In the last decade several initiatives have made annotated scalp EEG datasets of people with epilepsy available to researchers around the world. However, the format of the data varies between datasets. This heterogeneity of data along with a heterogeneity of methods used by researchers to validate algorithms makes comparison of algorithms difficult, ranking the best algorithms impossible and ultimately significantly hinders the research progress. This proposal built on ORD datasets and community guidelines and standards to propose a unified framework for the validation of seizure detection algorithms. The main objective was the development of tools and standards to unify the workflow for the validation of seizure detection algorithms in order to make data for seizure detection adhere to the principles of Findable, Accessible, Interpretable and Reusable data. To build this framework we proposed a standard data format based on existing guidelines. We then converted existing public datasets to this standard and uploaded the conversion code and standardized datasets to public repositories. We proposed a standardised methodology to evaluate algorithms and built a POC benchmark of state of the art algorithms. We centralised all this knowledge, datasets, standards tools and benchmark on a website that will help to speed the development of seizure detection algorithms. We published this framework in the leading journal in our field and presented our work in several conferences. This project and its outcomes were pivotal in setting the standards and methods required for epileptic seizure algorithm validation. Further work will be required on Open Research Infrastructure to allow the research community to federate around common tools and data