2,600
edits
Changes
Discover more about data (view source)
Revision as of 16:11, 6 February 2022
, 16:11, 6 February 2022no edit summary
<p class="author">by Kody Law, Andrew Stuart, and Konstantinos Zygalakis</p>
<p class="author">by Kody Law, Andrew Stuart, and Konstantinos Zygalakis</p>
<p>This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. </p>
<p>This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, together with the algorithms which are introduced and discussed, are all illustrated by the MATLAB software detailed in the book and made freely available online. The book is organized into nine chapters: the first contains a brief introduction to the mathematical tools around which the material is organized; the next four are concerned with discrete time dynamical systems and discrete time data; the last four are concerned with continuous time dynamical systems and continuous time data and are organized analogously to the corresponding discrete time chapters. This book is aimed at mathematical researchers interested in a systematic development of this interdisciplinary field, and at researchers from the geosciences, and a variety of other scientific fields, who use tools from data assimilation to combine data with time-dependent models. </p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Exploratory-data-analysis-with-MATLAB.jpg|150px|]]
<h3 style="text-decoration:none;">Exploratory data analysis with MATLAB, 3rd edition</h3>
<p class="author">by Wendy L Martinez, Angel R Martinez, and Jeffrey Solka</p>
<p>Exploratory Data Analysis with MATLAB, Third Edition presents EDA methods from a computational perspective and uses numerous examples and applications to show how the methods are used in practice. The authors use MATLAB code, pseudo-code, and algorithm descriptions to illustrate the concepts. The MATLAB code for examples, data sets, and the EDA Toolbox are available for download on the book's website. New to the Third Edition Random projections and estimating local intrinsic dimensionality Deep learning autoencoders and stochastic neighbor embedding Minimum spanning tree and additional cluster validity indices Kernel density estimation Plots for visualizing data distributions, such as beanplots and violin plots A chapter on visualizing categorical data;The authors put a computational emphasis on the methods used to visualise and summarise data before making model assumptions to generate hypotheses. They use MATLAB code and algorithmic descriptions to provide the user with state-of-the-art techniques for finding patterns and structure in data.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Open-data-structures.jpg|150px|Open data structures]]
<h3 style="text-decoration:none;">[https://open.umn.edu/opentextbooks/textbooks/171 Open data structures: an introduction]</h3>
<p class="author">by Pat Morin</p>
<p>Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code. Analyzed and implemented in Java, the data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and adjacency list representations; and B-trees. A modern treatment of an essential computer science topic, Open Data Structures is a measured balance between classical topics and state-of-the art structures that will serve the needs of all undergraduate students or self-directed learners.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Intro-to-data-technologies.jpg|150px|Introduction to data technologies]]
<h3 style="text-decoration:none;">[https://www.stat.auckland.ac.nz/~paul/ItDT/ Introduction to data technologies]</h3>
<p class="author">by Paul Murrell</p>
<p>Providing key information on how to work with research data, Introduction to Data Technologiespresents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve their awareness of what tasks can be achieved and describes the correct approach to perform these tasks. Practical examples demonstrate the most important points. The author first discusses how to write computer code using HTML as a concrete example. He then covers a variety of data storage topics, including different file formats, XML, and the structure and design issues of relational databases. After illustrating how to extract data from a relational database using SQL, the book presents tools and techniques for searching, sorting, tabulating, and manipulating data. It also introduces some very basic programming concepts as well as the R language for statistical computing.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<br>
[[Image:Quantitative-bioimaging.jpg|150px|Quantitative Bioimaging]]
<h3 style="text-decoration:none;">Quantitative Bioimaging: An Introduction to Biology, Instrumentation, Experiments, and Data Analysis for Scientists and Engineers</h3>
<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 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>