From big data to deep insight in developmental science

Wiley Interdisciplinary Reviews. Cognitive Science
Rick O Gilmore

Abstract

The use of the term 'big data' has grown substantially over the past several decades and is now widespread. In this review, I ask what makes data 'big' and what implications the size, density, or complexity of datasets have for the science of human development. A survey of existing datasets illustrates how existing large, complex, multilevel, and multimeasure data can reveal the complexities of developmental processes. At the same time, significant technical, policy, ethics, transparency, cultural, and conceptual issues associated with the use of big data must be addressed. Most big developmental science data are currently hard to find and cumbersome to access, the field lacks a culture of data sharing, and there is no consensus about who owns or should control research data. But, these barriers are dissolving. Developmental researchers are finding new ways to collect, manage, store, share, and enable others to reuse data. This promises a future in which big data can lead to deeper insights about some of the most profound questions in behavioral science.

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Citations

Jul 13, 2016·Topics in Cognitive Science·Robert L Goldstone, Gary Lupyan
Oct 30, 2016·Frontiers in Psychology·Joyse Medeiros, Jon Andoni Duñabeitia
Sep 23, 2020·New Directions for Child and Adolescent Development·Baptiste BarbotMarinus H van IJzendoorn
May 4, 2017·Annals of the New York Academy of Sciences·Rick O GilmoreTal Yarkoni
Aug 27, 2018·Prevention Science : the Official Journal of the Society for Prevention Research·Celia B Fisher, Deborah M Layman
Feb 23, 2021·Frontiers in Psychology·Maria AshworthJo Van Herwegen

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Software Mentioned

LookIt
Dropbox
Gmail
OSF
Bing
SurveyMonkey
SnapChat
Databrary
HomeBank
Jupyter

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