L2M – Integrated Data Science Platform for Complex Spatiotemporally Resolved Biomedical Applications

Reliable annotations set critical ground truths for modern data science initiatives. In life sciences, however, large volumes of physiologic data streams produce computational bottlenecks that hinder this workflow. A researcher’s only viable alternative is to adopt a DIY approach, exporting data to separate coding environments. This fragments the analysis from the visual context and corrodes scientific impact due to a lack of standardization and reproducibility.

To address this, we offer an interactive analysis platform specializing in large physiologic data streams. The software utilizes multi-resolution charting to enable instant visualization of massive datasets and features a dedicated annotation management system to ensure rigorous, standardized labeling. Uniquely, it integrates directly with user-configured IDEs to merge visualization with real-time code experimentation.

This project is positioned to capture a widening market gap. While the post-ChatGPT AI boom created vast tooling ecosystems for enterprise data science, academic and independent researchers remain underserved by rigid, incumbent tools. Through this internship, the intern will validate product-market fit to capture this high-value research market. Supported by North Forge, this project aims to accelerate the commercialization of Canadian intellectual property, fostering economic development by delivering a solution that restores precision and scalability to physiological research.

Faculty Supervisor:

Frederick A. Zeiler

Student:

Partner:

North Forge

Discipline:

Life Sciences

Sector:

Education

University:

University of Manitoba

Program: