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Life-science experimental methods generate vast and ever-increasing volumesof data, which provide highly valuable research resources. However, managementof these data is nontrivial and applicable software solutions are currentlysubject to intensive development. The solutions mainly fall into one of the twogroups: general data management systems (e.g. Onedata, iRODS, B2SHARE, CERNBox)or very specialised data management solutions (e.g. solutions for biomolecularsimulation data, biological imaging data, genomic data).  To bridge this gap between them, we provide Onedata4Sci, a prototype datamanagement solution, which is focused on the management of life science dataand covers four key steps of the data life cycle, i.e. data acquisition, useraccess, computational processing and archiving. Onedata4Sci is based on theOnedata data management system. It is written in Python, fully containerised,with the support for processing the stored data in Kubernetes. Theapplicability of Onedata4Sci is shown in three distinct use cases -- plantimaging data, cellular imaging data, and cryo-electron microscopy data. Despitethe use cases covering very different types of data and user patterns,Onedata4Sci demonstrated an ability to successfully handle all theseconditions. Complete source codes of Onedata4Sci are available on GitHub(https://github.com/CERIT-SC/onedata4sci), and its documentation and manual forinstallation are also provided.

Onedata4Sci: Life science data management solution based on Onedata