Using Big Data to Understand the Human Condition: The Kavli Human Project

David Steinberg.

Azmak et al. introduce the Kavli HUMAN Project (KHP), a unique and ambitious attempt to exploit big-data health analytics to study factors that contribute to good health. The KHP differs dramatically from typical large scale health studies in the depth of data that will be collected. Most such programs focus on very specific questions or rely on inter-subject comparisons. By contrast, the KHP will emphasize intra-subject comparisons, exploiting a complete record of the health, education, genetics, environmental, and lifestyle profiles of a large group of individuals at the within-subject level. Emerging evidence about the dynamic interplay between biology, behavior, and the environment points to a pressing need for large-scale, long-term synoptic dataset at the within-subject level.

The KHP will be a major step toward exploiting contemporary big data approaches to generate such a synoptic dataset —at least at moderate scale. The project aims to aggregate data from 2,500 New York City households in all five boroughs (roughly 10,000 individuals) whose biology and behavior will be measured using an unprecedented array of modalities over 20 years. It will also measure environmental conditions and events that KHP members experience using a geographic information system database of unparalleled scale, currently under construction in New York. In this manner, KHP will offer both synoptic and granular views of how human health and behavior evolve and what is responsible for inter-subject differences in their patterns. Azmak et al. argue that this in-depth data will allow for new discovery-based scientific approaches, rooted in big data analytics, with the potential to improve the health and quality of human life, particularly in urban environments.

Read the paper: 
Using Big Data to Understand the Human Condition: The Kavli Human Project. Okan Azmak, Hannah Bayer, Andrew Caplin, Miyoung Chun, Paul Glimcher, Steven Koonin, and Aristides Patrinos. Big Data, Volume 3, Number 3, 173-188.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s