Collaborative research: disciplinary improvements: enabling fair and open rare earth recovery from electronics through integrated data cyberinfrastructure

The National Science Foundation Office of Advanced Cyberinfrastructure awarded the University of Oklahoma $320,000 on January 1, 2027, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop CIR-REE, a cyberinfrastructure platform for rare earth element recovery data management and open science. The award runs through December 31, 2029, with work performed in Norman, Oklahoma.

CIR-REE unifies fragmented laboratory, industrial process, and materials documentation spanning rare earth recovery research and end-of-life electronics recycling. The platform integrates machine-actionable metadata, semantic data models, persistent identifiers, and polyglot data infrastructure to enable discovery, integration, curation, and reuse of heterogeneous rare earth element recovery data across materials researchers, data scientists, recyclers, libraries, and cyberinfrastructure stakeholders. Technically, the system combines physical preprocessing variables such as shredding granularity with chemical recovery descriptors including leaching kinetics, applying semantic knowledge representation, graph-enabled persistence, and physics-informed machine learning to support analysis and optimization of recovery pathways for neodymium and dysprosium from complex waste streams including hard disk drives. The infrastructure advances capability building through an interoperable data and inference platform, competency building through reusable standards and workflows for the rare earth recycling community, and community coordination across multiple stakeholder groups.

Abstract

The U.S. Transition to clean energy depends on reliable access to rare earth elements (rees), yet domestic recovery from end-of-life electronics is constrained by fragmented and poorly interoperable data spanning laboratory studies, industrial process records, and safety and materials documentation. In response, this project develops CIR-REE (cyberinfrastructure for rare earth element recovery), a socio-technical cyberinfrastructure for fair research data management and open science that lowers barriers to discovering, integrating, curating, and reusing cross-domain REE recovery data. CIR-REE couples machine-actionable metadata, semantic data models, persistent identifiers, polyglot data infrastructure, and open interfaces to support sustainable access to heterogeneous research products and workflows. CIR-REE is designed to advance capability building through the development of an interoperable data and inference platform, competency building through reusable standards, workflows, and data stewardship practices for the REE recycling community, and community building by enabling coordinated participation among materials researchers, data scientists, recyclers, libraries, and cyberinfrastructure stakeholders. Technically, the platform will unify physical preprocessing variables such as shredding granularity with chemical recovery descriptors such as leaching kinetics, and will integrate semantic knowledge representation, graph-enabled persistence, and physics-informed machine learning to transform static, disconnected records into an open, queryable, and reusable decision-support ecosystem. The resulting infrastructure will enable researchers to analyze and optimize recovery pathways for neodymium (ND) and dysprosium (DY) from complex waste streams such as hard disk drives, while establishing a scalable model for community-facing, open-science cyberinfrastructure in circular materials systems. This project generates broad societal impact by establishing CIR-REE as a sustainable, community-facing cyberinfrastructure for open REE recovery research. The platform will support the ingestion, curation, standardization, preservation, and reuse of heterogeneous data from end-of-life electronics through interoperable metadata, persistent identifiers, reusable workflows, and open interfaces. These capabilities will lower barriers to data-intensive materials research and provide a replicable model for long-term stewardship of circular-manufacturing data. Long-term sustainability will be reinforced through partnerships with electronic recycling companies, which will provide operational use cases, domain feedback, and continued data contributions, and through collaboration with cloud and data-computing partners to support scalable storage, computation, and public access. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are not planned for this award.