CEDAR is making data submission smarter and faster, so that scientific researchers and analysts can create and use better metadata. Through better interfaces, terminology, metadata practices, and analytics, CEDAR improves metadata from provider to end user.






“CEDAR has been an invaluable tool for tracking submissions of metadata and helping to reduce manual curation, ensure FAIR compliance, and provide a simpler user experience. The versatility of the fields and simple JSON schema outputs, coupled with controlled vocabulary through ontological branches, make for a very powerful and unique metadata platform.”

Daniel Cooper, PhDLINCS Data Coordination and Integration Center, University of MiamiLINCS-DCIC

“The CEDAR platform provides an easy-to-use solution for creating and reusing FAIR metadata. CEDAR’s metadata modeling, flexible but rigorous semantics, and ability to quickly produce structured metadata makes it perfect for ongoing Metadata 4 Machines workshops and emerging FAIR training courses.”

Erik SchultesInternational Science Coordinator, GO FAIR International Support and Coordination Office (GFISCO) GO FAIR

What is CEDAR?

The Center for Expanded Data Annotation and Retrieval (CEDAR) was established in 2014 to create a computational ecosystem for development, evaluation, use, and refinement of biomedical metadata.

Our approach centers on the use of metadata templates, which define the data elements needed to describe particular types of biomedical experiments. The templates include controlled terms and synonyms for specific data elements.  CEDAR uses a library of such templates to help scientists submit annotated datasets to appropriate online data repositories.

CEDAR is an end-to-end process that enables

  • community-based organizations to collaborate to create metadata templates,
  • investigators or curators to use the templates to define the metadata for individual experiments, and
  • scientists to search the metadata to access and analyze the corresponding online datasets.

Start using CEDAR today

Mailing List

Sign up for our free support mailing list to get your CEDAR questions answered. The lists are monitored daily by CEDAR team members and knowledgeable CEDAR users from our worldwide community.

User Guide

Learn how to use CEDAR.  The CEDAR User Guide contains everything you need to know to fill out metadata or create your own metadata form.

API Guide

The CEDAR Application Programming Interface (API) provides programmatic access to the CEDAR system using REST micro-services.


Watch the CEDAR videos in the Metadata Center Channel on YouTube.

What CEDAR does…

Create Forms

Create  user-friendly, shareable forms for collecting metadata, with nested form elements.

Compose Libraries

Build reusable form components that can be shared and published in libraries.


Collaborate with other users and groups of users to build forms and fill in forms with metadata.

Publish Forms

Publish your forms for others to use to enter metadata.

Submit Metadata

Submit data and metadata to NCBI repositories directly.

Version Content

Version your templates to maintain history.

How to cite CEDAR in your publication

We would be grateful if scientific publications resulting from projects that make use of CEDAR would include the following sentence in the acknowledgments section:

“This work was supported by grant 1U54AI117925 from the National Institutes of Health through the Big Data to Knowledge initiative.”

Please use the following reference citation for CEDAR:

Musen MA, Bean CA, Cheung K-H, Dumontier M, Durante KA, Gevaert O, Gonzalez-Beltran A, Khatri P, Kleinstein SH, O’Connor MJ et al.. 2015. The Center for Expanded Data Annotation and Retrieval. Journal of the American Medical Informatics Association, JAMIA. 22 (6) 1148-1152

Read all the CEDAR publications

  • “The ultimate Big Data challenge lies not in the data, but in the metadata —the machine-readable descriptions that provide data about the data. It is not enough to simply put data online; data are not usable until they can be ‘explained’ in a manner that both humans and computers can process.”

    Mark Musen, Professor of Medicine, Stanford University

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