The TUM is committed to the transparent handling of data generated during the course of the research process. This requires a sustainable research data management. The following guidelines were agreed by the TUM Board of Management.
The professional and responsible handling of research data is not only a staple of good scientific practice but essential for smooth research operations. The goal of sustainable research data management is to document and safeguard research results, while respecting disciplinary standards, and to make results available to the internal and external scientific community for further use, reproducibility and commercial application. Methods for handling research data should be anchored in both research and teaching. In addition to the Guidelines for Ensuring Good Scientific Practice and Dealing with Scientific Misconduct, the TUM Research Code of Conduct, the Patent Policy, and the Open Access Policy, the following guidelines are binding for all scientists working at TUM (Resolution of the TUM Board of Management 13 November 2018).
1. Research Data
The term “research data” includes all data and materials generated in the course of research through measurements, experiments, surveys, simulations, etc. Depending on the nature of scientific inquiry and discipline, this data is generated, processed, archived and published in different ways, i.e. it is highly heterogeneous.
2. Research Data Management
Research data management covers the entire life cycle of research data, from planning, through collection and processing, to its permanent archiving and possible publication. Research data are to be collected in accordance with the relevant disciplinary standards, specifying the tools and methods used in their production and processing, as well as the context in which they were generated, described with metadata and stored on a long-term basis and, where applicable, published. Metadata descriptions are to employ the established and standardized terminology of the respective discipline, taking into account interdisciplinary re-usability.
3. Data Management Plan
The data management plan is an important part of research data management. It regulates the storage of and access to research data, specifies responsibilities for data management, serves as a means of documentation and, if necessary, as verification of the use of grant funds. It is to be amended in the course of the project to reflect the current status. Any costs incurred for data management must be taken into account when applying for funding. TUM requires a data management plan to be drawn up and attached to the draft application before the start of a research project.
4. Responsibility and Accountability
Responsibility for the management of research data lies with the respective project head for the duration of the research project. Project heads are particularly obliged to ensure the long-term archiving of their research data. They pledge to adhere to good scientific practice, as well as to the relevant terms and conditions of funding and contractual agreements. This applies in particular to compliance with the relevant ethical, data protection and copyright provisions. Conflicting rights of third parties, such as from third-party funding contracts, must be observed.
5. Technical and Organizational Support
TUM supports its scientists in all matters related to research data management for the duration of a research project and provides discipline-specific consulting, training and data management services through the TUM Research Service Centre (Contact: firstname.lastname@example.org) in cooperation with the IT Service Center (ITSZ), the Leibniz Supercomputing Centre (LRZ) and the Office of Research Funding and Technology Transfer (TUM ForTe). In the future, the TUM Institute for Data Science, as a central scientific institution, will promote and support data-intensive research.
Munich, 13 November 2018
On behalf of the TUM Board of Management
Dr. Hans Pongratz
Senior Vice President