“Using Big Data to Understand the Human Condition: The Kavli HUMAN Project,” by Okan Azmak, Hannah Bayer, Andrew Caplin, Miyoung Chun, Paul Glimcher, Steven Koonin, and Aristides Patrinos, published in the journal Big Data (September 2016)
Until now, most large-scale studies of humans have either focused on very specific domains of inquiry or have relied on between-subjects approaches. While these previous studies have been invaluable for revealing important biological factors in cardiac health or social factors in retirement choices, no single repository contains anything like a complete record of the health, education, genetics, environmental, and lifestyle profiles of a large group of individuals at the within-subject level. This seems critical today because emerging evidence about the dynamic interplay between biology, behavior, and the environment point to a pressing need for just the kind of large-scale, long-term synoptic dataset that does not yet exist at the within-subject level. At the same time that the need for such a dataset is becoming clear, there is also growing evidence that just such a synoptic dataset may now be obtainable—at least at moderate scale—using contemporary big data approaches. To this end, we introduce the Kavli HUMAN Project (KHP), an effort 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 richly 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 co-evolve over the life cycle and why they evolve differently for different people. In turn, we argue that this will allow for new discovery-based scientific approaches, rooted in big data analytics, to improving the health and quality of human life, particularly in urban contexts.