Process Of Scientific Discovery Research Articles

Ex -identification and scientific discovery

ABSTRACT Consider the empirical laws established in the History of Science. The process of scientific discovery can be roughly explained by (Popper-Kuhn's) cycle which starts with collecting evidence, introducing a model to explain observations, predicting further observations, and confronting them with experimentation, reinforcing or disproving the model. We attempt to prove that the empirical evidence modeled by arithmetical operations can be automatically discovered with a small amount of information, i.e. with very short Blums' locking sequences, due to the suitable enumerations of arithmetical functions.

Redefining the scientific method: as the use of sophisticated scientific methods that extend our mind.

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.

A dynamic knowledge graph approach to distributed self-driving laboratories

The ability to integrate resources and share knowledge across organisations empowers scientists to expedite the scientific discovery process. This is especially crucial in addressing emerging global challenges that require global solutions. In this work, we develop an architecture for distributed self-driving laboratories within The World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph. We employ ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. We demonstrate the practical application of our framework by linking two robots in Cambridge and Singapore for a collaborative closed-loop optimisation for a pharmaceutically-relevant aldol condensation reaction in real-time. The knowledge graph autonomously evolves toward the scientist’s research goals, with the two robots effectively generating a Pareto front for cost-yield optimisation in three days.

Self-driving laboratories to autonomously navigate the protein fitness landscape

Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.

Solar Activity, Weather, and Climate: The Elusive Connection

Abstract The search for an explanation of correlations of weather and climate with solar activity has uncovered some subtle effects of the varying solar inputs, which include irradiance, energetic particles, electric fields induced by the solar wind, and the modulation of the galactic cosmic ray flux. Relevant phenomena have been identified in stratospheric chemistry and dynamics and in aerosols, atmospheric electricity, and clouds. The effects are small and appear intermittently in statistical analyses of observations. Correlations and theory permit responses to several solar inputs effective on the decadal time scales, making it difficult to distinguish them observationally. No mechanism for tropospheric decadal change can be considered to be definitely established, but for the day-to-day time scale there is clear observational evidence for clouds responding to solar wind–induced current flow (JZ) in the global electric circuit, due to changes in ionospheric potential, and independently from Forbush decreases of the cosmic ray flux. The responses to JZ also correlate separately with ionospheric potential changes due to changes in day-to-day thunderstorm activity. The identified mechanism is for electric charge effects on in-cloud scavenging, and this implies a decadal response, in view of decadal changes in JZ. However, a complete and quantitative model of the inferred electrically induced changes in cloud microphysics is not yet available. The failures, successes, and controversies of the search illustrate the somewhat chaotic process of scientific discovery.

Grow to learn: a metacognitive approach to early childhood teachers’ science professional development

ABSTRACT Background Science is an overlooked subject domain in early childhood education in the U.S. particularly in rural areas. The current state of early science education could be a result of insufficient teacher training and professional development (PD). Thus, we created an experimental PD program based on research about metacognition – Grow to Learn (GTL). GTL promotes preschool and kindergarten teachers’ and children’s holistic understanding of plant science concepts and the scientific discovery process through an immersive, hands-on sweet pea growing project. Purpose This study aims to determine the effect of GTL. Specifically, we examined the effect of GTL on preschool and kindergarten teachers’ science teaching efficacy and metacognitive awareness, and children’s cognitive skills and learning motivation. Design and method A mixed methods quasi-experimental design was used to determine the effect of GTL. Quantitative and qualitative data were collected before and after the PD program implementation using validated measurements. Sample A total of 11 preschool and kindergarten teachers and 76 children (Mage = 4.13 years) predominately from rural areas in North Idaho, U.S. completed the study. Results Results from multilevel repeated measures ANCOVA showed an increase in teachers’ procedural knowledge and children’s cognitive skills and learning motivation upon completing the program. A thematic analysis method was used to analyse qualitative data. Qualitative data analysis revealed evidence of children’s knowledge and learning skills gain, and teachers’ possible needs for classroom management training. Conclusions This study demonstrates the potential of conceptualizing early science education in the metacognition framework. Our results suggest that early childhood science teaching and learning could be improved by integrating metacognitive teaching and learning strategies in a PD program and by using immersive, hands-on activities as the broader inquiry learning context.

