title: Big data science: A literature review of nursing research exemplars.
abstract: Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge.
  
  
  
  The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals.
  
  
  
  A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice.
  
  
  
  Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods.
  
  
  
  There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, MN. Electronic address: westr006@umn.edu.
authors.forename: Bonnie L
authors.initials: BL
authors.lastname: Westra
authors.affiliation: College of Nursing, Medical University of South Carolina, Charleston, SC.
authors.forename: Martha
authors.initials: M
authors.lastname: Sylvia
authors.affiliation: Health Sciences Libraries, University of Minnesota, Minneapolis, MN.
authors.forename: Elizabeth F
authors.initials: EF
authors.lastname: Weinfurter
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, MN.
authors.forename: Lisiane
authors.initials: L
authors.lastname: Pruinelli
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, MN.
authors.forename: Jung In
authors.initials: JI
authors.lastname: Park
authors.affiliation: Cerner Corporation, Kansas City, MO.
authors.forename: Dianna
authors.initials: D
authors.lastname: Dodd
authors.affiliation: University of Florida College of Nursing, Gainesville, FL.
authors.forename: Gail M
authors.initials: GM
authors.lastname: Keenan
authors.affiliation: The College of St. Scholastica, Duluth, MN.
authors.forename: Patricia
authors.initials: P
authors.lastname: Senk
authors.affiliation: Duke University School of Nursing, Durham, NC.
authors.forename: Rachel L
authors.initials: RL
authors.lastname: Richesson
authors.affiliation: Ridgeview Medical Center, Waconia, MN.
authors.forename: Vicki
authors.initials: V
authors.lastname: Baukner
authors.affiliation: Kaiser Permanente, Oakland, CA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Cruz
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, MN.
authors.forename: Grace
authors.initials: G
authors.lastname: Gao
authors.affiliation: MediComp Systems, Alexandria, VA.
authors.forename: Luann
authors.initials: L
authors.lastname: Whittenburg
authors.affiliation: School of Nursing, University of Minnesota, Minneapolis, MN.
authors.forename: Connie W
authors.initials: CW
authors.lastname: Delaney
mesh_terms: D057225:Data Mining; D019992:Databases as Topic; D006801:Humans; D048108:Nursing Informatics / Q000379:methods*; D015399:Nursing Research / Q000379:methods*
keywords: Big data; Data science; Nurse scientist; Nursing informatics; Nursing research