Research collaboration data platform ensuring general data protection.

Translational data is of paramount importance for medical research and clinical innovation. It has the potential to benefit individuals and organizations, however, the protection of personal data must be guaranteed. Collecting diverse omics data and electronic health records (EHR), re-using the minimized data, as well as providing a reliable data transfer between different institutions are mandatory steps for the development of the promising field of big data and artificial intelligence in medical research. This is made possible within the proposed data platform in this research project. The established data platform enables the collaboration between public and commercial organizations by data transfer from various clinical systems into a cloud for supporting multi-site research while ensuring compliant data governance.

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital.

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.

Managing OMICS-Data: Considerations for the Design of a Clinical Research IT-Infrastructure.

Biomarker-based translational research enables deep insight into cellular processes and human diseases. As a result, high-throughput technologies promulgating a fast and cost-effective generation of data are widely used to advance our understanding in the molecular background of individuals. However, the increasing volume and complexity of data increases the need for sustainable infrastructures and state-of-the-art tools allowing management, analysis, and integration of OMICS data. To address these challenges, we have performed site visits of core facilities with a focus on high-throughput technologies to explore their (IT) infrastructure, organizational aspects, and data management strategies. Different stakeholders were interviewed regarding requirements and needs for dealing with high-throughput data. We have identified four different fields of action: (1) the interface from biorepositories to service providers of high-throughput technologies, (2) aspects within services providers, (3) the interface from service providers to bioinformatical analysis, and (4) organizational and other aspects. For each field, recommendations and strategies were developed for implementation of a seamless pipeline from biorepositories to highly specialized high-throughput laboratories including the sustainable management and integration of OMICS data.

Development of an information platform for new grid users in the biomedical field.

Bringing new users into grids is a top priority for all grid initiatives and one of the most challenging tasks. Especially in life sciences it is essential to have a certain amount of users to establish a critical mass for a sustainable grid and give feedback back to the technological middleware layer. Based on the presumable lack of grid IT knowledge it is notably more arduous to satisfy user demands although here the requirements are especially demanding. Therefore, the development of an information- and learning platform could support the efforts of grid experts to guide new users. By providing a platform about grid technology and their feasibilities for users of the community of biomedicine potential, users could be supported using the high potential of their discipline.

Perspectives of MediGRID.

Sustainability is a top priority for nearly all grid communities. The German grid communities in the area of life sciences are continuing their dissemination efforts in order to bring the grid to scientists. With cloud computing another concept for distributed IT infrastructures is on the rise. In this regard the grid has a different focus and matches better with life science compute power demands. A comparison of both grid and cloud in addition to the background and present status of the German life science grid give a contemporary impression of the future perspectives of MediGRID.