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<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>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>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">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>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>

<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">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>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.</p>

<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.</p>

<p><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><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>

<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="author">by the OECD</p>

<p class="author">by the OECD</p>

<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>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>

<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>

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

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

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

<p class="author">by the 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>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>

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

<p class="author">by 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>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>

<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>

<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>

<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">by Darine Benkalha</p>

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

<p>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>

<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>

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

<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">by 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>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>

<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>

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

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

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

<p class="author">by 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 (TBS) 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>

<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>

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

<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">by the 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>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>

<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>

<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>

<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 the Law Commission of Ontario</p>

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

<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>

<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>

<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">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>

<h3 style="text-decoration:none;">[https://www.stateofopendata.od4d.net/ State of Open Data]</h3>

<h3 style="text-decoration:none;">[https://www.stateofopendata.od4d.net/ State of Open Data]</h3>

<p class="author">by Tim Davies, Stephen B Walker, and Mor Rubinstein, on Open Data for Development</p>

<p class="author">by Tim Davies, Stephen B Walker, and Mor Rubinstein, on Open Data for Development</p>

<p>It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain.</p>

<p>It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain.</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">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>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<h3 style="text-decoration:none;">[https://arxiv.org/abs/1811.10154 Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead]</h3>

<p class="author">Cynthia Rudin</p>

<p class="author">Cynthia Rudin</p>

<p>Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</p>

<p>Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.</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">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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<h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3>

<h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3>

<p>Important data about women and girls is incomplete or missing. Through partnerships with UN agencies, governments, civil society, academics, and the private sector, Data2X is working for change.</p>

<p>Important data about women and girls is incomplete or missing. Through partnerships with UN agencies, governments, civil society, academics, and the private sector, Data2X is working for change.</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">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>

<h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3>

<h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3>

<p class="author">on Reddit</p>

<p class="author">on Reddit</p>

<p>DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</p>

<p>DataIsBeautiful is for visualizations that effectively convey information. Aesthetics are an important part of information visualization, but pretty pictures are not the sole aim of this subreddit.</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">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>

<h3 style="text-decoration:none;">[https://open.canada.ca/en Government of Canada Open Government]</h3>

<h3 style="text-decoration:none;">[https://open.canada.ca/en Government of Canada Open Government]</h3>

<p class="author">by David McCandless</p>

<p class="author">by David McCandless</p>

<p>Data, information, knowledge: we distil it into beautiful, useful graphics & diagrams. Information is Beautiful is dedicated to helping you make clearer, more informed decisions about the world. All our visualizations are based on facts and data: constantly updated, revised and revisioned.</p>

<p>Data, information, knowledge: we distil it into beautiful, useful graphics & diagrams. Information is Beautiful is dedicated to helping you make clearer, more informed decisions about the world. All our visualizations are based on facts and data: constantly updated, revised and revisioned.</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">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>

<h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3>

<h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3>

<p>The Open Data Charter is a collaboration between over 150 governments and organisations working to open up data based on a shared set of principles. We promote policies and practices that enable governments and CSOs to collect, share, and use well-governed data, to respond effectively and accountably to the following focus areas: anti-corruption, climate action and pay equity.</p>

<p>The Open Data Charter is a collaboration between over 150 governments and organisations working to open up data based on a shared set of principles. We promote policies and practices that enable governments and CSOs to collect, share, and use well-governed data, to respond effectively and accountably to the following focus areas: anti-corruption, climate action and pay equity.</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">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>

<h3 style="text-decoration:none;">[https://www.oecd-ilibrary.org/science-and-technology/oecd-digital-economy-papers_20716826 OECD Digital Economy Papers]</h3>

<h3 style="text-decoration:none;">[https://www.oecd-ilibrary.org/science-and-technology/oecd-digital-economy-papers_20716826 OECD Digital Economy Papers]</h3>

<p>The OECD Directorate for Science, Technology and Innovation (STI) undertakes a wide range of activities to better understand how information and communication technologies (ICTs) contribute to sustainable economic growth and social well-being. The OECD Digital Economy Papers series covers a broad range of ICT-related issues and makes selected studies available to a wider readership. They include policy reports, which are officially declassified by an OECD Committee, and occasional working papers, which are meant to share early knowledge.</p>

