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