
Management of scientific Big Data poses challenges at all stages of the data life cycle – acquisition, ingest, access, replication, preservation, etc. For scientific communities the data exploration – commonly considered as the 4th pillar besides experiment, theory and simulation – is of utmost importance to gain new scientific insights and knowledge.