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Discover more about data (view source)
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<p style="text-align:right; padding: 10px; margin-top: -10px; width:1130px"><strong>[https://wiki.gccollab.ca/Découvrez_plus_sur_les_données Français]</strong></p>
<p style="text-align:left; padding: 10px; margin-top: -10px; width:1130px"><strong>[https://wiki.gccollab.ca/Découvrez_plus_sur_les_données Français]</strong></p>
[[Image:DataCon2022-Banner-EN.png |100%|Data Conference 2022: Driving Data Value and Insights for All Canadians, 23 + 24 February 2022]]
[[Image:DataCon2022-Banner-EN.png |100%|Data Conference 2022: Driving Data Value and Insights for All Canadians, 23 + 24 February 2022]]
<p style="background-color: #f18f34; padding: 5px; width:1130px""><small>
<p style="background-color: #f18f34; padding: 5px; width:1130px""><small>
<strong>[https://www.csps-efpc.gc.ca/events/data-conference2022/index-eng.aspx Register now]</strong>
<strong>[https://vexpodev.z9.web.core.windows.net/en/#/2203/lobby Virtual Expo]</strong> |
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda Conference agenda]</strong>
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda Agenda]</strong> |
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong>
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong> |
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Networking_Missions Networking Missions]</strong> |
<strong>[https://wiki.gccollab.ca/GC_Data_Conference_2023/Discover_more_about_data Discover more about data 2023]</strong> |
<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Announcements Announcements]</strong>
</small></p>
</small></p>
<p style="color: DarkBlue; font-size:large;"><strong>Brought to you by Statistics Canada and the Canada School of Public Service with support from the GC Data Community</strong></p>
<p style="color: DarkBlue; font-size:large;"><strong>Brought to you by Statistics Canada and the Canada School of Public Service with support from the GC Data Community</strong></p>
<h1>Discover more about data</h1>
<h1>Discover more about data</h1>
<p><small>Are we missing something? Send us the details: [mailto:gcdc-cdgc@csps-efpc.gc.ca GC Data Community]</small></p>
<p><small>Are we missing something? Send us the details: [mailto:gcdc-cdgc@csps-efpc.gc.ca GC Data Community]</small></p>
<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I561 A Self-Directed Guide to Understanding Data]</strong> (online, self-paced)</p>
<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I561 A Self-Directed Guide to Understanding Data]</strong> (online, self-paced)</p>
<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I511 The Role of Data in Digital Government]</strong> (virtual classroom)</p>
<p><strong>[https://www.csps-efpc.gc.ca/catalogue/courses-eng.aspx?code=I511 The Role of Data in Digital Government]</strong> (virtual classroom)</p>
<p class="recco">Recommended by the Canada School of Public Service, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://catalogue.csps-efpc.gc.ca/catalog?pagename=Catalog&cm_locale=en Canada School of Public Service Learning catalogue]</h3>
<p>Browse the School's full catalogue of courses, events, programs and other learning tools. For recommended learning by theme or community, view our Learning paths.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.csps-efpc.gc.ca/digital-academy/index-eng.aspx Digital Academy]</h3>
<p>The CSPS Digital Academy was established by the Canada School of Public Service (CSPS) in 2018 to help federal public servants gain the knowledge, skills and mindsets they need in the digital age. It supports the principles of Canada's Beyond2020 initiative for an agile, inclusive and equipped workforce and advocates for a digital-first approach that aligns with Canada's Digital Standards. </p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/wtc/data-literacy Statistics Canada’s Data Literacy Training Initiative]</h3>
<p>Data literacy is the ability to derive meaningful information from data. It focuses on the competencies involved in working with data including the knowledge and skills to read, analyze, interpret, visualize and communicate data as well as understand the use of data in decision-making.</p>
<ul>
<li><strong>[https://www.statcan.gc.ca/eng/wtc/data-literacy/compentencies Data literacy competencies]</strong>: Data literacy competencies are the knowledge and skills you need to effectively work with data</li>
<li><strong>[https://www.statcan.gc.ca/eng/wtc/data-literacy/journey Data journey]</strong>: The data journey represents the key stages of the data process starting with finding and exploring data through to telling the data story</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>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<!-- *** 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.turing.ac.uk/news/can-data-trusts-be-backbone-our-future-ai-ecosystem Can data trusts be the backbone of our future AI ecosystem?]</h3>
<p class="author">Dr Aida Mehonic, on The Alan Turing Institute</p>
