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The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.

Abstract (Expand)

BACKGROUND: Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research … domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. OBJECTIVE: The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. METHODS: Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. RESULTS: The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Authors: T. Kirsten, F. Meineke, H. Loffler-Wirth, A. Uciteli, C. Beger, S. Staubert, M. Lobe, R. Hansel, F. G. Rauscher, J. C. Schuster, T. Peschel, H. Herre, J. Wagner, S. Zachariae, C. Engel, M. Scholz, E. Rahm, H. Binder, M. Loffler

Date Published: 3rd Aug 2022

Publication Type: Journal

Ontological Modelling and Execution of Phenotypic Queries in the Leipzig Health Atlas.

Abstract (Expand)

Sharing data is of great importance for research in medical sciences. It is the basis for reproducibility and reuse of already generated outcomes in new projects and in new contexts. FAIR data principles … are the basics for sharing data. The Leipzig Health Atlas (LHA) platform follows these principles and provides data, describing metadata, and models that have been implemented in novel software tools and are available as demonstrators. LHA reuses and extends three different major components that have been previously developed by other projects. The SEEK management platform is the foundation providing a repository for archiving, presenting and secure sharing a wide range of publication results, such as published reports, (bio)medical data as well as interactive models and tools. The LHA Data Portal manages study metadata and data allowing to search for data of interest. Finally, PhenoMan is an ontological framework for phenotype modelling. This paper describes the interrelation of these three components. In particular, we use the PhenoMan to, firstly, model and represent phenotypes within the LHA platform. Then, secondly, the ontological phenotype representation can be used to generate search queries that are executed by the LHA Data Portal. The PhenoMan generates the queries in a novel domain specific query language (SDQL), which is specific for data management systems based on CDISC ODM standard, such as the LHA Data Portal. Our approach was successfully applied to represent phenotypes in the Leipzig Health Atlas with the possibility to execute corresponding queries within the LHA Data Portal.

Authors: A. Uciteli, C. Beger, J. Wagner, A. Kiel, F. A. Meineke, S. Staubert, M. Lobe, R. Hansel, J. Schuster, T. Kirsten, H. Herre

Date Published: 24th May 2021

Publication Type: Journal

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