Description
Data Science needs data – more precisely it needs FAIR (Findable, Accessible, Interoperable, Reusable)[1], high quality data. Data Engineering approaches for the provision of such data are an important part of data science.
In our work, we develop such approaches mostly in the context of research data. Ongoing activities include the development of BEXIS2, an open source software for research data management, tools for quality control and curation support, approaches for semantic annotation of datasets, creation and linking of knowledge graphs from data, semantic search and provenance management.
In our poster, we will provide an overview of these approaches along the data life cycle.
[1] Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific data 3.1 (2016): 1-9.
