Fostering collaborative research environments using DataSHIELD

Data protection concerns and lack of IT infrastructure preclude widespread cross-institute research projects, hampering benefits of collaboration.


Due to varying degrees of informed consent from study participants and limitations originating from regulations and data privacy laws, sharing study datasets has been a great concern for many institutes that otherwise would like to collaborate in order to fully utilise the strength of their combined collected data. Hence, within the NFDI4Health framework, one aim is to enable institutes to participate in such research projects without actually

ceding control over their data. DataSHIELD is a software solution for secure data analysis of personal health data in the programming language R, in which data holders can keep their data behind a firewall on dedicated servers (Opal Servers) while researchers can remotely analyse data under tight control, send analyses requests and receive summary statistics back.

DataSHIELD Implementation Network

NFDI4Health has guided and supported the installation and update of DataSHIELD IT infrastructure at numerous institutes, in order to make them ready for federated data-analysis projects. This has supported contribution of epidemiological study data in INTIMIC, a European research project (“Differences in the microbiome composition in obese and non-obese individuals and its relationship to diet”), where a first analysis project “Federated analysis of the

association between body mass index and gu tmicrobiota composition among adults from multiple European observational studies” (Schwedhelm, C. et al., DGEpi 2023) has been successfully finalized. Furthermore, new DataSHIELD packages have been developed:

Figure 1: Roll-out of NFDI4Health-supported DataSHIELD infrastructure (installations and updates) in Data Holding Organisations across Germany (blue: fully implemented, orange: work in progress; as of 15.09.2023)

Implementation within NFDI4Health

Within NFDI4Health, there are three ongoing pilot projects which are currently in the phase of metadata collection and dataset harmonisation:

  1. Systematic investigation of methodological limitations in the derivation of exploratory dietary patterns
  2. Association of dietary sugar intake and body composition [among participants with genetic predisposition for a high sugar intake]
  3. Association of dietary sugar intake and glycemic load with chronic disease risk. 

What NFDI4Health offers

You are the representative of a Data Holding Organisation and want to make your data accessible by implementing DataSHIELD at your institute?

  • Comprehensive SOPs guiding the installation and configuration of Opal/DataSHIELD setup, data importation, and user management [SOP].
  • Troubleshooting support to address any challenges.

You are a research analyst with an intriguing analysis idea and want to use DataSHIELD for your project?

  • A seamlessly integrated analyst experience encompassing an analysis environment along with a unified credential system (work in progress).
  • Starter R scripts supporting the initial stages of analysis.
  • On-demand functions to enhance analysis capabilities.

Our Services

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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