and moderate stability tend to produce high impact publications. It is noteworthy that when the collaboration period is divided into different time intervals, similar findings can be found after the same analysis procedure is carried out. This indicates that our conclusions are robust. Overall, this study provides quantitative insights into the added value of long-term commitment and social trust associated with strong collaborative partnerships in international collaboration.

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.

Global Maps of Avian Leukosis Viruses: Research Trends and Themes Based on Networking

Avian leukosis virus (ALV) has a tremendous adverse impact on the poultry industry. Since its discovery, research on different aspects of ALV have been published. Due to the vast academic emphasis and economic importance of the ALV infection in poultry worldwide, this bibliometric analysis explored the scientific output associated with ALV utilizing the Web of Science (Core Collection) database. The relevant data were collected using the search query "AVIAN LEUKOSIS VIRUS", further refined by document types (article, book chapter, and proceedings paper). Finally, 1060 items with full records were imported in Plaintext and tab-delimited formats. The data analysis was carried out using MS Excel, VOS viewer, and R (Biblioshiny) software. Chinese and American research institutions produced the majority of papers during study time period. The Journal of Virology and Avian Diseases appeared as the favorite journal/source for publications. Apart from the avian leukosis virus and the ALV-J, the important keywords mentioned included avian leukosis virus subgroup j, chicken, and retrovirus. The analysis revealed substantial findings on ALV research, with a strong research response from the USA and China.

Women and key positions in scientific collaboration networks: analyzing central scientists’ profiles in the artificial intelligence ecosystem through a gender lens

Scientific collaboration in almost every discipline is mainly driven by the need of sharing knowledge, expertise, and pooled resources. Science is becoming more complex which has encouraged scientists to involve more in collaborative research projects in order to better address the challenges. As a highly interdisciplinary field with a rapidly evolving scientific landscape, artificial intelligence calls for researchers with special profiles covering a diverse set of skills and expertise. Understanding gender aspects of scientific collaboration is of paramount importance, especially in a field such as artificial intelligence that has been attracting large investments. Using social network analysis, natural language processing, and machine learning and focusing on artificial intelligence publications for the period from 2000 to 2019, in this work, we comprehensively investigated the effects of several driving factors on acquiring key positions in scientific collaboration networks through a gender lens. It was found that, regardless of gender, scientific performance in terms of quantity and impact plays a crucial part in possessing the “social researcher” role in the network. However, subtle differences were observed between female and male researchers in acquiring the “local influencer” role.

Impactful scientists have higher tendency to involve collaborators in new topics

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate. In particular, we find that highly productive scientists tend to have a higher fraction of single-topic collaborators, while highly cited-i.e., impactful-scientists have a higher fraction of multitopic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators in a new topic. We find that, compared to productive scientists, impactful scientists show strong preference of collaboration with high-impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.

The anatomy of social dynamics in escape rooms

From sport and science production to everyday life, higher-level pursuits demand collaboration. Despite an increase in the number of data-driven studies on human behavior, the social dynamics of collaborative problem solving are still largely unexplored with network science and other computational and quantitative tools. Here we introduce escape rooms as a non-interventional and minimally biased social laboratory, which allows us to capture at a high resolution real-time communications in small project teams. Our analysis portrays a nuanced picture of different dimensions of social dynamics. We reveal how socio-demographic characteristics impact problem solving and the importance of prior relationships for enhanced interactions. We extract key conversation rules from motif analysis and discuss turn-usurping gendered behavior, a phenomenon particularly strong in male-dominated teams. We investigate the temporal evolution of signed and group interactions, finding that a minimum level of tense communication might be beneficial for collective problem solving, and revealing differences in the behavior of successful and failed teams. Our work unveils the innovative potential of escape rooms to study teams in their complexity, contributing to a deeper understanding of the micro-dynamics of collaborative team processes.

Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems

Biological age is an important sociodemographic factor in studies on academic careers (research productivity, scholarly impact, and collaboration patterns). It is assumed that the academic age, or the time elapsed from the first publication, is a good proxy for biological age. In this study, we analyze the limitations of the proxy in academic career studies, using as an example the entire population of Polish academic scientists and scholars visible in the last decade in global science and holding at least a PhD (N = 20,569). The proxy works well for science, technology, engineering, mathematics, and medicine (STEMM) disciplines; however, for non-STEMM disciplines (particularly for humanities and social sciences), it has a dramatically worse performance. This negative conclusion is particularly important for systems that have only recently visible in global academic journals. The micro-level data suggest a delayed participation of social scientists and humanists in global science networks, with practical implications for predicting biological age from academic age. We calculate correlation coefficients, present contingency analysis of academic career stages with academic positions and age groups, and create a linear multivariate regression model. Our research suggests that in scientifically developing countries, academic age as a proxy for biological age should be used more cautiously than in advanced countries: ideally, it should be used only for STEMM disciplines.

