At The Hyve we have ample experience with:

  • Building and supporting research data infrastructures needed to store your research data
  • Providing consultancy to identify the relevant data management aspects
  • Supporting in writing the DMP required for a grant proposal and make sure it complies to the FAIR principles

You can also include The Hyve as a project partner in your proposal to deliver a research data management infrastructure, for example by using CKAN / Dataverse, Arvados, Podium, OMOP/OHDSI, cBioPortal or tranSMART, depending on the exact data types and needs in the consortium. (See our solutions)

The Hyve has previously built and delivered data management platforms in a.o. TraIT, IMI EMIF, ND4BB / IMI Translocation, IMI Ebola+ and Qualify, has substantially contributed to IMI eTRIKS, and also delivered similar infrastructures in a.o. BBMRI-NL and HealthRI, IMI BigData@Heart and IMI RADAR-CNS. (See our use cases)

Schedule an appointment or ask us for more information.

 

Research data management life cycle - data management plan

  Research Data Lifecycle (from a.o. UK Data Archive)

 

About data management plan

A data management plan (DMP) is mandatory for Horizon 2020, Innovative Medicine Initiative (IMI) and NWO grant proposals describing how research data is to be handled, both during and after a research project has finished. The DMP has to be in accordance with the FAIR principles. It forces researchers to think in advance about relevant data that is created and how this data can be re-used by other researchers.

Typically a data management plan contains sections about all stages of the data lifecycle, from creation to preservation and sharing for re-use.

The level of detail needed in the plan might differ depending on your funder and research topic. The plan will take into account concerns like costs of data management and storage, where the data is stored, in what stages the data is stored and security of the stored data.

 

The significance of good data management

Following good data management procedures ensures clear regulation and documentation of data capture, storage, analysis and access, which will increase the quality of your data and therefore your research. Good data management is not the objective, it rather is the means to increase the quality of knowledge discovery and innovation.

Following the FAIR principles will significantly decrease the risk of data loss, accelerate your research and ensure re-use of your data, increasing the impact of your data and resulting in more citations. In the end everybody benefits from a bigger variety of data available to you and your peers.

 

Do you have more questions? Let us know by filling in this contact form.

 

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