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

Toralf Kirsten; Frank A. Meineke; Henry Loeffler-Wirth; Christoph Beger; Alexandr Uciteli; Sebastian Stäubert; Matthias Löbe; René Hänsel; Franziska G. Rauscher; Judith Schuster; Thomas Peschel; Heinrich Herre; Jonas Wagner; Silke Zachariae; Christoph Engel; Markus Scholz; Erhard Rahm; Hans Binder; Markus Loeffler; on behalf of the LHA team

doi:10.1055/a-1914-1985

Methods of Information in Medicine, Table of Contents

CC BY-NC-ND 4.0 · Methods Inf Med 2022; 61(S 02): e103-e115
DOI: 10.1055/a-1914-1985

Original Article

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

Authors

Toralf Kirsten^*

¹Department of Medical Data Science, Leipzig University Medical Center, Leipzig, Germany

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
Frank A. Meineke^*

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Henry Loeffler-Wirth

⁴LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany
Christoph Beger

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Alexandr Uciteli

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Sebastian Stäubert

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Matthias Löbe

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
René Hänsel

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Franziska G. Rauscher

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
Judith Schuster

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Thomas Peschel

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Heinrich Herre

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Jonas Wagner

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
Silke Zachariae

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Christoph Engel

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany
Markus Scholz

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany
Erhard Rahm

⁵Department of Computer Sciences, Leipzig University, Leipzig, Germany
Hans Binder^§

⁴LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany
Markus Loeffler^§

²Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig University, Leipzig, Germany

³Interdisciplinary Centre for Bioinformatics, Leipzig University, Leipzig, Germany

⁴LIFE Research Centre for Civilization Diseases, Leipzig University, Leipzig, Germany

on behalf of the LHA team

Abstract

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.

Keywords

FAIR - data semantics - research data management - metadata - clinical trials - biomathematical models - ontology - risk prediction models - omics data

Full Text

References

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