Increasing trend of scientists to switch between topics

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.

Structure, motion, and multiscale search of traveling networks

Network models are widely applied to describe connectivity and flow in diverse systems. In contrast, the fact that many connected systems move through space as the result of dynamic restructuring has received little attention. Therefore, we introduce the concept of 'traveling networks', and we analyze a tree-based model where the leaves are stochastically manipulated to grow, branch, and retract. We derive how these restructuring rates determine key attributes of network structure and motion, enabling a compact understanding of higher-level network behaviors such as multiscale search. These networks self-organize to the critical point between exponential growth and decay, allowing them to detect and respond to environmental signals with high sensitivity. Finally, we demonstrate how the traveling network concept applies to real-world systems, such as slime molds, the actin cytoskeleton, and human organizations, exemplifying how restructuring rules and rates in general can select for versatile search strategies in real or abstract spaces.

Breaking down the relationship between disruption scores and citation counts

The emergence of the disruption score provides a new perspective that differs from traditional metrics of citations and novelty in research evaluation. Motivated by current studies on the differences among these metrics, we examine the relationship between disruption scores and citation counts. Intuitively, one would expect disruptive scientific work to be rewarded by high volumes of citations and, symmetrically, impactful work to also be disruptive. A number of recent studies have instead shown that such intuition is often at odds with reality. In this paper, we break down the relationship between impact and disruption with a detailed correlation analysis in two large data sets of publications in Computer Science and Physics. We find that highly disruptive papers tend to receive a higher number of citations than average. Contrastingly, the opposite is not true, as we do not find highly cited papers to be particularly disruptive. Notably, these results qualitatively hold even within individual scientific careers, as we find that-on average-an author's most disruptive work tends to be well cited, whereas their most cited work does not tend to be disruptive. We discuss the implications of our findings in the context of academic evaluation systems, and show how they can contribute to reconcile seemingly contradictory results in the literature.

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.

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.

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.

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.

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.

The power of peer networking for improving STEM faculty job applications: a successful pilot programme

To attain a faculty position, postdoctoral fellows submit job applications that require considerable time and effort to produce. Although mentors and colleagues review these applications, postdocs rarely receive iterative feedback from reviewers with the breadth of expertise typically found on an academic search committee. To address this gap, we describe an international peer-reviewing programme for postdocs across disciplines to receive reciprocal, iterative feedback on faculty applications. A participant survey revealed that nearly all participants would recommend the programme to others. Furthermore, our programme was more likely to attract postdocs who struggled to find mentoring, possibly because of their identity as a woman or member of an underrepresented population in STEM or because they changed fields. Between 2018 and 2021, our programme provided nearly 150 early career academics with a diverse and supportive community of peer mentors during the difficult search for a faculty position and continues to do so today. As the transition from postdoc to faculty represents the largest 'leak' in the academic pipeline, implementation of similar programmes by universities or professional societies would provide psycho-social support necessary to prevent attrition of individuals from underrepresented populations as well as increase the chances of success for early career academics in their search for independence.

Complex Network Evolution Model Based on Turing Pattern Dynamics

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.

Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines

We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts (cited journals), here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams-most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology.

Chaining and the temporal dynamics of scientists' publishing behaviour

Scientific progress, or scientific change, has been an important topic in the philosophy and history of science. Previous work has developed quantitative approaches to characterize the progression of science in different fields, but how individual scientists make progress through their careers is not well understood at a comprehensive scale. We characterize the regularity in the temporal dynamics of scientists' publishing behavior with computational algorithms that predict the historical emerging order of publications from individual scientists. We discover that scientists publish in ways following the processes of chaining that mirror those observed in historical word meaning extension, whereby novel ideas emerge by connecting to existing ideas that are proximal in semantic space. We report findings for predominant exemplar-based chaining that reconstructs the emerging order in the publications of 1,164 award-winning and random-sampled scientists from the fields of Physics, Chemistry, Medicine, Economics, and Computer Science over the past century. Our work provides large-scale evidence that scientists across different fields tend to share similar publishing behavior over time by taking incremental steps that build on their past research outputs.

Subject Area Risk Assessment of Four Hungarian Universities with a View to the QS University Rankings by Subject

Purpose

The aim of our paper is to investigate the role of a mentor leading a research team in the overall scientific performance of an academic institution and the possible risks of their departure with a special attention to their publication output.

