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Discover more about data (view source)

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[[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>

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<p class="author">by Virginia A Baldwin </p>

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<p class="author">Virginia A Baldwin </p>

<p>(In English) 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>

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<p class="author">by Feras A Batarseh and Ruixin Yang</p>

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<p class="author">Feras A Batarseh and Ruixin Yang</p>

<p>(In English) 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:SCC_Data_Gov_Roadmap_EN_COVER.png |150px|Canadian Data Governance Standardization Collaborative Roadmap]]

<h3 style="text-decoration:none;">[https://www.scc.ca/en/about-scc/publications/general/canadian-data-governance-standardization-roadmap Canadian Data Governance Standardization Collaborative Roadmap (June 2021)]</h3>

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<p class="author">~~by the~~ Canadian Data Governance Standardization Collaborative</p>

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<p class="author">Canadian Data Governance Standardization Collaborative</p>

<p>The Canadian Data Governance Standardization Roadmap tackles the challenging questions we face when we talk about standardization and data governance. It describes the current and desired Canadian standardization landscape and makes 35 recommendations to address gaps and explore new areas where standards and conformity assessment are needed.</p>

<p>SCC established the Canadian Data Governance Standardization Collaborative in 2019 to accelerate the development of industry-wide data governance standardization strategies. The Collaborative spent the past two years working together to build a standardization Roadmap. The Canadian Data Governance Standardization Collaborative is a group of 220 Canadians across government, industry, civil society, Indigenous organizations, academia, and standards development organizations.</p>

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[[Image:Invisible-Women-cover.jpg|150px|Invisible Women: Data Bias in a World Designed for Men, by Caroline Criado Pérez]]

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

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

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<p class="author">Caroline Criado Pérez</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 class="recco">Recommended by the Office of the CIO of Canada, Treasury Board of Canada Secretariat, 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>

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<p class="author">by James Michael Curran</p>

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<p class="author">James Michael Curran</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>

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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>

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<p class="author">by Jayajit Das and Ciriyam Jayaprakash</p>

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<p class="author">Jayajit Das and Ciriyam Jayaprakash</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>

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[[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>

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

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

<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>

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

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[[Image:Computer-age-statistical-inference.jpg|150px|Computer age statistical inference: Algorithms, evidence, and data science]]

<h3 style="text-decoration:none;">Computer age statistical inference: Algorithms, evidence, and data science</h3>

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<p class="author">by Bradley Efron and Trevor Hastie</p>

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<p class="author">Bradley Efron and Trevor Hastie</p>

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<p>The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.</p>

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<p>(In English) The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.</p>

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

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[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]

<h3 style="text-decoration:none;">Numbersense: How to Use Big Data to Your Advantage</h3>

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<p class="author">by Kaiser Fung</p>

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<p class="author">Kaiser Fung</p>

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<p>We live in a world of Big Data — and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it — whether we realize it or not. The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations. <i>Numbersense</i> gives you the insight into how Big Data interpretation works — and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p>

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<p>(In Engish) We live in a world of Big Data — and it's getting bigger every day. Virtually every choice we make hinges on how someone generates data . . . and how someone else interprets it — whether we realize it or not. The problem is, the more data we have, the more difficult it is to interpret it. From world leaders to average citizens, everyone is prone to making critical decisions based on poor data interpretations. <i>Numbersense</i> gives you the insight into how Big Data interpretation works — and how it too often doesn't work. You won't come away with the skills of a professional statistician, but you will have a keen understanding of the data traps even the best statisticians can fall into, and you'll trust the mental alarm that goes off in your head when something just doesn't seem to add up.</p>

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

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[[Image:Hands-on-machine-learning-with-Scikit-Learn-and-TensorFlow.jpg|150px|Hands-on machine learning with Scikit-Learn and TensorFlow]]

<h3 style="text-decoration:none;">Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems</h3>

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<p class="author">by Aurélien Géron</p>

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<p class="author">Aurélien Géron</p>

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<p>By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.</p>

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<p>(In English) By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.</p>

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

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[[Image:Intro-to-data-science.jpg|150px|Introduction to data science]]

<h3 style="text-decoration:none;">Introduction to data science: data analysis and prediction algorithms with R</h3>

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<p class="author">by Rafael Irizarry</p>

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<p class="author">Rafael Irizarry</p>

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<p>Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist's experience.</p>

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<p>(In English) Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist's experience.</p>

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

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[[Image:Intro-to-functional-data-analysis.jpg|150px|Introduction to functional data analysis]]

<h3 style="text-decoration:none;">Introduction to functional data analysis</h3>

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<p class="author">by Piotr Kokoszka and Matthew Reimherr</p>

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<p class="author">Piotr Kokoszka and Matthew Reimherr</p>

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<p>Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation.</p>

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<p>(In English) Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation.</p>

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

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[[Image:Doing-Bayesian-data-analysis.jpg|150px|Doing Bayesian data analysis]]

<h3 style="text-decoration:none;">Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan</h3>

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<p class="author">by John K Kruschke</p>

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<p class="author">John K Kruschke</p>

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<p>Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Amsterdam, Academic Press.