Scientific success from the perspective of the strength of weak ties

We present the first complete verification of Granovetter’s theory of social networks using a massive dataset, i.e. DBLP computer science bibliography database. For this purpose, we study a coauthorship network, which is considered one of the most important examples that contradicts the universality of this theory. We achieve this goal by rejecting the assumption of the symmetry of social ties. Our approach is grounded in well-established heterogeneous (degree-based) mean-field theory commonly used to study dynamical processes on complex networks. Granovetter’s theory is based on two hypotheses that assign different roles to interpersonal, information-carrying connections. The first hypothesis states that strong ties carrying the majority of interaction events are located mainly within densely connected groups of people. The second hypothesis maintains that these groups are connected by sparse weak ties that are of vital importance for the diffusion of information—individuals who have access to weak ties have an advantage over those who do not. Given the scientific collaboration network, with strength of directed ties measured by the asymmetric fraction of joint publications, we show that scientific success is strongly correlated with the structure of a scientist’s collaboration network. First, among two scientists, with analogous achievements, the one with weaker ties tends to have the higher h-index, and second, teams connected by such ties create more cited publications.

The Philosophy of Outliers: Reintegrating Rare Events Into Biological Science

Individual variation in morphology, physiology, and behavior has been a topic of great interest in the biological sciences. While scientists realize the importance of understanding diversity in individual phenotypes, historically the "minority" results (i.e., outlier observations or rare events) of any given experiment have been dismissed from further analysis. We need to reframe how we view "outliers" to improve our understanding of biology. These rare events are often treated as problematic or spurious, when they can be real rare events or individuals driving evolution in a population. It is our perspective that to understand what outliers can tell us in our data, we need to: (1) Change how we think about our data philosophically, (2) Fund novel collaborations using science "weavers" in our national funding agencies, and (3) Bridge long-term field and lab studies to reveal these outliers in action. By doing so, we will improve our understanding of variation and evolution. We propose that this shift in culture towards more integrative science will incorporate diverse teams, citizen scientists and local naturalists, and change how we teach future students.

Pandemics are catalysts of scientific novelty: Evidence from COVID ‐19

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First‐time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first‐time collaboration increased after the outbreak of COVID‐19, and international collaboration witnessed a sudden decrease. During COVID‐19, papers with more first‐time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

Collaboration enhances career progression in academic science, especially for female researchers

Collaboration and diversity are increasingly promoted in science. Yet how collaborations influence academic career progression, and whether this differs by gender, remains largely unknown. Here, we use co-authorship ego networks to quantify collaboration behaviour and career progression of a cohort of contributors to biennial International Society of Behavioral Ecology meetings (1992, 1994, 1996). Among this cohort, women were slower and less likely to become a principal investigator (PI; approximated by having at least three last-author publications) and published fewer papers over fewer years (i.e. had shorter academic careers) than men. After adjusting for publication number, women also had fewer collaborators (lower adjusted network size) and published fewer times with each co-author (lower adjusted tie strength), albeit more often with the same group of collaborators (higher adjusted clustering coefficient). Authors with stronger networks were more likely to become a PI, and those with less clustered networks did so more quickly. Women, however, showed a stronger positive relationship with adjusted network size (increased career length) and adjusted tie strength (increased likelihood to become a PI). Finally, early-career network characteristics correlated with career length. Our results suggest that large and varied collaboration networks are positively correlated with career progression, especially for women.

Cooperation patterns of members in networks during co-creation

Cooperation (i.e., co-creation) has become the principal way of carrying out creative activities in modern society. In co-creation, different participants can play two completely different roles based on two different behaviours: some participants are the originators who generate initial contents, while others are the revisors who provide revisions or coordination. In this study, we investigated different participants' roles (i.e., the originator vs. the revisor) in co-creation and how these roles affected the final cooperation-group outcome. By using cooperation networks to represent cooperative relationships among participants, we found that peripheral members (i.e., those in the periphery of the cooperation networks) and core members (i.e., those in the centre of the cooperation networks) played the roles of originators and revisors, respectively, mainly affecting the quantity versus the quality of their creative outcomes. These results were robust across the three different datasets and the three different indicators defining core and peripheral members. Previous studies have considered cooperation behaviours to be homogeneous, ignoring that different participants may play different roles in co-creation. This study discusses patterns of cooperation among participants based on a model in which different roles in co-creation are considered. Thus, this research advances the understanding of how co-creation occurs in networks.

