Nobel and novice: Author prominence affects peer review.

Peer review is a well-established cornerstone of the scientific process, yet it is not immune to biases like status bias, which we explore in this paper. Merton described this bias as prominent researchers getting disproportionately great credit for their contribution, while relatively unknown researchers get disproportionately little credit [R. K. Merton, Science 159, 56-63 (1968)]. We measured the extent of this bias in the peer-review process through a preregistered field experiment. We invited more than 3,300 researchers to review a finance research paper jointly written by a prominent author (a Nobel laureate) and by a relatively unknown author (an early career research associate), varying whether reviewers saw the prominent author's name, an anonymized version of the paper, or the less-well-known author's name. We found strong evidence for the status bias: More of the invited researchers accepted to review the paper when the prominent name was shown, and while only 23% recommended "reject" when the prominent researcher was the only author shown, 48% did so when the paper was anonymized, and 65% did when the little-known author was the only author shown. Our findings complement and extend earlier results on double-anonymized vs. single-anonymized review [R. Blank, Am. Econ. Rev. 81, 1041-1067 (1991); M. A. Ucci, F. D'Antonio, V. Berghella, Am. J. Obstet. Gynecol. MFM 4, 100645 (2022)].

The Role of CMC Statisticians: Co-Practitioners of the Scientific Method.

Chemistry, manufacturing, and control (CMC) statisticians play a key role in the development and lifecycle management of pharmaceutical and biological products, working with their non-statistician partners to manage product quality. Information used to make quality decisions comes from studies, where success is facilitated through adherence to the scientific method. This is carried out in four steps: (1) an objective, (2) design, (3) conduct, and (4) analysis. Careful consideration of each step helps to ensure that a study conclusion and associated decision is correct. This can be a development decision related to the validity of an assay or a quality decision like conformance to specifications. Importantly, all decisions are made with risk. Conventional statistical risks such as Type 1 and Type 2 errors can be coupled with associated impacts to manage patient value as well as development and commercial costs. The CMC statistician brings focus on managing risk across the steps of the scientific method, leading to optimal product development and robust supply of life saving drugs and biologicals.

The Pragmatist roots of scientific medicine: Reassessing Abraham Flexner's report on medical education.

Abraham Flexner's 1910 report on medical education is widely regarded as a watershed moment in the history of modern medicine in the US and beyond. Most commentators focus on its administrative and managerial impact, despite Flexner dedicating a sizeable portion of his report to a theoretical account of the kind of medicine that he seeks to implement. Close attention to these sections reveals a surprisingly coherent account of medicine that, based on a Deweyan Pragmatist philosophy of science, unites scientific investigator and medical practitioner in a new experimental paradigm of science. Flexner can develop an account that goes beyond a mere epistemic redefinition of medicine, providing the profession with a social, cultural, and ethical identity that avails itself of the extremely wide purview that Dewey granted to modern science. Due to the subsequent narrowing of philosophy of science to a delimited academic subdiscipline, these broad Pragmatist philosophical commitments at the roots of Flexner's scientific medicine remained a largely unexplored intellectual legacy.

COVID-19 and the Authority of Science.

In an attempt to respond effectively to the COVID-19 pandemic, policy makers and scientific experts who advise them have aspired to present a unified front. Leveraging the authority of science, they have at times portrayed politically favored COVID interventions, such as lockdowns, as strongly grounded in scientific evidence-even to the point of claiming that enacting such interventions is simply a matter of "following the science." Strictly speaking, all such claims are false, since facts alone never yield moral-political conclusions. More importantly, attempts to present a unified front have led to a number of other actions and statements by scientists and policy makers that erode the authority of science. These include actions and statements that: (1) mislead the public about epidemiological matters such as mortality rates, cause of death determinations, and computerized modeling, or fail to correct mainstream media sources that interpret such concepts in misleading ways; (2) incorporate moral-political opinions into ostensible statements of fact; and (3) misrepresent or misuse scientific expertise. The fundamental thesis of the paper is not primarily that such actions and statements have proliferated during the COVID-19 epidemic (though I think they have), but rather that they are unscientific and that presenting them as science undermines the authority of science. In the moral-political realm, the great power of science and the source of its authority derives from its agnosticism about fundamental moral-political claims. Science, for instance, has no built-in presumption that we should respect life, promote freedom, or practice toleration; nor does it tell us which of these values to prioritize when values conflict. Because of this agnosticism, science is recognized across a broad diversity perspectives as morally and politically impartial, and authoritative within its proper sphere. When it is infused with partisan bias, it loses that authority.

When Arne met J. L.: attitudes to scientific method in empirical semantics, ordinary language philosophy and linguistics.

