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Consulting Research Data Management (RDM)

Let us advise you on all the aspects of managing your social science research data and creating RDM plans

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Research data management includes the development and implementation of strategies and procedures for the protection, validation, and documentation of research data. This ensures the quality of the data and enables its usage, to answer new research questions, after the original research project has been completed. Accordingly, data management aims to protect data from loss or manipulation and to ensure its long-term discoverability and usability. 

Even if the data are not made available to other researchers after the end of the project, it is advisable to plan at the beginning of the research project how the data collection, analysis and storage will be carried out within the framework of the project. This helps to ensure that there are no "unpleasant surprises" at the end of the project because appropriate strategies are in place to deal with potential problems. This applies, for example, to the following areas: 

  • Informed consent, ownership rights and licensing. What legal and ethical regulations apply to the possibilities of reusing  of existing data in the project? If data are collected in the project, under what conditions can they later be made available to other scientists? What are the (legal and ethical) constraints? 
  • Integrity and replicability of research results. What information is needed to ensure replicability of research results? Were the processes of data collection and analysis documented in a comprehensible way so that they can be understood by third parties even without contact to the primary researchers? 
  • Data security and the risk of data loss. How will the data be accessed within the research project? How are backups made, where is the data stored, and who has access to the data?
  • Secure data erasure. Data that - for example, for reasons of data protection - are not intended for long-term backup must be securely erased at the end of the project without violation of the data confidentiality. 

The development and implementation of a data management strategy helps to ensure the quality of research and thus increase its value. This enables a planned use of resources and supports a smooth flow of the research process. This is another reason why currently many research funders also consider how data management is designed for the project when awarding funding. 

A data management plan (DMP) is a document in which data management strategies and planned actions are described and then backed up with resources and responsibilities. Such a DMP typically includes remarks on the following topics: 

  • Data collection (which data are collected, how are they collected?) 
  • Data protection and ethical aspects 
  • Legal aspects of research and data transfer for possibilities of reusing (copyright, data protection) 
  • Documentation and metadata 
  • Data security and backup 
  • Data selection for long-term digital preservation.