Automated gadget discovery in the quantum domain

In recent years, reinforcement learning (RL) has become increasingly successful in its application to the quantum domain and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent’s learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent’s policy.

Sustaining positive perceptions of science in the face of conflicting health information: An experimental test of messages about the process of scientific discovery

BackgroundThe public is often exposed to conflicting health information, with evidence of concerning consequences, yet little attention has been paid to identifying strategies that can mitigate its effects. ObjectiveThe current study tests whether three different approaches to communicating about the process of scientific discovery—a rational appeal using analogical evidence, a rational appeal using testimonial evidence, and a logic-based inoculation approach—could reduce the adverse effects of exposure to conflict by positively framing how people construe the scientific process, increasing their perceived knowledge about the scientific process, and helping them to respond to critiques about the scientific process, which, in turn, might make them less apt to counterargue the science they subsequently encounter in health news stories and other exposures to conflict. MethodsWe fielded a survey experiment in May 2022 with a national sample of U.S. adults (N = 1604). ResultsProviding any of the three messages about science prior to exposure to conflicting health information encouraged both positive construal of science and greater science knowledge perceptions and discouraged counterarguing science, compared to a control condition in which people were only exposed to conflict. Of the three messaging approaches tested, the testimonial evidence message was slightly more effective, but was also considered slightly more accurate, credible, and trustworthy. ConclusionsDeveloping and implementing messages that describe the process of scientific discovery could prove successful, not only in improving public perceptions of science but perhaps ultimately in better equipping people to make sense of conflicting information and its causes. However, additional research on such strategies is needed, particularly as part of larger interventions with multiple messages across multiple exposures, if they are to have implications for health and science communication.

Effectively integrating research argumentation in syllabus learning: A case study of reading journal articles in four fourth-year engineering fluid mechanics courses

In an emerging trend in engineering education students are engaged in the scientific discovery process through reading about research published in articles rather than textbooks. A research-based and curriculum-oriented intervention in an undergraduate course was designed to elucidate whether students can progressively gain reading skills when provided with selected articles explicitly related to fluid mechanics research. The aim was also to monitor student awareness of their own progress. A questionnaire was designed to evaluate how fourth-year fluid technology students perceived their reading skills during the teaching intervention. A progressive strategy was applied, including warm-up readings, in- and out-of-class assignments, and best practice lectures. Two parallel test exams and test readings were associated and quantitatively analyzed. Each cohort was randomly divided into two groups, and each group was assigned a different test reading before the lectures; the readings were then switched for the post-lecture assignment. The results demonstrate that the students acquired selective reading skills and awareness of accomplishment. The analyses indicated that learners gained an understanding of the core concepts and gave positive feedback on the teaching materials and schedule. These findings may serve as a guide for engineering educators to improve the preparation of undergraduate students.

Theory Development, Application and Contribution to Societal Development: A Journey in the Scientific Discovery Process

Theory Development, Application and Contribution to Societal Development: A Journey in the Scientific Discovery Process

Conceptual modeling as language design

AbstractConceptual modeling is a broad practice encompassing knowledge organization, domain modeling, and knowledge representation. It is best understood not as a scientific process of discovery, but as a constructive process of language design. This constructive process involves both explicating differences of meaning implicit in some discursive tradition and revising those differences to improve that tradition. Understood this way, conceptual modeling can serve as the basis for a paradigm of information research and practice that does not reproduce fundamental asymmetries between researchers and the people they study, or between practitioners and the people they serve. Progress within this paradigm will involve combining methods from what have up to now been different traditions or modes of conceptual modeling. It will require paying closer attention to the historical and structural dimensions of discursive traditions and reimagining the function of critique.

GFlowNets for AI-driven scientific discovery

GFlowNets provide a general probabilistic framework for accelerating the computational phase of the scientific discovery process, which is crucial for tackling pressing challenges posed by global pandemics and the climate crisis.

How many authors does it take to publish a high profile or classic paper?

Although the process of publishing a scientific paper has gotten simpler, it is increasingly difficult to publish a paper in high profile journals. We have analyzed the publishing data in the cell biology field and found several alarming trends developing over the last two decades. There is an emerging divide between scientist-run journals and professional-run high profile journals. How did this happen? What should we do? The core issue is whether the current standard for high profile journals hurts rather than helps the scientific discovery process. In this regard, we suggest that the editors and scientists should direct their focus on the potential impact and rigor of the work instead of the "perfection" or "completeness" of the study.

Computational Scientific Discovery in Psychology

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Distribution of labor, productivity and innovation in collaborative science

In this paper, we investigate the process of scientific discovery using an under-exploited source of information: the Polymath projects. Polymath projects are an original attempt to solve a series mathematical problems collectively and in a collaborative online environment. To investigate the Polymath experiment, we analyze all the posts related to the projects that have resulted in a peer-reviewed publication. We focus in particular on the organization of the scientific labor and on the innovations that result from the contributions of the different authors. We find that a high presence of occasional contributors increases the productivity of the most active users and the overall productivity of the forums (i.e., the number of posts grows super-linearly with the number of contributors). We argue that, in large-scale collaborations, the serendipitous interaction between occasional contributors can be crucial to the scientific process, and individual contributions from occasional participants can open new directions of research.

How to enhance scientific literacy? Review of interventions focused on improving high school students’ scientific reasoning skills and attitudes toward science

Scientific literacy is one of the crucial skills necessary for making informed decisions and evaluating evidence. Despite the fact that the overall results of Slovak students in scientific literacy are lower than the average for OECD countries, there is still a lack of interventions focusing explicitly on fostering scientific literacy in the educational system. Therefore, this literature review aims to determine: 1. types of interventions that can be used to foster scientific literacy; 2. the main features of interventions responsible for their effectiveness. The authors conducted a content analysis of 40 empirical scientific papers, focusing on two aspects of scientific literacy: scientific reasoning ability and attitudes toward science. The study indicates that scientific reasoning ability can be effectively improved with the interventions focused on 1. scientific argumentation; 2. understanding nature of science; 3. process of scientific discovery. Interventions focused on improving attitudes toward science share similar characteristics as interventions focused on scientific reasoning and can be classified similarly. Other aspects, such as the level of guidance, teaching strategy, gender, and domain of the intervention can also play an important role.

Искусственный интеллект для науки и наука для искусственного интеллекта

In 2016, Hiroaki Kitano proposed that artificial intelligence (AI) will be able to overcome a number of human cognitive limitations that slow down the process of scientific discovery [Kitano 2016 web]. Since then, the odds of AI being awarded the Nobel Prize have been widely discussed, particularly within aca­demic community [Engineering for Research Symposium web 2020]. At the AI Journey 2021 conference, some renowned representatives of four scientific dis­ciplines (physics, mathematics, neurobiology, philosophy) discussed this issue and then co-authored this article [AI 2021 web]. In the first part of our paper, we critically analyze the role of AI technologies in natural science research: how useful they can be for fundamental science, what the potential of AI in natural and exact sciences is, and what principal limitations it has. Another part of our article discusses a counter-question of what science can do for the future re­search into AI. Today, it is impossible to imagine machine learning without lin­ear algebra, physics of materials, and brain research. All this falls under what is now commonly referred to as AI, a general umbrella term [Russel, Norvig 2021]. Thus, having served the birth of AI once, how can physics, mathematics, and neuroscience serve it today?

Perceptions of Creativity in Artistic and Scientific Processes

This paper presents the results of a factorial survey research on perceptions of artistic and scientific creativity in humans and AI. A general reluctance at at- tributing creativity to artificial systems is well-documented in the literature on the theme. Aim of this survey is to test whether this reluctance is equally strong when participants evaluate scenarios where human and artificial agents are involved in processes of scientific discovery and scenarios where they are en- gaged in artistic creation processes. The starting hypothesis of the study is that participants should be less hesitant at attributing creativity to artificial agents when the latter engage in scientific discovery processes. Findings, however, dis- confirm this assumption, showing that participants attribute significantly less creativity to artificial actors than to human ones, and even more so when they are involved in scientific processes.

Is Stanislaw Lem’s Solaris a “scientific” novel?

The given article deals with one of the most well-known Polish writers Stanislaw Lem and his novel Solaris. It is a rather controversial work which aroused a lot of discussions and disagreements. The investigation tries to prove that Solaris is not a typical classical sci-fi novel and there are several things giving evidence to the fact. One of the most fundamental dividing lines in science is that of “the observer/the observed” and this binary opposition is violated as the scientists on the planet do not do any research. The observer and the observed are polarized, with the observer inevitably claiming the dominant role, the ocean in question was never really “studied” properly because it does not let itself be studied. What is more, scientists on board the ship almost do not perform any complicated technical experiments. The things Lem describes are partly just regular ones, of everyday use, and partly related to space exploration. But the fact that they are seen in such a mundane environment, in disorder, is already indicating that something is wrong with the potential “scientific” life on the station. Solaris (ocean) itself can be read (among other things) as a metaphor of death. The ocean, obviously, possesses some information about the people who come within its reach. It is unpredictable even in terms of its external appearance. The ocean reproduces some of the objects/images it finds in human minds, and then tries to master it. The ocean obviously triggers something in humans and makes them face certain phenomena or feelings, often, as it seems, quite traumatic. So, the ocean is a kind of a container of information, a large organ that preserves and transforms it. Lem’s Solaris makes us face a number of important questions. It posits itself, seemingly, as a sci-fi novel that invests in describing the scientific process of research and discovery. However, it soon becomes clear that Lem is doing all he can to question the idea of “science” as it exists in the modern mind, emphasizing the idea of the unity of the universe.

Unnatural Selection? An Analysis of the Factors Associated with Unpublished Prospective Clinical Research in Radiation Oncology

<h3>Purpose/Objective(s)</h3> Factors associated with successful publication of clinical trials in radiation oncology have not yet been identified. We investigated whether publication bias was associated with factors such as funding source, size, phase of trial, study length, randomized design, and trial origin. <h3>Materials/Methods</h3> Clinical trials between 2000-2005 were extracted by filtering for "radiation oncology" using the National Institutes of Health database, clinicaltrials.gov. The dates were chosen to select for studies that had adequate time to accrue. Studies were excluded if they were incomplete, observational, Phase 4, or lacked sufficient descriptions of their methods. Included studies underwent independent samples <b>t-</b>tests and bivariate analyses to evaluate for publication status using the Pearson Chi-Square test through JMP. <h3>Results</h3> 538 studies were candidates for analysis. Multi-center trial sites, government funding, Phase III status, and randomized allocation were factors independently associated with increased likelihood of publication. The average number of study arms, lengths of the study in years, and number of participants were all significantly greater in trials that achieved publication. <h3>Conclusion</h3> The results of the review demonstrate publication bias. Less than half of the completed clinical trials in radiation oncology from 2000-2005 were published; Phase III status, multi-center sites, non-industry funding, randomized allocation larger participant pools, and a longer study duration all contributed to successful publication of those trials. Ascertaining facilitative and inhibitory factors to publication may facilitate transparency in medical literature and thereby improve clinical advancements and the scientific discovery process.