Understanding and predicting human mobility in urban areas is very important. Unfortunately, there are no large, standardized, and open datasets to compare different models. Here, we introduce a new open, anonymized dataset of 100k individual mobility trajectories. Check it out!

Fantastic opportunity for PhD students in systems and data science: NIH-funded 2-week interdisciplinary program discussing developing methods for applying health interventions (focused on community health) in your research!

Interested in #datascience #machinelearning #networkscience and #nutrition ? Join us for a lot of fun this April!
👇👇👇👇👇👇👇👇👇👇👇👇👇
nutrition-ml-school.org

Yes, exactly! Mobility data is a large-scale, accessible sensor of human behavior, and now we know it predicts diet-related disease. We can study it at a global scale to better understand different food landscapes impact different populations!

Human mobility data, made up of locations captured by smartphones, provides a new way to observe where people get food. This study shows it’s a powerful indicator of population diet and diet-related disease.

nature.com/articles/s4174…

Phone geolocation data tells us a lot about where & what people eat, & their risk for diseases like diabetes. This signals an exciting opportunity to use big mobility data in food studies (& rely less on murky self-reports). Team science at its best! rdcu.be/drc5S

A fantastic opportunity for researchers interested in understanding how environmental exposures impact health behaviors, among other impactful topics!

In Jan '21, the NIH convened the Precision Nutrition: Research Gaps and Opportunities Workshop to bring together experts to discuss issues in better understanding & addressing precision nutrition. Our report in American Society for Nutrition's American Journal of Clinical Nutrition is now avail academic.oup.com/ajcn/advance-a…