U.S. Department of Health and Human ServicesHHS National Institutes of HealthNIH National Center for Advancing Translational SciencesNCATS
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Understand Data Principles

Understand What is Data

Data can be defined as a collection of information. Data usually include quantitative and qualitative information. Quantitative data include measurable information, such as height and weight. Qualitative data is descriptive information, such as gender and ethnicity. The goals of registry determine what kinds of data are collected. Registries typically collect both quantitative and qualitative.

Determining what kinds of data and how much of it needs to be collected requires thoughtful consideration. Because little is known about many rare diseases, you may be tempted to collect as much information as you can. But it’s important that you do not overwhelm the people providing the information to your registry. You must take care to balance what you need to know (essential data) with what you would like to know (nonessential data). It is especially important that the information you collect is of high quality. Low-quality data cannot be used for research. High-quality data are verifiable, accurate, and are an exact fit for the registry’s intended use. The data you collect must be capable of delivering the insight you hope to gain.

Data quality is determined by the following:

  1. Accessibility – Are the data available? Are the data easily and quickly obtained?
  2. Completeness – Is the data complete? Are the necessary questions included on your registry data collection forms? Were all the questions answered?
  3. Accuracy – Are the data correct and reliable?
  4. Objectivity – Are the data free of bias?
  5. Relevancy – Do the data address the questions your registry is attempting to answer?

Make sure you collect only high-quality data for your registry by following the FAIR Guiding Principles for scientific data management :

Findability – Data should be easily found.
Accessibility – Once the data is found, it should be accessible.
Interoperability – Data needs to be integrated with other data and to be able to work with various different applications.
Reusable – Data should be able to be re-used for different research and/or purposes.

We created the RaDaR Tool: Data Quality Control Checklist to assist you in evaluating the quality of data.