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A Position Statement on Population Data Science: The science of data about people

McGrail, Kim; Jones, Kerina; Akbari, Ashley; Bennett, Tell; Boyd, Andrew; Carinci, Fabrizio; Cui, Xinjie; Denaxas, Spiros; Dougall, Nadine; Ford, David; Kirby, Russell S; Kum, Hye-Chung; Moorin, Rachael; Moran, Ros; O'Keefe, Christine; Preen, David; Quan, Hude; Sanmartin, Claudia; Schull, Michael; Smith, Mark; Williams, Christine; Williamson, Tyler; Wyper, Grant; Kotelchuck, Milton


Kim McGrail

Kerina Jones

Ashley Akbari

Tell Bennett

Andrew Boyd

Fabrizio Carinci

Xinjie Cui

Spiros Denaxas

David Ford

Russell S Kirby

Hye-Chung Kum

Rachael Moorin

Ros Moran

Christine O'Keefe

David Preen

Hude Quan

Claudia Sanmartin

Michael Schull

Mark Smith

Christine Williams

Tyler Williamson

Grant Wyper

Milton Kotelchuck


Information is increasingly digital, creating opportunities to respond to pressing issues about human populations using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate sociotechnical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.

Journal Article Type Article
Acceptance Date Nov 30, 2017
Online Publication Date Feb 22, 2018
Publication Date Feb 22, 2018
Deposit Date Feb 1, 2018
Publicly Available Date May 29, 2018
Journal International Journal of Population Data Science
Print ISSN 2399-4908
Publisher Swansea University
Peer Reviewed Peer Reviewed
Volume 3
Issue 1
Keywords Digital information, population data,
Public URL
Contract Date May 29, 2018


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