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ABSTRACT This paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps. 

In this position paper, we describe research on knowledge graph-empowered materials science prediction and discovery. The research consists of several key components including ontology mapping, materials data annotation, and information extraction from unstructured scholarly articles. We argue that although big data generated by simulations and experiments have motivated and accelerated the data-driven science, the distribution and heterogeneity of materials science-related big data hinders major advancements in the field. Knowledge graphs, as semantic hubs, integrate disparate data and provide a feasible solution to addressing this challenge. We design a knowledge-graph based approach for data discovery, extraction, and integration in materials science. 

Researchers across nearly every discipline seek to leverage ontologies for knowledge discovery and computational tasks; yet, the number of machine readable materials science ontologies is limited. The work presented in this paper explores the Processing, Structure, Properties and Performance (PSPP) framework for accelerating the development of materials science ontologies. We pursue a case study framed by the creation of an Aerogel ontology and a Battery Cathode ontology and demonstrate the Helping Interdisciplinary Vocabulary Engineer for Materials Science (HIVE4MAT) as a proof of concept showing PSPP relationships. The paper includes background context covering materials science, the PSPP framework, and faceted analysis for ontologies. We report our research objectives, methods, research procedures, and results. The findings indicate that the PSPP framework offers a rubric that may help guide and potentially accelerate ontology development.