©2019 by Digital Ecosystem Research Challenge.

This project has been made possible in part by the Government of Canada.

ABOUT

The Digital Ecosystem Research Challenge is a collaborative effort which aims to support research and innovative exploration into the ways in which digital media impact the 2019 Federal Election in Canada. We are collecting social media, online news, web, and survey data at a large scale. We want to help teams from academia and civil society access this data through our Research Awards. Learn more about our projects.

POST-ELECTION CONFERENCE

We will be publishing a public report and hosting a conference to share the results of our collaboration. On February 20-21, 2020​ all award-winning project teams will convene at the University of Ottawa to share their insights and help set a policy and research agenda. A portion of the conference will be open to the public. This fall we will be sharing information about how to attend.

WHO CAN PARTICIPATE?

Project teams are made up of academic researchers and civil society groups with an academic-based partner from across Canada and abroad. Members of the public, policy makers, and others will be invited to our post-election conference.

FAQ

Q: How will proposals be evaluated?

A: Proposals will be evaluated based on relevance/importance of the project, feasibility and capacity of the team, as well as their unique contribution to the larger collaboration. Unique contribution is important because we hope to support research which uses a range of methods and focuses on a variety of topics and issue areas.

  1. Relevance and unique contribution (30%): Contribute to better understanding the information ecosystem, specifically the uses and/or impacts of digital media, in the lead up to and during the 2019 federal election. This includes originality, significance and expected contribution to knowledge on information ecosystems in Canada, the potential to inform policy and practice, and potential to increase digital literacy among Canadians. This also includes fit for the larger collaboration in terms of uniqueness of contribution so that a wide range of methods, data, and sub-topics can be addressed.

  2. Feasibility and plan to achieve excellence (40%): Appropriateness of the methodology and of the work plan, including timelines for the design and conduct of the activity; and probability that the objectives will be met; feasibility of accessing data in a timely manner; quality and appropriateness of knowledge mobilization plan including effective dissemination, exchange and engagement with stakeholders within and/or beyond the research community, where applicable; appropriateness of the requested budget.

  3. Capacity and expertise to succeed (30%): Quality, quantity and significance of expertise of the applicant or team in relation to the proposed project. Past relevant experience and published work of team members relative to their roles in the project and to the stage of their career. Experience working in collaboration and/or other interactions with stakeholders.

 

Q: What data is available?

A: There will be social media, online news, web, and survey data. More specifically, we have identified some data types that we definitely want to collect such as Twitter data, online data, content from news outlets in Canada, and survey data matched with web traffic monitoring data. We are still confirming the exact data types we will include and so may be able to accommodate new requests. Please contact Professor Taylor Owen to discuss your data needs and plans as soon as possible (ideally before you submit your proposal).

 

Q: I am confused about how this data is made available to Award winners, can you provide more details?

This is a new approach to collaborative research which comes with a number of ethical and legal questions to consider. Different data has to be treated differently and so this process largely depends on the specific proposal. For some data types, data will be processed by a team of data scientists based at McGill and survey researchers at the University of Toronto. They will then provide virtual access to the specific dataset(s) needed by a given Award recipient. The data will sometimes be available in raw format, but will often instead be provided at various levels of aggregation. For example, if you want to examine the online response to all digital news related to political party leaders (or some other topic/set of topics) you would work with a data scientist to create a query and the data scientist will then create that subset of data from our larger collection for your own further analysis. Your team and the data scientist will work to test the query to make sure it is reliable and valid given your research questions. This is likely to be an iterative process which you should think of as an important part of your research. Make sure to reserve time for this type of testing into your timeline for the project. Award winners will participate in a data briefing call the week of July 22, 2019 where they will receive more details. You are strongly encouraged to reach out to Professor Taylor Owen to discuss your data needs and plans. You are also welcome to collect your own original data independently.

Q: What types of studies can be funded?
A: We encourage research using any method or mix of methods and may include tool development or licensing. Projects can focus on any topic relevant to the election such as misinformation, disinformation, political polarization, political debate and engagement, political advertising, election integrity, or other related areas. While an academic partner is always required, public facing projects are encouraged. 

Q: I am from a civil society group or am an individual outside of academia, can I apply?

A: Yes, but you need to have a partner at an academic institution. We are unable to transfer funds to non-academic institutions. We encourage you to reach out to academics who work in your area of focus to establish a collaboration.

OTHER QUESTIONS?