Implementing FAIR Digital Objects in the EOSC-Life Workflow Collaboratory

作者：Carole Goble, Stian Soiland‐Reyes, Finn Bacall, Stuart Owen, Alan Williams, Ignacio Eguinoa, Bert Droesbeke, Simone Leo, Luca Pireddu, Laura Rodríguez‐Navas, José Ma Fernández, Salvador Capella-Gutiérrez, Hervé Ménager, Björn Grüning, Beatriz Serrano‐Solano, Philip Ewels, Frederik Coppens · 发表于：UvA-DARE (University of Amsterdam) · 年份：2021 · DOI：10.5281/zenodo.4605654 · 被引用次数：73 · 研究领域：Research Data Management Practices、Scientific Computing and Data Management、Image Processing and 3D Reconstruction

The practice of performing computational processes using workflows has taken hold in the biosciences as the discipline becomes increasingly computational. The COVID-19 pandemic has spotlighted the importance of systematic and shared analysis of SARS-CoV-2 and its data processing pipelines. This is coupled with a drive in the community towards adopting FAIR practices (Findable, Accessible, Interoperable, and Reusable) not just for data, but also for workflows, and to improve the reproducibility of processes, both manual and computational. EOSC-Life brings together 13 of the Life Science ‘ESFRI’ research infrastructures to create an open, digital and collaborative space for biological and medical research. The project is developing a cloud-based workflow collaboratory to drive implementation of FAIR workflows across disciplines and RI boundaries, and foster tool- focused collaborations and reuse between communities via the sharing of data analysis workflows. The collaboratory aims to provide a framework for researchers and workflow specialists to use and reuse workflows. As such it is an example of the Canonical Workflow Frameworks for Research (CWFR) vision in practice. EOSC-Life is made up of established research infrastructures ranging from biobanking and clinical trial management, through to coordinating biomedical imaging and plant phenotyping to multi-omic and systems-based data analysis. The heterogeneity of the disciplines is reflected in the diversity of their data ana...