2 research outputs found
CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows
Here we present CaosDB, a Research Data Management System (RDMS) designed to
ensure seamless integration of inhomogeneous data sources and repositories of
legacy data. Its primary purpose is the management of data from biomedical
sciences, both from simulations and experiments during the complete research
data lifecycle. An RDMS for this domain faces particular challenges: Research
data arise in huge amounts, from a wide variety of sources, and traverse a
highly branched path of further processing. To be accepted by its users, an
RDMS must be built around workflows of the scientists and practices and thus
support changes in workflow and data structure. Nevertheless it should
encourage and support the development and observation of standards and
furthermore facilitate the automation of data acquisition and processing with
specialized software. The storage data model of an RDMS must reflect these
complexities with appropriate semantics and ontologies while offering simple
methods for finding, retrieving, and understanding relevant data. We show how
CaosDB responds to these challenges and give an overview of the CaosDB Server,
its data model and its easy-to-learn CaosDB Query Language. We briefly discuss
the status of the implementation, how we currently use CaosDB, and how we plan
to use and extend it