<p>(In English) Here at The Alan Turing Institute, we’re interested in how data trusts could help to shape the future artificial intelligence (AI) ecosystem. At present, lots of well-intentioned initiatives to create machine learning algorithms fail because of the lack of training datasets, and this is true both in the private and the public sector. A data trust could enable safe and secure data sharing that would allow the UK to develop and deploy AI systems to benefit society and the economy.</p>
<p class="recco">Recommended by [https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Chantal_Bernier Chantal Bernier], a Data Conference 2022 speaker</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.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592 Directive on Automated Decision-Making]</h3>
<p>The Government of Canada is increasingly looking to utilize artificial intelligence to make, or assist in making, administrative decisions to improve service delivery. The Government is committed to doing so in a manner that is compatible with core administrative law principles such as transparency, accountability, legality, and procedural fairness. Understanding that this technology is changing rapidly, this Directive will continue to evolve to ensure that it remains relevant.</p>
<p class="recco">Recommended by Statistics 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">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Benoit_Deshaies 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://www.ineteconomics.org/perspectives/blog/hijacked-and-paying-the-price-why-ransomware-gangs-should-be-designated-as-terrorists Hijacked and Paying the Price - Why Ransomware Gangs Should be Designated as Terrorists]</h3>
<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>
<p>(In English) Ransomware gangs have been causing extensive damage. It’s time that the government takes them more seriously.</p>
<p class="recco">Recommended by the GC Data Community</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">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.cigionline.org/publications/patching-our-digital-future-unsustainable-and-dangerous/ Patching Our Digital Future Is Unsustainable and Dangerous]</h3>
<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>
<p>(In English) In recent years, the world has witnessed an alarming number of high-profile cyber incidents, harmful information and communications technology (ICT) practices, and internationally wrongful acts through the misuse of ICTs. Over the last 30 years, a unique and strategic vulnerability has been brought to society — by allowing poorly coded or engineered, commercial-off-the-shelf products to permeate and power every aspect of our connected society. These products and services are prepackaged with exploitable weaknesses and have become the soft underbelly of government systems, critical infrastructures and services, as well as business and household operations. The resulting global cyber insecurity poses an increasing risk to public health, safety and prosperity. It is critical to become much more strategic about how new digital technologies are designed and deployed, and hold manufacturers of these technologies accountable for the digital security and safety of their products. The technology industry has fielded vulnerable products quickly — now, it is crucial to work together to reduce the risks created and heal our digital environment as fast as society can. This paper was first published as part of CIGI’s recent essay series, Governing Cyber Space during a Crisis in Trust.</p>
<p class="recco">Recommended by the GC Data Community</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">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Cynthia_Rudin 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://nrc.canada.ca/en/research-development/products-services/technical-advisory-services/ai-accelerator-government-canada AI Accelerator for the Government of Canada]</h3>
<p>The AI Accelerator is a new service from the NRC that helps Government of Canada departments and agencies harness the power of artificial intelligence (AI).</p>
<p class="recco">Recommended by the National Research Council of Canada</p><p class="recco"></p>
<h3 style="text-decoration:none;">[https://busrides-trajetsenbus.csps-efpc.gc.ca/ Busrides]</h3>
<p>Busrides is a product of the Canada School of Public Service Digital Academy, and a destination created to deepen your understanding of everything digital and government.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?DGUID=2021A000011124 Census of Population]</h3>
<p>A detailed statistical portrait of Canada and its people by their demographic, social and economic characteristics.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/covid19?HPA=1 COVID-19: A data perspective]</h3>
<p>A series of articles on various subjects which explore the impact of COVID-19 on the socio-economic landscape. New articles will be released periodically.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<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>(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://www.thedatalodge.com/ The Data Lodge]</h3>
<p>(In English) Data and analytics are the linchpin of digital transformation, yet culture is the hardest part. Data literacy is the missing link, and the key to cracking the culture code. We believe that the foundation of nurturing a data literate workforce (from executives, to data and analytics professionals, to your front-line associates) is fostering a shared language around the use of data, or Information as a Second Language® (ISL). And that cracking the culture code starts with making this language personal.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science Data Science Centre]</h3>
