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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: 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: 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:

This service bulletin presents federal government personnel (Full-Time Equivalent - FTE*) engaged in S&T activities.

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This release provides a provincial distribution of the research and development activity in the ten provinces.

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This service bulletin presents the geographic distribution of federal government science and technology expenditures. The distribution is done every two years and the previous data available are for the period 1992-93.

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The Higher Education sector is composed of "all universities, colleges of technology and other institutes of post-secondary education, whatever their source of finance or legal status.

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This service bulletin presents the geographic distribution of federal government science and technology expenditures.

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Gross domestic expenditures on research and development (GERD) represents total R&D expenditures performed in a country's national territory during a given year.

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This study examines the differences in strategies and activities pursued by a sample of more-successful and less-successful group of growing small- and medium-sized enterprises. Amongst other matters, it examines different functional strategies -- the importance of management, human resource practices, marketing, financing, and the innovativeness of the firm. Innovative activities are the most important determinants of success; that is, for a wide range of industries, they serve to discriminate between the more- and the less-successful firms better than any other variable. Almost all of the strategy questions that relate to innovative activity receive higher scores from the more-successful group of firms than from the less-successful group of firms. This is also the case for innovative activities -- whether a firm possesses an R&D unit, its expenditure on R&D relative to total investment, and its R&D-to-sales ratio.

Description:

Many analysts see high technology industries as the way to a healthy and wealthy economy. This article reviews some of the concepts and definitions of high technology, selects one definition and examines the 1977 to 1986 employment and earnings dynamics in these industries.

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.