Fresh teams are associated with original and multidisciplinary research

Teamwork is one of the most prominent features in modern science. It is now well understood that team size is an important factor that affects the creativity of the team. However, the crucial question of how the character of research studies is related to the freshness of a team remains unclear. Here, we quantify the team freshness according to the absence of prior collaboration among team members. Our results suggest that papers produced by fresher teams are associated with greater originality and a greater multidisciplinary impact. These effects are even stronger in larger teams. Furthermore, we find that freshness defined by new team members in a paper is a more effective indicator of research originality and multidisciplinarity compared with freshness defined by new collaboration relationships among team members. Finally, we show that the career freshness of team members is also positively correlated with the originality and multidisciplinarity of produced papers.

Interplay between success and patterns of human collaboration: case study of a Thai Research Institute

Networks of collaboration are notoriously complex and the mechanisms underlying their evolution, although of high interest, are still not fully understood. In particular, collaboration networks can be used to model the interactions between scientists and analyze the circumstances that lead to successful research. This task is not trivial and conventional metrics, based on number of publications and number of citations of individual authors, may not be sufficient to provide a deep insight into the factors driving scientific success. However, network analysis techniques based on centrality measures and measures of the structural properties of the network are promising to that effect. In recent years, it has become evident that most successful research works are achieved by teams rather than individual researchers. Therefore, researchers have developed a keen interest in the dynamics of social groups. In this study, we use real world data from a Thai computer technology research center, where researchers collaborate on different projects and team up to produce a range of artifacts. For each artifact, a score that measures quality of research is available and shared between the researchers that contributed to its creation, according to their percentage of contribution. We identify several measures to quantify productivity and quality of work, as well as centrality measures and structural measures. We find that, at individual level, centrality metrics are linked to high productivity and quality of work, suggesting that researchers who cover strategic positions in the network of collaboration are more successful. At the team level, we show that the evolution in time of structural measures are also linked to high productivity and quality of work. This result suggests that variables such as team size, turnover rate, team compactness and team openness are critical factors that must be taken into account for the success of a team. The key findings of this study indicate that the success of a research institute needs to be assessed in the context of not just researcher or team level, but also on how the researchers engage in collaboration as well as on how teams evolve.

Network modularity controls the speed of information diffusion

The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature-the modular structure-strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation.

Publishing in English is associated with an increase of the impact factor of Brazilian biodiversity journals

English is the lingua franca for scientific communication, but some journals, especially in developing countries, still publish non-English studies. A shift towards publishing in English may promote internationalization and more visibility of scientific journals. Here we compared quality indexes between Brazilian journals that have always published in English and journals that have published in languages other than English. We also investigated whether a temporal shift towards publishing in English led to elevated quality measures. Our analyses covered 16 Brazilian biodiversity journals and accounted for 12640 papers published since 2007. The mean impact factor was on average 55% higher in journals that have published consistently in English, compared to the so-called multilanguage journals. The proportion of publications in English increased to nearly three times the original value in multilanguage journals between 2007 and 2016, and the impact factor tripled during this period. At the same time, the Qualis-Capes classifications (B1-B2-B3) tended to fall. Publishing in English can be a first step to increased visibility, and this is particularly important for biodiversity journals, since Brazilian ecosystems are considered of interest to the international scientific community and nature conservation.

A Bibliometric Analysis in Industry 4.0 and Advanced Manufacturing: What about the Sustainable Supply Chain?

During the last decade, different concepts, methodologies, and technologies have appeared, evolving industry toward what we know today as the fourth industrial evolution or Industry 4.0 (I4.0) and Advanced Manufacturing (AM). Based on both, Supply Chain (SC) is presented as the relevant process that sets the sustainability of manufacturing and, therefore, is defined as a key term in a sustainable approach to I4.0. However, there are no studies that analyze the evolution of science in the fields of I4.0 and AM together. In order to fill this gap, the aim of this research work is to analyze the tendencies of science research related to I4.0 and AM by conducting a bibliometric and network analysis and also to generate a new contribution through the analysis of scientific trends related to SC and Sustainable Supply Chain (SSC) within this scientific context, for the time span 2010–2019. The results show that the number of publications is growing exponentially and the most active countries are Germany and the U.S., with Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University being the most productive organization and Tecnologico de Monterrey the most collaborative. The analysis of the scientific terms allows us to conclude that the research field is in a growth phase, generating up to almost 4500 new terms in 2019.

