Good research requires responsible data management.

Data management = preservation + innovation

In short, data management can be defined as the creation, storage, maintenance, disclosure, archiving and sustainable preservation of research data. Not only would it be terrible if all your important research data would be lost. And not only can you save a lot of time if you can retrieve your data more easily. But if we can connect data sets, we can see the bigger picture, find solutions that cannot be found in specialized areas alone. Innovation happens where disciplines meet. The innovative power of research depends on our ability to connect datasets.

DataHub offers the tools and services to help you with this.

Choose the level that is right for you

ESSENTIAL

A simple way to get started with research data management.

STANDARD

For researchers that work with personal data or want to take research data management one step further.

ADVANCED

For researchers that want to leverage the power of data and dive deep into data sets.

Get started

Take the next step

Take the deep dive

What’s included:

Safe storage of data
Basic meta data
Create a data management section
Introduction into data management knowledge
FAQ’s to help you get started

All essential services, plus:

Create a full data management plan
Anonymisation of sensitive (human) data
Work with e-health records
Discover data
Structure your data sets with standards
Ask questions per mail

All the standard services plus:

Advanced custom storage facilities
Use data sets of other researchers
Custom data structuring
Custom solutions
Personal support

About DataHub

At DataHub, we provide data management services for (non-) clinical studies. DataHub is an institutional data broker that provides data management services to researchers from the academic hospital and university. Our cross-organisational infrastructure enables researchers from multiple disciplines to reuse valuable data from the hospital, the university, and beyond. DataHub works according to the FAIR principles, which specify that data and metadata should be findable, accessible, interoperable and reusable. We continuously improve our services in order to provide added value to researchers who want to do more with their data.