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Line 509: − − <!-- *** ARTICLES + POSTS *** --> − − <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">Derek Alton</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">Darine Benkalha</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">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> − − <h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/resources Data science resources]</h3> − <p class="author">from the Data Science Network for the Federal Public Service</p> − <p>For data science enthusiasts: Find resources, training, tools, and communities.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <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">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 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">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> − − <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>(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> − − <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> − <p class="author">Karine Joizil</p> − <p>(In French - original title: <strong>Le projet de loi 95 : De la volonté de l’État québécois de permettre un accès et une utilisation optimale de ses données</strong>) 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> − <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.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>(In English) 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> − − <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>(In English) 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> − − <!-- *** WEBSITES *** --> − − <h2>Websites</h2> − <h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3> − <p>(In English) 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> − − <h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3> − <p class="author">on Reddit</p> − <p>(In English) 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> − − <h3 style="text-decoration:none;">[https://open.canada.ca/en Government of Canada Open Government]</h3> − <p>Open Government is about making government more accessible to everyone. Participate in conversations, find data and digital records, and learn about open government.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <h3 style="text-decoration:none;">[https://informationisbeautiful.net/ Information is beautiful]</h3> − <p class="author">by David McCandless</p> − <p>(In English) 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> − − <h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3> − <p>(In English) 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> − − <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 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.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3> − <p>(In English) Open Government Data (OGD) is a philosophy- and increasingly a set of policies - that promotes transparency, accountability and value creation by making government data available to all. Public bodies produce and commission huge quantities of data and information. By making their datasets available, public institutions become more transparent and accountable to citizens. By encouraging the use, reuse and free distribution of datasets, governments promote business creation and innovative, citizen-centric services.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3> − <p>(In English) 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> − − <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 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.data.gov/ US Government Open Data]</h3> − <p>(In English) Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <h3 style="text-decoration:none;">[https://data.worldbank.org/ World Bank Open Data]</h3> − <p>Free and open access to global development data.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <!-- *** TOOLS *** --> − − <h2>Tools</h2> − − [[Image:Data-interoperatiblity_guide-UN.png|150px|Data Interoperability Guide]] − <h3 style="text-decoration:none;">[https://unstats.un.org/wiki/display/InteropGuide/Introduction Data Interoperability Guide]</h3> − <p class="author">Luis Gonzalez, on the UN Statistics Wiki</p> − <p>(In English) 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> − − <h3 style="text-decoration:none;">[http://opendatatoolkit.worldbank.org/en/index.html Starting an Open Data Initiative]</h3> − <p class="author">from Worldbank</p> − <p>The Open Government Data Toolkit is designed to help governments, Bank staff and users understand the basic precepts of Open Data, then get “up to speed” in planning and implementing an open government data program, while avoiding common pitfalls.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − [[Image:Data-Ethics-Canvas.jpg|150px|The Data Ethics Canvas]] − <h3 style="text-decoration:none;">[https://theodi.org/article/the-data-ethics-canvas-2021/ The Data Ethics Canvas]</h3> − <p class="author">Dave Tarrant, James Maddison, Olivier Thereaux</p> − <p>(In English) 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> − <br> − − <!-- *** PEOPLE *** --> − − <!--<h2>People to follow</h2> − <h3 style="text-decoration:none;">This</h3> − <p>Description</p> − <h3 style="text-decoration:none;">That</h3> − <p>Description</p>--> − − <!