James A. Evans

Tradition and Innovation in Scientists’ Research Strategies

What factors affect a scientist’s choice of research problem? Qualitative research in the history and sociology of science suggests that this choice is patterned by an “essential tension” between productive tradition and risky innovation. We examine this tension through Bourdieu’s field theory of science, and we explore it empirically by analyzing millions of biomedical abstracts from MEDLINE. We represent the evolving state of chemical knowledge with networks extracted from these abstracts. We then develop a typology of research strategies on these networks. Scientists can introduce novel chemicals and chemical relationships (innovation) or delve deeper into known ones (tradition). They can consolidate knowledge clusters or bridge them. The aggregate distribution of published strategies remains remarkably stable. High-risk innovation strategies are rare and reflect a growing focus on established knowledge. An innovative publication is more likely to achieve high impact than a conservative one, but the additional reward does not compensate for the risk of failing to publish. By studying prizewinners in biomedicine and chemistry, we show that occasional gambles for extraordinary impact are a compelling explanation for observed levels of risky innovation. Our analysis of the essential tension identifies institutional forces that sustain tradition and suggests policy interventions to foster innovation.

The prediction of human tumor radiosensitivity in situ: An approach using chromosome aberrations detected by fluorescence in situ hybridization☆☆☆

No method of predicting the radiation sensitivity of individual human tumors is presently available, and recently published data show that other factors, in addition to the intrinsic radiosensitivity of the tumor cells, may play a role in the in vivo response of human tumors. Since these factors likely involve the tumor milieu (e.g., cell-cell contact and tumor hypoxia), an in situ assay of radiosensitivity is required. Although an analysis based on chromosome damage is the only suitable assay that would fit the requirements of sensitivity and speed of analysis, conventional examination of chromosome damage is impractical. By allowing the visualization of chromosomes in interphase cells, the technique of premature chromosome condensation (PCC) overcomes the need to culture the tumor cells in vitro, but the technical problem remains of counting a small excess number of breaks over the often large pretreatment chromosome number. We demonstrate here that the combination of fluorescence in situ hybridization (FISH) with PCC enormously simplifies the problem by focusing the analysis on a single chromosome. It also allows exchange aberrations to be scored easily. We demonstrate that the FISH technology may also be used to estimate radiation sensitivity from stable reciprocal translocations in metaphase identified by combining whole chromosome painting with a second color hybridization to the repeat sequences common to the centromeres. Since the frequency of stable translocations should correlate with initial chromosome damage, and since these translocations are not preferentially lost from the irradiated tumor cell population by cell death, an estimate of tumor cell killing following 1-5 dose fractions should be possible. Each of these two methods has its advantages, and a careful study of the two should establish which is superior for routine use to determine tumor radiosensitivity in situ.

Industry collaboration, scientific sharing, and the dissemination of knowledge

Robert Merton famously characterized modern science as distinct from other social spheres by the importance of sharing. In contrast, secrecy is often claimed the most frequent method companies employ to benefit from their discoveries. This study interrogates these claims, and then uses fieldwork on academic research with the popular plant model Arabidopsis thaliana and the companies that support it to explore the nature of sharing in academy and industry. Using archival materials and panel models, the study then examines the consequences of industry collaboration, how it influences sharing between academic scientists and the reach of their ideas and materials. Interviews with academic scientists and industrial research managers reveal differences in sharing. Academics are practiced at communicating discoveries and sharing materials, but occasionally withhold to secure credit or barter to maximize it. In contrast, companies manage their ideas and resources for longer-term control. The difference is not that academic scientists never keep secrets, but that many do so badly. Statistical findings suggest that industry sponsorship influences scientists to reduce their sharing of research materials and methods, but it increases the reception of scientists’ early-stage manuscripts, probably as a substitute, enabling competing labs to infer a closed lab’s methods. Industry’s influence also affects sharing indirectly by sponsoring research in less crowded areas. In this way, industry curbs the demand as well as the supply of sharing in science. As a result, industry sponsorship limits the social, organizational, and geographic distance that sponsored ideas travel over time. Scientists find it difficult to enroll other academics in their research findings as they become enrolled in an industry project of avoiding and mitigating competing science.

