Data publication

Data publication for personalized health data as new standard

NFDI4Health creates procedures for FAIR publication of health studies without compromising data protection.

Publication of study data

NFDI4Health has established a metadata standard and process for the publication of health studies to make health data FAIR:

Based on existing international standards, NFDI4Health has developed a generic information model for describing health-related studies and is in communication with various groups to establish this information model as a national and international standard. Read more. Read more about the implementation guideline of the information model.
This information model also includes information on data sharing possibilities and the corresponding access conditions. Together with a persistent identifier, all (meta)data are thus published in order to speak of a data publication. The data itself remains under the full control of its primary custodians and access can be requested through the established application procedures. Read more about the publication guidelines and the metadata schema.
For health study publication, NFDI4Health has developed the German Central Health Study Hub as an infrastructure. It allows study metadata to be collected via a user-friendly web-based data collection template or application programming interface. The German Central Health Study Hub also provides the ability to publish study documents individually, with the publication of questionnaires and variable catalogues being particularly desirable, as these contain the most detailed information about available data. In this way, health studies with associated data become more discoverable, and researchers can use the information to evaluate the content of data collections and learn about access conditions and requirements for the data. Read more.

Building blocks to enable publication of personal health data as well as training and information material about the publication process are available to the community. Acceptance and usage of publication process are key to a success and hence further close interaction with the community is curial.

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.