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<p>(In English) Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan. Amsterdam, Academic Press.

Provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data.</p>

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

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[[Image:Data-assimilation.jpg|150px|Data Assimilation]]

<h3 style="text-decoration:none;">Data Assimilation: A Mathematical Introduction (Texts in Applied Mathematics Book 62)</h3>

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<p class="author">by Kody Law, Andrew Stuart, and Konstantinos Zygalakis</p>

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<p class="author">Kody Law, Andrew Stuart, and Konstantinos Zygalakis</p>

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<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>

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<p>(In English) 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>

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[[Image:Analyse-des-donnees-textuelles.jpg|150px|Analyse des données textuelles]]

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<h3 style="text-decoration:none;">~~Analyse des données textuelles~~</h3>

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<h3 style="text-decoration:none;">Textual data analysis</h3>

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<p class="author">Ludovic Lebart, Bénédicte Pincemin et Céline Poudat</p>

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<p class="author">Ludovic Lebart, Bénédicte Pincemin, and Céline Poudat</p>

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<p>~~L’analyse~~ des données textuelles (~~ADT~~) ~~permet d’explorer et de visualiser les recueils de textes les plus divers~~ : ~~œuvres littéraires~~, ~~transcriptions d’entretien~~, ~~discours politiques~~, ~~dossiers de presse~~, documents ~~d’archives~~, ~~enquêtes en ligne avec~~ questions ~~ouvertes~~, ~~fichiers de réclamations~~, ~~sondages de~~ satisfaction. ~~Le présent ouvrage procède à une présentation rigoureuse des méthodes de l’ADT~~, ~~qui combinent statistique exploratoire~~, ~~visualisations~~, ~~procédures de~~ validation ~~quantitative et approche~~ qualitative.</p>

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<p>(In French - original title: Analyse des données textuelles) Textual data analysis (TDA) makes it possible to explore and visualize a wide range of text collections: literary works, interview transcripts, political speeches, press files, archival documents, online surveys with open-ended questions, complaint files, and satisfaction surveys. This book provides a rigorous presentation of TDA methods, which combine exploratory statistics, visualizations, quantitative validation procedures, and qualitative approaches.</p>

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

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

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<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>

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<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>

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<p>(In English) 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>

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[[Image:Big-data-et-tracabilite-numerique.jpg|150px|Big data et traçabilité numérique]]

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<h3 style="text-decoration:none;">Big data ~~et traçabilité numérique~~</h3>

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<h3 style="text-decoration:none;">Big data and digital tracking</h3>

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<p class="author">Pierre-Michel Menger et Simon Paye (~~éditeurs~~)</p>

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<p class="author">Pierre-Michel Menger and Simon Paye (editors)</p>

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<p>~~Les~~ traces ~~numériques de l’activité des individus~~, ~~des entreprises~~, ~~des~~ administrations~~, des réseaux sociaux sont devenues un gisement considérable~~. ~~Comment ces données sont-elles prélevées~~, ~~stockées~~, ~~valorisées~~, ~~et vendues ? Et que penser des algorithmes qui convertissent en outil de contrôle et de~~ persuasion ~~l’information sur les comportements, les actes de travail et les échanges~~ ? ~~Les~~ big data ~~sont-elles à notre~~ service ~~ou font-elles de nous les rouages consentants du capitalisme informationnel et relationnel~~ ? ~~Les~~ sciences ~~sociales enquêtent sur les enjeux sociaux~~, ~~éthiques~~, ~~politiques et économiques de ces~~ transformations. ~~Mais elles sont elles aussi de plus en plus consommatrices de données numériques de masse~~. ~~Cet ouvrage collectif explore l’expansion de la traçabilité numérique dans ces deux~~ dimensions~~, marchande et scientifique~~. ~~L’ouvrage est dirigé par~~ Pierre-Michel Menger, ~~professeur au~~ Collège de France ~~et titulaire de la chaire « Sociologie du travail créateur »~~, ~~et par~~ Simon Paye, ~~maître de conférences à l’université de~~ Lorraine, ~~sociologue du travail et des groupes professionnels~~.</p>

