Changes

<h3 style="text-decoration:none;">Invisible Women: Data Bias in a World Designed for Men</h3>

<h3 style="text-decoration:none;">Invisible Women: Data Bias in a World Designed for Men</h3>

<p class="author">by Caroline Criado Pérez</p>

<p class="author">by Caroline Criado Pérez</p>

<p>Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.</p>

<p>(In English) Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives. Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in <i>Invisible Women​</i>, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.</p>

<p>Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in <i>Invisible Women​</i>, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<br>

<br>

<h3 style="text-decoration:none;">Introduction to data analysis with R for forensic scientists (Vol. 21)</h3>

<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 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>(In English) 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>

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

<br>

<br>

<h3 style="text-decoration:none;">Systems immunology: an introduction to modeling methods for scientists</h3>

<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 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>(In English) 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>

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

<br>

<br>

<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>(In English) 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>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

<p class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, a GC Data Community partner</p>

[[Image:Livre-blanc-une-science-ouverte-dans-une-republique-numerique-guide-strategique.jpg|150px|Qu’est-ce que le text et data mining ?]]

[[Image:Livre-blanc-une-science-ouverte-dans-une-republique-numerique-guide-strategique.jpg|150px|Qu’est-ce que le text et data mining ?]]

<h3 style="text-decoration:none;">[https://books.openedition.org/oep/1716?lang=fr Qu’est-ce que le text et data mining ?]</h3>

<h3 style="text-decoration:none;">[https://books.openedition.org/oep/1716?lang=fr What is text and data mining?]</h3>

<p class="author">Direction de l’Information Scientifique et Technique, CNRS</p>

<p class="author">Scientific and Technical Information Directorate, CNRS</p>

<p>Le data mining est un concept jeune qui apparaît en 1989 sous un premier nom de KDD (Knowledge Discovery in Databases, en français ECD pour Extraction de Connaissances à partir des Données). Le terme de « text and data mining » est apparu pour la première fois dans le domaine du marketing au début des années 1990. Ce concept, tel qu’appliqué aux services marketing, est étroitement lié au concept du « one-to-one relationship » (Michael Berry et Gordon Linoff, créateurs du data mining dans le m).</p>

<p>(In French - original title: Qu’est-ce que le text et data mining ?) Data mining is a new concept that first appeared in 1989 under the name KDD (Knowledge Discovery in Databases). The term "text and data mining" first appeared in the marketing field in the early 1990s. This concept, as applied to marketing services, is closely linked to the concept of the "one-to-one relationship" (Michael Berry and Gordon Linoff, creators of data mining in M).</p>

<p class="recco">Recommandé par Agriculture et Agroalimentaire Canada, un partenaire de la Communauté des données du GC.</p>

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

<br>

<br>

<br>

<br>