Planning data management
PRACTICAL GUIDE 6
What are research data?
The OECD defines research data as factual records (numerical scores, textual records, images and sounds) used as primary sources for scientific research, and that are commonly accepted in the scientific community as necessary to validate research findings.
Why manage research data?
From the start of your research you will collect, produce and use data. Research data management (RDM) is part of the research process. It covers all activities that deal with collecting, describing, storing, processing, analysing, archiving and accessing data.
How do you manage research data?
Data management must often be anticipated at the start of a project by presenting a data management plan (DMP). This document helps you consider how to organise your data, files and supporting documents during and after the project. Many research funders, e.g. the German Research Research Foundation (DFG), the European Commission and the 9th research framework programme “Horizon Europe” require proof of a DMP. The DMP is a continuous document that must be updated during your research project.
Recommendations for managing research data in economics
- Research data management in the social, behavioural and economic sciences – guidelines of the German Data Forum (in German) (PDF)
- Research data management in economics (PDF)
- Managing Research Data: “Economic Sciences” (PDF)
Research data management helps you react to challenges relating to data, during and after the project. This can also apply to data administration which is often designed more consistently and sensibly after prior conception. Responsibilities, access rights and clear planning make it easier for you and the research team to administrate the data and make them accessible and reusable for others, if necessary. At the end of the project it facilitates the archiving and transfer of datasets.
What should a data management plan include?
Collection and documentation: Here you need to describe the type, format and scope of data that you will collect. The format of the created data usually depends on the software you use. It will affect the options for joint usage and long-term archiving. The initial descriptions in a data management plan will not only help you create a documentation, but also the metadata. This documentation is useful for understanding your data, and you will enrich it during the productive phase.
Storing and archiving: How will you save and archive the data while you carry out your research? Where will they be archived when the project will have ended? Who will be responsible for data recovery if something happens? The earlier you consider this, the better.
Legal and security-related questions: What are the protective regulations applicable to your data? Which methods must you apply to guarantee the safety of personal or other sensitive data? Which regulations must you comply with if you want to obtain personal data beyond the immediate intended use? You should take care to familiarise yourself with the European General Data Protection Regulation (GDPR).
For the joint usage of data and long-term archiving please consider the following:
- Who may possibly want to use your data?
- What criteria may apply to the selection of data to be transferred?
- How long should data be archived?
- In which data repository could you deposit the data?
- How you can identify your data (persistent identifier/DOI)?
Responsibilities and resources: Specify the roles and responsibilities of people who work on the project, especially in the case of alliance projects where many researchers, institutions and groups with different working methods collaborate.
Good luck with your research!
Date: March 2021
Questions, comments and notes are welcome at open-science@zbw.eu