News about CEDAR

CEDAR now supports YAML metadata

CEDAR has long been able to hand users a template, element, field or an instance as YAML — a clean, human-readable format that is easy to skim, edit by hand, and track in version control. With the 2.9.0 release, that street now runs both ways: CEDAR accepts YAML too. You can author and edit templates, elements, fields, and instances as plain YAML.

Nothing about CEDAR’s underlying model has changed. Templates, elements and field remain in JSON Schema and metadata remains in JSON-LD; YAML is simply another serialization of those artifacts. Every template, element, field or instance round-trips through YAML and back to the identical JSON Schema or JSON-LD, so YAML is a convenience for authoring and review — not a new format to adopt or migrate to.

There is nothing new to learn and no separate endpoint to call. Every artifact route negotiates the representation per request, so JSON and YAML clients work side by side: ask for YAML and you receive YAML, send YAML and CEDAR ingests it.

A small example makes the difference concrete. Here is a real CEDAR template — a study with a single text field — as YAML:

type: template
name: Study
id: https://repo.metadatacenter.org/templates/3b2af8ae
status: draft
version: 0.0.1
children:
- key: Title
  type: text-field
  name: Title
  description: The title of the study
  configuration:
    required: true
modelVersion: 1.6.0
createdOn: '2024-03-27T13:57:17-07:00'
modifiedOn: '2024-06-14T11:42:47-07:00'
createdBy: https://metadatacenter.org/users/6d21a887
modifiedBy: https://metadatacenter.org/users/6d21a887

That is the whole template. The equivalent CEDAR JSON Schema — the same artifact with its @context, JSON-LD typing, per-field _valueConstraints and _ui blocks, and property definitions spelled out — runs many times longer, and CEDAR still generates and stores all of it. YAML just lets a person read and write the parts that carry meaning.

We also support a compact presentation mode, which omits provenance fields for brevity. Here is the above template in compact form:

type: template
name: Study
id: https://repo.metadatacenter.org/templates/3b2af8ae
children:
- key: Title
  type: text-field
  name: Title
  description: The title of the study
  configuration:
    required: true

The YAML format is documented in the CEDAR YAML specification, and the full API — including the YAML media types — is browsable in the interactive API reference.

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

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

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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