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CaosDB—Research Data Management for complex, changing, and automated research workflows

MPG-Autoren
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Fitschen,  Timm
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons182785

Schlemmer,  Alexander
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173583

Luther,  Stefan
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zitation

Fitschen, T., Schlemmer, A., Hornung, D., tom Wörden, H., Parlitz, U., & Luther, S. (2019). CaosDB—Research Data Management for complex, changing, and automated research workflows. Data, 42(2): 83. doi:10.3390/data4020083.


Zitierlink: https://hdl.handle.net/21.11116/0000-0003-CA67-6
Zusammenfassung
We present CaosDB, a Research Data Management System (RDMS) designed to ensure
seamless integration of inhomogeneous data sources and repositories of legacy data in a FAIR way.
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 its data model, the
CaosDB Server 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.