<p>The OECD Directorate for Science, Technology and Innovation (STI) undertakes a wide range of activities to better understand how information and communication technologies (ICTs) contribute to sustainable economic growth and social well-being. The OECD Digital Economy Papers series covers a broad range of ICT-related issues and makes selected studies available to a wider readership. They include policy reports, which are officially declassified by an OECD Committee, and occasional working papers, which are meant to share early knowledge.</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">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>

<h3 style="text-decoration:none;">[https://www.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3>

<h3 style="text-decoration:none;">[https://www.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3>

<h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3>

<h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3>

<p>The ODI is a non-profit with a mission to work with companies and governments to build an open, trustworthy data ecosystem. We work with a range of organisations, governments, public bodies and civil society to create a world where data works for everyone.</p>

<p>The ODI is a non-profit with a mission to work with companies and governments to build an open, trustworthy data ecosystem. We work with a range of organisations, governments, public bodies and civil society to create a world where data works for everyone.</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">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>

<h3 style="text-decoration:none;">[https://www.opengovpartnership.org/ Open Government Partnership]</h3>

<h3 style="text-decoration:none;">[https://www.opengovpartnership.org/ Open Government Partnership]</h3>

<p>In 2011, government leaders and civil society advocates came together to create a unique partnership—one that combines these powerful forces to promote transparent, participatory, inclusive and accountable governance. Seventy-eight countries and seventy-six local governments — representing more than two billion people — along with thousands of civil society organizations are members of the Open Government Partnership (OGP).</p>

<p>In 2011, government leaders and civil society advocates came together to create a unique partnership—one that combines these powerful forces to promote transparent, participatory, inclusive and accountable governance. Seventy-eight countries and seventy-six local governments — representing more than two billion people — along with thousands of civil society organizations are members of the Open Government Partnership (OGP).</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">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>

<h3 style="text-decoration:none;">[https://www.data.gov/ US Government Open Data]</h3>

<h3 style="text-decoration:none;">[https://www.data.gov/ US Government Open Data]</h3>

<p class="author">by Luis Gonzalez, on the UN Statistics Wiki</p>

<p class="author">by Luis Gonzalez, on the UN Statistics Wiki</p>

<p>Over the years, countless systems that do not talk to one another have been created within and across organizations for the purposes of collecting, processing and disseminating data for development. With the proliferation of different technology platforms, data definitions and institutional arrangements for managing, sharing and using data, it has become increasingly necessary to dedicate resources to integrate the data necessary to support policy-design and decision-making. Interoperability is the ability to join-up and merge data without losing meaning (JUDS 2016). In practice, data is said to be interoperable when it can be easily re-used and processed in different applications, allowing different information systems to work together. Interoperability is a key enabler for the development sector to become more data-driven.</p>

<p>Over the years, countless systems that do not talk to one another have been created within and across organizations for the purposes of collecting, processing and disseminating data for development. With the proliferation of different technology platforms, data definitions and institutional arrangements for managing, sharing and using data, it has become increasingly necessary to dedicate resources to integrate the data necessary to support policy-design and decision-making. Interoperability is the ability to join-up and merge data without losing meaning (JUDS 2016). In practice, data is said to be interoperable when it can be easily re-used and processed in different applications, allowing different information systems to work together. Interoperability is a key enabler for the development sector to become more data-driven.</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">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>

<h3 style="text-decoration:none;">[http://opendatatoolkit.worldbank.org/en/index.html Starting an Open Data Initiative]</h3>

<h3 style="text-decoration:none;">[http://opendatatoolkit.worldbank.org/en/index.html Starting an Open Data Initiative]</h3>

<p class="author">by Dave Tarrant, James Maddison, Olivier Thereaux</p>

<p class="author">by Dave Tarrant, James Maddison, Olivier Thereaux</p>

<p>The Data Ethics Canvas is a tool for anyone who collects, shares or uses data. It helps identify and manage ethical issues – at the start of a project that uses data, and throughout. It encourages you to ask important questions about projects that use data, and reflect on the responses. The Data Ethics Canvas provides a framework to develop ethical guidance that suits any context, whatever the project’s size or scope.</p>

<p>The Data Ethics Canvas is a tool for anyone who collects, shares or uses data. It helps identify and manage ethical issues – at the start of a project that uses data, and throughout. It encourages you to ask important questions about projects that use data, and reflect on the responses. The Data Ethics Canvas provides a framework to develop ethical guidance that suits any context, whatever the project’s size or scope.</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">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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