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<p>(In French - original title: Big data et traçabilité numérique) The digital traces of the activity of individuals, companies, administrations and social networks have become a considerable source of data. How is this data collected, stored, valued and sold? And what about the algorithms that convert information on behaviors, work acts and exchanges into a tool for control and persuasion? Are big data at our service or do they make us the consenting cogs of informational and relational capitalism? Social sciences investigate the social, ethical, political and economic stakes of these transformations. But they are also more and more consumers of mass digital data. This collective work explores the expansion of digital tracking in both its commercial and scientific dimensions. The book is edited by Pierre-Michel Menger, professor at the Collège de France and holder of the chair "Sociology of creative work", and by Simon Paye, lecturer at the University of Lorraine, sociologist of work and professional groups.</p>

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

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

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[[Image:Big-data-et-societe.jpg|150px|Big Data et société]]

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<h3 style="text-decoration:none;">Big Data ~~et société~~: ~~Industrialisation des médiations symboliques~~</h3>

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<h3 style="text-decoration:none;">Big Data and society: Industrialization of symbolic mediations</h3>

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<p class="author">André Mondoux et Marc Ménard (~~éditeurs~~)</p>

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<p class="author">André Mondoux and Marc Ménard (editors)</p>

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<p>Le Big Data (~~ou mégadonnées~~) ~~suscite des discours porteurs de~~ visions ~~économiques prometteuses~~ : ~~efficience du microciblage~~, ~~meilleurs rendements par gestion prédictive~~, ~~algorithmes et~~ intelligence ~~artificielle~~, ~~villes intelligentes~~ . ~~bref~~, ~~toute une économie des données qui trouverait son achèvement véritable dans une créativité enfin libérée de tout joug disciplinaire~~, ~~idéologique et politique~~. ~~L'éclatement des individualités émancipées sonde le~~ social ~~tel qu'il est porté par ces discours de promotion~~. ~~En effet~~, ~~force est de constater que le~~ social ~~est relativement~~ absent, ~~pour l'instant~~, ~~des réflexions que l'on présente comme névralgiques pour un avenir meilleur~~. ~~Ce phénomène soulève d'importantes et préoccupantes~~ questions, ~~que ce soit concernant l'intégrité de la vie privée~~ face ~~à la marchandisation des données personnelles~~, ~~les dynamiques - économiquement productives - de la~~ surveillance ~~corporative~~, ~~les rapports de pouvoir induits par les GAFAM~~ (Google, Apple, Facebook, Amazon et Microsoft), ~~les pièges du temps réel ou encore la dynamique algorithmique et sa tendance à suppléer les lois~~ (~~le politique~~) ~~par les faits~~ (~~le réel enfin rendu indéniable grâce aux données quantifiables~~). ~~Ce premier ouvrage collectif du Groupe de recherche sur l'information et la surveillance au quotidien~~ (GRISQ) ~~envisage le~~ Big Data ~~comme producteur d'effets en même temps que produit de dynamiques sociales~~. ~~Il intéressera les étudiants et les chercheurs du domaine de la~~ communication ~~qui s'interrogent sur le vaste univers des mégadonnées~~.</p>

+

<p>(In French - original title: Big Data et société: Industrialisation des médiations symboliques) Big Data (or megadata) gives rise to speeches carrying promising economic visions: efficiency of micro-targeting, better yields through predictive management, algorithms and artificial intelligence, smart cities... in short, a whole economy of data that would find its true completion in a creativity finally liberated from all disciplinary, ideological, and political yokes. The breakdown of emancipated individualities probes the social as it is carried by these promotional discourses. Indeed, it is necessary to note that the social is relatively absent, for the moment, from the reflections that are presented as neuralgic for a better future. This phenomenon raises important and worrying questions, whether it be about the integrity of privacy in the face of the commodification of personal data, the economically productive dynamics of corporate surveillance, the power relationships induced by the GAFAMs (Google, Apple, Facebook, Amazon and Microsoft), the pitfalls of real time, or the algorithmic dynamic and its tendency to replace laws (politics) with facts (reality finally made undeniable by quantifiable data). This first collective work of the Research Group on Information and Surveillance in Daily Life (GRISQ) considers Big Data as a producer of effects as well as a product of social dynamics. It will be of interest to students and researchers in the field of communication who wonder about the vast universe of megadata.</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>

