Grants and Contributions
About this information
In June 2016, as part of the Open Government Action Plan, the Treasury Board of Canada Secretariat (TBS) committed to increasing the transparency and usefulness of grants and contribution data and subsequently launched the Guidelines on the Reporting of Grants and Contributions Awards, effective April 1, 2018.
The rules and principles governing government grants and contributions are outlined in the Treasury Board Policy on Transfer Payments. Transfer payments are transfers of money, goods, services or assets made from an appropriation to individuals, organizations or other levels of government, without the federal government directly receiving goods or services in return, but which may require the recipient to provide a report or other information subsequent to receiving payment. These expenditures are reported in the Public Accounts of Canada. The major types of transfer payments are grants, contributions and \'other transfer payments\'.
Included in this category, but not to be reported under proactive disclosure of awards, are (1) transfers to other levels of government such as Equalization payments as well as Canada Health and Social Transfer payments. (2) Grants and contributions reallocated or otherwise redistributed by the recipient to third parties; and (3) information that would normally be withheld under the Access to Information Act and the Privacy Act.
$30,000.00
Oct 5, 2020
For-profit organization
YOUTH - Regression analysis and machine learning on health data
957275
The aim of the project is to develop complex statistical models that will be used in web applications for pharmacoepidemiology.
$36,000.00
May 5, 2020
Academia
Machine Learning for Prediction of Water Pollution from Mine Wastes
947180
The general objective of this project is to develop machine learning tools to analyze long-term water quality monitoring data on mine sites. The team will access and extract data from the water quality database to be built. Then suitable machine learning algorithms will be selected and applied through computer coding to analyze these data.
$120,000.00
Oct 1, 2020
For-profit organization
Enhancement and Support of Machine Learning Products in Cancer Radiotherapy
957398
Develop a product roadmap that includes a consideration for customer requirements, market needs, competition pressure, ongoing support systems, and regulatory compliance. Execute the product roadmap to develop, test, and, build planned versions of products and deliver to customers worldwide.
$4,500.00
Aug 10, 2017
Hardware-Based Machine Learning for Epileptic Seizure Detection and Prevention
URU
$4,500.00
Aug 10, 2017
Real-Estate Price Estimation and Modeling through Statistical Machine Learning
URU
$50,000.00
Apr 4, 2022
For-profit organization
Developing a Motorsport Data Analysis System (MDAS) with Machine Learning
988070
The MDAS system is designed to analyze motorsport drivers practicing in simulators and deliver actionable insights to improve their performance. The system will expose this analysis via APIs to be consumed by external applications owned by the firm.
$300,564.00
Sep 9, 2022
Academia
Robust and Resilient Machine Learning for Connected Health-Care Systems
984858
The Project focuses on building resilient and secure machine learning (ML)
techniques for connected health-care systems.
$75,000.00
Apr 1, 2024
For-profit organization
Machine Learning Extensions for Switchboard, an audio software development kit
1018509
The goal of this project is to design and implement a series of extensions that allow developers to easily integrate and ship audio-based machine learning models inside of applications that contain the Switchboard SDK (software development kit).
$50,000.00
May 13, 2024
For-profit organization
ARP - Diagnosis of Li-Ion batteries using machine learning.
1019096
Development of management and diagnostic algorithms for Li-ion rechargeable batteries.
$25,000.00
May 5, 2020
Academia
Machine Learning for Prediction of Water Pollution from Mine Wastes
947180
The general objective of this project is to develop machine learning tools to analyze long-term water quality monitoring data on mine sites. The team will access and extract data from the water quality database to be built. Then suitable machine learning algorithms will be selected and applied through computer coding to analyze these data.