Anatomy of funded research in science

Mapping philanthropic support of science

While philanthropic support for science has increased in the past decade, there is limited quantitative knowledge about the patterns that characterize it and the mechanisms that drive its distribution. Here, we map philanthropic funding to universities and research institutions based on IRS tax forms from 685,397 non-profit organizations. We identify nearly one million grants supporting institutions involved in science and higher education, finding that in volume and scope, philanthropy is a significant source of funds, reaching an amount that rivals some of the key federal agencies like the NSF and NIH. Our analysis also reveals that philanthropic funders tend to focus locally, indicating that criteria beyond research excellence play an important role in funding decisions, and that funding relationships are stable, i.e. once a grant-giving relationship begins, it tends to continue in time. Finally, we show that the bipartite funder-recipient network displays a highly overrepresented motif indicating that funders who share one recipient also share other recipients and we show that this motif contains predictive power for future funding relationships. We discuss the policy implications of our findings on inequality in science, scientific progress, and the role of quantitative approaches to philanthropy.

Factors influencing the work of researchers in Scientific Initiation: A systematic review protocol

INTRODUCTION

Scientific Initiation (SI) is an educational activity that allows students to begin their scientific training and research under the guidance of an experienced researcher. While several studies have examined students' perceptions of SI, research on the perspective of researchers working in this field is currently lacking. Thus, this study's aim is to describe the protocol design for conducting a systematic review. At the same time, the review aims also to identify factors influencing the work of researchers in SI and explore the motivations leading researchers to engage in research projects within institutions and their respective impacts.

METHOD AND ANALYSES

Literature search will be done using the bibliographic databases, including Academic Search Premier, APA PsycNet, CINAHL Plus, ERIC, SocINDEX, Scopus, and Web of Science. The search strategy was guided by the PICo framework (Population, phenomenon of Interest, and Context). The preparation and development of this protocol following guidelines were employed: Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015), Peer Review of Electronic Search Strategies 2015 (PRESS 2015), and PRISMA-Search (PRISMA-S). Original, peer-reviewed articles that examine the factors related to the work of researchers in SI will be included without any language or publication date restrictions. Qualitative, quantitative, and mixed-methods studies will be screened by two independent researchers. The included studies will be analyzed to identify factors, policies, and their impacts obtained analytically. Findings will be objectively categorized and synthetically represented through figures, diagrams, and graphic models. The risk of bias will be assessed using the Critical Appraisal Skills Program (CASP) and the Downs and Black checklists. A third senior reviewer will resolve any discrepancies.

DISCUSSION

We aim to understand the factors that drive researchers to engage in SI research through the dissemination of the findings of this systematic review. This may aid the development of institutional strategies and actions that can support the enhancement of SI programs and encourage greater researcher participation.

The drivers of global news spreading patterns

The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the GDELT database. Unlike previous research, we focus on the sequential mention of events across various countries, thus incorporating a temporal dimension into the analysis of news dissemination networks. Our results show a significant imbalance in online news spreading. We identify news superspreaders forming a tightly interconnected rich club, exerting significant influence on the global news agenda. To further investigate the mechanisms underlying news dissemination and the shaping of global public opinion, we model countries' interactions using a gravity model, incorporating economic, geographical, and cultural factors. Consistent with previous studies, we find that countries' GDP is one of the main drivers to shape the worldwide news agenda.

Ranking mobility and impact inequality in early academic careers

How difficult is it for an early career academic to climb the ranks of their discipline? We tackle this question with a comprehensive bibliometric analysis of 57 disciplines, examining the publications of more than 5 million authors whose careers started between 1986 and 2008. We calibrate a simple random walk model over historical data of ranking mobility, which we use to 1) identify which strata of academic impact rankings are the most/least mobile and 2) study the temporal evolution of mobility. By focusing our analysis on cohorts of authors starting their careers in the same year, we find that ranking mobility is remarkably low for the top- and bottom-ranked authors and that this excess of stability persists throughout the entire period of our analysis. We further observe that mobility of impact rankings has increased over time, and that such rise has been accompanied by a decline of impact inequality, which is consistent with the negative correlation that we observe between such two quantities. These findings provide clarity on the opportunities of new scholars entering the academic community, with implications for academic policymaking.

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.

Higher-order rich-club phenomenon in collaborative research grant networks

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National Science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more impactful in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs.

