Radboudumc Technology Center Data stewardship

Research data management (RDM)

• Research Data Lifecycle (RDL)

  We provide support for individual researchers, departments and consortia in all four phases of the Research Data Lifecycle.

• FAIR research data management

  • Explore the four phases of the Research Data Lifecycle.
  • Follow our FAIR RDM course to get a head start in the FAIR RDM terminology, tools and regulation.
  • Contact our data stewards for support and advice on tailored RDM solutions that are compliant with the Radboudumc policy (see the general contact button on the RTC DS main webpage).

• Data management plan (DMP)

  • A DMP is mandatory for PhD candidates, required by most funders and necessary to acquire local feasibility approval.
  • To get started, follow the DMP training.
  • Make sure to select the Radboudumc template in DMPonline (intranet) for NWO and ZonMW funded studies and for local feasibility applications.
  • Make use of the example texts for data management paragraph in PhD theses or EU funded studies.
  • Ask for advice or have your DMP reviewed by our data stewards (see the general contact button on the RTC DS main webpage).

Training

• FAIR Research Data Management (RDM)
  Two half days introduction to RDM, covering terminology, tools and solutions.
• Data Management Plan (DMP)
  This 1-hour online course teaches you how to make a DMP using the DMP online tool.
• Digital Research Environment (DRE)
  A 1.5 hour course including hands-on session to get researchers started with the DRE.
• R basics for data preparation and presentation
  A 5-day course to learn the basics in R data preparation and presentation.
• Training offered by the Radboud University
  Browse the RU workshops calendar for training on the Radboud Data Repository (RDR), EndNote, ATLAS.ti and more

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.

Open Science

Open Science is about free access to scientific knowledge, data and methods throughout a research project, increasing cooperation, transparency and reproducibility of research.

Three important aspects of Open Science are:

1. Pre-registration of your research project, allows you to claim your ideas in an early stage, and increases the credibility of your results. Many journals (>300) also use the Registered reports publishing format: methods and proposed analyses are pre-registered and peer-reviewed prior to research being conducted. Manuscripts that survive pre-study peer review receive an in-principle acceptance that will not be revoked based on the outcomes, but only on failings of quality assurance, following through on the registered protocol, or unresolvable problems in reporting clarity or style.
2. Open access to methods for data collection and analysis. This makes your results more reproducible, enhancing the value of your findings. Moreover, other researchers can credit your work when they reuse your methods and procedures.
3. Open access to research data so that others can verify your results, and reuse your data.

More information by Radboud University and partners

For more information on Open Science, contact the experts at the RU Library, or visit the following websites:

• Open Science - Universiteitsbibliotheek RU
• Open Science - Nationaal Programma Open Science
• Center for Open Science

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