Accessible data quality assessments for better research

NFDI4Health makes data quality assessment accessible to promote FAIR research with versatile concepts and tools.
Autumn Workhop Data Quality and Initial Data Analysis, November 2022 © TMF

Dealing with data quality in the health sciences is characterized by an impressive paradox. On the one hand, reliable scientific work depends on the availability of high data quality. On the other hand, while a lot of effort goes into the design and conduct of studies this is not so much the case for the reproducible and transparent conduct of data quality assessments. NFDI4Health is therefore dedicated to lower the boundaries for efficient implementation of such assessments. This refers to development of concepts and tools, by making them available, and by marketing them in the community. The considerable interest in this topic was underlined by the 2022 autumn workshop that has been funded by the NFDI and conducted in collaboration with the German Society for Medical Informatics, Biometry and Epidemiology (GMDS), the Technology and Methods Platform for Networked Medical Research (TMF), the German Society for Epidemiology (DGEpi), the German Region of the International Biometric Society (IBS -DR), the German Society for Social Medicine and Prevention (DGSMP), the international initiative STRengthening Analytical Thinking for Observational Studies (STRATOS) in November in Berlin. About 140 scientists and other research personnel participated in this two-day workshop. A user group for data quality assessments is currently in preparation.

Raising awareness for concepts and tools to assess data quality is an important step on the path towards an efficient and transparent health science.

For further information, see here.


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


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.
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