<p>In this rapidly-changing digital era, statistical agencies need to find innovative ways to harness the power of data. Statistics Canada is embracing the possibilities of data science to better serve the information needs of Canadians.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network Data Science Network for the Federal Public Service]</h3>
<p>Looking for a dynamic space to collaborate and learn about data science? Join the new Data Science Network for the Federal Public Service. Our vision is to create a vibrant community of data science enthusiasts and to offer a dynamic space for members to collaborate and learn about data science.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.datatothepeople.org/ Data To The People]</h3>
<p>(In English) Data To The People are recognised global experts and industry leaders in building and nurturing data literacy. We equip leaders and organisations with the tools to assess individual and organisational data literacy, and design bespoke programs for them to improve the data competency of their workforce.</p>
<p class="recco">Recommended by Statistics Canada, 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>(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://www.statcan.gc.ca/en/interact?HPA=1 Interact with data]</h3>
<p>Find Statistics Canada videos, data visualizations, infographics, and thematic maps.</p>
<p class="recco"><p class="recco">Recommended by Statistics Canada, 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>
<h3 style="text-decoration:none;">Statistics</h3>
<ul>
<li><strong>[https://energy-information.canada.ca/en Canadian Centre for Energy Information (CCEI)]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/subjects-start/digital_economy_and_society Digital economy and society statistics]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/topics-start/poverty Dimensions of Poverty Hub]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/subjects-start/environment Environment statistics]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/topics-start/gender_diversity_and_inclusion Gender, diversity and inclusion statistics]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/subjects-start/housing Housing statistics]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/subjects-start/labour_ Labour statistics]</strong></li>
<li><strong>[https://www.statcan.gc.ca/en/subjects-start/indigenous_peoples Statistics on Indigenous peoples]</strong></li>
<li><strong>[https://www144.statcan.gc.ca/tdih-cdit/index-eng.htm?HPA=1 Transportation Data Hub ]</strong></li>
</ul>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<!-- *** TOOLS *** -->
<h2>Tools</h2>
<h3 style="text-decoration:none;">[https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html Algorithmic Impact Assessment Tool]</h3>
<p>The Algorithmic Impact Assessment (AIA) is a mandatory risk assessment tool intended to support the Treasury Board’s Directive on Automated Decision-Making (“the Directive”). The tool is a questionnaire that determines the impact level of an automated decision-system. It is composed of 48 risk and 33 mitigation questions. Assessment scores are based on many factors including systems design, algorithm, decision type, impact and data.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www150.statcan.gc.ca/n1/pub/89-20-0006/892000062021001-eng.htm Framework for Responsible Machine Learning Processes at Statistics Canada ]</h3>
<p>This document is a handbook for practitioners developing and implementing Machine Learning (ML) processes. It provides guidance and practical advice on how to responsibly develop these automated processes within Statistics Canada but could be adopted by any organization. They can be applied to processes that are put in production or that are dealing with research.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
[[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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[[Image:StatsCAN-app-EN.png|150px|The StatsCAN app]]
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/sc/mobile-applications StatsCAN app]</h3>
<p class="author">Statistics Canada</p>
<p>This free app lets you tap into expert analysis, fun facts, visuals, short stories and insight that bring together data, tools and articles to provide you with the latest information on Canada's economy, society and environment.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
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<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/mystatcan/login?HPA=1 My StatCan]</h3>
<p class="author">Statistics Canada</p>
<p>My StatCan is a customizable one-stop portal that allows you to: Bookmark and quickly access your favourite articles, reports, data tables, indicators, and more; Receive email notifications on our latest data releases; Participate in online discussions on the StatCan Blog, chat with an expert and Question of the month.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www150.statcan.gc.ca/n1/pub/71-607-x/71-607-x2020010-eng.htm?HPA=1 Canadian Statistical Geospatial Explorer]</h3>
