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-# K6.1-B Introduction
-Maintainer: Christian Löschen @ TU Dresden
-n
-# 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
-Participants should be aware about the risk of dark data and the benefits of data management. They should know about theoretical concepts of data management and practical strategies and techniques to implement aligned data management solutions. They should be able to describe and structure data, find and use metadata standards. They should know what to consider when publishing research data and how to find suitable data respositories.
-# Outcomes
-  * Understanding the risk of losing (knowledge about) data
-  * Knowing about RDM concepts
-  * Knowing techniques and abilities to minimize the risk of data loss
-  * Able to adequately describe and structure data
-  * Able to apply metadata schemas and create metadata profiles
-  * Knowing what to consider when publishing data
-  * Able to find suitable data repositories
-# Subskills