Researchers will often ask us about our data analysis workflow. Most of our projects fall into two main categories. First, when a project starts after a proposal is awarded Second, when we provide Data Science support for research teams, Departments, Health Systems, companies, governments, or other organizations. Here are our main principles: Continuous delivery. The overarching principle behind our workflow is continuous delivery. In other words, we deliver a new set of results every week, usually on a Friday. [Read More]
Streamlining data collection by machine learning-based prediction of physical tests and self-reported scores.
Ava has a significant challenge ahead of her. She is the Principal Investigator for a cohort of patients with cardiac conditions, and tests – such as the Timed Up and Go (TUG) – and self-reported scores are vital to the success of her program. However, primary care physicians involved with the project consider a time-consuming data collection protocol to be a deal-breaker. In other words, if the pace of their clinics slows down, this will make their lives a nightmare, and they will be less likely to participate. [Read More]
Proposal preparation workflow at SporeData
We are frequently asked about our workflow when writing a grant proposal, and so we thought about outlining it in a post: A discussion is held among all collaborators regarding the central area of research, what we want to accomplish, and the set of methods we plan on using. SporeData’s team releases a one-page Specific Aims section. The first goal of this section is to allow the whole team to contribute toward the design. [Read More]