Data harmonisation

Data harmonisation and bridging across the health domains through standards

To make health studies and their data FAIR (Findable, Accessible, Interoperable and Reusable) we have developed guidelines and standards for metadata description and data sharing.

NFDI4Health has developed data publication guidelines, common metadata description standards and adaptations of health data interoperability standards to harmonize the description of studies and their corresponding metadata. This renders such metadata more meaningful and comparable and, thus, the studies and their data ‘FAIR’.

The data publication policy that we have developed describes recommendations and requirements for the publication of research data from health-related studies, with a focus on the services developed by NFDI4Health. It classifies resource types to be published, such as study descriptions or types of study documents or data collections, outlines licensing for such resources and usage of perennial identifiers, as well as defines formatting and metadata description requirements for the published resources and their data.

For the metadata that describes the resources, the publication policy refers to the tailored metadata schema that we have developed, which is implemented in the NFDI4Health services. This metadata schema combines common elements and their controlled vocabularies (value sets) relevant for all domains and use cases covered by NFDI4Health. It is designed in a modular fashion and also comprises modules that are more specific for certain sub-domains. Mappings to domain-overarching (e.g. DataCite) and to health-domain specific standards, as well as to other metadata schemas and clinical trial registries provide the basis for interfacing to external resources.

To further enhance the interoperability of data captured in the NFDI4Health services, we also make use of health-domain specific ontologies (e.g. SNOMED CT) and data interoperability standards, e.g. by implementing the metadata schema as profile in HL7 FHIR.

Data publication guidelines, a tailored and interoperable metadata schema that maps to established standards, as well as adaptations of data interoperability standards and domain-specific ontologies provide the basis for the harmonized collection of health-related studies, their metadata and data.

Health Study Hub

The German Central Health Study Hub allows researchers to publish their project characteristics, documents and data related to their research project in a FAIR manner or to find information about past and ongoing studies.

Data Train

The Data Train cross-disciplinary graduate training programme, a core element of the NFDI4Health training approach, aims at building the next generation of data-savvy researchers in the biomedical sciences.

Personal Health Train

To foster data-driven innovation in medicine, we developed a distributed analysis infrastructure that enables research on sensitive data without prior data sharing while supporting diverse data formats.

Local Data Hub

The LDH is the local node in the federated concept of NFDI4Health. We develop and promote the dissemination of a unified data sharing platform based on the FAIR principles in line with the NFDI4Health standards.

Data publication

Health data, as collected in clinical trials and epidemiological, as well as public health studies, cannot be freely published, but are valuable datasets whose reuse is of high importance for health research. NFDI4Health has established a metadata standard and process for the publication of health studies to make health data FAIR.

Data harmonisation

To make health studies and their data FAIR we have developed guidelines and standards for metadata description and data sharing. We have developed data publication guidelines, common metadata description standards and adaptations of health data interoperability standards to harmonize the description of studies and their corresponding metadata.

Data Quality Assessment

It is a paradox: on the one hand, good scientific work depends on high data quality. On the other hand, a lot of effort is put into the design and conduct of studies, but not into data quality assessments. We help to facilitate the efficient performance of such assessments with versatile concepts and tools

DataSHIELD

Expansion of decentralised research projects with DataSHIELD: Until now, data protection concerns and the lack of special IT infrastructure have prevented the expansion of cross-institutional research projects. DataSHIELD is intended to solve that problem.