Choosing experiments to accelerate collective discovery

Significance Scientists perform a tiny subset of all possible experiments. What characterizes the experiments they choose? And what are the consequences of those choices for the pace of scientific discovery? We model scientific knowledge as a network and science as a sequence of experiments designed to gradually uncover it. By analyzing millions of biomedical articles published over 30 y, we find that biomedical scientists pursue conservative research strategies exploring the local neighborhood of central, important molecules. Although such strategies probably serve scientific careers, we show that they slow scientific advance, especially in mature fields, where more risk and less redundant experimentation would accelerate discovery of the network. We also consider institutional arrangements that could help science pursue these more efficient strategies. A scientist’s choice of research problem affects his or her personal career trajectory. Scientists’ combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity’s importance corresponds to its degree centrality, and a problem’s difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies.

Modeling the transmission of community-associated methicillin-resistant Staphylococcus aureus: a dynamic agent-based simulation

BackgroundMethicillin-resistant Staphylococcus aureus (MRSA) has been a deadly pathogen in healthcare settings since the 1960s, but MRSA epidemiology changed since 1990 with new genetically distinct strain types circulating among previously healthy people outside healthcare settings. Community-associated (CA) MRSA strains primarily cause skin and soft tissue infections, but may also cause life-threatening invasive infections. First seen in Australia and the U.S., it is a growing problem around the world. The U.S. has had the most widespread CA-MRSA epidemic, with strain type USA300 causing the great majority of infections. Individuals with either asymptomatic colonization or infection may transmit CA-MRSA to others, largely by skin-to-skin contact. Control measures have focused on hospital transmission. Limited public health education has focused on care for skin infections.MethodsWe developed a fine-grained agent-based model for Chicago to identify where to target interventions to reduce CA-MRSA transmission. An agent-based model allows us to represent heterogeneity in population behavior, locations and contact patterns that are highly relevant for CA-MRSA transmission and control. Drawing on nationally representative survey data, the model represents variation in sociodemographics, locations, behaviors, and physical contact patterns. Transmission probabilities are based on a comprehensive literature review.ResultsOver multiple 10-year runs with one-hour ticks, our model generates temporal and geographic trends in CA-MRSA incidence similar to Chicago from 2001 to 2010. On average, a majority of transmission events occurred in households, and colonized rather than infected agents were the source of the great majority (over 95%) of transmission events. The key findings are that infected people are not the primary source of spread. Rather, the far greater number of colonized individuals must be targeted to reduce transmission.ConclusionsOur findings suggest that current paradigms in MRSA control in the United States cannot be very effective in reducing the incidence of CA-MRSA infections. Furthermore, the control measures that have focused on hospitals are unlikely to have much population-wide impact on CA-MRSA rates. New strategies need to be developed, as the incidence of CA-MRSA is likely to continue to grow around the world.

Weaving the fabric of science: Dynamic network models of science's unfolding structure

Abstract Science is a complex system. Building on Latours actor network theory, we model published science as a dynamic hypergraph and explore how this fabric provides a substrate for future scientific discovery. Using millions of abstracts from MEDLINE, we show that the network distance between biomedical things (i.e., people, methods, diseases, chemicals) is surprisingly small. We then show how science moves from questions answered in one year to problems investigated in the next through a weighted random walk model. Our analysis reveals intriguing modal dispositions in the way biomedical science evolves: methods play a bridging role and things of one type connect through things of another. This has the methodological implication that adding more node types to network models of science and other creative domains will likely lead to a superlinear increase in prediction and understanding.

Attention to local health burden and the global disparity of health research.

