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SOLID Data: What it Means

For the U.S. to meet the growing demand for skills and credential data, it is essential that this information be structured, open, linked, interoperable, and durable (SOLID). Credential Engine ensures this by advancing CTDL, the only comprehensive open standard for describing and linking credentials, learning, and work ecosystems, as the foundation for this work.

CTDL: The CTDL data structure is the stable foundation of everything we do. By using CTDL, all information is published as SOLID data, ensuring it can be connected, reused, and scaled across education, workforce, and technology systems. At the same time, CTDL is a living language: it continues to evolve as new types of data and use cases emerge. What must evolve now is the infrastructure around CTDL—to scale publishing and consuming, strengthen data quality, and enable AI-powered services that make the dataset more transparent, discoverable, interoperable, and reusable.

SOLID 

To meet growing global demand for trustworthy education and workforce data, public information that supports learn-and-work ecosystems must be structured for reuse at scale. Credential Engine advances this goal by ensuring that credential, skill, job, and related ecosystem data is SOLID: structured, open, linked, interoperable, and durable.

  • Structured: Standardized, machine-readable formats so it can be checked for consistency, compared, and used across learn-and-work ecosystems. Structured data enables reliable interpretation.  
  • Open: Open access and licensing for both commercial and non-commercial use support transparency, innovation, and broad access. Open data enables reuse and remixing. 
  • Linked: Connected through persistent identifiers and semantic relationships so it can be discovered, combined, and understood across credentials, skills, jobs, and pathways. Linked data enables meaningful connections across types and sources. 
  • Interoperable: Shared technical and semantic standards so it can be reliably exchanged and used across platforms, tools, and jurisdictions without custom integration.  Interoperable data conveys meaning and value across different systems and contexts. 
  • Durable: Persistent identifiers, governance, and long-term infrastructure ensure stability, referenceability, and trust over time. Durable data is reliable now and in the future.

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Tags: Credential Engine, Credential Transparency, CTDL, Data
Fact Sheets

Recognition of Prior Learning: Helping People Move Forward

Recognition of prior learning (RPL) is the process of providing formal acknowledgment and credit for knowledge, skills, and abilities people have gained through work experience, military service, self-study, volunteering, and/or previous education. This includes credit for prior learning (CPL), transfer credit between institutions, and validation of non-traditional learning experiences. RPL empowers people to move forward and build on what they already know rather than starting over, accelerating pathways to credentials and careers.

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Fact Sheets

Recognition of Prior Learning: Helping People Move Forward

Recognition of prior learning (RPL) is the process of providing formal acknowledgment and credit for knowledge, skills, and abilities people have gained through work experience, military service, self-study, volunteering, and/or previous education. This includes credit for prior learning (CPL), transfer credit between institutions, and validation of non-traditional learning experiences. RPL empowers people to move forward and build on what they already know rather than starting over, accelerating pathways to credentials and careers.

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