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-# K6.1-B Introduction
-# Background
-Research Data Management (RDM) has become a hype within the past years: Funding agencies demand for data management plans (DMP), research institutions set up data management policies and guidelines, and national projects aim on sustainably establishing research data infrastructures (NFDI). But why?
-Data is one of the most important assets in science. Ever faster growing data needs to be handled during project lifetime and beyond. Good scientific practice demands for long-standing and traceable research outcomes. Therefore it is important not to lose track of the origin and processing of data. Additionally, aligned data management routines potentially increase the efficiency of research processes and groups.
-Nonetheless, past has shown that data tends to get lost, but usually not physically: Knowledge about data gets lost over time, due to common staff turnover in science and inappropriate or missing documentation. This leads to large and expensive data silos, stuffed with useless, 'dark' data, time-consuming and frustrating to deal with.
-# Aim
-# Outcomes
-# Subskills