Funding for Agri-food Data Canada is provided in part by the Canada First Research Excellence Fund
One goal of Agri-food Data Canada is to increase the value of data and other digital object outputs, by developing sound data management practices. This is accomplished by following the FAIR principles.
The FAIR principles state that digital research outputs, including data sets, should be findable, accessible, interoperable and reusable for both people and machines. Agri-food Data Canada (ADC) will work with researchers to develop tools that enable researchers to apply the FAIR principles to their work and to improve automatic, computer-driven manipulation of data and other digital outputs.
To improve data FAIRness Agri-food Data Canada is:
- Creating a semantic engine that will help researchers create and use better machine-actionable, reusable, and accessible descriptions and governance for their data, projects, algorithms, tools, workflows, and other digital research outputs.
- Collaborating on projects that will enable the federation of data silos. A federation of these silos will require processes that allow data access, authentication, and data transfer standards. These technologies will ensure that data, metadata, and access rights travel with the data from source to destination within the ADC federation.
- Developing tools to help researchers with data provenance and traceability. Cryptographically verifiable information about data origin and creation processes will allow researchers to transform data, audit and evaluate the quality of and trust in data, model authenticity, and implement access control for derived data.
- Creating a culture of FAIR data by developing knowledge-sharing resources such as webinars, training, and teaching materials. ADC works with partners to align our approaches and contribute to the global research community.
Agri-food Data Canada is creating a data ecosystem serving agri-food sustainability, guided by the FAIR principles. We are working with researchers to develop the tools and infrastructure needed to achieve better data management outcomes and ultimately, to maximize the impact and reach of their research.