Life Science (LS) communities must increase the sustainability of their data resources, software tools, and workflows, to allow their long-term re-use by the wider scientific community, especially in future applications involving machine-based analyses. Here, we describe key findings regarding sustainable LS resources, based on experiences from the EOSC-Life project. EOSC-Life involves 13 European LS Research Infrastructures. It lays the foundation for an open, digital and collaborative space for biological and medical research. We describe organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the LS domain. Using lessons from 27 scientific projects selected via open calls, we demonstrate the efficiency of the EOSC-Life support model for sustainable FAIR data management and explore the complex sustainability needs for sensitive- and industry-related data resources. We describe cross-disciplinary sharing of best practices, and how this contributes to the sustainability of knowledge across communities, through building of training resources. We formulate a set of recommendations, focusing on working with experts, and communicating outcomes, to establish strong credibility and recognition of research projects. Furthermore, demonstrating and practising also permit a more sustainable adoption of reproducible research outputs. Recommended measures include sustainable training methodologies, as well as ensuring high quality metadata to drive the sustainable reusability of scientific objects. Responsibilities and roles to ensure sovereignty, sustainable services and tools must also be attributed. Finally, our analysis demonstrates how data harmonisation facilitates interoperability of tools, data, solutions and a better understanding of concepts, functionalities and semantics in the LSs.

This research is mainly a product of the EOSC-Life European programme funding from the European Union's Horizon Europe research and innovation programme under grant agreement Nº824087. Complementary support was provided through EU funded project AgroServ (grant agreement Nº101058020), EU funded project BY-COVID (grant agreement Nº101046203), EU funded project DANUBIUS-IP (grant agreement Nº101079778), EU funded project EMPHASIS-GO (grant agreement Nº101079772), EU funded project FAIRplus (IMI grant agreement Nº802750), EU funded project FAIRsharing (Wellcome grant agreement Nº212930/Z/18/Z), EU funded project ISIDORe (grant agreement Nº101046133), EU funded project Precision Toxicology (grant agreement Nº965406), UKRI DASH (grant agreement NºMR/V038966/1). Special thanks to T. Biro and her radical collaboration team from Research Data Alliance who gave us great inspiration on how to lead this radical collaboration work.