Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are not only struggle with data volume, but also fail to address the fragmentation of research results across domains, hampering scientific reproducibility, and cross-domain collaboration, while raising concerns about data sovereignty and governance. Here we propose a practical framework for FAIR and federated Data Ecosystems that combines decentralized, distributed systems with existing research infrastructure to enable seamless cross-domain collaboration. Based on established patterns from data commons, data meshes, and data spaces, our approach introduces a layered architecture consisting of governance, data, service, and application layers. Our framework preserves domain-specific expertise and control while facilitating data integration through standardized interfaces and semantic enrichment. Key requirements include adaptive metadata management, simplified user interaction, robust security, and transparent data transactions. Our architecture supports both compute-to-data as well as data-to-compute paradigms, implementing a decentralized peer-to-peer network that scales horizontally. By providing both a technical architecture and a governance framework, FAIR and federated Data Ecosystems enables researchers to build on existing work while maintaining control over their data and computing resources, providing a practical path towards an integrated research infrastructure that respects both domain autonomy and interoperability requirements.