Research Data Management
Research Data Management is a scholarly work, published in 2020 in ''it - Information Technology''. The main subjects of the publication include data quality, computer science, data sharing, and scientific workflow system. Introduction to research data managementMore and more areas in science, even systematic and theory-based sciences such as Mathematics, are evolving into data-driven sciences where a lot of data are used or produced to support the research work.These can result from measurements (from experiments in labs or more recently from always-on-sensors such as microphones and cameras in the Internet of Things) or from modelling and simulation processes.A lot of research areas from natural sciences, medical sciences, engineering, among others, are more and more data-driven.For these, research data management is becoming a crucial issue.A completely different area are the less measurementand sensor-data-driven humanities research areas (Digital Humanities), in which work is very text-or documentcentered.We can call these document-driven sciences.Also completely different are sciences relying on nondigital artifacts such as soil, water, or material samples being first class research objects that have to be "stored" to be able to reproduce research results afterwards.We can call these artifact-based sciences.Both document-driven and artifact-based sciences will also rely on digital infrastructures for digital documents and scanned historical texts, as well as on digital infrastructures for secondary (derived) data from experiments with non-digital artifacts and metadata for these experiments and artifacts.Research data management is therefore also a crucial issue for these types of sciences.