Tag Archive for: ontology-based metadata

New Java Library: CEDAR Artifact Library Now Available

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We’ve released a new Java library to make working with CEDAR metadata easier for developers: the CEDAR Artifact Library.

This lightweight library provides Java classes for modeling and manipulating CEDAR metadata artifacts—such as templates, template elements, fields, and instances. It’s designed to simplify the process of working with CEDAR’s JSON-based metadata structures by offering an intuitive, object-oriented interface for developers building tools, pipelines, or services that work with machine-actionable metadata.

Key capabilities include:

  • Parsing and serializing CEDAR JSON metadata
  • Full object models for templates, fields, elements, and instances
  • Built-in knowledge of CEDAR’s schema structure
  • Round-trip support for reading and writing JSON
  • Easy integration into Java-based metadata tools and validation workflows
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New Paper in Scientific Data: How CEDAR Enables Standardized Metadata Across HuBMAP—and Now SenNet

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We’re pleased to announce the publication of a new paper in Scientific Data titled
“Ensuring Adherence to Standards in Experiment-Related Metadata Entered Via Spreadsheets”
(Read the paper).

This paper outlines how the CEDAR Workbench has played a central role in ensuring that experiment-related metadata collected by the HuBMAP consortium adheres to FAIR principles and community standards—despite being entered by researchers using spreadsheets. It also describes the expansion of this approach into the SenNet program, demonstrating how metadata consistency and quality can scale across large, decentralized biomedical research consortia.

The Challenge: Researcher-Friendly Interfaces with Standards-Driven Metadata

Biomedical consortia like HuBMAP face a major challenge: researchers need to enter metadata that is rich, precise, and standards-compliant—but they overwhelmingly prefer working in spreadsheets. This poses a tension between usability and data quality.

Rather than force researchers to abandon familiar tools, the CEDAR team collaborated with HuBMAP to design a solution that lets researchers continue using spreadsheets while ensuring the underlying metadata adheres to formal standards.

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Empowering Rich Metadata Entry: How Dryad and OSF Are Using the CEDAR Embeddable Editor

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As scientific data sharing becomes increasingly vital to research transparency and reuse, the role of high-quality metadata has never been more important. Yet, creating structured, standards-aligned metadata remains a complex task—especially when researchers are forced to navigate unfamiliar tools or metadata standards outside their everyday workflows. That’s where the CEDAR Embeddable Editor (CEE) steps in.

Developed by the CEDAR team, the CEE is a lightweight, standards-compliant Web Component that enables third-party platforms to embed machine-actionable metadata templates directly into their interfaces. Instead of sending researchers off to external metadata portals, the CEE brings structured metadata authoring directly into the systems where scientists already work—lowering friction, improving consistency, and aligning with the FAIR principles.

Recently, two major research data platforms—Dryad and the Open Science Framework (OSF)—have integrated the CEE to enhance their metadata workflows.

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New Paper in Scientific Data: Modeling Community Standards as Metadata Templates

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We’re pleased to highlight the publication of a foundational paper from the CEDAR team in Scientific Data:
“Modeling community standards for metadata as templates makes data FAIR”
by Mark A. Musen, Martin J. O’Connor, Erik Schultes, and colleagues
? Read the paper

The Problem: Abstract Guidelines, Inconsistent Metadata

The FAIR principles (Findable, Accessible, Interoperable, and Reusable) have become central to modern research data policy—but implementing them at scale remains difficult. Many scientific domains have published metadata reporting guidelines, but these are typically in free-text form, making them difficult to apply in practice or enforce systematically.

The result? Metadata that is often inconsistent, incomplete, or lacking the semantic richness needed to enable true data reuse and discovery.

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