Background Biomedical research projects deal with data management requirements from multiple sources like funding agencies’ guidelines, publisher policies, discipline best practices, and their own users’ needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability. Results We introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform. Conclusions Management of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.

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Background Secondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called “real world data” are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital. Methods We identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools. Results We demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements. Conclusion Though the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

Introduction: Ensuring scientific reproducibility and compliance with documentation guidelines of funding bodies and journals is a topic of greatly increasing importance in biomedical research. Failure to comply, or unawareness of documentation standards can have adverse effects on the translation of research into patient treatments, as well as economic implications. In the context of the German Research Foundation-funded collaborative research center (CRC) 1002, an IT-infrastructure sub-project was designed. Its goal has been to establish standardized metadata documentation and information exchange benefitting the participating research groups with minimal additional documentation efforts. Methods: Implementation of the self-developed menoci-based research data platform (RDP) was driven by close communication and collaboration with researchers as early adopters and experts. Requirements analysis and concept development involved in person observation of experimental procedures, interviews and collaboration with researchers and experts, as well as the investigation of available and applicable metadata standards and tools. The Drupal-based RDP features distinct modules for the different documented data and workflow types, and both the development and the types of collected metadata were continuously reviewed and evaluated with the early adopters. Results: The menoci-based RDP allows for standardized documentation, sharing and cross-referencing of different data types, workflows, and scientific publications. Different modules have been implemented for specific data types and workflows, allowing for the enrichment of entries with specific metadata and linking to further relevant entries in different modules. Discussion: Taking the workflows and datasets of the frequently involved experimental service projects as a starting point for (meta-)data types to overcome irreproducibility of research data, results in increased benefits for researchers with minimized efforts. While the menoci-based RDP with its data models and metadata schema was originally developed in a cardiological context, it has been implemented and extended to other consortia at GÃűttingen Campus and beyond in different life science research areas.