Progress in Multi-Disciplinary Data Life Cycle Management

Modern science is most often driven by data. Improvements in state-of-the-art technologies and methods in many scientific disciplines lead not only to increasing data rates, but also to the need to improve or even completely overhaul their data life cycle management.Communities usually face two kinds of challenges: generic ones like federated authorization and authentication infrastructures and data preservation, and ones that are specific to their community and their respective data life cycle. In practice, the specific requirements often hinder the use of generic tools and methods.The German Helmholtz Association project ''Large-Scale Data Management and Analysis" (LSDMA) addresses both challenges: its five Data Life Cycle Labs (DLCLs) closely collaborate with communities in joint research and development to optimize the communities data life cycle management, while its Data Services Integration Team (DSIT) provides generic data tools and services.We present most recent developments and results from the DLCLs covering communities ranging from heavy ion physics and photon science to high-throughput microscopy, and from DSIT