Quantification In Science
Download Quantification In Science PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Quantification In Science book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Quantification in Science
In any scientific discipline, progress from the abstraction of con cepts to factual quantification is unavoidable. Within the many fields of present-day science, quantification is a reality that touches daily a large percentage of the population, whether individuals are involved with sciences and technologies directly in their occupa tional endeavors, or in the scholastic learning processes, or in their roles as consumers of products as well as recipients of services. With such a large spectrum of applications, the quantification of physical concepts requires a clear understanding of the funda mentals by a varied audience with different levels of education. For quite a few years I have felt the need for a publication that would explain the quantities used in the various sciences, especially in physics and technology, in simple terms, easily understood by a lay audience. I thought that such a work should include definitions of the units involved, their numerical values, the various systems in which they are incorporated, and a practical means of converting each unit into an equivalent one in a different system. Furthermore, to give a realistic context to the units, I thought that it would be beneficial to present them in the historical process in which they were conceived. Therefore, I have included biographical notes about those scientists whose names were used to name the units themselves.
The Quantified Scholar
Author: Juan Pablo Pardo-Guerra
language: en
Publisher: Columbia University Press
Release Date: 2022-08-30
Since 1986, the British government, faced with dwindling budgets and growing calls for public accountability, has sought to assess the value of scholarly work in the nation’s universities. Administrators have periodically evaluated the research of most full-time academics employed in British universities, seeking to distribute increasingly scarce funding to those who use it best. How do such attempts to quantify the worth of knowledge change the nature of scholarship? Juan Pablo Pardo-Guerra examines the effects of quantitative research evaluations on British social scientists, arguing that the mission to measure academic excellence resulted in less diversity and more disciplinary conformity. Combining interviews and original computational analyses, The Quantified Scholar provides a compelling account of how scores, metrics, and standardized research evaluations altered the incentives of scientists and administrators by rewarding forms of scholarship that were closer to established disciplinary canons. In doing so, research evaluations amplified publication hierarchies and long-standing forms of academic prestige to the detriment of diversity. Slowly but surely, they reshaped academic departments, the interests of scholars, the organization of disciplines, and the employment conditions of researchers. Critiquing the effects of quantification on the workplace, this book also presents alternatives to existing forms of evaluation, calling for new forms of vocational solidarity that can challenge entrenched inequality in academia.
Uncertainty Quantification and Predictive Computational Science
This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.