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Science and knowledge are studied by researchers across many disciplines, examining how they are developed, what their current boundaries are and how we can advance them. By integrating evidence across disparate disciplines, the holistic field of science of science can address these foundational questions. This field illustrates how science is shaped by many interconnected factors: the cognitive processes of scientists, the historical evolution of science, economic incentives, institutional influences, computational approaches, statistical, mathematical and instrumental foundations of scientific inference, scientometric measures, philosophical and ethical dimensions of scientific concepts, among other influences. Achieving a comprehensive overview of a multifaceted field like the science of science requires pulling together evidence from the many sub-fields studying science across the natural and social sciences and humanities. This enables developing an interdisciplinary perspective of scientific practice, a more holistic understanding of scientific processes and outcomes, and more nuanced perspectives to how scientific research is conducted, influenced and evolves. It enables leveraging the strengths of various disciplines to create a holistic view of the foundations of science. Different researchers study science from their own disciplinary perspective and use their own methods, and there is a large divide between quantitative and qualitative researchers as they commonly do not read or cite research using other methodological approaches. A broader, synthesizing paper employing a qualitative approach can however help provide a bridge between disciplines by pulling together aspects of science (economic, scientometric, psychological, philosophical etc.). Such an approach enables identifying, across the range of fields, the powerful role of our scientific methods and instruments in shaping most aspects of our knowledge and science, whereas economic, social and historical influences help shape what knowledge we pursue. A unifying theory is then outlined for science of science - the new-methods-drive-science theory.

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Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology. Science textbooks, science dictionaries, and science institutions often state that scientists follow, and should follow, the universal scientific method of testing hypotheses using observation and experimentation. Yet, scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study. Using data on all major discoveries across science including all Nobel Prize and major non-Nobel Prize discoveries, we can address the question of the extent to which "the scientific method" is actually applied in making science's groundbreaking research and whether we need to expand this central concept of science. This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)-with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis. Empirical evidence thus challenges the common view of the scientific method. Adhering to it as a guiding principle would constrain us in developing many new scientific ideas and breakthroughs. Instead, assessing all major discoveries, we identify here a general, common feature that the method of science can be reduced to: making all major discoveries has required using sophisticated methods and instruments of science. These include statistical methods, particle accelerators, and X-ray methods. Such methods extend our mind and generally make observing, experimenting, and testing hypotheses in science possible, doing so in new ways and ensure their replicability. This provides a new perspective to the scientific method-embedded in our sophisticated methods and instruments-and suggests that we need to reform and extend the way we view the scientific method and discovery process.

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

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Few things have impacted our lives as much as science and technology, but how we developed science and civilisation is one of the most challenging questions that has not yet been well explained. Attempting to identify the central driver, leading scientists have highlighted the role of culture, cooperation and geography. They focus thus on broad factors that are important basic preconditions but that we cannot directly influence. To better address the question, this paper integrates evidence from evolutionary biology, cognitive science, methodology, archaeology and anthropology. The paper identifies 9 main preconditions necessary for contemporary science, which include 6 main preconditions for civilisation. Using a kind of quasi-experimental research design we observe that some cultures (experimental groups) met the preconditions while other cultures (control groups) did not. Among the preconditions, we explain how our mind's evolved methodological abilities (to observe, solve problems and experiment) have directly enabled acquiring knowledge about the world and collectively developing increasingly sophisticated methods (such as mathematics and more systematic experimentation) that have enabled science and civilisation. We have driven the major revolutions throughout our history - the palaeolithic technological and agricultural revolutions and later the so-called scientific, industrial and digital revolutions - by using our methodological abilities in new ways and developing new methods and tools, i.e. through methodological revolutions. Viewing our methods as the main mechanism through which we have directly developed scientific and technological knowledge, and thus science and civilisation, provides a new framework for understanding science and the history of science. Viewing humans as homo methodologicus, using an expanding methodological toolbox, provides a nuanced explanation of how we have been directly able to meet our needs, solve problems and develop vast bodies of technological and scientific knowledge. By better understanding the origin and foundations of science, we can better understand their limits and, most importantly, how to push those limits. We can do so especially by addressing the evolved cognitive constraints and biases we face and improving the methods we use.

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Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science.

