Summary: Many aspects of data management are an essential part of scientific research. Depending on the discipline, there are already established standards and procedures, in other disciplines they are just emerging. The following page gives you a rough overview of the different tasks in research data management.
Table of Contents

Planning and grant applications

Well-planned data management does not only support the active research phase in a project, but also lays the foundation for the subsequent processes, e.g., archiving, preparation of data for re-use, publication, thus guaranteeing the data quality from the start. Therefore, in the context of project application, research funding agencies have already started to request information about research data management processes, for example in the form of a mandatory data management plan. In addition, research organizations have published publishing guidelines and policies for the handling of research data.


  • Plan data management to facilitate the actual research process, e.g. via a data management plan. RDMO supports your planning process.
  • Clarify legal issues before data collection, e.g. when collecting personal data
  • Check if you can re-use already existing data
  • Plan for sufficient and redundant research data storage as well as resources in a data archive
  • Define workflow policies for storage and backup as well as for the exchange of data
  • If you plan to publish your data choose a repository and consider additional requirements, e.g. metadata standards, licences, data formats

Collection and analysis

Data formats and metadata standards should be selected and defined before beginning data collection and analysis, preferably during the planning process. This ensures that not only the actual data collection and analysis, but also the subsequent processes such as archiving and data provisioning are taken into account. In addition to the mandatory discipline-specific formats and standards, we also recommend open formats that are important for the long-term use of data and access. By providing additional documentation that describes, for example, data structures, workflows, and protocols, the sharing of research data within a workgroup, larger research team, or network can be improved.


  • Use open formats for files and metadata standards
  • Structure your data: apply naming conventions for files and directories
  • Document your data: provide metadata and additional information needed for data reproduction, data sharing, re-use and archiving
  • Store your research data on an enterprise level storage system which guaranties data integrity, geo-redundancy and backup
  • Version your data
  • Save work force by considering subsequent processes such as publication and archiving and benefit from integrating those requirement herein

Archiving and publication

Research data archiving is the long-term storage of scientific research data. Ensuring distribution, access control and data security archiving guarantees that none of your data is lost, forgotten or rendered useless, e.g. by being locked-in older file formats or storage media. Funding agencies have different policies regarding the archiving and publication of research data. For example, in the new Guidelines for Safeguarding Good Research Practice (Code of Conduct) the DFG outlines

When scientific and academic findings are made publicly available, the research data (generally raw data) on which they are based are generally archived in an accessible and identifiable manner for a period of ten years at the institution where the data were produced or in cross-location repositories. This practice may differ depending on the subject area.1

Similarly, various academic journals increasingly demand research data to be published together with the research article or in a public data repository.


  • If not done during the data collection or analysis processes prepare your data to match the requirements of the data archive, publishers’ and repositories’, e.g., data formats and data documentation
  • Archiving entails format validation and integrity checking of your data, n.b. archiving is not a simple backup as file formats and storage devices may become inaccessible over time.
  • Define the level others may access your data, e.g., via selecting a suitable licence for publication and archiving
  • Transfer your research data into a suitable data repository and, if applicable, define regulations on access level, embargo period or on-request

  1. Deutsche Forschungsgemeinschaft (2019): Guidelines for Safeguarding Good Research Practice. Code of Conduct, p.20