CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows
by
Timm Fitschen 1,2,‡
, Alexander Schlemmer 1,3,*,‡, Daniel Hornung 1,†, Henrik tom Wörden 1,2,†, Ulrich Parlitz 1,2,3 and Stefan Luther 1,2,3,4
Timm Fitschen 1,2,‡, Alexander Schlemmer 1,3,*,‡, Daniel Hornung 1,†, Henrik tom Wörden 1,2,†, Ulrich Parlitz 1,2,3 and Stefan Luther 1,2,3,4
1
Max Planck Institute for Dynamics and Self-Organization, 37077 Göttingen, Germany
2
Institute for the Dynamics of Complex Systems, Georg-August-Universität, 37077 Göttingen, Germany
3
German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, 37075 Göttingen, Germany
4
Institute of Pharmacology and Toxicology, University Medical Center Göttingen, 37075 Göttingen, Germany
*
Author to whom correspondence should be addressed.
†
Current address: Indiscale GmbH i.G., 37075 Göttingen, Germany.
‡
These authors contributed equally to this work.
Data 2019, 4(2), 83; https://doi.org/10.3390/data4020083
Received: 29 April 2019 / Revised: 5 June 2019 / Accepted: 6 June 2019 / Published: 10 June 2019
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.
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Keywords:
RDMS; research data management; FAIR; database; ACID
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MDPI and ACS Style
Fitschen, T.; Schlemmer, A.; Hornung, D.; tom Wörden, H.; Parlitz, U.; Luther, S. CaosDB—Research Data Management for Complex, Changing, and Automated Research Workflows. Data 2019, 4, 83.
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