Public use and public funding of science

Knowledge of how science is consumed in public domains is essential for understanding the role of science in human society. Here we examine public use and public funding of science by linking tens of millions of scientific publications from all scientific fields to their upstream funding support and downstream public uses across three public domains-government documents, news media and marketplace invention. We find that different public domains draw from various scientific fields in specialized ways, showing diverse patterns of use. Yet, amidst these differences, we find two important forms of alignment. First, we find universal alignment between what the public consumes and what is highly impactful within science. Second, a field's public funding is strikingly aligned with the field's collective public use. Overall, public uses of science present a rich landscape of specialized consumption, yet, collectively, science and society interface with remarkable alignment between scientific use, public use and funding.

Rank Has Its Privileges: Explaining Why Laboratory Safety Is a Persistent Challenge

Environmental, health, and safety management systems have become common in research settings to improve laboratory safety through systematic observation and self-regulation. However, there is scant empirical evidence assessing whether these surveillance and inspection systems meet their intended objectives. Using data from safety inspections in research laboratories at a large university, we investigate whether conducting inspections, and recording and reporting findings back to the formally responsible actors (i.e., principal investigator scientists) lead to the improvement of regulatory compliance. Our analyses identify a population of well-funded, high-status, tenured researchers whose non-compliant practices persist. Our interviews with environmental, health, and safety personnel suggest that higher-status actors disengage from the regulatory system, the compliance officers, and the system's feedback process by their variable recognition and acknowledgment of relevant regulations, attention to the inspection reports, and responses to the feedback concerning repair of the unsafe situation. This study extends previous literature on regulatory compliance by providing evidence for the role of power and status in explaining actor-level non-compliant behavior.

Connections matter: a proxy measure for evaluating network membership with an application to the Seventh Research Framework Programme

Although the topic of networks has received significant attention from the scientific literature, it remains to be seen whether it is possible to quantify the degree to which an organisation benefits from being part of a network. Starting from the concept of network value and that of Metcalfe’s Law, this paper introduces and defines the collective network effect (CNE). CNE is based on the concept that a network member is not only affected by its friends but also by the friends of its friends. By taking into account network connection patterns, CNE provides a proxy for quantifying the benefit of network membership. We computed the CNE for the nodes of a large network built using the whole set of common projects among the participants of the 7th Framework Programme for Research and Technological Development of the European Commission. The obtained results show that nodes with a higher CNE have access to substantially more conspicuous fundings than nodes with a lower CNE. In general, such a measure could supplement other centrality measures and be useful for organisations and companies aiming to evaluate both their current situation and the potential partners they should link with in order to extract the highest benefits from network membership.

Scientific evidence based rare disease research discovery with research funding data in knowledge graph

BACKGROUND

Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study.

METHODS

To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery.

RESULTS

Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research.

CONCLUSION

We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases.

The unintended consequences of the pandemic on non-pandemic research activities

Research about the Covid-19 pandemic has taken center stage in shaping the work of many scholars, inter alia highlighting the importance of research in addressing the grand challenges humanity faces. However, the pandemic has also ushered in increased administrative, teaching and out of work commitments for many researchers, leading to concerns that academics will become less willing to invest time in obtaining resources to undertake non-Covid-related projects. Using a large-scale survey of business, economics and management researchers, coupled with their publication histories and additional institutional data, we examine how far individuals experienced the focus on the pandemic as ‘crowding out’ interest in, and undermining their confidence in applying for grants for work not focused on the pandemic. We found 40% of the sample agree that the pandemic has impaired their confidence in applying for non-pandemic-related grants and ‘crowded out’ other projects. Researchers with current and prior grants, particularly those with the most experience of holding grants, scholars whose work ‘impacted’ beyond academia, and early career researchers, disproportionately considered themselves to be most affected. We also found that researchers’ perceptions differed based on institutional characteristics. We discuss the implications of these findings for grant providers and national research agencies as well as for individual academic researchers and the institutions in which they work.

Funding acknowledgements in scientific publications: A literature review

The topic of acknowledgements has produced abundant research since the 1970s, though, as previous studies point out, the value of acknowledgements has not yet been demonstrated and further research is limited by lack of conceptualization. This study focuses on funding acknowledgements (FAs), considering that funding represents an important input in the scientific process. In this context, 183 scientific publications retrieved from Scopus from the 1970s until June 2020 were analyzed, with the aim of systematizing conceptually this body of research and contributing to a theory of acknowledgements. Results are summarized into the following main themes: the meaning of FAs; data sources for acknowledgements; the process of funding; association of funding with productivity, impact, and collaboration; and other aspects affected by funding. The literature reviewed shows that a theory of acknowledgements based on the reward triangle, as in previous studies, is unable to capture the extreme complexity of the scientific activity affecting and being affected by FAs. Funding bodies appear as clear and influential actors in the scientific communication system, making important decisions on the research that is supported, and influencing the type of knowledge produced. Funding agencies hold a responsibility regarding the data that they may collect on their programs, as well as the normalization policies they need to develop so that funded authors can reference with less ambiguity the financial source of their projects. Finally, the need to assess the impact of research funding beyond the scientific community that is, the societal impact, is also addressed.

The effect of competitive public funding on scientific output: A comparison between China and the EU

Public funding is believed to play an important role in the development of science and technology. However, whether public funding and, in particular, competitive funding from public agencies actually helps to increase scientific output (i.e. publications) remains a matter of debate. By analysing a dataset of co-publications between China and the EU and a dataset of joint project collaborations in European Framework Programs for Research and Innovation [FP7 and Horizon 2020 (H2020)], we investigate whether different public funding agencies’ competitive assets have different impact on the volume of publication output. Our results support the hypotheses that competitively funded research output varies by funding sources, so that a high level of funding does not necessarily lead to high scientific output. Our results show that FP7/H2020 funded projects do not have a positive contribution to the output of joint publications between China and the EU. Interestingly, cooperation in the form of jointly writing proposals to these EU programmes, especially when they are not granted by the European Commission, can contribute significantly to joint scientific publications in a later stage. This applies in particular to cases where funding from China is involved. Our findings highlight the key role that funding agencies play in influencing research behaviour. Our results indicate that Chinese funding triggers a high number of publications, whereas research funded by the EU does so to a much lower extent, arguably due to the EU’s strong focus on social impact and its funding schemes as tools to promote European integration.

Robustness and rich clubs in collaborative learning groups: a learning analytics study using network science

Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group's robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.

Concentration of Danish research funding on individual researchers and research topics: Patterns and potential drivers

The degree of concentration in research funding has long been a principal matter of contention in science policy. Strong concentration has been seen as a tool for optimizing and focusing research investments but also as a damaging path towards hypercompetition, diminished diversity, and conservative topic selection. While several studies have documented funding concentration linked to individual funding organizations, few have looked at funding concentration from a systemic perspective. In this article, we examine nearly 20,000 competitive grants allocated by 15 major Danish research funders. Our results show a strongly skewed allocation of funding towards a small elite of individual researchers, and towards a select group of research areas and topics. We discuss potential drivers and highlight that funding concentration likely results from a complex interplay between funders’ overlapping priorities, excellence-dominated evaluation criteria, and lack of coordination between both public and private research funding bodies.

The evolution of knowledge within and across fields in modern physics

The exchange of knowledge across different areas and disciplines plays a key role in the process of knowledge creation, and can stimulate innovation and the emergence of new fields. We develop here a quantitative framework to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time. Drawing on a comprehensive data set on scientific production in modern physics and on the patterns of citations between articles published in the various fields in the last 30 years, we are then able to map, over time, how the ideas developed in a given field in a certain time period have influenced later discoveries in the same field or in other fields. The analysis of knowledge flows internal to each field displays a remarkable variety of temporal behaviours, with some fields of physics showing to be more self-referential than others. The temporal networks of knowledge exchanges across fields reveal cases of one field continuously absorbing knowledge from another field in the entire observed period, pairs of fields mutually influencing each other, but also cases of evolution from absorbing to mutual or even to back-nurture behaviors.

Predicting success in the worldwide start-up network

By drawing on large-scale online data we are able to construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent companies, while the links model the flow of employees and the associated transfer of know-how across companies. We use network centrality measures to assess, at an early stage, the likelihood of the long-term positive economic performance of a start-up. We find that the start-up network has predictive power and that by using network centrality we can provide valuable recommendations, sometimes doubling the current state of the art performance of venture capital funds. Our network-based approach supports the theory that the position of a start-up within its ecosystem is relevant for its future success, while at the same time it offers an effective complement to the labour-intensive screening processes of venture capital firms. Our results can also enable policy-makers and entrepreneurs to conduct a more objective assessment of the long-term potentials of innovation ecosystems, and to target their interventions accordingly.

Quantifying the dynamics of failure across science, startups and security

Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation^1-7, human dynamics^8-11 and learning^12-17, we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.

Early-career setback and future career impact

Setbacks are an integral part of a scientific career, yet little is known about their long-term effects. Here we examine junior scientists applying for National Institutes of Health R01 grants. By focusing on proposals fell just below and just above the funding threshold, we compare near-miss with narrow-win applicants, and find that an early-career setback has powerful, opposing effects. On the one hand, it significantly increases attrition, predicting more than a 10% chance of disappearing permanently from the NIH system. Yet, despite an early setback, individuals with near misses systematically outperform those with narrow wins in the longer run. Moreover, this performance advantage seems to go beyond a screening mechanism, suggesting early-career setback appears to cause a performance improvement among those who persevere. Overall, these findings are consistent with the concept that "what doesn't kill me makes me stronger," which may have broad implications for identifying, training and nurturing junior scientists.

Concentration or dispersal of research funding?

The relationship between the distribution of research funding and scientific performance is a major discussion point in many science policy contexts. Do high shares of funding handed out to a limited number of elite scientists yield the most value for money, or is scientific progress better supported by allocating resources in smaller portions to more teams and individuals? In this review article, we seek to qualify discussions on the benefits and drawbacks of concentrating research funds on fewer individuals and groups. Based on an initial screening of 3,567 articles and a thorough examination of 92 papers, we present a condensation of central arguments. Further, we juxtapose key findings from 20 years of empirical research on the relation between the size of research grants and scientific performance. Overall, the review demonstrates a strong inclination toward arguments in favor of increased dispersal. A substantial body of empirical research also exhibits stagnant or diminishing returns to scale for the relationship between grant size and research performance. The findings question the rationale behind current funding trends and point toward more efficient ways to allocate resources. In addition, they highlight the need for more research on the interplay between science-internal mechanisms and policy priorities in accelerating concentration of funding.

Quantifying and predicting success in show business

In certain artistic endeavours—such as acting in films and TV, where unemployment rates hover at around 90%—sustained productivity (simply making a living) is probably a better proxy for quantifying success than high impact. Drawing on a worldwide database, here we study the temporal profiles of activity of actors and actresses. We show that the dynamics of job assignment is well described by a “rich-get-richer” mechanism and we find that, while the percentage of a career spent active is unpredictable, such activity is clustered. Moreover, productivity tends to be higher towards the beginning of a career and there are signals preceding the most productive year. Accordingly, we propose a machine learning method which predicts with 85% accuracy whether this “annus mirabilis” has passed, or if better days are still to come. We analyse actors and actresses separately, also providing compelling evidence of gender bias in show business.

For most actors sustained productivity defines success. Here the authors study the careers of actors and identify a "rich-get-richer" mechanism with respect to productivity, the emergence of hot streaks and the presence of gender bias, and are able to predict whether the most productive year of an actor is yet to come.

Funder Restrictions on Application Numbers Lead to Chaos

Restricting application rates is an attractive way for funders to reduce time and money wasted evaluating uncompetitive applications. However, mathematical models show that this could induce chaotic cycles in total application numbers, increasing uncertainty in the funding process. One emergent property is that smaller institutions spend disproportionally more time unfunded.

The Matthew effect in science funding

A classic thesis is that scientific achievement exhibits a "Matthew effect": Scientists who have previously been successful are more likely to succeed again, producing increasing distinction. We investigate to what extent the Matthew effect drives the allocation of research funds. To this end, we assembled a dataset containing all review scores and funding decisions of grant proposals submitted by recent PhDs in a €2 billion granting program. Analyses of review scores reveal that early funding success introduces a growing rift, with winners just above the funding threshold accumulating more than twice as much research funding (€180,000) during the following eight years as nonwinners just below it. We find no evidence that winners' improved funding chances in subsequent competitions are due to achievements enabled by the preceding grant, which suggests that early funding itself is an asset for acquiring later funding. Surprisingly, however, the emergent funding gap is partly created by applicants, who, after failing to win one grant, apply for another grant less often.

Quantifying the diaspora of knowledge in the last century

Academic research is driven by several factors causing different disciplines to act as "sources" or "sinks" of knowledge. However, how the flow of authors' research interests - a proxy of human knowledge - evolved across time is still poorly understood. Here, we build a comprehensive map of such flows across one century, revealing fundamental periods in the raise of interest in areas of human knowledge. We identify and quantify the most attractive topics over time, when a relatively significant number of researchers moved from their original area to another one, causing what we call a "diaspora of the knowledge" towards sinks of scientific interest, and we relate these points to crucial historical and political events. Noticeably, only a few areas - like Medicine, Physics or Chemistry - mainly act as sources of the diaspora, whereas areas like Material Science, Chemical Engineering, Neuroscience, Immunology and Microbiology or Environmental Science behave like sinks.

Interdisciplinary research has consistently lower funding success

Interdisciplinary research is widely considered a hothouse for innovation, and the only plausible approach to complex problems such as climate change. One barrier to interdisciplinary research is the widespread perception that interdisciplinary projects are less likely to be funded than those with a narrower focus. However, this commonly held belief has been difficult to evaluate objectively, partly because of lack of a comparable, quantitative measure of degree of interdisciplinarity that can be applied to funding application data. Here we compare the degree to which research proposals span disparate fields by using a biodiversity metric that captures the relative representation of different fields (balance) and their degree of difference (disparity). The Australian Research Council's Discovery Programme provides an ideal test case, because a single annual nationwide competitive grants scheme covers fundamental research in all disciplines, including arts, humanities and sciences. Using data on all 18,476 proposals submitted to the scheme over 5 consecutive years, including successful and unsuccessful applications, we show that the greater the degree of interdisciplinarity, the lower the probability of being funded. The negative impact of interdisciplinarity is significant even when number of collaborators, primary research field and type of institution are taken into account. This is the first broad-scale quantitative assessment of success rates of interdisciplinary research proposals. The interdisciplinary distance metric allows efficient evaluation of trends in research funding, and could be used to identify proposals that require assessment strategies appropriate to interdisciplinary research.

The Anatomy of the Global Football Player Transfer Network: Club Functionalities versus Network Properties

Professional association football is a game of talent. The success of a professional club hinges largely on its ability of assembling the best team. Building on a dataset of player transfer records among more than 400 clubs in 24 world-wide top class leagues from 2011 to 2015, this study aims to relate a club’s success to its activities in the player transfer market from a network perspective. We confirm that modern professional football is indeed a money game, in which larger investment spent on the acquisition of talented players generally yields better team performance. However, further investigation shows that professional football clubs can actually play different strategies in surviving or even excelling this game, and the success of strategies is strongly associated to their network properties in the football player transfer network.

Bias in Research Grant Evaluation Has Dire Consequences for Small Universities

Federal funding for basic scientific research is the cornerstone of societal progress, economy, health and well-being. There is a direct relationship between financial investment in science and a nation's scientific discoveries, making it a priority for governments to distribute public funding appropriately in support of the best science. However, research grant proposal success rate and funding level can be skewed toward certain groups of applicants, and such skew may be driven by systemic bias arising during grant proposal evaluation and scoring. Policies to best redress this problem are not well established. Here, we show that funding success and grant amounts for applications to Canada's Natural Sciences and Engineering Research Council (NSERC) Discovery Grant program (2011-2014) are consistently lower for applicants from small institutions. This pattern persists across applicant experience levels, is consistent among three criteria used to score grant proposals, and therefore is interpreted as representing systemic bias targeting applicants from small institutions. When current funding success rates are projected forward, forecasts reveal that future science funding at small schools in Canada will decline precipitously in the next decade, if skews are left uncorrected. We show that a recently-adopted pilot program to bolster success by lowering standards for select applicants from small institutions will not erase funding skew, nor will several other post-evaluation corrective measures. Rather, to support objective and robust review of grant applications, it is necessary for research councils to address evaluation skew directly, by adopting procedures such as blind review of research proposals and bibliometric assessment of performance. Such measures will be important in restoring confidence in the objectivity and fairness of science funding decisions. Likewise, small institutions can improve their research success by more strongly supporting productive researchers and developing competitive graduate programming opportunities.