<p class="author">Statistics Canada</p>
<p>The Canadian Statistical Geospatial Explorer empowers users to discover Statistics Canada’s geo‑enabled data down to the smallest level of detail available, the dissemination area. Users can find, explore then export data in various formats to use in their workflows. Users can also customize the map and change basemaps (satellite imagery, topography, etc.) to view data in a different context.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://wiki.gccollab.ca/File:TheDataLodge_Where_to_go_to_learn_more.pdf Where to go to learn more, from The Data Lodge] <small>(PDF)</small></h3>
<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Valerie_A_Logan Valerie Logan]</p>
<p>(English only) Key data literacy resources and webinars.</p>
<p class="recco">Recommended by the GC Data Community</p>
<!-- *** NEWSLETTERS + BLOGS *** -->
<h2>Newsletters, blogs, and feeds</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>
<p class="recco">Recommended by the GC Data Community</p>
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<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>
<h3 style="text-decoration:none;">[https://www150.statcan.gc.ca/n1/dai-quo/index-eng.htm?HPA=1 The Daily]</h3>
<p class="author">from Statistics Canada</p>
<p>In the news: daily releases.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/data-science/network/newsletter Data Science Network for the Federal Public Service newsletter]</h3>
<p>Keep up to speed on the latest news in the world of data science by subscribing to the Network newsletter: Data Science Bits and Bytes—your source of info for all things data science in the Government of Canada and beyond.</p>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/o1/en/plus?HPA=1 StatsCAN Plus]</h3>
<p class="author">from Statistics Canada</p>
<p>Stay on top of the country’s statistical news throughout the day!</p>
<p class="recco">Recommended by Statistics 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>
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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>
<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>
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<h2>Books and reports</h2>
<h2>Books and reports</h2>
<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>(In English) This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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[[Image:Les-donnees-administratives-publiques-dans-l-espace-numerique.jpg|150px|Les données administratives publiques dans l'espace numérique]]
<h3 style="text-decoration:none;">[https://www.worldcat.org/title/donnes-administratives-publiques-dans-lespace-numrique/oclc/1281673459/editions?referer=di&editionsView=true Public administrative data in the digital space]</h3>
<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Pierre_Desrochers Pierre Desrochers]</p>
<p>(In French - original title: <strong>Les données administratives publiques dans l'espace numérique</strong>) The purpose of this book is to explore the issues related to the collection and use of administrative data. These data, accumulated by government departments and agencies in the course of their daily activities, are becoming more and more interesting as statistical and technological tools develop and allow their exploitation. Administrative data is data that is routinely collected when individuals register or conduct transactions, or created during record-keeping activities related to service delivery. The implications of megadata, or Big Data, the advent of machine learning and artificial intelligence are significant for organizations and their information governance. They raise many issues of quality, accuracy, privacy and interpretation.</p>
<p class="recco">Recommended by the GC Data Community</p>
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<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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[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]
[[Image:Number-Sense-cover.jpg|150px|Numbersense: How to Use Big Data to Your Advantage, by Kaiser Fung]]
<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>(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>
<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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<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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<p>(In French - original title: <strong>Analyse des données textuelles</strong>) 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>
<p>(In French - original title: <strong>Analyse des données textuelles</strong>) 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>
<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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<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>(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>
<p class="recco">Recommended by Agriculture and Agri-Food Canada, a GC Data Community partner</p>
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<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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