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ONSEN Common Data Elements


Woman in Suit wearing safety glasses in a large room with computers and printers
Jacquelyn Y. Taylor, PhD, RN is having DNA samples from her blood pressure study analyzed.

The goal of the Common Data Elements section of ONSEN is to inform existing nurse scientists and academic faculty training nurse scientists seeking to expand their scholarship into Omics, and to develop strategies to promote Omics nursing science. These efforts are all aimed at achieving the Genomic Nursing Science Blueprint research recommendations more quickly, efficiently, effectively, and collaboratively.

Definition of Common Data Element (CDE)

Image of Text quoted in main paragraph reading: "a data element that is common to multiple data sets across different studies."Definition:

The National Institutes of Health (NIH) defines a CDE: as "a data element that is common to multiple data sets across different studies." ( or fundamental logical units of data pertaining to one kind of information1


Blood Pressure: to measure and report on Blood pressure (BP), the CDE could be captured and reported a systolic BP value and diastolic BP value using the measurement unit= mmHg. Further method specification, such as "Using sphygmomanometer (correct cuff size) and right upper arm, after sitting quietly for 5 minutes, cuff pressure inflated 30 mmHg above the palpated SBP and deflated 2 mmHg per second" would help assure that data reported from multiple studies were assessed and reported in a consistent way.

  1. 1 Cohen MZ, Thompson CB, Yates B, et al. Implementing common data elements across studies to advance research. Nurs Outlook 2015;63(2):181-188


Nurse working with patient
Kathleen Hickey, Ed, RN, FNP, ANP, APNG working with a patient reprogramming his implantable cardioverter defibrillator (ICD) which is used to treat/terminate any future arrhythmias.

How will the use of CDEs help advance nursing science and research?

  • Leveraging results across multiple studies
  • Access to larger samples
  • Support for secondary data analysis
  • Pooling common data increases statistical power
  • Answer important questions with more diverse sample
  • Compare groups with similar outcomes
  • Use of more sophisticated analyses
  • Understanding complex interactions across studies