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Be prepared for data management and sharing requirements using common data elements

Dr. Karyn Onyeneho
Karyn ONYENEHO,
Advisor,
Division of Neuroscience (DN)
.
smiling woman in black top with dark hair that is pulled back
Maria Fe LANFRANCO GALLOFRE,
Division of Neuroscience (DN).

It’s been nearly one year since the NIH 2023 Final Policy for Data Management and Sharing (DMS) went into effect. As part of our continuing series to help scientific teams learn to develop a DMS Plan, we wanted to share some tips for addressing the third of six core elements of the policy — “data standards” — and the important role of Common Data Elements (CDEs).

Getting to know Core Element 3 and CDEs

Core Element 3 of the DMS Policy requires applicants to describe what standards will be applied to the scientific data and associated metadata (data about the data) generated by the proposed research. In other words, applicants need to explain the types of rules the data and metadata will follow that enable information to be collected, formatted, described, and shared in a consistent manner.

How can applicants achieve this consistency? The answer lies in CDEs. CDEs are data elements or variables, defined and used in the same way across multiple clinical research studies to standardize data collection. They are structured as precisely defined questions and answers with a specified format or set of values. Multiple CDEs can be curated into questionnaire surveys, case report forms, patient registries, and similar things.

For example, investigators interested in studying significant underlying medical conditions of COVID-19 testing or diagnosis may use the NIH-endorsed CDE named “Comorbidity or Underlying Condition Type” from the NIH CDE Repository. This CDE defines a set of medical conditions such as Alzheimer’s disease and related dementias, cancer, type II diabetes, or hypertension underlying COVID-19 that can be applied to data collection.

Advantages of CDEs

NIH encourages the use of CDEs to improve data quality, enhance rigor and reproducibility, and promote data harmonization. CDEs make research faster and more efficient while reducing burdens on data repositories. Other advantages of using CDEs include:

  • Enabling cross-study comparisons, data aggregation, and meta-analyses
  • Simplifying training and operations across research teams
  • Producing timely, relevant scientific findings
  • Aligning evidence of clinical studies with evidence from “real-world data” (e.g., electronic health records, mobile/wearable devices, and patient-reported outcomes)

Check out some online resources or reach out for help!

We hope this overview is helpful. For more guidance on using CDEs in your research, there are several additional online resources available:

If you have questions about CDEs, please contact the NLM Support Center or leave a comment below. For specific questions related to how to develop and apply CDEs for your research, contact your NIA program officer.

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