Questions about research data management? Please contact firstname.lastname@example.org
The amount of research data is increasing explosively and solutions for storing, sharing, retrieving and reusing the information are important for both the institutions and for developing new research. Here you can read about the strategies and find practical examples of good data management.
Research data management (RDM) entails the responsible handling of research data before, during and after the research project. This includes processes for collecting, processing, analysing, storing, sharing, archiving and disposing of research data.
Roskilde University (RUC) has a data management policy which provides guidelines to researchers and students to manage research data appropriately (see box to the left). RUC's data management policy is based on the idea that research data should be "as open as possible, as closed as necessary". When research leads to the publication of results, relevant data that validates the research should, as far as possible, be attached to the publication.
Some research funds (i.a. Horizon Europe) require that research data are made Findable, Accessible, Interoperable and Reusable (FAIR). For more information about FAIR, please read our FAIR LibGuide. More information is also available in the article from Mark Wilkinson et al. (see box below) and the FAIR Principles at GO FAIR.
We can support you with RDM and making your data FAIR. Please contact us via eScience Services at RUC's serviceportal (requires RUC login) or via email@example.com.
The Danish Universities have developed an eLearning course "Research Data Management". The course is divided into three modules (RDM, FAIR, Data Management Plans), introducing the general concepts and terms used in RDM. Each module takes approx. 20 minutes. See more here or at the respective sites in this LibGuide.