CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows

TL;DR

CaosDB is a research data management system tailored for biomedical sciences, supporting complex, evolving workflows, diverse data sources, and automation needs, with a flexible data model and query language.

Contribution

It introduces CaosDB, a novel RDMS designed to handle complex, changing biomedical research data workflows with semantic data modeling and user-friendly querying.

Findings

Supports integration of heterogeneous data sources

Facilitates workflow adaptation and standard development

Enables automation of data acquisition and processing

Abstract

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.