"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.
Romain DavidArina RybinaJean-Marie BurelJean-Karim HérichéPauline AudergonJan-Willem BoitenFrederik CoppensSara CrockettKatrina ExterSven FahrnerMaddalena FratelliCarole GoblePhilipp GormannsTobias GrantnerBjoern Andreas GrueningKim Tamara GurwitzJohn M HancockHenriette HarmsePetr HolubNick JutyGeoffrey KarnbachEmma KarouneAntje KepplerJessica KlemeierCarla LancelottiJean-Luc LegrasAllyson L ListerDario Livio LongoRebecca LudwigBénédicte MadonMarzia MassimiVera MatserRafaele MatteoniMichaela Th MayrhoferChristian OhmannMaria PanagiotopoulouHelen ParkinsonIsabelle PerseilClaudia PfanderRoland PieruschkaMichael RaessAndreas RauberAudrey S RichardPaolo RomanoAntonio RosatoÁlex Sánchez-PlaSusanna-Assunta SansoneSiddhant TripathiBeatriz Serrano-SolanoJing TangZiaurrehman TanoliJonathan TeddsHarald WagenerMartin WeiseHans Victor WesterhoffRudolf WittnerJonathan J EwbankNiklas BlombergPhilip GribbonPublished in: The EMBO journal (2023)
The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.