Machine learning has advanced rapidly in recent years, driven in large part by frictionless reproducibility enabled through shared datasets, shared code, and competitive benchmarks. In contrast, spectrum research has largely lacked the infrastructure needed to support comparable reproducibility and community-wide evaluation, limiting the ability to produce robust, comparable evidence for both scientific progress and data-driven spectrum policy. In this paper, we present SCORE (Standardized, Controlled, Open, Reproducible Evaluation), a general, end-to-end platform that brings the principles of frictionless reproducibility to spectrum-aware machine learning. The platform integrates curated datasets, a standardized submission interface, automated and isolated execution, and public leader-boards to enable fair, transparent, and repeatable evaluation and comparison of spectrum-related methods and systems. We demonstrate the flexibility of SCORE through two distinct use cases: incumbent detection using wideband IQ data as part of a national data and algorithm competition, and environment detection using signals of opportunity. Together, these instantiations show that benchmark-driven, reproducible workflows can be effectively adapted to spectrum research, providing a scalable foundation for spectrum-aware learning and enabling rigorous, evidence-based evaluation to directly support future data-driven spectrum policy and regulatory decision-making.