[[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 class="author">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>(In English) 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>

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[[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>

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<p class="author">by Paul Murrell</p>

+

<p class="author">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>(In English) 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>

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[[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 class="author">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>(In English) 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>

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[[Image:Open-gov-data-report.jpg|150px|Open Government Data Report]]

<h3 style="text-decoration:none;">[https://www.oecd-ilibrary.org/governance/open-government-data-report_9789264305847-en Open Government Data Report: Enhancing Policy Maturity for Sustainable Impact]</h3>

−

+

<p>This report provides an overview of the state of open data policies across OECD member and partner countries, based on data collected through the OECD Open Government Data survey (2013, 2014, 2016/17), country reviews and comparative analysis. The report analyses open data policies using an analytical framework that is in line with the OECD OUR data Index and the International Open Data Charter. It assesses governments’ efforts to enhance the availability, accessibility and re-use of open government data. It makes the case that beyond countries’ commitment to open up good quality government data, the creation of public value requires engaging user communities from the entire ecosystem, such as journalists, civil society organisations, entrepreneurs, major tech private companies and academia. The report also underlines how open data policies are elements of broader digital transformations, and how public sector data policies require interaction with other public sector agendas such as open government, innovation, employment, integrity, public budgeting, sustainable development, urban mobility and transport. It stresses the relevance of measuring open data impacts in order to support the business case for open government data.</p>

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

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[[Image:Good-practice-principles-for-data-ethics-in-the-public-sector.png|150px|Good Practice Principles for Data Ethics in the Public Sector]]

<h3 style="text-decoration:none;">[https://www.oecd.org/gov/digital-government/good-practice-principles-for-data-ethics-in-the-public-sector.htm Good Practice Principles for Data Ethics in the Public Sector]</h3>

−

<p class="author">~~by the~~ OECD Digital Government and Data Unit</p>

+

<p class="author">OECD Digital Government and Data Unit</p>

−

<p>Taking values-based common actions that place human rights at the core of digital government and data policies. The Good Practice Principles for Data Ethics in the Public Sector support the ethical use of data in digital government projects, products, and services to ensure they are worthy of citizens' trust. The document introduces 10 Good Practice Principles for Data Ethics in the Public Sector, including a set of specific actions which can support their implementation.</p>

+

<p>(In English) Taking values-based common actions that place human rights at the core of digital government and data policies. The Good Practice Principles for Data Ethics in the Public Sector support the ethical use of data in digital government projects, products, and services to ensure they are worthy of citizens' trust. The document introduces 10 Good Practice Principles for Data Ethics in the Public Sector, including a set of specific actions which can support their implementation.</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>

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[[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 class="author">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>(In English) 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>

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[[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 Bruce Ratner</p>

+

<p class="author">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>(In English) 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>

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[[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 class="author">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>(In English) 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>

<br>

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[[Image:Data-mining-with-R.jpg|150px|Data Mining with R: Learning with Case Studies]]

<h3 style="text-decoration:none;">Data Mining with R: Learning with Case Studies, Second Edition</h3>

−

<p class="author">by Luis Torgo</p>

+

<p class="author">Luis Torgo</p>

−

<p>Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining.</p>

+

<p>(In English) Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining.</p>

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

<br>

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[[Image:R-for-political-data-science.jpg|150px|R for Political Data Science: A Practical Guide]]

<h3 style="text-decoration:none;">R for Political Data Science: A Practical Guide</h3>

−

<p class="author">by Francisco Urdinez and Andrés Cruz (editors)</p>

+

<p class="author">Francisco Urdinez and Andrés Cruz (editors)</p>

−

<p>R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.</p>

+

<p>(In English) R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.</p>

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

<br>

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[[Image:The-social-dynamics-of-open-data.jpg|150px|The Social Dynamics of Open Data]]

<h3 style="text-decoration:none;">The Social Dynamics of Open Data</h3>

−

<p class="author">by François van Schalkwyk, Stefaan G Verhulst, and Gustavo Magalhaes</p>

+

<p class="author">François van Schalkwyk, Stefaan G Verhulst, and Gustavo Magalhaes</p>

