What’s going on related to CEDAR?

The ARMS architecture: an AI agent takes a legacy metadata record and CEDAR template ID, uses MCP tools to query CEDAR and BioPortal, and outputs a standardized metadata record.

New Paper Accepted at AMIA 2026: CEDAR + AI for Automated Metadata Standardization

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We’re pleased to share that a new paper from the CEDAR team has been accepted for presentation at the 2026 AMIA Annual Symposium: “Automated Standardization of Legacy Biomedical Metadata Using an Ontology-Constrained LLM Agent.”

CEDAR’s core idea is that community metadata standards should be machine-actionable—encoded as templates that specify not just which fields are required, but exactly which values are allowed, drawn from controlled vocabularies and ontologies. This paper shows what becomes possible when those structured constraints are placed in the hands of an AI agent: messy, noncompliant legacy metadata can be cleaned up and standardized automatically, at scale.

The Problem: Millions of Messy Legacy Records

Public repositories are full of metadata authored before tools like CEDAR existed—idiosyncratic field names, free-text values, and inconsistent adherence to standards. That looseness is a fundamental barrier to FAIR data. Large language models can help interpret and rewrite such records, but on their own they don’t reliably produce the canonical terms a standard requires, and their training knowledge goes stale as ontologies evolve.

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Integration workflow of the CEDAR Embeddable Editor within a third-party Web application, from JSON Schema/JSON-LD templates to JSON-LD metadata instances.

New Paper in the Data Science Journal: Author Once, Publish Everywhere with the CEDAR Embeddable Editor

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We’re pleased to announce the publication of a new paper in the Data Science Journal titled “Author Once, Publish Everywhere: Portable Metadata Authoring with the CEDAR Embeddable Editor.”

The paper takes a comprehensive look at the CEDAR Embeddable Editor (CEE)—the lightweight, interoperable Web Component that brings structured, standards-based metadata authoring directly into third-party platforms. Instead of sending researchers off to a separate tool, the CEE embeds metadata creation into the environments where they already work, helping make research data findable, accessible, interoperable, and reusable (FAIR).

The Challenge: Rich Metadata Without Leaving the Workflow

High-quality, “rich” metadata are essential for FAIR data, and the CEDAR Workbench has long provided tools to design machine-actionable metadata templates that encode community standards in a computable form. But the original model required researchers to leave their native platforms and engage with a separate, centralized editor—a barrier that limited its integration into routine research and data-submission workflows.

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Diagram of the CEDAR template model: community standards are formalized as template fields, elements, and templates, populated as metadata instances that describe datasets.

New Paper in AI Magazine: Knowledge Engineering for Open Science

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We’re pleased to announce the publication of a new paper in AI Magazine titled “Knowledge Engineering for Open Science: Building and Deploying Knowledge Bases for Metadata Standards.”

Synthesizing ten years of CEDAR research, the paper makes the case that the metadata templates at the heart of CEDAR are a modern form of declarative knowledge base—capturing, in symbolic form, how a community of scientists believes its data should be described so that datasets can be findable, accessible, interoperable, and reusable (FAIR).

The Big Idea: Metadata Standards as Knowledge Bases

Decades ago, hand-built knowledge bases powered intelligent systems by encoding expert knowledge in an inspectable, editable form. The paper argues that CEDAR templates play the same role for open science: they encode a community’s metadata reporting guidelines as machine-actionable templates that enumerate the attributes of an experiment and link those attributes to ontologies and value sets. Unlike patterns learned from data, these standards often don’t yet exist in any “learnable” form—so capturing them still requires the careful, expert-driven knowledge engineering the field has practiced for decades.

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CEDAR 2.8.0 Now Live: RRID and PubMed ID Field Support

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This latest update strengthens CEDAR’s ability to support rich, standardized scientific metadata through the addition of new external authority field types.

What’s New

RRID Field Support
CEDAR now supports the RRID (Research Resource Identifier) field type. Template authors can include RRID fields in their templates and instances, allowing users to record standardized identifiers for reagents, tools, and model organisms. This enhancement improves metadata consistency, reproducibility, and interoperability across datasets.

PubMed ID Field Support
CEDAR now provides a PubMed ID field type for linking scientific publications directly within metadata instances. Authors can reference PubMed IDs to automatically connect to the corresponding publications in PubMed, streamlining citation management and ensuring clear traceability to supporting literature.

Learn More

For more information about CEDAR features, visit the CEDAR manual or explore our GitHub repository.