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Description: This study examines the economic footprint created by the Canadian research and development pharmaceutical sector on the Canadian economy in 2021, including a focus on the contribution of Innovative Medicines Canada’s members. While the impact of the sector’s medical research is well known, less known are the economic impacts of the sector on the Canadian economy, such as the value generated, the jobs supported and the investments made.

Description: Technologies the business or organization plans to adopt or incorporate over the next 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Challenges faced by business or organization when adopting or incorporating technologies, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Use of artificial intelligence (AI) by businesses and organizations in producing goods or delivering services over the last 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Extent to which artificial intelligence (AI) has reduced tasks previously performed by employees and the impact of AI use on total employment, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Changes made by business or organization when using artificial intelligence (AI) to produce goods or deliver services, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: New or significantly improved goods or services brought onto the market from 2020 to 2023, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Innovation of new products or services and most significant recent product innovation was new to its market, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: This article explores trends in patent applications made by Canadian-resident businesses for advanced technologies from 2001 to 2019, drawing on Eurostat's aggregation of high-tech patents. Approximately one-third of applications fall under high-tech categories, the bulk of which were associated with Communication, Computer, and Automated business equipment technologies. While these fields saw growth until 2012, a subsequent decline occurred, notably in Computer and Electronic Product Manufacturing. Biotechnology, Semiconductors, and Lasers showed limited dynamism, while aviation technology applications surged by nearly twentyfold over the period.

Description: This study compares the Government of Canada’s direct and indirect measures to support R&D, as captured by business innovation and growth support (BIGS) programs and the Scientific Research and Experimental Development (SR&ED) tax incentive program. BIGS and SR&ED are two central instruments that the Canadian government uses to stimulate R&D expenditures in the business sector.

Description: Technologies the business or organization plans to adopt or incorporate over the next 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Challenges faced by business or organization when adopting or incorporating technologies, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Use of artificial intelligence (AI) by businesses and organizations in producing goods or delivering services over the last 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Extent to which artificial intelligence (AI) has reduced tasks previously performed by employees and the impact of AI use on total employment, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Changes made by business or organization when using artificial intelligence (AI) to produce goods or deliver services, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: New or significantly improved goods or services brought onto the market from 2020 to 2023, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: Innovation of new products or services and most significant recent product innovation was new to its market, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, second quarter of 2024.

Description: This dashboard shows traffic count data that is obtained from traffic camera imagery using a computer vision-based system developed at the Data Exploration and Integration Lab (DEIL) at Statistics Canada. The system periodically pulls traffic imagery from the Application Programmable Interfaces (APIs) of municipal and provincial traffic camera programs. Vehicle detection was implemented using the open source You Only Look Once version 3 (YOLOv3) object detection model that was trained on the Common Objects in Context (COCO) dataset. The output of the model is used to generates real-time counts of the detected vehicles (cars, trucks, buses, motorcycles).

Description:

Percentage of enterprises that used specific types of advanced or emerging technologies, by North American Industry Classification System (NAICS) code and enterprise size, based on a one-year observation period. Advanced technologies include material handling, supply chain or logistics technologies; design or information control technologies; processing or fabrication technologies; clean technologies; security or advanced authentication systems; business intelligence technologies; and other types of advanced technologies. Emerging technologies include nanotechnology, biotechnology, geomatics or geospatial technologies, artificial intelligence (AI), integrated Internet of Things (IoT) systems, blockchain technologies, and other types of emerging technologies.

Description:

Percentage of enterprises that did not adopt or use advanced technologies for specific reasons, by North American Industry Classification System (NAICS) code and enterprise size, based on a one-year observation period. Reasons for not adopting or using advanced technologies include not convinced of economic benefit; difficulty in obtaining financing; high cost of advanced technologies; investment not necessary for continuing operations; lack of technical skills required to support this type of investment; organizational culture too inflexible; decisions made by parent, affiliates or subsidiary businesses; lack of technical support or services (from consultants or vendors); lack of information regarding advanced technology; difficulty in integrating new advanced technologies with existing systems, standards and processes; other reasons for not adopting or using advanced technologies; and adoption or use of advanced technologies not applicable to this business’s activities.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description:

This paper presents an open-source system that was developed for automatic estimation of building height from street-view images using Deep Learning (DL), advanced image processing techniques, and geospatial data. The goal of the developed system is to ultimately be used to enrich the Open Database of Buildings (ODB), that was published by Statistics Canada, as a part of the Linkable Open Data Environment (LODE). Some of the obtained results for building-height estimation are presented. Some challenging cases and the scalability of the system are discussed as well.

Description:

This infographic uses data from the Business Innovation and Growth Support and Linkable File Environment. It shows the relative distribution of beneficiaries and value of federal support related to business innovation and growth in 2018 by employment size, revenue size and industry.