−

<p>The Social Dynamics of Open Data is a collection of peer reviewed papers presented at the 2nd Open Data Research Symposium (ODRS) held in Madrid, Spain, on 5 October 2016. Research is critical to developing a more rigorous and fine-combed analysis not only of why open data is valuable, but how it is valuable and under what specific conditions. The objective of the Open Data Research Symposium and the subsequent collection of chapters published here is to build such a stronger evidence base. This base is essential to understanding what open data's impacts have been to date, and how positive impacts can be enabled and amplified. Consequently, common to the majority of chapters in this collection is the attempt by the authors to draw on existing scientific theories, and to apply them to open data to better explain the socially embedded dynamics that account for open data's successes and failures in contributing to a more equitable and just society. </p>

+

<p>(In English) The Social Dynamics of Open Data is a collection of peer reviewed papers presented at the 2nd Open Data Research Symposium (ODRS) held in Madrid, Spain, on 5 October 2016. Research is critical to developing a more rigorous and fine-combed analysis not only of why open data is valuable, but how it is valuable and under what specific conditions. The objective of the Open Data Research Symposium and the subsequent collection of chapters published here is to build such a stronger evidence base. This base is essential to understanding what open data's impacts have been to date, and how positive impacts can be enabled and amplified. Consequently, common to the majority of chapters in this collection is the attempt by the authors to draw on existing scientific theories, and to apply them to open data to better explain the socially embedded dynamics that account for open data's successes and failures in contributing to a more equitable and just society. </p>

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

<br>

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<h2>Articles and posts</h2>

<h3 style="text-decoration:none;">[https://derekalton.medium.com/building-a-framework-to-grow-ecosystems-a-rough-rough-draft-7b93ad73ed08 Building a framework to grow ecosystems… a rough rough draft]</h3>

−

<p class="author">by Derek Alton</p>

+

<p class="author">Derek Alton</p>

−

<p>Any ecosystem starts with a base foundation. These are the rivers and streams, the mountains and earth, the sun, rain and general climate. It is from this base foundation that an ecosystem grows. This foundation needs to have some level of sustainability for life to take hold. Likewise a social ecosystem requires a base infrastructure that is stable and secure to develop on. This could be physical infrastructure like roads and buildings with electricity and hydro but since we live now in a digital age, this is increasingly digital infrastructure: things like broadband connection and the world wide web (and all the protocols that underpin it). It is important to understand what infrastructure is required for your ecosystem to thrive and make sure it is sustainably available.</p>

+

<p>(In English) Any ecosystem starts with a base foundation. These are the rivers and streams, the mountains and earth, the sun, rain and general climate. It is from this base foundation that an ecosystem grows. This foundation needs to have some level of sustainability for life to take hold. Likewise a social ecosystem requires a base infrastructure that is stable and secure to develop on. This could be physical infrastructure like roads and buildings with electricity and hydro but since we live now in a digital age, this is increasingly digital infrastructure: things like broadband connection and the world wide web (and all the protocols that underpin it). It is important to understand what infrastructure is required for your ecosystem to thrive and make sure it is sustainably available.</p>

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

<h3 style="text-decoration:none;">[https://medium.com/opendatacharter/spotlight-a-plea-from-the-odcs-iwg-data-standardisation-matters-4d26329a18bb A plea from the ODC’s IWG: Data standardisation matters]</h3>

−

<p class="author">by Darine Benkalha</p>

+

<p class="author">Darine Benkalha</p>

−

<p>A re-cap of ODC’s Implementation Working Group meeting held last September 2021.</p>

+

<p>(In English) A re-cap of ODC’s Implementation Working Group meeting held last September 2021.</p>

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

<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network/data-visualizations Creating Compelling Data Visualizations]</h3>

−

<p class="author">by Alden Chen, Statistics Canada</p>

+

<p class="author">Alden Chen, Statistics Canada</p>

<p>Data visualization is a key component in many data science projects. For some stakeholders, especially subject matter experts and executives who may not be technical experts, it is the primary avenue by which they see, understand and interact with data projects. Consequently, it is important that visualizations communicate insights as clearly as possible. But too often, visualizations are hindered by some common flaws that make them difficult to interpret, or worse yet, are misleading. This article will review three common visualization pitfalls that both data communicators and data consumers should understand, as well as some practical suggestions for getting around them.</p>

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

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<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/eng/data-science/network/automated-systems Responsible use of automated decision systems in the federal government]</h3>

−

<p class="author">by Benoit Deshaies, Treasury Board of Canada Secretariat, and Dawn Hall, Treasury Board of Canada Secretariat</p>