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Current questions in ecology revolve around instabilities in the dynamics on spatial networks and particularly the effect of node heterogeneity. We extend the master stability function formalism to inhomogeneous biregular networks having two types of spatial nodes. Notably, this class of systems also allows the investigation of certain types of dynamics on higher-order networks. Combined with the generalized modeling approach to study the linear stability of steady states, this is a powerful tool to numerically asses the stability of large ensembles of systems. We analyze the stability of ecological metacommunities with two distinct types of habitats analytically and numerically in order to identify several sets of conditions under which the dynamics can become stabilized by dispersal. Our analytical approach allows general insights into stabilizing and destabilizing effects in metapopulations. Specifically, we identify self-regulation and negative feedback loops between source and sink populations as stabilizing mechanisms and we show that maladaptive dispersal may be stable under certain conditions.

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Biomedical research, especially pharmaceutical research, has been criticised for engaging in practices that lead to over-estimations of the effectiveness of medical treatments. A central issue concerns the reporting of absolute and relative measures of medical effectiveness. In this paper we critically examine proposals made by Jacob Stegenga to (a) give priority to the reporting of absolute measures over relative measures, and (b) downgrade the measures of effectiveness (effect sizes) of the treatments tested in clinical trials (Stegenga, 2015a). After exposing significant flaws in a central case study used by Stegenga to bolster his first proposal (a), we go on to argue that neither of these proposals is defensible (a or b). We defend the practice, in line with the New England Journal of Medicine, of reporting both absolute and relative measures whenever feasible.

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Climate change, nutrition, poverty and medical drugs are widely discussed and pressing issues in science, policy and society. Despite these issues being of great importance for the quality of our lives it remains unclear how well people understand them. Specifically, do particular demographic and socioeconomic factors explain variation in public understanding of these four concepts? To what extent are people's changes in understanding associated with changes in their behaviour? Do people judge scientific practices relying on the more descriptive concepts of climate change and effective medical drugs to be more objective (less controversial) than practices relying on the more value-laden concepts of poverty and healthy nutrition? To address these questions, an experimental survey and regression analyses are conducted using data collected from about one thousand participants across different continents. The study finds that public understanding of science is generally low. A smaller proportion of people were able to correctly identify the common explanation accepted internationally among the scientific community for climate change and effectiveness of medical drugs (42% and 43% of participants in the study, respectively) than for poverty and healthy nutrition (61% and 65% of participants, respectively). Older age and political non-conservativeness were the strongest predictors of correctly understanding these four concepts. Greater levels of education and political non-conservativeness were in turn the strongest predictors of people's reported changes in their behaviour based on their improved understanding of these concepts. Because climate change is among the least understood scientific concepts but is arguably the greatest challenge of our time, better efforts are needed to improve how media, awareness campaigns and education systems mediate information on the topic in order to tackle the large knowledge deficits that constrain behavioural change.

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BACKGROUND: Randomised controlled trials (RCTs) are commonly viewed as the best research method to inform public health and social policy. Usually they are thought of as providing the most rigorous evidence of a treatment's effectiveness without strong assumptions, biases and limitations. OBJECTIVE: This is the first study to examine that hypothesis by assessing the 10 most cited RCT studies worldwide. DATA SOURCES: These 10 RCT studies with the highest number of citations in any journal (up to June 2016) were identified by searching Scopus (the largest database of peer-reviewed journals). RESULTS: This study shows that these world-leading RCTs that have influenced policy produce biased results by illustrating that participants' background traits that affect outcomes are often poorly distributed between trial groups, that the trials often neglect alternative factors contributing to their main reported outcome and, among many other issues, that the trials are often only partially blinded or unblinded. The study here also identifies a number of novel and important assumptions, biases and limitations not yet thoroughly discussed in existing studies that arise when designing, implementing and analysing trials. CONCLUSIONS: Researchers and policymakers need to become better aware of the broader set of assumptions, biases and limitations in trials. Journals need to also begin requiring researchers to outline them in their studies. We need to furthermore better use RCTs together with other research methods. Key messages RCTs face a range of strong assumptions, biases and limitations that have not yet all been thoroughly discussed in the literature. This study assesses the 10 most cited RCTs worldwide and shows that trials inevitably produce bias. Trials involve complex processes - from randomising, blinding and controlling, to implementing treatments, monitoring participants etc. - that require many decisions and steps at different levels that bring their own assumptions and degree of bias to results.

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