Most studies on global health inequality consider unequal health care and socio-economic conditions but neglect inequality in the production of health knowledge relevant to addressing disease burden. We demonstrate this inequality and identify likely causes. Using disability-adjusted life years (DALYs) for 111 prominent medical conditions, assessed globally and nationally by the World Health Organization, we linked DALYs with MEDLINE articles for each condition to assess the influence of DALY-based global disease burden, compared to the global market for treatment, on the production of relevant MEDLINE articles, systematic reviews, clinical trials and research using animal models vs. humans. We then explored how DALYs, wealth, and the production of research within countries correlate with this global pattern. We show that global DALYs for each condition had a small, significant negative relationship with the production of each type of MEDLINE articles for that condition. Local processes of health research appear to be behind this. Clinical trials and animal studies but not systematic reviews produced within countries were strongly guided by local DALYs. More and less developed countries had very different disease profiles and rich countries publish much more than poor countries. Accordingly, conditions common to developed countries garnered more clinical research than those common to less developed countries. Many of the health needs in less developed countries do not attract attention among developed country researchers who produce the vast majority of global health knowledge—including clinical trials—in response to their own local needs. This raises concern about the amount of knowledge relevant to poor populations deficient in their own research infrastructure. We recommend measures to address this critical dimension of global health inequality.

Benchmarking ontologies: bigger or better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.

Science of science

The whys and wherefores of SciSci The science of science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field. In a Review, Fortunato et al. explain that the underlying rationale is that with a deeper understanding of the precursors of impactful science, it will be possible to develop systems and policies that improve each scientists ability to succeed and enhance the prospects of science as a whole. Science, this issue p. eaao0185 BACKGROUND The increasing availability of digital data on scholarly inputs and outputs—from research funding, productivity, and collaboration to paper citations and scientist mobility—offers unprecedented opportunities to explore the structure and evolution of science. The science of science (SciSci) offers a quantitative understanding of the interactions among scientific agents across diverse geographic and temporal scales: It provides insights into the conditions underlying creativity and the genesis of scientific discovery, with the ultimate goal of developing tools and policies that have the potential to accelerate science. In the past decade, SciSci has benefited from an influx of natural, computational, and social scientists who together have developed big data–based capabilities for empirical analysis and generative modeling that capture the unfolding of science, its institutions, and its workforce. The value proposition of SciSci is that with a deeper understanding of the factors that drive successful science, we can more effectively address environmental, societal, and technological problems. ADVANCES Science can be described as a complex, self-organizing, and evolving network of scholars, projects, papers, and ideas. This representation has unveiled patterns characterizing the emergence of new scientific fields through the study of collaboration networks and the path of impactful discoveries through the study of citation networks. Microscopic models have traced the dynamics of citation accumulation, allowing us to predict the future impact of individual papers. SciSci has revealed choices and trade-offs that scientists face as they advance both their own careers and the scientific horizon. For example, measurements indicate that scholars are risk-averse, preferring to study topics related to their current expertise, which constrains the potential of future discoveries. Those willing to break this pattern engage in riskier careers but become more likely to make major breakthroughs. Overall, the highest-impact science is grounded in conventional combinations of prior work but features unusual combinations. Last, as the locus of research is shifting into teams, SciSci is increasingly focused on the impact of team research, finding that small teams tend to disrupt science and technology with new ideas drawing on older and less prevalent ones. In contrast, large teams tend to develop recent, popular ideas, obtaining high, but often short-lived, impact. OUTLOOK SciSci offers a deep quantitative understanding of the relational structure between scientists, institutions, and ideas because it facilitates the identification of fundamental mechanisms responsible for scientific discovery. These interdisciplinary data-driven efforts complement contributions from related fields such as scientometrics and the economics and sociology of science. Although SciSci seeks long-standing universal laws and mechanisms that apply across various fields of science, a fundamental challenge going forward is accounting for undeniable differences in culture, habits, and preferences between different fields and countries. This variation makes some cross-domain insights difficult to appreciate and associated science policies difficult to implement. The differences among the questions, data, and skills specific to each discipline suggest that further insights can be gained from domain-specific SciSci studies, which model and identify opportunities adapted to the needs of individual research fields. The complexity of science. Science can be seen as an expanding and evolving network of ideas, scholars, and papers. SciSci searches for universal and domain-specific laws underlying the structure and dynamics of science. ILLUSTRATION: NICOLE SAMAY Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise—for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.

Novel opportunities for computational biology and sociology in drug discovery

Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies.