Deriving insights for research data sharing from a large-scale data sharing initiative in Canada

This project examines the challenges of sharing large amounts of health research data in Canada. Advances in computing now let researchers explore bigger sets of data and ask new questions. However, the teams running data-sharing platforms often underestimate how much time and effort is needed for coordination, paperwork, and collaboration. They also sometimes fail to account for the diverse needs and incentives of various partners who contribute data. We will study the COVID-19 Immunity Task Force (CITF) Databank, which brings together data from over seventy health studies in Canada. Our goal is to understand the work and resources needed to collect, organize, and share this data so it can be reused for future research. By learning from the CITF Databank's experience, we hope to suggest ways to improve future projects of this kind. Aims: We will gather the views of research teams that provided data and the scientists and staff who designed and managed the databank and compare their perspectives with trends observed from their email communications. This will help us create practical advice for running similar data-sharing projects. Methods: First, we will review internal CITF Databank documents-such as contracts, agreements, and emails-and use machine learning to spot patterns in communication and coordination. Second, we will interview researchers to learn about whether the CITF Databank met their needs and expectations and what could be improved. Expected Results: We aim to produce clear guidelines that can help future data-sharing projects avoid common problems, work together more smoothly, and make Canada's health research data more valuable for science and public health.