Design/methodology/approach

By using SciVal subject area data, we composed a formula describing the level of vulnerability of any given university in the case of losing any of its leading mentors, identifying other risk factors by dividing their careers into separate stages.

Findings

It turns out that the higher field-weighed citation impact is, the better position universities reach in the rankings by subject and the vulnerability of institutions highly depends on the mentors, especially in view of their contribution to the topic clusters.

Research limitations

The analysis covers the publication output of leading researchers working at four Hungarian universities, the scope of the analysis is worth being extended.

Practical implications

Our analysis has the potential to give an applicable systemic approach as well as a data collection scheme to university managements so as to formulate an inclusive and comprehensive research strategy involving the introduction of a reward system aimed at publications and further encouraging national and international research cooperation.

Originality/value

The methodology and the principles of risk assessment laid down in our paper are not restricted to measuring the vulnerability level of a limited group of academic institutions, they can be appropriately used for investigating the role of mentors or leading researchers at every university across the globe.

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.

Unexpected advantages of exploitation for target searches in complex networks

Exploitation universally emerges in various decision-making contexts, e.g., animals foraging, web surfing, the evolution of scientists' research topics, and our daily lives. Despite its ubiquity, exploitation, which refers to the behavior of revisiting previous experiences, has often been considered to delay the search process of finding a target. In this paper, we investigate how exploitation affects search performance by applying a non-Markovian random walk model, where a walker randomly revisits a previously visited node using long-term memory. We analytically study two broad forms of network structures, namely, (i) clique-like networks and (ii) lollipop-like networks and find that exploitation can significantly improve search performance in lollipop-like networks, whereas it hinders target search in clique-like networks. Moreover, we numerically verify that exploitation can reduce the time needed to fully explore the underlying networks using 550 diverse real-world networks. Based on the analytic result, we define the lollipop-likeness of a network and observe a positive relationship between the advantage of exploitation and lollipop-likeness.

Inequality of authors’ reference reuse

This brief communication finds a clear and universal inequality of authors’ reference reuse behaviour. We observe that a few references are reused many times in an author’s oeuvre while most of his or her references only occur in the reference list for quite a limited number of times. A power law distribution depicts such an inequality. We particularly utilise the power value, [Formula: see text], to characterise the nuanced difference of such inequalities. A pilot study based upon Microsoft Academic Graph (MAG) shows that the [Formula: see text] tends to be normally distributed, regardless of whether it is from a citing or a cited perspective. Our empirical study also reveals that the [Formula: see text] of highly cited publications tends to be greater than that of lowly cited ones, yet we also observe a saturation when the number of citations increases.

Patterns of interest change in stack overflow

Stack Overflow is currently the largest programming related question and answer community, containing multiple programming areas. The change of user's interest is the micro-representation of the intersection of macro-knowledge and has been widely studied in scientific fields, such as literature data sets. However, there is still very little research for the general public, such as the question and answer community. Therefore, we analyze the interest changes of 2,307,720 users in Stack Overflow in this work. Specifically, we classify the tag network in the community, vectorize the topic of questions to quantify the user's interest change patterns. Results show that the change pattern of user interest has the characteristic of a power-law distribution, which is different from the exponential distribution of scientists' interest change, but they are all affected by three features, heterogeneity, recency and proximity. Furthermore, the relationship between users' reputations and interest changes is negatively correlated, suggesting the importance of concentration, i.e., those who focus on specific areas are more likely to gain a higher reputation. In general, our work is a supplement to the public interest changes in science, and it can also help community managers better design recommendation algorithms and promote the healthy development of communities.

Quantifying the rise and fall of scientific fields

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.

The emergence of graphene research topics through interactions within and beyond

Scientific research is an essential stage of the innovation process. However, it remains unclear how a scientific idea becomes applied knowledge and, after that, a commercial product. This paper describes a hypothesis of innovation based on the emergence of new research fields from more mature research fields after interactions between the latter. We focus on graphene, a rising field in materials science, as a case study. First, we used a co-clustering method on titles and abstracts of graphene papers to organize them into four meaningful and robust topics (theory and experimental tests, synthesis and functionalization, sensors, supercapacitors and electrocatalysts). We also demonstrated that they emerged in the order listed. We then tested all topics against the literature on nanotubes and batteries, the possible parent fields of theory and experimental tests, as well as supercapacitors and electrocatalysts. We found incubation signatures for all topics in the nanotube papers collection and weaker incubation signatures for supercapacitors and electrocatalysts in the battery papers collection. Surprisingly, we found and confirmed that the 2004 breakthrough in graphene created a stir in both the nanotube and battery fields. Our findings open the door for a better understanding of how and why new research fields coalesce.

