Common Data Elements, Part 4 of 4

Table of Contents

• Introduction
• What Are NIH Common Data Elements?
• The NIH CDE Task Force & Governance
• NIH CDE Repository: What It Is and Why It Matters
• Using the Repository: How to Search and What You’ll Find
  • Example: Administrative/Address CDEs
  • Example: Patient-Reported Outcomes (NINR)
• Approval Criteria for NIH-Endorsed CDEs
• FAIR Data, Interoperability, and Metadata
• Recommendations for Future-Ready Data Sharing
• Conclusion
• Key Takeaways

Introduction

This final installment (Part 4 of 4) focuses on the NIH Common Data Elements (CDEs)—what they are, how they’re governed, where to find them, and how to use them to make your research accurate, consistent, and interoperable. You’ll learn to navigate the NIH CDE Repository, understand endorsement criteria, and align your studies with FAIR data principles.

What Are NIH Common Data Elements?

Common Data Elements (CDEs) are standardized, precisely defined questions, variables, and response options used across studies. Their purpose is to enable harmonized data collection, facilitating reuse, pooling, and meta-analysis across diseases, disciplines, and institutes.

The NIH CDE Task Force & Governance

The NIH CDE Task Force is a trans-NIH community of practice that:

• Reviews CDE collections submitted by NIH-recognized bodies (Institutes, Centers, trans-NIH committees).
• Determines whether they meet endorsement criteria for NIH-funded research.
• Maintains the NIH CDE Repository, a central access point for recommended or required CDEs.
• Coordinates with disease-focused CDE programs (e.g., NINDS CDEs).

NIH CDE Repository: What It Is and Why It Matters

Launched in 2015, the NIH Common Data Element Repository:

• Provides human- and machine-readable CDE definitions.
• Hosts CDEs recommended or required by NIH Institutes and Centers.
• Advances FAIR data sharing (Findable, Accessible, Interoperable, Reusable).
• Was prioritized in the NLM strategic plan as a key enabler of data standardization.

The Repository includes:

• Cross-disciplinary toolkits and toolboxes.
• Disease-focused CDEs (e.g., NINDS).
• Consistent controlled vocabularies and metadata for computational readiness.

Using the Repository: How to Search and What You’ll Find

The Repository lets you:

• Browse by collection (e.g., NINDS, NINR) and see counts of available CDEs.
• Search by keyword, data type, domain, or value set.
• View each CDE’s:
  • Name and definition
  • Prompt/question text
  • Permissible values / value sets
  • Data type, format, coding system(s)
  • Usage and provenance

Example: Administrative/Address CDEs

• City Name, County Name, Address Line
  Each includes a standard definition, format constraints, and (where applicable) code systems—so your administrative data are collected uniformly across sites.

Example: Patient-Reported Outcomes (NINR)

• Survey item (e.g., “It is hard for me to play or go out with my friends as much as I’d like.”)
  The repository shows the exact prompt, response options/value set (with labels and codes), and context of use—allowing direct reuse with interoperable coding.

Approval Criteria for NIH-Endorsed CDEs

The NIH CDE Governance Committee reviews and approves CDE collections using the NIH Scientific Data Council criteria:

1. Clear, unambiguous definition of the variable and measure (prompt + response).
2. Documented reliability and validity (psychometrics/evidence).
3. Human- and machine-readable specification (structure + metadata).
4. Designation by a recognized NIH body (Institute, Center, or NIH committee).
5. Licensing/IP clarity (open use preferred; if restricted, conditions are explicit).

FAIR Data, Interoperability, and Metadata

CDEs operationalize FAIR:

• Findable: Persistent identifiers and searchable metadata.
• Accessible: Public repository access with clear use conditions.
• Interoperable: Standardized vocabularies, codified value sets, computable formats.
• Reusable: Clear definitions, provenance, reliability/validity evidence, and licensing.

Using CDEs means your datasets come metadata-ready for machine processing and cross-study synthesis.

Recommendations for Future-Ready Data Sharing

A core best practice (echoed in recent data-sharing guidance):

• Ensure every shared dataset/document is paired with concise, public, consistently structured discovery metadata that describes:
  • What the object is,
  • How it can be accessed,
  • Under what conditions (licensing, access controls).
• Build/participate in metadata repositories that align with FAIR, use standard terminologies, and expose computable CDEs to maximize discoverability (human + machine) and reuse.

Conclusion

The NIH CDE ecosystem—anchored by the Task Force, Governance Committee, and the CDE Repository—gives researchers practical tools to standardize data capture, satisfy FAIR expectations, and accelerate interoperable science. By selecting endorsed CDEs and pairing them with high-quality metadata, you boost your study’s immediate rigor and its long-term impact through reusability and integration.

Key Takeaways

• CDEs = standardized variables (prompts, responses, codes) enabling consistent, reusable research data.
• The NIH CDE Repository (since 2015) hosts endorsed CDEs that are human- and machine-readable.
• Governance criteria require clear definitions, psychometric support, computability, NIH designation, and transparent licensing.
• CDEs operationalize FAIR—making datasets findable, accessible, interoperable, and reusable.
• Pair all shared data with concise, public, structured discovery metadata—this is essential for discoverability and reuse.
• Start with disease-focused CDEs (e.g., NINDS) and cross-cutting toolkits; reuse value sets and formats to ensure interoperability across studies.