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<h2>Books and reports</h2>
<h2>Books and reports</h2>
[[Image:Intro-to-online-ecological-and-environmental-data.jpg|150px|Introduction to Online Ecological and Environmental Data]]
<h3 style="text-decoration:none;">[https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1140&context=libraryscience Introduction to Online Ecological and Environmental Data]</h3>
<p class="author">by Virginia A. Baldwin </p>
<p>The advent of the Internet and proliferation of materials on it has brought significant and rapid change in scholarly communication. Perhaps more gradually has come the posting of research data for sharing with other researchers in the field. This volume describes several projects that have made environmental and ecological researchers' data freely available online. Librarians from the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), from one regional agency based in Oregon, one university, and one research corporation describe aspects of the online data projects developed by their respective institutions. A sixth paper, from a librarian at State University of New York University at Buffalo, follows the development of online research data in a specific field, acid rain research, from a variety of types of research programs. A common theme in these papers is the interdisciplinary involvement of researchers who produce and use data in the fields of environmental and ecological studies.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Federal-data-science.jpg|150px|Federal data science]]
<h3 style="text-decoration:none;">Federal data science: transforming government and agricultural policy using artificial intelligence</h3>
<p class="author">by Feras A. Batarseh and Ruixin Yang</p>
<p>Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Inro-to-data-analysis-with-R-for-Forensic-Scientists.jpg|150px|Introduction to data analysis with R for forensic scientists]]
<h3 style="text-decoration:none;">Introduction to data analysis with R for forensic scientists (Vol. 21)</h3>
<p class="author">by James Michael Curran</p>
<p>Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research. Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to R Considerations and techniques for the visual display of data through graphics; An overview of statistical hypothesis tests and the reasoning behind them; A comprehensive guide to the use of the linear model, the foundation of most statistics encountered; An introduction to extensions to the linear model for commonly encountered scenarios.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Systems-immunology.jpg|150px|Systems immunology: an introduction to modeling methods for scientists]]
<h3 style="text-decoration:none;">Systems immunology: an introduction to modeling methods for scientists</h3>
<p class="author">by Jayajit Das and Ciriyam Jayaprakash</p>
<p>This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:SCC_Data_Gov_Roadmap_EN_COVER.png |150px|Canadian Data Governance Standardization Collaborative Roadmap]]
[[Image:SCC_Data_Gov_Roadmap_EN_COVER.png |150px|Canadian Data Governance Standardization Collaborative Roadmap]]