-- *** NEWSLETTERS + BLOGS *** --> − − <h2>Newsletters and blogs</h2> − [[Image:GCDC-round-EN-FR.png|150px|GC Data Community]] − <h3 style="text-decoration:none;">[https://mailchi.mp/e0872fde637e/gc-data-community-mailing-list-sign-up-inscription-la-liste-de-diffusion-de-la-communaut-des-donnes-du-gc GC Data Community monthly newsletter]</h3> − <p>Subscribe to keep up-to-date on data-related events, releases, jobs, and more throughout the Government of Canada.</p> − <br> − <br> − <br> − <br> − − [[Image:The-AI-ethics-brief-newsletter.PNG|150px|The AI Ethics Brief]] − <h3 style="text-decoration:none;">[https://brief.montrealethics.ai/ The AI Ethics Brief]</h3> − <p class="author">from the Montreal AI Ethics Institute</p> − <p>(In English) The Montreal AI Ethics Institute is an international non-profit organization democratizing AI ethics literacy. Subscribe to get full access to the newsletter and have the latest from the field of AI ethics delivered right to your inbox every week. Never miss an update from the work being done at the Montreal AI Ethics Institute and our thoughts on research and development in the field from around the world.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − − <!-- *** PODCASTS *** --> − − <h2>Podcasts</h2> − [[Image:Women-in-data-science-podcast.PNG|150px|Women in Data Science podcast]] − <h3 style="text-decoration:none;">[https://www.widsconference.org/podcast.html Women in Data Science podcast]</h3> − <p class="author">from Stanford University</p> − <p>(In English) Leading women in data science share their work, advice, and lessons learned along the way with Professor Margot Gerritsen from Stanford University. Hear about how data science is being applied and having impact across a wide range of domains, from healthcare to finance to cosmology to human rights and more.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − <br> − − [[Image:Data-sceptic-podcast.PNG|150px|Data Skeptic podcast]] − <h3 style="text-decoration:none;">[https://dataskeptic.com/ Data Skeptic podcast]</h3> − <p>(In English) Your trusted podcast, centered on data science, machine learning, and artificial intelligence.</p> − <p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p> − <br> − <br> − − [[Image:Ehsayers-podcast-eng.jpg|150px|Eh Sayers podcast]] − <h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/sc/podcasts Eh Sayers podcast]</h3> − <p class="author">from Statistics Canada</p> − <p>Join us as we meet with experts from Statistics Canada and from across the nation to ask and answer the questions that matter to Canadians.</p> − <br>
Discover more about data (view source)
Revision as of 10:19, 13 February 2022
, 10:19, 13 February 2022no edit summary
<li><strong>[https://www.statcan.gc.ca/eng/wtc/data-literacy/catalogue Learning catalogue]</strong>: Data literacy training available from Statistics Canada</li>
<li><strong>[https://www.statcan.gc.ca/eng/wtc/data-literacy/catalogue Learning catalogue]</strong>: Data literacy training available from Statistics Canada</li>
</ul>
</ul>
<!-- *** ARTICLES + POSTS *** -->
<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">Derek Alton</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">Darine Benkalha</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">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>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/resources Data science resources]</h3>
<p class="author">from the Data Science Network for the Federal Public Service</p>
<p>For data science enthusiasts: Find resources, training, tools, and communities.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<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">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 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">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>
<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>(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>
<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>
<p class="author">Karine Joizil</p>
<p>(In French - original title: <strong>Le projet de loi 95 : De la volonté de l’État québécois de permettre un accès et une utilisation optimale de ses données</strong>) 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>
<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.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>(In English) 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>
<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>(In English) 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>
<!-- *** WEBSITES *** -->
<h2>Websites</h2>
<h3 style="text-decoration:none;">[https://data2x.org/ Data2x]</h3>
<p>(In English) 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>
<h3 style="text-decoration:none;">[https://www.reddit.com/r/dataisbeautiful/top/?t=all /r/DataIsBeautiful]</h3>
<p class="author">on Reddit</p>
<p>(In English) 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>
<h3 style="text-decoration:none;">[https://open.canada.ca/en Government of Canada Open Government]</h3>
<p>Open Government is about making government more accessible to everyone. Participate in conversations, find data and digital records, and learn about open government.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://informationisbeautiful.net/ Information is beautiful]</h3>
<p class="author">by David McCandless</p>
