Radboudumc Technology Center Data stewardship

Our impact

We collaborate with internal and external partners to share knowledge and develop new solutions and platforms for research data management.

Many researchers recognize the importance of research data management and the FAIR principles, however, applying them is often difficult and time-consuming. We aim to address this by:

• Providing clear guidelines and instructions;
• Developing secure & intuitive tools and infrastructure;
• Sharing knowledge and connecting experts.

Policy and guidelines

Throughout the Research Data Lifecycle, policy and guidelines apply. These documents are available on Qportal and IQS. Read them before you start your project.

Policies and guidelines on Qportal

• General Radboud policy
• Integral Quality System (IQS)
• SOP Research Data Management (RDM)
• SOP Management and archiving of scientific human-related research ('Beheer en archivering wetenschappelijk mensgebonden onderzoek')

Example text for Data Management Plan (DMP)

Use the DMPonline tool to prepare your DMP and make use of the guidelines and example answers for each topic. Make sure to use the Radboudumc template, also when preparing a DMP for funders such as ZonMW and NWO.

FAIR manual on Qportal

Making your data FAIR is a challenging task. For researchers it is difficult to decide where to start and what choices to make in the process. To help researchers, Health-RI have written a FAIR manual that guides you step-by-step to apply the FAIR principles to research data. Click here to access it.

FAIR principles

The FAIR principles help to make your data have impact beyond your research project and publications, and increase the visibility of your scientific achievements.

Making data FAIR

FAIR is an acronym for ‘Findable’, ‘Accessible’, ‘Interoperable’ and ‘Reusable’.

• Findable: The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.
• Accessible: Once the user finds the required data, they need to know how can the data be accessed, possibly including authentication and authorization.
• Interoperable: The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.
• Reusable: The ultimate goal of FAIR is to optimize the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

More information by Radboud University

The Radboud University has established the following minimum requirements concerning data "FAIRness": 'Research data underlying scientific publications authored by Radboud University or Radboudumc employees should be Findable and Accessible.'

The FAIR guidelines are known to many researchers but are not yet universally applied in practice. We at Radboudumc are obliged to have all research data comply at least with the F (sustainably findable) and the A (proper access management). Read more on the website of the Radboud University.

Digital services of other RTCs

• Software and applications

  • AI algoritm development
    • RTC Deep learning
  • Fiji, QuPath, Cellpose - support for main open-source image processing/analysis software
    • RTC Microscopy
  • Grand challenge - platform for AI development
    • RTC Deep Learning
  • Kaluza, Flowjo, flow cytomertry clustering analysis in R and in Omiq - functional management and support on software for multicolor flow cytometry analysis
    • RTC Flow Cytometry

• Analysis

  • Advice on AI and machine learning - for projects with data from general practices
    • RTC Health Data
  • Automation of image processing and analysis solutions
    • RTC Microscopy
  • Anonymization of imaging data
    • RTC Imaging
  • Cost-effective analyses
    • RTC Health Economics
  • Custom solutions for image processing and analysis
    • RTC Microscopy
  • Data preparation for AI
    • RTC Deep Learning
  • DNA sequencing
    • RTC Genomics
  • Metagenomics
    • RTC Bioinformatics
  • Methodological and statistical advice
    • RTC Health Data
  • Protein structure analysis
    • RTC Bioinformatics
  • RNA sequencing
    • RTC Bioinformatics
  • Single cell analysis
    • RTC Bioinformatics
  • Statistical analysis
    • RTC Biostatistics, RTC Health Economics

• Data access and sharing

  • Complex (online) data collection
    • RTC Health Data
  • Sharing imaging data
    • RTC Imaging
  • Supply of healthcare data of +/- 100 general practices
    • RTC Health Data

• Infrastructure

  • Advice and assistance in setting up research website - for example: research portals, integration of Castor EDC with research websites
    • RTC Health Data
  • Design and management of databases for research data
    • RTC Health Data

• Courses

  • AI for health
    • RTC Deep Learning
  • How to generate and analyze high-quality multi-color flow cytometry data - hands-on analysis in Kaluza (and/or Flowjo, Omiq) (in development)
    • RTC Flow Cytometry
  • Introduction in using R
    • RTC Biostatistics
  • Statistics for clinical researchers
    • RTC Biostatistics