Peer Review

https://publons.com/publon/10.1162/qss_a_00193

Social benefits and individual costs of creativity in art and science: A statistical analysis based on a theoretical framework

In this study, we statistically identified and characterized the relationship between the long-run social benefits of creativity and the in-life individual costs (in terms of happiness and health) of creativity. To do so, we referred to a theoretical framework that depicts a creator’s life. We generated a balanced dataset of 200 creators (i.e., composers, painters, mathematicians and physicists, and biologists and chemists born between 1770 and 1879), and calculated standardized evaluations of the long-run social benefits in different domains (performances, exhibitions, citations). We performed regression analysis and identified the statistical determinants of the relationship between a creator’s social benefits and the costs to their happiness and health. We found that creativity represented an individual cost for all four creator groups, with a larger impact on happiness than on health; the cost was greater if creativity was based more on divergent than on convergent thinking or if authors faced greater language issues. The impacts of long-run social benefits on individual happiness and health were similar in the arts and sciences if institutional differences were taken into account.

Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles

The outbreak of coronavirus disease 2019 (COVID-19) has had a significant repercussion on the health, economy, politics and environment, making coronavirus-related issues more complicated and difficult to adequately address by relying on a single field. Interdisciplinary research can provide an effective solution to complex issues in the related field of coronavirus. However, whether coronavirus-related research becomes more interdisciplinary still needs corroboration. In this study, we investigate interdisciplinary status of the coronavirus-related fields via the COVID-19 Open Research Dataset (CORD-19). To this end, we calculate bibliometric indicators of interdisciplinarity and apply a co-occurrence analysis method. The results show that co-occurrence relationships between cited disciplines have evolved dynamically over time. The two types of co-occurrence relationships, Immunology and Microbiology & Medicine and Chemical Engineering & Chemistry, last for a long time in this field during 1990-2020. Moreover, the number of disciplines cited by coronavirus-related research increases, whereas the distribution of disciplines is uneven, and this field tends to focus on several dominant disciplines such as Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. We also measure the disciplinary diversity of COVID-19 related papers published from January to December 2020; the disciplinary variety shows an upward trend, while the degree of disciplinary balance shows a downward trend. Meanwhile, the comprehensive index ²D^s demonstrates that the degree of interdisciplinarity in coronavirus field decreases between 1990 and 2019, but it increases in 2020. The results help to map the interdisciplinarity of coronavirus-related research, gaining insight into the degree and history of interdisciplinary cooperation.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

Research hotspots and trends of microRNA in periodontology and dental implantology: a bibliometric analysis

Background

Periodontal disease is a leading cause of tooth loss, and microRNA (miRNA) has been shown to regulate various biological processes. This study aimed to quantitatively analyze the literature related to miRNA in periodontology and dental implantology and summarize the research hotspots and trends in this field.

Methods

Literature records from 1985 to 2020 were obtained from the Web of Science Core Collection database. After manual selection, the data was used for cooperative network analysis, keyword co-occurrence analysis, and reference co-citation analysis and visualized by CiteSpace.

Results

A total of 287 papers were analyzed between 2007 and 2020, and more than 95% of them were published in the past decade. The largest number of publications were from China, followed by the USA and Japan. The direct cooperation among the productive institutions was not close. At present, most of the research belongs to the discipline of dentistry, oral surgery, cell biology, and molecular biology. Literature clusters generated by reference co-citation analysis and keyword co-occurrence network showed that previous studies mainly focused on four hotspots: periodontal ligament stem cells (PDLSCs), the pathological process of periodontitis, osteogenic differentiation/bone regeneration, and the competing endogenous RNA (ceRNA) network.

Conclusions

The therapeutic potential of miRNA in promoting bone formation and how the ceRNA network contributes to miRNA regulation at a deeper level have become the two main research trends of this field.

