About Seedcase
Who we are
The Seedcase project is developed by a team at the Steno Diabetes Center Aarhus and Department of Clinical Medicine at Aarhus University in Aarhus, Denmark, and is funded by a five year grant from the Novo Nordisk Foundation.
Why we build Seedcase
In clinical and health research, especially for small- to mid-sized research groups, funding for building modern, open source software infrastructures for managing and using data is limited. This lack of funding has naturally led to organizational challenges for managing existing and incoming data for many research initiatives, including the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) initiative (1,2), a national research collaboration and database initiative established in 2010 with continual enrollment of persons with type 2 diabetes. DD2 is one of our collaborators and key stakeholders of Seedcase.
We hope that Seedcase will improve and extend the existing DD2 research infrastructure into an open national state-of-the-art research infrastructure that will provide easy and transparent access to this resource for researchers, clinicians and stakeholders, thus enabling excellent data science driven research. Within the DD2 setting, an open, transparent, and easy access to this constantly growing resource has the potential of greatly improving the interest in, use of, and scientific impact of this resource, thus leading to substantial scientific and medical advancements, individualised treatment and improved human health in not only persons with type 2 diabetes, but population overall.
Seedcase will not be limited to only DD2. Our secondary aim to create this framework in such a way that other research groups and companies, who are unable to adequately invest in building infrastructures of this type, can relatively easily implement it, and modify as needed, for their own purposes. By building this framework, we hope to help propel research groups and companies across Denmark (and globally) to quickly getting updated on modern, scalable, and efficient approaches to working with data.