University Information and Library Service (HIBS) University Information and Library Service (HIBS)

Handling data is essential for the scientific research process. Data is generated, processed, and utilized in various forms in research. With the increasing volume of data and the opportunities for reuse brought about by digitalization, the importance of managing research data is also growing.

DMP template of HAW Hamburg: Use our structured template (Word document) to create a data management plan (DMP) for funding applications and research projects. Further information on DMPs, guidance, and templates can be found in the lower section of this page.

Research data can be defined as any data generated in the context of scientific research activities. Due to the diverse range of scientific methods, disciplines, and research interests, there exists a wide variety of different types of research data, most of which are now digital. In research, data are created, processed, and utilized in forms such as measurement data, laboratory values, survey data, interviews, texts, or audiovisual documents. Even testing procedures, such as simulations or questionnaires, can be included in this category.

The goal of research data management is to develop methods, procedures, and strategies that ensure the systematic handling of research data and secure their sustainable usability. From data creation to reuse, research data management supports your scientific research throughout the data lifecycle – from planning, creation, and preparation of data to data analysis, as well as addressing questions related to archiving, securing, publishing, sharing, and reusing data.

Research data management supports:

• The fundamental organization of the research process: Proactive planning of data handling in research projects is an essential part of scientific practice.
• Compliance with funders’ requirements: Many funding agencies consider robust research data management a prerequisite for funding.
• Data reusability: Metadata and licenses enable other researchers to reuse data.
• Transparency and traceability of research processes: Documenting data and analysis steps ensures research traceability and forms the foundation for reproducibility of results.
• Increasing the visibility of research and creating new collaboration opportunities: Published data, in particular, can enhance the visibility of your research projects and initiate collaborations.
• Citeability of data: By publishing your data as standalone publications or supplements and assigning persistent identifiers like DOIs, your research data becomes citable and permanently referable.

With the four fundamental FAIR Data Principles, FORCE11, an international coalition of individuals from scientific research, libraries, archives, publishers, and research funding, has formulated key requirements for research data that enable sustainable use. The German Research Foundation (DFG) also explicitly points out in its "Guidelines for Safeguarding Good Scientific Practice" that access to research data should comply with the FAIR principles. These four requirements for research data are:

• Findable: Persistent and globally unique identifiers (e.g., DOI) and extensive metadata ensure optimal findability and citability of research data.
• Accessible: Access to research data and metadata should be simple and possible using an open, free, and machine-readable protocol.
• Interoperable: To link research data in a machine-readable way over the long term, data must be comparable, and metadata should be based on controlled vocabularies, classifications, etc.
• Reusable: Research data and metadata should be comprehensively described, documented, and clearly and legally licensed to ensure their reuse.

The complete FAIR principles were published in Scientific Data in 2016.

Data Management Plans (DMP) are an important tool in the context of research data management. They document the handling of research data within research projects and should ideally be created before the actual research process begins. Due to the mandatory requirements for managing research data from funding agencies such as the DFG or the EU, DMPs are becoming increasingly common.

To create a DMP, you can use the HAW Hamburg template (Word document). It provides structured support for planning and documenting your handling of research data – for example, in the context of funding applications. Useful guidance and example answers to individual DMP questions can be found in an annotated checklist provided by TU Hamburg.

• Checklist (PDF, German) from the DFG on handling research data
• Annotated DFG checklist (PDF) with examples from TU Hamburg
• Checklist (PDF) for a Data Management Plan from the Digital Curation Center
• Official DMP template for EU Horizon Europe (Version 1.0, as of May 5, 2021)
• Sample DMP (PDF, German) from HU Berlin for DFG applications
• Sample DMP (PDF, German) from HU Berlin for BMBF applications
• Examples for DMPs from the Data Management Planning Tool of the University of California
• Exemplary DMPs from various disciplines by the LIBER Research Data Management Working Group on zenodo.org