• Energy Research

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.

"Be SURE - Be SUstainable REcommendations" The main goals and challenges for the Life Science (LS) 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 LS 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 LS Research Infrastructures (RIs), 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 FAIR data management, 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. IN PRESS EMBO Journal: https://www.embopress.org/journal/14602075 AVAILABLE SOON at : https://doi.org/10.15252/embj.2023115008  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.

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.