Life Science (LS) communities must increase the sustainability of their data resources, software tools, and workflows so that the wider scientific community can re-use and make use of the available resources over the long-term, especially in future applications involving machine-based analyses. In this poster, we describe key findings from the EOSC-Life project in developing and establishing sustainable resources, tools and cloud based LS solutions. EOSC-Life brought 13 European LS Research Infrastructures together and laid the foundation for an open, digital and collaborative space for life sciences research. We describe organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the LS domain. Building on lessons from 27 scientific projects selected and applying the "radical collaboration" method used in other Research Data Alliance publication (e.g. Pickering et al., 2021), we demonstrate the efficiency of the EOSC-Life support model for sustainable FAIR data management in a paper accepted in EMBO-Journal and explore complex sustainability needs including those associated with sensitive-data associated with sensitive- and industry-related data resources. We describe how best practices can be shared across disciplines and how this contributes to sustaining knowledge across communities by building training resources. Additionally, this poster presents lessons learned from the EOSC-Life project as a set of recommendations which emphasise working with experts and communicating outcomes to increase the credibility and recognition of research projects. Furthermore, demonstrating and practising these recommendations also permits a more sustainable adoption of reproducible research outputs. We have called these recommendations BeSURE (BE SUstainable REcommendations ). The BeSURE recommendations include sustainable training methodologies and ensure high-quality metadata to drive the sustainable reusability of scientific objects. Rôles and responsibilities must be shared to ensure sovereignty, sustainable services and tools. Finally, our analysis shows how data harmonisation facilitates the interoperability of tools, data, and solutions and improves the understanding of concepts, functionalities and semantics in the life sciences.