Labfront's experienced data scientists provide expertise, insights, and help explore with you on your project. We focus on data analysis and insight generation, freeing you to concentrate on your research.
Eliminate the overhead costs and time involved in expanding your in-house team. Our on-demand service model allows you to access top data scientists exactly when you need them, without the lengthy recruitment process or expenses of full-time hires.
All of the Garmin wearable data gets pulled out and reported to you in a usable way. We couldn’t have done our study without it.
Our team are alumni from the Center for Dynamical Biomarkers (Beth Israel Deaconess Medical Center/Harvard Medical School) and have over 20+ years of experience. That’s why researchers from Stanford to MIT trust them to handle their data analysis needs.
Labfront's analysts consistently go above and beyond with positivity, a sterling work ethic, and an ever-present willingness to assist, making our collaboration a genuine pleasure.
Our services are designed to support your research from data exploration to advanced analysis. With our sophisticated signal processing, we unearth hidden insights from physiological data, allowing you to concentrate on the insights that matter.
Smaller projects, typically involving a single researcher, with budgets around $100,000 - $200,000.
Features:
Bring on experts to understand and explore your Labfront data, specializing in heart rate from ECG and PPG-derived BBI analyses (HRV, DFA, MSE, etc.), sleep data (actigraphy, cardiopulmonary coupling), and continuous glucose data.
Medium-sized projects with a few researchers with budgets between $300,000 and $500,000.
Features:
Cross analyze data from a non-Labfront data source, like multi-lead EEG or others. Can include some custom development for machine learning.
Large-scale, multi-team, or high-complexity projects, typically above $1,000,000.
Features:
Help guide data capture methods from multiple data sources and even retroactive data. Support from initial project planning to paper publication, which includes full data cleaning to completed analysis. Includes learning to develop new algorithms.