PropertyValue
?:about
?:abstract
  • Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered. (xsd:string)
?:contributor
?:dateModified
  • 2020 (xsd:gyear)
?:datePublished
  • 2020 (xsd:gyear)
?:doi
  • 10.1038/s41366-020-0532-8 ()
?:duplicate
?:hasFulltext
  • true (xsd:boolean)
is ?:hasPart of
?:inLanguage
  • en (xsd:string)
?:isPartOf
?:issn
  • 1476-5497 ()
?:linksDOI
?:linksURN
is ?:mainEntity of
?:name
  • Evidence from big data in obesity research: international case studies (xsd:string)
?:provider
?:publicationType
  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
?:reference
?:sourceInfo
  • GESIS-SSOAR (xsd:string)
  • In: International Journal of Obesity, 44, 2020, 1028-1040 (xsd:string)
rdf:type
?:url
?:urn
  • urn:nbn:de:0168-ssoar-70720-3 ()
?:volumeNumber
  • 44 (xsd:string)