Task Area 5
In health studies, data are collected to understand diseases and improve treatments. However, the reuse of this data is often restricted due to inadequate standardisation, as well as regulatory and technical hurdles. In order to make health data accessible and FAIR (Findable, Accessible, Interoperable, and Reusable), Task Area 5 tests and implements the services and infrastructures developed within the framework of NFDI4Health in various Use Cases from scientific practice.
Clinical, epidemiological and public health studies typically generate structured and quality-assured health data that can be used to investigate the development and dynamics of diseases at the individual and population level as well as the effectiveness of preventive, diagnostic and therapeutic interventions. Access to detailed information about a large and unselected number of participants and patients is crucial in this context. This is the only way to create risk profiles of these target groups in order to support personalised prevention and medicine and to find new intervention options that ultimately improve medical care and public health. Currently, more and more data is being made available in the form of large primary data collections, but also secondary data sources such as data from disease registers or health insurances. However, the findability, sharing, and reuse of this data for research purposes are currently limited for various reasons. These include:
Lack of standardised metadata for data discoverability and a wide methodological diversity in the identification and collection of health information across studies,
Incomplete knowledge about relevant studies and the type of data collected,
Regulatory, administrative, ethical, and legal challenges regarding data access and the merging of data across populations or data sources,
Lack of knowledge among potential data users in research data management, data preparation, and data analysis, and
Limitations in data analysis infrastructures for utilising study data.
Many of these challenges are already addressed in other working areas of NFDI4Health. Task Area 5 aims to implement or pilot developed solutions, such as services and infrastructure components, in specific Use Cases that reflect the core needs of health research. Overall, national health data can subsequently get FAIRer.
Our "Use Cases"
The infrastructure components developed by NFDI4Health are implemented and tested in 4 Use Cases in the areas of Nutritional Epidemiology/Epidemiology of chronic diseases, Secondary data and record linkage, Clinical studies and Radiomics/Imaging AI:
Contact
Prof. Dr. Iris Pigeot
TA5-Lead
Measure-Lead T5.3 “Use case Secondary data and record linkage”
E-Mail: pigeot@leibniz-bips.de
Phone: +49 (0)228 73-60351
Leibniz Institute for Prevention Research and Epidemiology – BIPS GmbH
Achterstraße 30
D-28359 Bremen
Prof. Dr. Matthias Schulze
TA5-Lead
Measure-Lead T5.1 “Use case ‘Nutritional epidemiology’”
E-Mail: mschulze@dife.de
Phone: +49 (0)33 200 88 - 2434
German Institute of Human Nutrition Potsdam-Rehbruecke
Arthur-Scheunert-Allee 114-116
14558 Nuthetal
Prof. Dr. Ute Nöthlings
Measure-Lead T5.1 “Use case ‘Nutritional epidemiology’”
E-Mail: noethlings@uni-bonn.de
Phone: +49 (0)228 73 60490
University of Bonn,
Institute for Nutrition and Food Sciences
Fiedrich-Hirzebruch-Allee 7
53115 Bonn
Prof. Dr. Tobias Pischon
Measure-Lead: T5.2 “Use case ‘Epidemiology of chronic diseases’”
E-Mail: tobias.pischon@mdc-berlin.de
Phone: +49 (0)30 9406-4563
Max Delbrück Center for Molecular Medicine
Robert-Rössle-Straße 10
13125 Berlin, Deutschland
Prof. Dr. Hajo Zeeb
Phone: +49 (0)421 218-56-902
Prof. Dr. Wolfgang Ahrens
Measure-Lead T5.3 “Use case ‘Secondary data and record linkage”
E-Mail: ahrens@leibniz-bips.de
Phone: +49 (0)421 218-56822
Leibniz Institute for Prevention Research and Epidemiology – BIPS GmbH
Achterstraße 30
D-28359 Bremen
Dr. Oana Brosteanu
Matthias Löbe
Prof. Dr. Horst Hahn
Measure-Lead T5.6 Use Case “Radiomics / Imaging AI”
E-Mail: horst.hahn@mevis.fraunhofer.de
Phone: +49 (0)421 218-59002
Fraunhofer Institute for Digital Medicine MEVIS
Max-von-Laue-Str. 2
28359 Bremen