Does the mobility of scientists disrupt their collaboration stability?

To explore to what extent the mobility of scientists disrupts the stability of their research collaboration, we designed a measure − Collaboration Stability After Moving ( CSAM) − for scientists, retrieved 4343 US-related scientists’ curricula vitae (CVs) from the Open Researcher and Contributor ID (ORCID) website and publication records in the Web of Science database and applied a linear regression model to the dataset. Our findings include the following: (1) the more times a scientist moved, the more she or he is inclined to co-author with previous collaborators, (2) cross-country mobility disrupts the stability of research collaboration more than domestic mobility and (3) the stability of research collaboration correlates with scientists’ cultural background, cross-country work experience and research areas.

Scientific elite revisited: patterns of productivity, collaboration, authorship and impact

Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns to those of ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel Prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel Prize. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science.

Elites, communities and the limited benefits of mentorship in electronic music

While the emergence of success in creative professions, such as music, has been studied extensively, the link between individual success and collaboration is not yet fully uncovered. Here we aim to fill this gap by analyzing longitudinal data on the co-releasing and mentoring patterns of popular electronic music artists appearing in the annual Top 100 ranking of DJ Magazine. We find that while this ranking list of popularity publishes 100 names, only the top 20 is stable over time, showcasing a lock-in effect on the electronic music elite. Based on the temporal co-release network of top musicians, we extract a diverse community structure characterizing the electronic music industry. These groups of artists are temporally segregated, sequentially formed around leading musicians, and represent changes in musical genres. We show that a major driving force behind the formation of music communities is mentorship: around half of musicians entering the top 100 have been mentored by current leading figures before they entered the list. We also find that mentees are unlikely to break into the top 20, yet have much higher expected best ranks than those who were not mentored. This implies that mentorship helps rising talents, but becoming an all-time star requires more. Our results provide insights into the intertwined roles of success and collaboration in electronic music, highlighting the mechanisms shaping the formation and landscape of artistic elites in electronic music.

Effects of homophily and academic reputation in the nomination and selection of Nobel laureates

In collective decision-making, a group of independent experts propose individual choices to reach a common decision. This is the case of competitive events such as Olympics, international Prizes or grant evaluation, where groups of experts evaluate individual performances to assign resources, e.g. scores, recognitions, or funding. However, there are systems where evaluating individual's performance is difficult: in those cases, other factors play a relevant role, leading to unexpected emergent phenomena from micro-scale interactions. The Nobel assignment procedure, rooted on recommendations, is one of these systems. Here we unveil its network, reconstructed from official data and metadata about nominators, nominees and awardees between 1901 and 1965, consisting of almost 12,000 individuals and 17,000 nominations. We quantify the role of homophily, academic reputation of nominators and their prestige neighborhood, showing that nominees endorsed by central actors - who are part of the system's core because of their prestigious reputation - are more likely to become laureate within a finite time scale than nominees endorsed by nominators in the periphery of the network. We propose a mechanistic model which reproduces all the salient observations and allows to design possible countermeasures to mitigate observed effects.

Measuring quality and outcomes of research collaborations: An integrative review

INTRODUCTION

Although the science of team science is no longer a new field, the measurement of team science and its standardization remain in relatively early stages of development. To describe the current state of team science assessment, we conducted an integrative review of measures of research collaboration quality and outcomes.

METHODS

Collaboration measures were identified using both a literature review based on specific keywords and an environmental scan. Raters abstracted details about the measures using a standard tool. Measures related to collaborations with clinical care, education, and program delivery were excluded from this review.

RESULTS

We identified 44 measures of research collaboration quality, which included 35 measures with reliability and some form of statistical validity reported. Most scales focused on group dynamics. We identified 89 measures of research collaboration outcomes; 16 had reliability and 15 had a validity statistic. Outcome measures often only included simple counts of products; publications rarely defined how counts were delimited, obtained, or assessed for reliability. Most measures were tested in only one venue.

CONCLUSIONS

Although models of collaboration have been developed, in general, strong, reliable, and valid measurements of such collaborations have not been conducted or accepted into practice. This limitation makes it difficult to compare the characteristics and impacts of research teams across studies or to identify the most important areas for intervention. To advance the science of team science, we provide recommendations regarding the development and psychometric testing of measures of collaboration quality and outcomes that can be replicated and broadly applied across studies.

Increasing trend of scientists to switch between topics

Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.

Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined

In this study, I developed operational versions of Gini’s and Hirsch’s indexes that can be applied to characterize each researcher’s publication history (PH) as heterodox, orthodox, and interdisciplinary. In addition, the new indicators warn against anomalies that potentially arise from tactical or opportunistic citation and publication behaviors by authors and editors, and can be calculated from readily available information. I split the original Hirsch index into nested indexes to isolate networking activity, as well as to distinguish scientific production (number of articles) from scientific productivity (rate of production), and used nested Gini indexes to identify intentional and successful intertopical and interdisciplinary research. I applied the most popular standardizations (i.e., per author and per year), and used simple methodologies (i.e., least-squares linear and cubic fitting, whole-career vs. subperiods, two-dimensional graphs). I provide three representative numerical examples based on an orthodox multidisciplinary PH, a heterodox PH from the social sciences, and an orthodox unidisciplinary PH from the physical sciences. Two additional numerical examples based on PHs from the life and health sciences show that the suggested PH characterization can be applied to different disciplines where different publication and citation practices prevail. Software is provided to help readers explore the use of these indicators.

Best practices and inclusion of team science principles in appointment promotion and tenure documents in research intensive schools of nursing

BACKGROUND

Nurse scientists are highly sought after and find satisfaction in serving as members of interdisciplinary research teams. These teams also tend to be highly productive. However, nurse scientists in academia also have to reach certain productivity milestones to be promoted and receive tenure that may be incongruent with team science principles.

PURPOSE

This study therefore sought to examine whether APT documents in research intensive nursing schools incorporate team science principles.

METHODS

Qualitatively analyzed the appointment, promotion and tenure documents of 18 U.S. based research intensive schools of nursing with over $2 million in NIH funding in fiscal year 2014.

FINDINGS

The study found that only 8 of 18 documents included any reference to team science principles and even these mentions were largely negligible. There were few best practices to recommend across documents. By not recognizing team science within these documents, nursing risks marginalization within the larger scientific community by limiting mentorship and learning opportunities for early career nurse scientists.

DISCUSSION

Schools of nursing should revisit their promotion and tenure criteria and include a greater commitment to encouragement of team science.

Multiscale impact of researcher mobility

International mobility facilitates the exchange of scientific, institutional and cultural knowledge. Yet whether globalization and advances in virtual communication technologies have altered the impact of researcher mobility is a relevant and open question that we address by analysing a broad international set of 26 170 physicists from 1980 to 2009, focusing on the 10-year period centred around each mobility event to assess the impact of mobility on research outcomes. We account for secular globalization trends by splitting the analysis into three periods, measuring for each period the effect of mobility on researchers' citation impact, research topic diversity, collaboration networks and geographical coordination. In order to identify causal effects we leverage statistical matching methods that pair mobile researchers with non-mobile researchers that are similar in research profile attributes prior the mobility event. We find that mobile researchers gain up to a 17% increase in citations relative to their non-mobile counterparts, which can be explained by the simultaneous increase in their diversity of co-authors, topics and geographical coordination in the period immediately following migration. Nevertheless, we also observe that researcher's completely curtail prior collaborations with their source country in 11% of the cross-border mobility events. As such, these individual-level perturbations fuel multiscale churning in scientific networks, e.g. rewiring the connectivity of individuals and ideas and affecting international integration. Together these results provide additional clarity on the complex relationship between human capital mobility and the dynamics of social capital investment, with implications for immigration and national innovation system policy.

Cross-disciplinary evolution of the genomics revolution

Born out of the Human Genome Project (HGP), the field of genomics evolved with phenomenal speed into a dominant scientific and business force. While other efforts were intent on estimating the economic impact of the genomics revolution, we shift focus to the social and cultural capital generated by bridging together biology and computing-two of the constitutive disciplines of "genomics". We quantify this capital by measuring the pervasiveness of bio-computing cross-disciplinarity (XD) in genomics research during and after the HGP. To provide interlocking perspectives at the career and epistemic levels, we assembled three data sets to measure XD via (i) the collaboration network between 4190 biology and computing faculty from 155 departments in the United States, (ii) cross-departmental affiliations within a comprehensive set of human genomics publications, and (iii) the application of computational concepts and methods in research published in a preeminent genomics journal. Our results show the following: First, research featuring XD collaborations has higher citation impact than other disciplinary research-an effect observed at both the career and individual article levels. Second, genomics articles featuring XD methods tend to have higher citation impact than epistemically pure articles. Third, XD researchers of computing pedigree are drawn to the biology culture. This statistical evidence acquires deeper meaning when viewed against the organizational and knowledge transfer mechanisms revealed by the data models. With cross-disciplinary initiatives set to dominate the agenda of funding agencies, our case study provides a framework for appreciating the long-term effects of these initiatives on science and its standard-bearers.