<p>(In English) 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>
<h3 style="text-decoration:none;">[https://opendatacharter.net/ International Open Data Charter]</h3>
<p>(In English) 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>
<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 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.oecd.org/gov/digital-government/open-government-data.htm OECD Open Government data]</h3>
<p>(In English) Open Government Data (OGD) is a philosophy- and increasingly a set of policies - that promotes transparency, accountability and value creation by making government data available to all. Public bodies produce and commission huge quantities of data and information. By making their datasets available, public institutions become more transparent and accountable to citizens. By encouraging the use, reuse and free distribution of datasets, governments promote business creation and innovative, citizen-centric services.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://theodi.org/ Open Data Institute]</h3>
<p>(In English) 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>
<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 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.data.gov/ US Government Open Data]</h3>
<p>(In English) Find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://data.worldbank.org/ World Bank Open Data]</h3>
<p>Free and open access to global development data.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Data-interoperatiblity_guide-UN.png|150px|Data Interoperability Guide]]
<h3 style="text-decoration:none;">[https://unstats.un.org/wiki/display/InteropGuide/Introduction Data Interoperability Guide]</h3>
<p class="author">Luis Gonzalez, on the UN Statistics Wiki</p>
<p>(In English) 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>
<h3 style="text-decoration:none;">[http://opendatatoolkit.worldbank.org/en/index.html Starting an Open Data Initiative]</h3>
<p class="author">from Worldbank</p>
<p>The Open Government Data Toolkit is designed to help governments, Bank staff and users understand the basic precepts of Open Data, then get “up to speed” in planning and implementing an open government data program, while avoiding common pitfalls.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
[[Image:Data-Ethics-Canvas.jpg|150px|The Data Ethics Canvas]]
<h3 style="text-decoration:none;">[https://theodi.org/article/the-data-ethics-canvas-2021/ The Data Ethics Canvas]</h3>
<p class="author">Dave Tarrant, James Maddison, Olivier Thereaux</p>
<p>(In English) 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>
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<h2>Newsletters and blogs</h2>
[[Image:GCDC-round-EN-FR.png|150px|GC Data Community]]
<h3 style="text-decoration:none;">[https://mailchi.mp/e0872fde637e/gc-data-community-mailing-list-sign-up-inscription-la-liste-de-diffusion-de-la-communaut-des-donnes-du-gc GC Data Community monthly newsletter]</h3>
<p>Subscribe to keep up-to-date on data-related events, releases, jobs, and more throughout the Government of Canada.</p>
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[[Image:The-AI-ethics-brief-newsletter.PNG|150px|The AI Ethics Brief]]
<h3 style="text-decoration:none;">[https://brief.montrealethics.ai/ The AI Ethics Brief]</h3>
<p class="author">from the Montreal AI Ethics Institute</p>
<p>(In English) The Montreal AI Ethics Institute is an international non-profit organization democratizing AI ethics literacy. Subscribe to get full access to the newsletter and have the latest from the field of AI ethics delivered right to your inbox every week. Never miss an update from the work being done at the Montreal AI Ethics Institute and our thoughts on research and development in the field from around the world.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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<h2>Podcasts</h2>
[[Image:Women-in-data-science-podcast.PNG|150px|Women in Data Science podcast]]
<h3 style="text-decoration:none;">[https://www.widsconference.org/podcast.html Women in Data Science podcast]</h3>
<p class="author">from Stanford University</p>
<p>(In English) Leading women in data science share their work, advice, and lessons learned along the way with Professor Margot Gerritsen from Stanford University. Hear about how data science is being applied and having impact across a wide range of domains, from healthcare to finance to cosmology to human rights and more.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Data-sceptic-podcast.PNG|150px|Data Skeptic podcast]]
<h3 style="text-decoration:none;">[https://dataskeptic.com/ Data Skeptic podcast]</h3>
<p>(In English) Your trusted podcast, centered on data science, machine learning, and artificial intelligence.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Ehsayers-podcast-eng.jpg|150px|Eh Sayers podcast]]
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/sc/podcasts Eh Sayers podcast]</h3>
<p class="author">from Statistics Canada</p>
<p>Join us as we meet with experts from Statistics Canada and from across the nation to ask and answer the questions that matter to Canadians.</p>
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<h2>Books and reports</h2>
<h2>Books and reports</h2>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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