+

<p class="author">Benoit Deshaies, Treasury Board of Canada Secretariat, and Dawn Hall, Treasury Board of Canada Secretariat</p>

−

<p>Data scientists play an important role in assessing data quality and building models to support automated decision systems. An understanding of when the Directive on Automated Decision-Making applies and how to meet its requirements can support the ethical and responsible use of these systems. In particular, the explanation requirement and the guidance (Guidance on Service and Digital, section 4.5.3.) from the Treasury Board of Canada Secretariat ~~(TBS)~~ on model selection are of high relevance to data scientists.</p>

+

<p>Data scientists play an important role in assessing data quality and building models to support automated decision systems. An understanding of when the Directive on Automated Decision-Making applies and how to meet its requirements can support the ethical and responsible use of these systems. In particular, the explanation requirement and the guidance (Guidance on Service and Digital, section 4.5.3.) from the Treasury Board of Canada Secretariat on model selection are of high relevance to data scientists.</p>

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

<h3 style="text-decoration:none;">[https://ec.europa.eu/isa2/eif_en The New European Interoperability Framework]</h3>

−

<p class="author">~~by the~~ European Commission</p>

+

<p class="author">European Commission</p>

<p>The European Interoperability Framework (EIF) is part of the Communication (COM(2017)134) from the European Commission adopted on 23 March 2017. The framework gives specific guidance on how to set up interoperable digital public services.</p>

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

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<h3 style="text-decoration:none;">[https://towardsdatascience.com/how-i-would-learn-data-science-if-i-had-to-start-over-f3bf0d27ca87 How I Would Learn Data Science (If I Had to Start Over)]</h3>

<p class="author">by Ken Jee, on Towards Data Science</p>

−

<p>Lessons learned from my data science journey.</p>

+

<p>(In English) Lessons learned from my data science journey.</p>

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

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<h3 style="text-decoration:none;">[https://www.mccarthy.ca/fr/references/blogues/techlex/le-projet-de-loi-95-de-la-volonte-de-letat-quebecois-de-permettre-un-acces-et-une-utilisation-optimale-de-ses-donnees ~~Le projet de loi~~ 95 : ~~De la volonté de l’État québécois de permettre un accès et une utilisation optimale de ses données~~]</h3>

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<h3 style="text-decoration:none;">[https://www.mccarthy.ca/fr/references/blogues/techlex/le-projet-de-loi-95-de-la-volonte-de-letat-quebecois-de-permettre-un-acces-et-une-utilisation-optimale-de-ses-donnees Bill 95: The Quebec government's desire to allow access to and optimal use of its data]</h3>

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<p class="author">~~par~~ Karine Joizil</p>

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<p class="author">Karine Joizil</p>

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<p>~~Dans le monde~~ de la ~~recherche, cette réforme était souhaitée depuis longtemps notamment par le Scientifique en chef du Québec~~ et ~~les fonds~~ de ~~recherche pour qui l’accès à ces~~ données ~~sera d’une grande utilité~~.</p>

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<p>(In French - original title: Le projet de loi 95 : De la volonté de l’État québécois de permettre un accès et une utilisation optimale de ses données) In the research world, this reform has been desired for a long time, notably by the Chief Scientist of Quebec and the research funds, for whom access to these data will be of great use.</p>

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<p class="recco">~~Recommandé par le Bureau du DPI du~~ Canada, ~~Secrétariat du Conseil du Trésor du~~ Canada, ~~un partenaire de la Communauté des données du~~ GC.</p>

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

<h3 style="text-decoration:none;">[https://www.lco-cdo.org/wp-content/uploads/2021/04/LCO-Regulating-AI-Critical-Issues-and-Choices-Toronto-April-2021-1.pdf Regulating AI: Critical Issues and Choice] <small>PDF</small></h3>

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<p class="author">~~by the~~ Law Commission of Ontario</p>

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<p class="author">Law Commission of Ontario</p>

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<p>This ~~paper identifies~~ a ~~series~~ of ~~important legal~~ and ~~policy issues that Canadian policymakers should consider when contemplating~~ regulatory framework~~(s) for~~ AI and ADM systems ~~that aid government decision-making~~.</p>

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<p>(In English) This report is a ground-breaking analysis of how to regulate AI and automated decision-making (ADM) systems used by governments and other public institutions. The report discusses key choices and options, identifies regulatory gaps, and proposes a comprehensive framework to ensure governments using AI and ADM systems protect human rights, ensure due process and promote public participation.</p>

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

Sharon.twiss

2,600

edits