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"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling. | LitMetric

AI Article Synopsis

  • - The EOSC-Life consortium aims to enhance data reuse and sustainability in life sciences through collaborative efforts among 13 European research infrastructures, focusing on large-scale and computational research.
  • - Key barriers to sustainability identified include organisational, technical, financial, and legal/ethical challenges, which need to be addressed to improve resource management.
  • - The initiative advocates for adhering to FAIR principles and promotes data harmonisation and cross-disciplinary training, leading to better interoperability of tools and data in life science research.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10690449PMC
http://dx.doi.org/10.15252/embj.2023115008DOI Listing

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