In the autumn of 1959, Arne Naess and J. L. Austin, both pioneers of empirical study in the philosophy of language, discussed their points of agreement and disagreement at a meeting in Oslo. This article considers the fragmentary record that has survived of that meeting, and investigates what light it can shed on the question of why the two philosophers apparently found so little common ground, given their shared commitment to the importance of data in the study of language. Naess and Austin held different views about two significant aspects of the relationship between scientific method and philosophical investigation. The first aspect concerns the nature of experimental data; Naess used the statistical analysis of data collected from non-philosophical informants while Austin advocated deliberation leading to agreement over usage by a few skilled experts. The second aspect relates to their respective attitudes to the role of theory in philosophical inquiry, attitudes which drew on discussions of scientific method, and its relevance to philosophy, from the early decades of the twentieth century. This article traces the evidence for these views on scientific method in Naess's and Austin's respective published work, and in the record of their Oslo meeting. It concludes with a brief overview of opinions about scientific method manifest in the decades since that meeting in various branches of linguistics. These opinions speak to the enduring importance of attitudes to scientific method in relation to our study and understanding of human language.

Hypotheses in Kant's philosophy of science.

In this paper I extend the case for a necessitation account of particular laws in Kant's philosophy of science by examining the relation between reason's hypothetical use in the Appendix to the Transcendental Dialectic and the legitimate hypotheses identified in the Doctrine of Method. Building on normative accounts of reason's ideas, I argue that reason's hypothetical use does not describe the connections between objects and their grounds, which lie beyond the reach of the understanding, but merely prescribes the relations between appearances and their conditions, for which the understanding must seek. A legitimate hypothesis, I suggest, is a proposition we hold to be true that fills in one or several of those relations. The problematic character of hypotheses requires that we evaluate our reasons for holding them to be true. While natural modality is grounded in the nature of things, which cannot be fully known, our reasons for assent can and must be grounded on features of objects that are epistemically available to us.

Failures to disagree are essential for environmental science to effectively influence policy development.

While environmental science, and ecology in particular, is working to provide better understanding to base sustainable decisions on, the way scientific understanding is developed can at times be detrimental to this cause. Locked-in debates are often unnecessarily polarised and can compromise any common goals of the opposing camps. The present paper is inspired by a resolved debate from an unrelated field of psychology where Nobel laureate David Kahneman and Garry Klein turned what seemed to be a locked-in debate into a constructive process for their fields. The present paper is also motivated by previous discourses regarding the role of thresholds in natural systems for management and governance, but its scope of analysis targets the scientific process within complex social-ecological systems in general. We identified four features of environmental science that appear to predispose for locked-in debates: (1) The strongly context-dependent behaviour of ecological systems. (2) The dominant role of single hypothesis testing. (3) The high prominence given to theory demonstration compared investigation. (4) The effect of urgent demands to inform and steer policy. This fertile ground is further cultivated by human psychological aspects as well as the structure of funding and publication systems.

Why and how we should join the shift from significance testing to estimation.

A paradigm shift away from null hypothesis significance testing seems in progress. Based on simulations, we illustrate some of the underlying motivations. First, p-values vary strongly from study to study, hence dichotomous inference using significance thresholds is usually unjustified. Second, 'statistically significant' results have overestimated effect sizes, a bias declining with increasing statistical power. Third, 'statistically non-significant' results have underestimated effect sizes, and this bias gets stronger with higher statistical power. Fourth, the tested statistical hypotheses usually lack biological justification and are often uninformative. Despite these problems, a screen of 48 papers from the 2020 volume of the Journal of Evolutionary Biology exemplifies that significance testing is still used almost universally in evolutionary biology. All screened studies tested default null hypotheses of zero effect with the default significance threshold of p = 0.05, none presented a pre-specified alternative hypothesis, pre-study power calculation and the probability of 'false negatives' (beta error rate). The results sections of the papers presented 49 significance tests on average (median 23, range 0-390). Of 41 studies that contained verbal descriptions of a 'statistically non-significant' result, 26 (63%) falsely claimed the absence of an effect. We conclude that studies in ecology and evolutionary biology are mostly exploratory and descriptive. We should thus shift from claiming to 'test' specific hypotheses statistically to describing and discussing many hypotheses (possible true effect sizes) that are most compatible with our data, given our statistical model. We already have the means for doing so, because we routinely present compatibility ('confidence') intervals covering these hypotheses.

A Contemporary Message from Mendel's Logical Empiricism.

The gene is one of the most fundamental concepts in life sciences, having been developed in the mold of the Mendelian paradigm of heredity, which shaped genetics across 150 years. How could Mendel possibly be so prophetic in the middle of 19th century, using only the small garden of the monastery as his experimental breeding field? I believe that we are indebted to Mendel's mastery of the scientific method, which was far ahead of his time. Although his experimental technology was literally garden-variety, Mendel's excellence in the method of science, algebra, and logical analysis helped him in designing the right experiment and in interpreting the results insightfully. This may be valuable to recall in today's technology-focused culture, where the center of interest tends to be on the generation and description of high-throughput datasets from specialized genomics screens. As Mendel's story suggests, progress in 21st century genetics may also depend on the development of robust concepts and generalizations.

Improving Preservice Primary Teachers' Understanding of the Nature of Methods of Science Through Reflective Reading of News Articles.

A study is presented that analyzed the pedagogical efficacy of reading opinion articles about methods of science, published in the media, in order to improve the meta-scientific understanding of 52 preservice primary teachers (PPTs) with regard to the topic. To this end, an activity was designed taking an explicit and reflective approach. The design of the activity required a short teaching intervention when being implemented in class in order to facilitate its integration into the program of the subject of science teaching. Before doing the activity, the PPTs' prior conceptions about the nature of methods of science were diagnosed using the Opinions about Science, Technology and Society Questionnaire (COCTS, in its Spanish acronym). The activity consisted of reading the articles, and then responding in small work groups to a series of questions for reflection and debate on the topic. The groups' responses were then shared and discussed in class. Once the activity had finished, the PPTs responded to the questionnaire again (post-test) in order to evaluate how their conceptions had progressed. There was an improvement in their understanding of various aspects of the nature of methods of science (e.g., scientists use a variety of methods in their research depending on the object of study, the context, and the resources available, or that the scientific method is an idealized, simplistic, and therefore poor representation of how scientists do research). These results show that the activity was effective in getting the PPTs to reflect and learn about the topic. Finally, the limitations of the study are discussed (e.g., the limited time frame to implement the activity and evaluate results), and some future research perspectives are given to improve the understanding of PPTs about the nature of methods of science.

The fundamental principles of reproducibility.

Reproducibility is a confused terminology. In this paper, I take a fundamental view on reproducibility rooted in the scientific method. The scientific method is analysed and characterized in order to develop the terminology required to define reproducibility. Furthermore, the literature on reproducibility and replication is surveyed, and experiments are modelled as tasks and problem solving methods. Machine learning is used to exemplify the described approach. Based on the analysis, reproducibility is defined and three different degrees of reproducibility as well as four types of reproducibility are specified. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic.

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.

Mendel proposed an experimentally verifiable paradigm of particle-based heredity that has been influential for over 150 years. The historical arguments have been reflected in the near past as Mendel's concept has been diversified by new types of omics data. As an effect of the accumulation of omics data, a virtual gene concept forms, giving rise to genetical data science. The concept integrates genetical, functional, and molecular features of the Mendelian paradigm. I argue that the virtual gene concept should be deployed pragmatically. Indeed, the concept has already inspired a practical research program related to systems genetics. The program includes questions about functionality of structural and categorical gene variants, about regulation of gene expression, and about roles of epigenetic modifications. The methodology of the program includes bioinformatics, machine learning, and deep learning. Education, funding, careers, standards, benchmarks, and tools to monitor research progress should be provided to support the research program.

Data Analysis will not Result in Knowledge Production about Sepsis.

How to cite this article: Sampath S. Data Analysis will not Result in Knowledge Production about Sepsis. Indian J Crit Care Med 2021;25(7):750-751.

The best predictions in experimental biology are critical and persuasive.

A powerful way to evaluate scientific explanations (hypotheses) is to test the predictions that they make. In this way, predictions serve as an important bridge between abstract hypotheses and concrete experiments. Experimental biologists, however, generally receive little guidance on how to generate quality predictions. Here, we identify two important components of good predictions - criticality and persuasiveness - which relate to the ability of a prediction (and the experiment it implies) to disprove a hypothesis or to convince a skeptic that the hypothesis has merit. Using a detailed example, we demonstrate how striving for predictions that are both critical and persuasive can speed scientific progress by leading us to more powerful experiments. Finally, we provide a quality control checklist to assist students and researchers as they navigate the hypothetico-deductive method from puzzling observations to experimental tests.

Fake news and fake research: Why meta-research matters more than ever.

Research is in a crisis of credibility, and this is to the peril of all paediatricians. Billions of dollars are being wasted each year because research is not planned, badly conducted or poorly reported, and this is on a background of rapidly reducing research budgets. How can paediatricians, families and patients make informed treatment choices if the evidence base is absent or not trustworthy? This article discusses why meta-research now matters more than ever, how it can help solve this crisis of credibility and how this should lead to more efficient and effective clinical care. The field of meta-research or research-on-research is the ultimate big picture approach to identifying and solving issues of bias, error, misconduct and waste in research. Meta-researchers value authenticity over aesthetics and quality over quantity. The utility of meta-research does not rely on accusations or critical assessments of individual research, but through highlighting where and how the scientific method and research standards across all fields can be improved. Meta-researchers study, analyse and critique the research pathway, focusing on elements such as methods (how to conduct), evaluation (how to test), reporting (how to communicate), reproducibility (how to verify) and incentives (how to reward). In the current climate it is now more critical than ever that we make use of meta-research and prioritise high-quality high-impact research, ultimately leading to improved patient outcomes.

Reviewer bias in single- versus double-blind peer review.

Peer review may be "single-blind," in which reviewers are aware of the names and affiliations of paper authors, or "double-blind," in which this information is hidden. Noting that computer science research often appears first or exclusively in peer-reviewed conferences rather than journals, we study these two reviewing models in the context of the 10th Association for Computing Machinery International Conference on Web Search and Data Mining, a highly selective venue (15.6% acceptance rate) in which expert committee members review full-length submissions for acceptance. We present a controlled experiment in which four committee members review each paper. Two of these four reviewers are drawn from a pool of committee members with access to author information; the other two are drawn from a disjoint pool without such access. This information asymmetry persists through the process of bidding for papers, reviewing papers, and entering scores. Reviewers in the single-blind condition typically bid for 22% fewer papers and preferentially bid for papers from top universities and companies. Once papers are allocated to reviewers, single-blind reviewers are significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors, top universities, and top companies. The estimated odds multipliers are tangible, at 1.63, 1.58, and 2.10, respectively.

Development of low-cost cardiac and skeletal muscle laboratory activities to teach physiology concepts and the scientific method.

Anatomy and Physiology courses taught at community colleges tend to focus laboratory hours primarily on anatomy as opposed to physiology. However, research demonstrates that, when instructors utilize active learning approaches (such as in laboratory settings) where students participate in their own learning, students have improved outcomes, such as higher test scores and better retention of material. To provide community college students with opportunities for active learning in physiology, we developed two laboratory exercises to engage students in cardiac and skeletal muscle physiology. We utilized low-cost SpikerBox devices to measure electrical activity during cardiac (electrocardiogram) and skeletal muscle (electromyogram) contraction. Laboratory activities were employed in Anatomy and Physiology courses at two community colleges in southeast Michigan. A 2-h laboratory period was structured with a 20-min slide presentation covering background material on the subject and experiments to examine the effects of environmental variables on nervous system control of cardiac and skeletal muscle contraction. Students were asked to provide hypotheses and proposed mechanisms, complete a results section, and provide conclusions for the experiments based on their results. Our laboratory exercises improved student learning in physiology and knowledge of the scientific method and were well-received by community college students enrolled in Anatomy and Physiology. Our results demonstrate that the use of a SpikerBox for cardiac and skeletal muscle physiology concepts is a low-cost and effective approach to integrate physiology activities into an Anatomy and Physiology course.

Drawing on the Past to Shape the Future of Synthetic Yeast Research.

Some years inspire more hindsight reflection and future-gazing than others. This is even more so in 2020 with its evocation of perfect vision and the landmark ring to it. However, no futurist can reliably predict what the world will look like the next time that a year's first two digits will match the second two digits-a numerical pattern that only occurs once in a century. As we leap into a new decade, amid uncertainties triggered by unforeseen global events-such as the outbreak of a worldwide pandemic, the accompanying economic hardship, and intensifying geopolitical tensions-it is important to note the blistering pace of 21st century technological developments indicate that while hindsight might be 20/20, foresight is 50/50. The history of science shows us that imaginative ideas, research excellence, and collaborative innovation can, for example, significantly contribute to the economic, cultural, social, and environmental recovery of a post-COVID-19 world. This article reflects on a history of yeast research to indicate the potential that arises from advances in science, and how this can contribute to the ongoing recovery and development of human society. Future breakthroughs in synthetic genomics are likely to unlock new avenues of impactful discoveries and solutions to some of the world's greatest challenges.

Models of the Gene Must Inform Data-Mining Strategies in Genomics.

The gene is a fundamental concept of genetics, which emerged with the Mendelian paradigm of heredity at the beginning of the 20th century. However, the concept has since diversified. Somewhat different narratives and models of the gene developed in several sub-disciplines of genetics, that is in classical genetics, population genetics, molecular genetics, genomics, and, recently, also, in systems genetics. Here, I ask how the diversity of the concept impacts data-integration and data-mining strategies for bioinformatics, genomics, statistical genetics, and data science. I also consider theoretical background of the concept of the gene in the ideas of empiricism and experimentalism, as well as reductionist and anti-reductionist narratives on the concept. Finally, a few strategies of analysis from published examples of data-mining projects are discussed. Moreover, the examples are re-interpreted in the light of the theoretical material. I argue that the choice of an optimal level of abstraction for the gene is vital for a successful genome analysis.