• We have varied experience in implementing individual reports and dynamic visualisations for explorative analyses, clinical data safety monitoring boards and centralized clinical monitoring.
  • As data centre we monitor quality and completeness of the captured data, perform explorative and feasibility analyses for (sub) studies and pre-process data for statistical analyses.
  • We develop and maintain software packages at the interface between data management and data analysis and possess extensive expertise in implementing secure solutions for data sharing of sensitive data.

Our services

Integration of data from different sources including clinical routine

We offer support and consultation regarding preparation and consolidation of your data.

Data centre

We manage your data from registries and cohort studies and support you in the development of research questions. Moreover, we clarify all questions regarding the feasibility of (sub)studies.

[Translate to English:] JKuhle & PBenkert

Interview with a responsible data scientist

Data centre

Reporting and data evaluation

We create reports, visualizations and explorative analyses, using e.g. dynamic web applications.


Centralised monitoring

We implement algorithms to monitor data quality and critical metrics of your clinical trial as support of clinical monitoring.

Data Safety Monitoring Boards

We create reports and visualizations as a basis for decision-making for Data Safety Monitoring Boards / Data Monitoring Committees.

Custom software packages

We develop and maintain software packages for the simplification, automation and reproduction of recurring tasks at the interface between databases and data analysis. Our R package to import data from the clinical data management sytem secuTrial to R can be downloaded on CRAN. The latest version can be found on GitHub.

Data Access Committees

We offer assistance regarding Data Sharing and during the development of a Data Management Plan. A Data Access Committee enables dissemination of data according to the FAIR principle while ensuring data protection.

FAIR Data Principle
These four FAIR principles form the basis for a sustainable re-use of research data

Research data should be provided with a digital object identifier (DOI) and should be easy to find.

Research data, or at least the associated metadata, should be easily accessible and access to the research data should be described.

Research data should be easy to interpret and use public and free data formats and standards as far as possible.

Data collection and processing are well documented so that research data can be easily reused.

For further information & questions


We are part of the Research Data Management Initiative at the University of Basel: