2,600
edits
Changes
Discover more about data (view source)
Revision as of 17:22, 6 February 2022
, 17:22, 6 February 2022no edit summary
<p class="author">by Raimund J Ober, E Sally Ward, and Jerry Chao</p>
<p class="author">by Raimund J Ober, E Sally Ward, and Jerry Chao</p>
<p>Quantitative bioimaging is a broad interdisciplinary field that exploits tools from biology, chemistry, optics, and statistical data analysis for the design and implementation of investigations of biological processes. Instead of adopting the traditional approach of focusing on just one of the component disciplines, this textbook provides a unique introduction to quantitative bioimaging that presents all of the disciplines in an integrated manner. The wide range of topics covered include basic concepts in molecular and cellular biology, relevant aspects of antibody technology, instrumentation and experimental design in fluorescence microscopy, introductory geometrical optics and diffraction theory, and parameter estimation and information theory for the analysis of stochastic data.</p>
<p>Quantitative bioimaging is a broad interdisciplinary field that exploits tools from biology, chemistry, optics, and statistical data analysis for the design and implementation of investigations of biological processes. Instead of adopting the traditional approach of focusing on just one of the component disciplines, this textbook provides a unique introduction to quantitative bioimaging that presents all of the disciplines in an integrated manner. The wide range of topics covered include basic concepts in molecular and cellular biology, relevant aspects of antibody technology, instrumentation and experimental design in fluorescence microscopy, introductory geometrical optics and diffraction theory, and parameter estimation and information theory for the analysis of stochastic data.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Intro-to-hierarchical-bayesian-modeling-for-ecological-data.jpg|150px|Introduction to hierarchical Bayesian modeling for ecological data]]
<h3 style="text-decoration:none;">Introduction to hierarchical Bayesian modeling for ecological data</h3>
<p class="author">by Eric Parent and Etienne Rivot</p>
<p>Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Datagives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors' website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Statistical-and-machine-learning-data-mining.jpg|150px|Statistical and machine-learning data mining]]
<h3 style="text-decoration:none;">Statistical and machine-learning data mining: techniques for better predictive modeling and analysis of big data</h3>
<p class="author">by </p>
<p>Focusing on uniquely large-scale statistical models that effectively consider big data identifying structures (variables) with the appropriate predictive power in order to yield reliable, robust, relevant large scale analyses, this edition incorporates 13 chapters, as well as explanations of the author's own GenIQ model.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
<br>
[[Image:Shifting-the-balance.jpg|150px|Shifting the Balance]]
<h3 style="text-decoration:none;">Shifting the Balance: How Top Organizations Beat the Competition by Combining Intuition with Data</h3>
<p class="author">by Mark Schrutt</p>
<p>Digital transformation expert Mark Schrutt reveals how the world's top companies are using vast amounts of data to inform their decisions, disrupt industries, and get closer to their customers. Businesses that continue to rely only on intuition do so at their peril. What if you had the data you always wanted and could tell what was truly an emerging trend that would forever change your industry? Shifting the Balance analyzes the turn towards data-driven decision-making and describes how best-in-class organizations use data to shift their field of vision so it is forward-looking instead of reactive.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
<br>