Benefits of RDM
RDM ensures that your research data are FAIR (Findable, Accessible, Interoperable, and Reusable). It makes research verifiable and reproducible, and funders and publishers often require it. RDM also has many advantages for you as a researcher. It enables new research with existing datasets and prevents data from becoming lost or accessed by unauthorised persons. Also, it increases efficiency, and ultimately it maximises your research output.
9 Golden Rules
1. Contact your data steward as early as possible
Each faculty at Maastricht University (UM) has its own data steward. This is your first point of contact for RDM- and FAIR-related questions. If necessary, the data steward will involve other specialists for additional expertise (e.g. the information manager).
At UM, several other support services for Research Data Management are available.
2. Write a Data Management Plan and keep it up to date
Writing a Data Management Plan (DMP) will help you identify and mitigate potential risks before collecting data. It also saves you time in later phases of your research. The DMP is a dynamic document in which you can add or change the information in your project.
The UM and MUMC+ have developed an institutional DMP. This DMP is available in the DMPonline tool.
Need support? You can contact your faculty’s data steward.
3. Learn about RDM
Do you want to learn more about RDM? You can find more information on the RDM portal.
Check out the RDM workshops and trainings provided by the University Library.
4. Make your data FAIR throughout your research project
The FAIR principles are an integral part of the Open Science movement towards transparent and reproducible research. Following the FAIR principles will help you to improve the quality of your data, which can ultimately help in maximising your research output and impact.
Watch the video below to learn more about the FAIR principles.
Video credits: Martínez-Lavanchy, P.M., Hüser, F.J., Buss, M.C.H., Andersen, J.J., Begtrup, J.W. (2019). ‘FAIR Principles’. In: Holmstrand, K.F., den Boer, S.P.A., Vlachos, E., Martínez-Lavanchy, P.M., Hansen, K.K. (Eds.), Research Data Management (eLearning course). doi: 10.11581/dtu:00000049
5. Be compliant with the GDPR
As a researcher, you are responsible for being compliant with the GDPR when processing personal data. Find out more about your legal obligations when processing this data by visiting UM’s privacy webpage.
Research projects that process personal data must be recorded in your faculty’s data processing register by the information manager. This must be done before data collection starts.
6. Take notice of relevant guidelines and requirements
When collecting data, be compliant with the national [PDF], Maastricht University’s, and the research funders’ guidelines and requirements for managing research data such as preregistration, a DMP, and research data archiving.
7. Submit your research protocol for ethical review
Ethical review of scientific research involving human participants or personal data is done by several ethical review committees within the University.
Find information on which ethics committee you will need to apply to.
8. Use UM infrastructure for storing and exchanging research data
During your research project, only use storage solutions and collaborative tools offered by UM. Contact your faculty’s data steward or information manager to find out more about the different available tools.
Do not use public cloud services to store and share your research data (e.g. Dropbox, Google Drive, Google Docs, OneDrive, etc.). There are risks associated with using services that UM does not have a data processing agreement with. Furthermore, using such services to store personal data is not compliant with the GDPR.
9. Publish your research following the FAIR principles
Your research data must be registered and archived at the end of your project, conforming to the FAIR principles. This enhances your recognition as a researcher and maximises your research output. UM is committed to becoming a FAIR University by 2023.
Contact the faculty data steward for advice on a suitable repository (for example, DataverseNL) and for assistance with making your data FAIR.