Changes

[[Image:Intro-to-online-ecological-and-environmental-data.jpg|150px|Introduction to Online Ecological and Environmental Data]]

[[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>

<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 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>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>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

[[Image:Federal-data-science.jpg|150px|Federal data science]]

[[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>

<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 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>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>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>

[[Image:Data-Feminism-cover.jpg|150px|Data Feminism, by  Catherine D'Ignazio and Lauren F. Klein]]

[[Image:Data-Feminism-cover.jpg|150px|Data Feminism, by  Catherine D'Ignazio and Lauren F. Klein]]

<h3 style="text-decoration:none;">Data Feminism: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.</h3>

<h3 style="text-decoration:none;">Data Feminism: A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.</h3>

<p class="author">by Catherine D'Ignazio and Lauren F. Klein</p>

<p class="author">by Catherine D'Ignazio and Lauren F Klein</p>

<p>Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In <i>Data Feminism</i>, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

<p>Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In <i>Data Feminism</i>, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.

<p>[https://mitpress.mit.edu/books/data-feminism <i>Data Feminism</i>] offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.</p>

<p>[https://mitpress.mit.edu/books/data-feminism <i>Data Feminism</i>] offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.</p>

[[Image:Statistical-and-machine-learning-data-mining.jpg|150px|Statistical and machine-learning data mining]]

[[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>

<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 class="author">by Bruce Ratner</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>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>

<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>