Research Data Management

The Data Management Plan (DMP) is already required by many funding organizations in project proposals (section "Handling of Research Data"), but it is also becoming more common in other projects and is increasingly applied to bachelor's or master's theses that also generate data important for research. The goal of the DMP is to identify problems in handling research data early, improve understanding, and enhance the reusability of the data. The possible contents of a DMP, based on the questionnaire of the German Research Foundation (DFG), are listed below:

Data Description

• How are new data generated in your project?
  (e.g., through measurements, collections, surveys, ...)
• Are existing data reused?
  (e.g., existing datasets from old projects or from third parties)
• What types of data, in terms of data formats, are generated in your project and how are they processed further? (e.g., texts as .TXT, tables as .CSV, images as .JPG, videos as .MP4, ...)
• To what extent do these accrue or what data volume is expected?
  (e.g., 100MB of text data, 100GB of video material, ...)

Documentation and Data Quality

• What approaches are followed to comprehensively describe the data?
  (e.g., use of existing metadata or documentation standards or ontologies)
• What measures are taken to ensure high data quality?
  (e.g., which metadata is automatically generated or regularly checked and maintained by whom?)
• What digital methods and tools are required to use the data?
  (e.g., eLabFTW for experimental data, SPSS for survey data, or MAXQDA for data annotations)

Storage and Technical Security During the Project

• How are data stored and secured during the project runtime?
  (e.g., is the SCC storage used? What does the technical infrastructure look like? At what intervals are the data backed up, or is there a backup concept?)
• How is the security of sensitive data ensured during the project runtime?
  (e.g., access and usage management)

Legal Obligations and Framework Conditions

• What legal peculiarities exist in connection with handling research data in your project?
  (e.g., are there personal data according to GDPR? Must data be anonymized or pseudonymized?)
• Are there expected impacts or restrictions regarding later publication or accessibility?
  (e.g., can part of the data only be published in a database?)
• How are usage and copyright aspects as well as ownership issues taken into account?
  (e.g., what usage licenses are available? Has the copyright of the collected data been considered?)
• Are there important scientific codes of conduct or professional standards that should be considered?
  (e.g., are data used from a scientific archive? Are there usage restrictions?)

Data Exchange and Permanent Accessibility of Data

• With whom and how are data exchanged?
  (e.g., does another research institution have access to the data?)
• Which data is provided for reuse or is important as evidence of results?
  (e.g., on which repository are the datasets, and with which open tools can they be viewed and reused? What usage license do they receive?)
• Do you plan long-term storage or archiving of your data in a suitable infrastructure?
  (e.g., in a digital archive or a project management tool like GitLab? Are there embargoes? When can the research data be used by third parties?)

Responsibilities and Resources

• Who is responsible for the appropriate handling of research data?
  (e.g., description of roles and responsibilities within the project)
• What resources are required to implement appropriate handling of research data in the project?
  (e.g., hardware, software, personnel, licenses, costs, time, ...)
• Who is responsible for curating the data after the end of the project runtime?
  (e.g., for publication, maintenance, and/or inquiries)