Description: Release published in The Daily – Statistics Canada’s official release bulletin

Description:

Recent improvements in robotics have rekindled ancient fears about the impact of robotics on humankind. Unfortunately, existing data seldom distinguishes robots from other types of automation, so research into their impact so far has been difficult. This article introduces research from a new Statistics Canada dataset, Robots!, on the impact of robots at the firm-level. The article examines the impact of robot investment on firm performance and employment at the enterprise level.

Description:

Prior to the COVID-19 pandemic, advances in artificial intelligence and robotics raised concerns that automation might lead to relatively high unemployment rates in the coming years. This Economic Insights article examines the degree to which Canadians’ views about the impact of automation on net job creation in 1989 materialized three decades later.

Description:

This infographic describes the invesment in robots in the Canadian economy and its impact on employment.

Description:

Recent advances in artificial intelligence have rekindled ancient fears that robots will replace humans in the economy. Previous waves of automation changed but did not reduce labour’s role, but robots’ human-like flexibility could make this time different. Whether or not it will is an empirical question that has lacked suitable data to answer. This paper describes the creation of a dataset to fill the evidence gap in Canada. Robots! is firm-level panel data on robot adoption created using Canadian import data. The data identify a substantial amount of the robot investment in the Canadian economy from 1996 to 2017. Although many robots are imported by robotics wholesalers or programmers for resale, the majority of them can be attributed to their final (direct) adopting firm. The data can be used to study the impact of robot adoption at the economic region, industry or firm-level.

Description:

This study examines how employment and organizations have changed in response to robot adoption. As robotics and artificial intelligence (AI) become increasingly used by firms as the next engine of innovation and productivity growth, their effects on labour, firm practices and productivity have become a subject of growing importance. The study provides the most comprehensive evidence possible at the level of individual businesses on the employment and organizational effects of robot investments.

Description: As the ninth goal outlined in the 2030 Agenda for Sustainable Development, Canada and other UN member states have committed to build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation by 2030. This 2020 infographic provides an overview of indicators underlying the ninth Sustainable Development Goal in support of industry, innovation and infrastructure, and the statistics and data sources used to monitor and report on this goal in Canada.

Description: This classification system was developed conjointly by the Social Sciences and Humanities Research Council of Canada (SSHRC), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Foundation for Innovation (CFI), the Canadian Institutes of Health Research (CIHR), and Statistics Canada which is the custodian. This shared standard classification, inspired by the Frascati Model 2015 of the Organisation for Economic Co-operation and Development (OECD), will be used by the federal granting agencies and Statistics Canada to collect, and disseminate data related to research and development in Canada. The Canadian Research and Development Classification (CRDC) first official version was the 2020 Version 1.0, now being replaced by CRDC Version 2.0. The CRDC is revised within 2 years for minor changes, and every five years for major revisions. CRDC 2020 Version 2.0 is composed of 3 main pieces: the type of activity or TOA (with 3 categories), the field of research or FOR (with 1,671 fields at the lowest level) and socioeconomic objective or SEO (with 85 main groups at the lowest level).

Description:

The concordance table provides a link between data tables and the survey questions from the Survey of Innovation and Business Strategy (SIBS).

Description:

Summary of the technical workshop on Estimates of Research and Development in the Higher Education Sector (HERD), held in Ottawa on October 16, 2009. Data users and experts from universities and colleges, granting councils and provincial and federal government departments proposed general and detailed recommendations for the methodology applied in estimating the HERD.

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The issue of biotechnology in agriculture has generated much debate. This activity is designed to help students better understand biological systems for producing materials and their advantages over synthetic systems.

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This activity looks at the different ways in which technology is used on the farm.

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This report focusses on new studies that analyse information and communications technology industries, science-based industries, high-technology industries and firms, the knowledge-based economy, and knowledge workers.

Description:

Statistics Canada's annual Economic Conference provides a forum for the exchange of empirical research among business, government, research and labour communities. The conference is also a means to promote economic and socio-economic analyses while subjecting existing data to critical assessment as part of an ongoing process of statistical development and review. This year's theme was Innovation in an Evolving Economy. At the May 6-7, 2002 conference there were 12 presentations, based directly on the analysis of Science, Innovation and Electronic Information Division (SIEID) data. These presentations were given by SIEID analysts, by Statistics Canada analysts in other groups, by facilitated access researchers and by analysts using published or commissioned estimates.

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The Quebec Institute of Statistics hosted a forum for Statistics Canada and provincial government experts dealing with the subject of science and technology statistics.

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This paper describes the experiences, the issues and the expectations of the many different players involved in the implementation of document imaging for the Canadian Census of Agriculture.

Description:

Statistics Canada is engaged in a project "Information System for Science and Technology" which purpose is to develop useful indicators of activity and a framework to tie them together into a coherent picture of science and technology (S&T) in Canada. The Working papers series is used to publish results of the different initiatives conducted within this project. The produced data are related to the activities, linkages and outcomes of S&T. Several key areas are covered such as: innovation, technology diffusion, human resources in S&T and interrelations between different actors involved in S&T. This series also presents important data tabulations taken from regular surveys on R&D and S&T and made possible because of the existing Project.