Skip Navigation LinksHome > May/June 2014 - Volume 63 - Issue 3 > Mother Lodes and Mining Tools: Big Data for Nursing Science
Text sizing:
A
A
A
Nursing Research:
doi: 10.1097/NNR.0000000000000041
Editorial

Mother Lodes and Mining Tools: Big Data for Nursing Science

Henly, Susan J. PhD, RN

Free Access
Article Outline
Collapse Box

Author Information

Susan J. Henly, PhD, RN, is Editor, Nursing Research.

The editor has no conflicts of interest to report.

Corresponding author: Susan J. Henly, PhD, RN, Professor, School of Nursing, University of Minnesota, 5-140 WDH, 308 Harvard St. SE, Minneapolis, MN, 55455 (e-mail: henly003@umn.edu).

The idea of big data science sometimes seems to exist as hyperbole carried to excess. The scientific potential is said to be boundless, the scientific approaches are said to be spectacular, and the scientific frontiers are said to be endless. Yet, even enthusiasts are issuing cautions (Lazer, Kennedy, King, & Vespignani, 2014), and uptake in nursing seems to be lagging (Broome, 2014). Why? Has the potential of e-science been oversold? Is it possible there isn’t interesting information out there after all—that we’re mining a nonexistent mother lode? Or are we stymied and not really sure how to proceed?

The e-science arising from big data is a transformed science, and its world is foreign to those of us trained in classical scientific paradigms (Bell, 2009). Data are no longer scarce but superabundant. The data collection challenge morphs to a data capture endeavor. Theory-based modeling gives way to data mining and visualization—because dimensionality of data is huge, types of data are diverse, and interconnections among data are innumerable. Instead of aiming for explanation, the triple goal is to describe, integrate, and predict. It’s a nearly new enterprise!

The domain of nursing science as the science of health is vast, stretching from molecular health to global health, on time scales from nanoseconds to centuries. As the many omic sciences emerge—genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, exposomics, and more—relevance to nursing science will become more apparent (e.g., Lyon, Starkweather, Montpetit, Menzies, & Jallo, 2014). These are all big data sciences, challenging us to think and discover in new ways.

The biopsychosocial–spiritual holism of human health and being is much valued in practice. Big data and e-science are creating realistic opportunities to incorporate this holistic perspective into our research. Sensors, tracking devices, and real-time self-reports are generating torrents of health-related data that, if captured and mined, have potential to extend the reach of nurse scientists and allow discovery of complex spatiotemporal associations. Already, the quantified self-movement is demonstrating the utility of this approach as a foundation for a person-centered science of health.

Nursing Research welcomes big data science-based submissions. We’re preparing for these articles in many ways. We’re posting announcements about big data resources and workshops on the Nursing Research social media sites. We’re inviting nurse scientists and colleagues in related fields working with big data to join our scientific review panel. We’re inviting data miners to join the quantitative review panel to ensure informed, high-quality methodological review. We’re adding color to our pages so that important elements in images and figures that display findings can be highlighted and communicated. Our expectation is that big data articles will stimulate new thoughts about nursing science and new ways to explore and understand health.

Back to Top | Article Outline

References

Broome M.( 2014;, March). Back to the future: Redeveloping our science in resource challenged environments. Paper presented at the Annual Meeting of the Midwest Nursing Research Society, St. Louis, MO.

Hey T., Tansley S., & Tolle K.(Eds.). ( 2009;). Jim Gray on eScience: A transformed scientific method. In The fourth paradigm: Data-intensive scientific discovery (pp. xvii-xxxi). , Redmond, WA: Microsoft Research.

Lazer D., Kennedy R., King G., & Vespignani A.( 2014;). The parable of Google Flu: Traps in big data analysis. Science. , 343:, 1203–1205.

Lyon D. E., Starkweather A. R., Montpetit A., Menzies V., & Jallo N.( 2014;). A biobehavioral perspective on telomere length and the exposome. Biological Research for Nursing.

Advance on-line publication. Retrieved from http://brn.sagepub.com/content/early/2014/03/03/1099800414522689.abstract. DOI: 10.1177/1099800414522689


Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins

Login