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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>

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<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]]

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<strong>[https://~~www~~.~~csps-efpc~~.gc.ca/~~events~~/~~data-conference2022~~/~~index-eng.aspx Register now~~]</strong>  ~~      ~~  

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<strong>[https://vexpodev.z9.web.core.windows.net/en/#/2203/lobby Virtual Expo]</strong>  |  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda ~~Conference agenda~~]</strong>  ~~      ~~  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Agenda Agenda]</strong>  |  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong>      ~~  ~~  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Speakers Conference speakers]</strong>  |  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Networking_Missions Networking Missions]</strong>  |  

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~~<!--~~<strong>[https://~~www~~.~~csps-efpc.gc~~.ca/~~events/data-conference2022/index-eng.aspx Download your Data Conference Networking Missions~~]</strong~~>--~~>

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<strong>[https://wiki.gccollab.ca/GC_Data_Conference_2023/Discover_more_about_data Discover more about data 2023]</strong>  |  

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<strong>[https://wiki.gccollab.ca/Data_Conference_2022_Announcements Announcements]</strong>

</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>

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~~<h1>** DRAFT ** Discover more about data</h1>~~

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<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>

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<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>

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<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>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<h3 style="text-decoration:none;">[https://www.csps-efpc.gc.ca/digital-academy/index-eng.aspx Digital Academy]</h3>

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<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>

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<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>

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<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>

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<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>

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<p class="author">Dr Aida Mehonic, on The Alan Turing Institute</p>

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<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>

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<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>

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<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>

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<h3 style="text-decoration:none;">[https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592 Directive on Automated Decision-Making]</h3>

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<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>

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<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>

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

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<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>

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

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

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<h3 style="text-decoration:none;">[https://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>

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<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>

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<p>(In English) Ransomware gangs have been causing extensive damage. It’s time that the government takes them more seriously.</p>

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<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>

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

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<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>

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<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>

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<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>

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<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Melissa_Hathaway Melissa Hathaway]</p>

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<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>

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<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>

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<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>

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

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<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>

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<h2>Websites</h2>

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<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>

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<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>

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<p class="recco">Recommended by the National Research Council of Canada</p><p class="recco"></p>

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<h3 style="text-decoration:none;">[https://busrides-trajetsenbus.csps-efpc.gc.ca/ Busrides]</h3>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<h3 style="text-decoration:none;">[https://www12.statcan.gc.ca/census-recensement/index-eng.cfm?DGUID=2021A000011124 Census of Population]</h3>

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<p>A detailed statistical portrait of Canada and its people by their demographic, social and economic characteristics.</p>

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<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/covid19?HPA=1 COVID-19: A data perspective]</h3>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<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>

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<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>

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<h3 style="text-decoration:none;">[https://www.thedatalodge.com/ The Data Lodge]</h3>

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<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>

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<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/data-science Data Science Centre]</h3>

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<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>

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<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/data-science/network Data Science Network for the Federal Public Service]</h3>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<h3 style="text-decoration:none;">[https://www.datatothepeople.org/ Data To The People]</h3>

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<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>

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<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>

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<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>

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<h3 style="text-decoration:none;">[https://www.statcan.gc.ca/en/interact?HPA=1 Interact with data]</h3>

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<p>Find Statistics Canada videos, data visualizations, infographics, and thematic maps.</p>

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<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>

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<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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<h3 style="text-decoration:none;">Statistics</h3>

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<ul>

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<li><strong>[https://energy-information.canada.ca/en Canadian Centre for Energy Information (CCEI)]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/subjects-start/digital_economy_and_society Digital economy and society statistics]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/topics-start/poverty Dimensions of Poverty Hub]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/subjects-start/environment Environment statistics]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/topics-start/gender_diversity_and_inclusion Gender, diversity and inclusion statistics]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/subjects-start/housing Housing statistics]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/subjects-start/labour_ Labour statistics]</strong></li>

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<li><strong>[https://www.statcan.gc.ca/en/subjects-start/indigenous_peoples Statistics on Indigenous peoples]</strong></li>

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<li><strong>[https://www144.statcan.gc.ca/tdih-cdit/index-eng.htm?HPA=1 Transportation Data Hub ]</strong></li>

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</ul>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

<h2>Tools</h2>

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<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>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<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>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

[[Image:Data-interoperatiblity_guide-UN.png|150px|Data Interoperability Guide]]

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<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>

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<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>

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<p class="author">Statistics Canada</p>

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<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>

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<p class="recco">Recommended by Statistics Canada, a GC Data Community partner</p>

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<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>

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<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Valerie_A_Logan Valerie Logan]</p>

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<p>(English only) Key data literacy resources and webinars.</p>

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<p class="recco">Recommended by the GC Data Community</p>

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<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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<br>

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<p class="recco">Recommended by the GC Data Community</p>

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<p class="author">from Statistics Canada</p>

<p>In the news: daily releases.</p>

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<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/data-science/network/newsletter Data Science Network for the Federal Public Service newsletter]</h3>

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<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>

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<p>(In English) This book provides a complete overview of computational immunology, from basic concepts to mathematical modeling at the single molecule, cellular, organism, and population levels. It showcases modern mechanistic models and their use in making predictions, designing experiments, and elucidating underlying biochemical processes. It begins with an introduction to data analysis, approximations, and assumptions used in model building. Core chapters address models and methods for studying immune responses, with fundamental concepts clearly defined. Readers from immunology, quantitative biology, and applied physics will benefit from the following: Fundamental principles of computational immunology and modern quantitative methods for studying immune response at the single molecule, cellular, organism, and population levels. An overview of basic concepts in modeling and data analysis. Coverage of topics where mechanistic modeling has contributed substantially to current understanding. Discussion of genetic diversity of the immune system, cell signaling in the immune system, immune response at the cell population scale, and ecology of host-pathogen interactions.</p>

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

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<br>

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[[Image:Les-donnees-administratives-publiques-dans-l-espace-numerique.jpg|150px|Les données administratives publiques dans l'espace numérique]]

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<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>

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<p class="author">[https://wiki.gccollab.ca/Data_Conference_2022_Speakers#Pierre_Desrochers Pierre Desrochers]</p>

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<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>

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<p class="recco">Recommended by the GC Data Community</p>

<br>

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

<br>

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

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

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

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<br>

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

<br>

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<br>

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

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<br>

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

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<br>

Samuel.burke

Administrators

875

edits

Changes

Discover more about data (view source)

Revision as of 14:30, 21 February 2023