In the past decade, numerous initiatives have provided annotated scalp EEG datasets of individuals with epilepsy for global researchers. However, data format discrepancies and varying validation methods impede algorithm comparisons, rendering ranking and research progress challenging. This proposal extends ORD datasets, community standards, and guidelines to introduce a unified framework for validating seizure detection algorithms. The primary goal is to create tools and standards streamlining the algorithm validation process, aligning data with Findable, Accessible, Interpretable, and Reusable (FAIR) principles. To establish this framework, existing public annotated datasets will be curated, and their data format standardized. Software tools will be developed to convert original datasets into this standardized format. Methodologies for algorithm evaluation will also be standardized to create a benchmark for cutting-edge algorithms. All this knowledge, datasets, standards, tools, and benchmarks will be centralized on a website, expediting seizure detection algorithm development.