Influence of intrinsic motivations on the continuity of scientific knowledge contribution to online knowledge-sharing platforms

Scientific knowledge contribution to online knowledge-sharing platforms has long been regarded as instrumental behavior based on utilitarian considerations. Employing cognitive evaluation theory, this study examines scientific expert users' behavioral metrics to understand the factors responsible for users continuing to contribute their scientific knowledge for an extended period or a very short span. We found that expert users' intrinsic motivations, which has received little attention in recent studies, constitute an important indicator of sustained online scientific knowledge contribution. Furthermore, although social rewards fail to predict the continuity of scientific knowledge contribution, they prolong the duration of knowledge contribution by enhancing the intrinsic motivations of expert users. In conclusion, a self-reinforcement mechanism underlies the relationship of intrinsic motivation with social rewards, which governs continuous online scientific knowledge contribution behavior.

Finding scientific communities in citation graphs: Articles and authors

Understanding the nature and organization of scientific communities is of broad interest. The “Invisible College” is a historical metaphor for one such type of community that refers to a small group of scientists working on a problem of common interest. The scientific and social behavior of such colleges has been the subject of case studies that have examined limited samples of the scientific enterprise. We introduce a metamethod for large-scale discovery that consists of a pipeline to select themed article clusters, whose authors can then be analyzed. A sample of article clusters produced by this pipeline was reviewed by experts, who inferred significant thematic relatedness within clusters, suggesting that authors linked to such clusters may represent valid communities of practice. We explore properties of the author communities identified by our pipeline, and the publication and citation practices of both typical and highly influential authors. Our study reveals that popular domain-independent criteria for graphical cluster quality must be carefully interpreted in the context of searching for author communities, and also suggests a role for contextual criteria.

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.

Characterizing dissimilarity of weighted networks

Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.

Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks

The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.

COVID-19 research risks ignoring important host genes due to pre-established research patterns

It is known that research into human genes is heavily skewed towards genes that have been widely studied for decades, including many genes that were being studied before the productive phase of the Human Genome Project. This means that the genes most frequently investigated by the research community tend to be only marginally more important to human physiology and disease than a random selection of genes. Based on an analysis of 10,395 research publications about SARS-CoV-2 that mention at least one human gene, we report here that the COVID-19 literature up to mid-October 2020 follows a similar pattern. This means that a large number of host genes that have been implicated in SARS-CoV-2 infection by four genome-wide studies remain unstudied. While quantifying the consequences of this neglect is not possible, they could be significant.

Trends in Glucocerebrosides Research: A Systematic Review

Glucocerebrosides are sphingolipid components of cell membranes that intervene in numerous cell biological processes and signaling pathways and that deregulation is implicated in human diseases such as Gaucher disease and Parkinson's disease. In the present study, we conducted a systematic review using document co-citation analysis, clustering and visualization tools to explore the trends and knowledge structure of glucocerebrosides research as indexed in the Science Citation Index Expanded database (1956-present). A co-citation network of 5,324 publications related to glucocerebrosides was constructed. The analysis of emerging categories and keywords suggested a growth of research related to neurosciences over the last decade. We identified ten major areas of research (e.g., clusters) that developed over time, from the oldest (i.e., on glucocerebrosidase protein or molecular analysis of the GBA gene) to the most recent ones (i.e., on drug resistance in cancer, pharmacological chaperones, or Parkinson's disease). We provided for each cluster the most cited publications and a description of their intellectual content. We moreover identified emerging trends in glucocerebrosides research by detecting the surges in the rate of publication citations in the most recent years. In conclusion, this study helps to apprehend the most significant lines of research on glucocerebrosides. This should strengthen the connections between scientific communities studying glycosphingolipids to facilitate advances, especially for the most recent researches on cancer drug resistance and Parkinson's disease.

Impact of individual actions on the collective response of social systems

In a social system individual actions have the potential to trigger spontaneous collective reactions. The way and extent to which the activity (number of actions—A) of an individual causes or is connected to the response (number of reactions—R) of the system is still an open question. We measure the relationship between activity and response with the distribution of efficiency, a metric defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\eta =R/A$$\end{document}η=R/A. Generalizing previous results, we show that the efficiency distribution presents a universal structure in three systems of different nature: Twitter, Wikipedia and the scientific citations network. To understand this phenomenon, we develop a theoretical framework composed of three minimal statistical models that contemplate different levels of dependence between A and R. The models not only are able to reproduce the empirical activity-response data but also can serve as baselines or null models for more elaborated and domain-specific approaches.

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.

Measuring similarity in co-occurrence data using ego-networks

The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystems, and brain networks. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here, we show that traditional methods based on the aggregated network can bring unwanted indirect relationships. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.