Welcome to the MatSci YAMZ
The collaborative dictionary for materials science metadata. Designed for researchers and professionals, YAMZ provides a shared space to define, discuss, and refine key terms used across the materials science community. By contributing definitions, commenting, and voting, you help build a living, community-driven vocabulary that promotes clarity, interoperability, and shared understanding in materials research.
Get Started
Ready to explore the language of materials science? Use the search bar to discover terms, browse definitions, and see how the community describes key concepts in materials metadata. You can also contribute by refining existing entries or adding new terms to expand the shared vocabulary.
- Add a term the community hasn't defined yet.
- Write clear definitions and examples grounded in real materials science usage.
- Every submission starts the community review process. Votes and comments help refine it over time.
- Browse the full dictionary alphabetically, or filter by material, property, and process tags.
- Compare multiple community definitions side by side for the same term.
- See vote counts and edit history to gauge how settled a definition is.
- Review and critique existing definitions for clarity and precision.
- Weigh in when contributors disagree, and help the community converge on consensus.
- Share references or datasets that strengthen a term's accuracy.
Who We Are
MatSci YAMZ is a community-driven initiative from Drexel University's Metadata Research Center that standardizes the complex language of materials science to accelerate global discovery. By integrating expert crowdsourcing with human-in-the-loop AI, the platform provides a collaborative “Metadata Zoo” where researchers define, discuss, and vote on terminology in real time. This ensures that scientific data adheres to FAIR principles — making it Findable, Accessible, Interoperable, and Reusable for both human researchers and machine-learning applications.
Mission Statement
The mission of MatSci-YAMZ is to strengthen the semantic infrastructure across materials science by providing a collaborative, AI-supported space for defining and refining disciplinary terminology. The MatSci-YAMZ hybrid model integrates crowdsourcing and human-in-the-loop AI, and enables researchers to collaboratively build and vote on persistent controlled vocabulary terms. MatSci-YAMZ supports semantic transparency and advances metadata standards development, interdisciplinary communication, and AI-ready research.
Learn More About Us
Learn more about our team at the Metadata Research Center.