From big data to deep insight in developmental science

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)112-126
Number of pages15
JournalWiley Interdisciplinary Reviews: Cognitive Science
Issue number2
StatePublished - Mar 1 2016

All Science Journal Classification (ASJC) codes

  • General Neuroscience
  • General Psychology


Dive into the research topics of 'From big data to deep insight in developmental science'. Together they form a unique fingerprint.

Cite this