News about CEDAR

CEDAR Offers Support for CDEs from caDSR

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We are pleased to report that CEDAR template creators can now import from over 60,000 of NCI’s caDSR Common Data Elements (CDEs) to build new Fields in CEDAR. Using CEDAR’s search, browsing, and viewing services template builders can easily build a form based partly or entirely on CDEs from caDSR.

Over the last 18 months, CEDAR developers have collaborated with the NCI to adapt CEDAR capabilities to the unique characteristics of CDEs. By representing these CDEs as CEDAR Fields, we have made them fully accessible to CEDAR users. CEDAR already handled many of the specialized features that are found in the caDSR templates, and the CEDAR team added some features to support particular CDE workflows.

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CEDAR in the GO FAIR Funder Study

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After providing contributions to the GO FAIR project over the last 18 months, CEDAR will be a significant participant in GO FAIR’s FAIR Funder Implementation Study.

This collaborative project will demonstrate a new level of integrated and FAIR metadata, making data projects funded by research agencies demonstrably more Findable, Accessible, Interoperable, and Reusable. As one of the founding collaborators, CEDAR has played a significant role in defining, describing, and implementing services that will improve metadata collection for funded research.

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Publication: CEDAR offers metadata recommendations from mined rules

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Marcos Martinez-Romero and his co-authors have published a new paper describing CEDAR’s updated implementation of intelligent authoring. The new methods use rule mining to generate recommendations based on previously entered metadata in the CEDAR system, and offer the users only the most likely recommendations given previous metadata entries for the template.

Suggested values seen by users
The intelligent authoring metadata recommendations take into account rules derived from previously entered metadata with the same values.

You can find instructions for setting up this capability in the CEDAR User Manual page Understanding the Suggestion System.

Martínez-Romero M, O’Connor MJ, Egyedi AL, Willrett D, Hardi J, Graybeal J, Musen MA. Using association rule mining and ontologies to generate metadata recommendations from multiple biomedical databases. Database. Volume 2019, 10 June 2019. https://doi.org/10.1093/database/baz059.