Okan Azmak New York University
Hannah Bayer New York University
Andrew Caplin New York University
Miyoung Chun The Kavli Foundation
Paul Glimcher New York University
Steven Koonin New York University
Aristides Patrinos New York University
The Kavli HUMAN Project
There is ever-increasing evidence that from early in life our biology, the events we encounter, and the choices we make leave deep imprints on our minds and bodies that impact our future well-being, health, longevity—every aspect of our lives and our communities. Yet our scholarly understanding of this “bio-behavioral complex,” this rich set of feedback effects between biology, behavior, and environment, remains surprisingly incomplete here at the beginning of the era of big data. As scientists working today, there is no escaping the fact that we lack some of the most basic longitudinal data about the bio-behavioral complex in domains ranging from education to finance to health. We have made radical advances ranging from the Human Genome Project, to the revolution in cognitive neuroscience, to the development of predictive psychological assays to innovations in social outcome measurement. But while our understanding of each of these subdomains has grown, we have made only incremental progress in uniting these many measurements in a manner that yields detailed behavioral phenotypes that characterize the myriad ways in which humans express their genetic endowment in different environmental settings.
This ignorance, with all its costs, is particularly surprising given two critical revolutions that have swept across our academic and cultural landscapes: the development of massive discovery datasets in other scientific domains and the growth of the measurement technologies by which corporate big data has gained a deepening understanding of each of the isolated subdomains mentioned above. If one were to unite these many existing classes of available big data at the within-subject level, we believe that one could without a doubt produce a discovery dataset that would revolutionize the social and natural human sciences.
As an example of the role massive discovery datasets have played in recent scientific inquiry, consider the Sloan Digital Sky Survey. Until the 1990s, individual astronomers studied specific galaxies and quasars by booking time on established telescopes and searching the heavens for isolated data types relevant to their question at hand. In this way, astronomers laboriously aggregated small datasets ideally suited to resolving single hypotheses. In the late 1990s, however, the Sloan Foundation and its partners developed an automated telescopic system in New Mexico, the Apache Point Telescope, and began the robotic collection of a massive database that now catalogs photometric observations on over 500 million celestial objects across a huge range of data types. This kind of big data transformed galactic-level cosmology from a small data science to a big data science and has catalyzed a renaissance in astronomy and the initiation of many other astronomical catalogs of high scholarly impact. But despite the success of this big data approach with outward-pointing telescopes over the last decade, we have made no similar advances in our study of humanity with an inward-facing telescope.
One reason for this lapse in the study of humanity might be largely technical. Until very recently, we simply have not had the techniques and instruments required to build massive datasets at the scale and precision required to answer fundamental questions about the human condition. Over the course of the last decade, however, advances in computers, smartphones, the Internet, and large-scale biological measurement have made it possible to construct automated counterparts to the Sloan Apache Point Telescope for the study of humanity. In fact, isolated proprietary databases of this kind are now becoming commonplace. For example, Google regularly tracks the geolocations of hundreds of millions of people, credit-reporting companies track financial data about individuals to the level of individual purchases, and health insurance companies track medical and health related data at a similar granularity. Oddly though, no group has attempted to aggregate these datasets at the within-subject level in an effort to produce a Sloan Digital Sky Survey for Humanity.
In this article and the four that follow, we pose a simple question driven by these twin revolutions, the rise of truly massive discovery datasets in the physical and the natural sciences and the development of unconnected datasets on human health and behavior: What would be the advantage of generating a truly comprehensive longitudinal dataset that captured nearly all aspects of a representative human population's biology, behavior, and environment? In the pages that follow we argue not only that the aggregation of such a dataset is now possible, but also that it would provide fundamental advances in a host of bio-behavioral areas that could revolutionize scholarship and policy.
To read the full article click here.
Big Data, published by Mary Ann Liebert, Inc., is a highly innovative, peer-reviewed journal, provides a unique forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing. GEN presents here one article "Using Big Data to Understand the Human Condition: The Kavli HUMAN Project." The views expressed here are those of the authors and are not necessarily those of Big Data, Mary Ann Liebert, Inc., publishers, or their affiliates. No endorsement of any entity or technology is implied.