Research Data Management Policy of Julius-Maximilians-Universität Würzburg
Research findings can only be generated, harnessed, and disseminated if research data are managed in a responsible and transparent manner. The present policy should be read in conjunction with JMU’s guidelines for safeguarding good scientific practice and procedures concerning scientific misconduct as well as the University’s IT strategy and open access policy; it applies to all academic staff at Julius-Maximilians-Universität Würzburg, both in their capacity as researchers and in their capacity as teachers training the next generation of scholars and scientists.
On 20 March 2017, Julius-Maximilians-Universität adopted this research data management policy to ensure that research data are documented and made accessible in a sustainable and structured manner. The policy assigns equal importance to assuring the quality and reproducibility of research data, optimising them for re-use, and acknowledging their value as a basis for innovation.
- For the context of this policy, research data should be interpreted as any data and samples that are generated, developed, and/or analysed in the course of research activities. Different academic disciplines employ different research approaches and generate different types of data in different formats and quantities and with different degrees of structuredness and aggregation that differ in their impact and potential for re-use. Research data can be generated through processes including experiments, surveys, the study of sources, the re-analysis of existing datasets, creative processes, and software development. Research objects, too, can constitute research data. Research data underpin research publications and form an essential part of these. When managing research data, and, in particular, when making them available for use and re-use, institutions and individuals must comply with the legal provisions identified in no. 5.
- Research data management (RDM) means any methods, processes, regulations, and technologies aimed at assuring the quality and long-term usability of research data. RDM comprises all processes from data generation and processing, structuring, documentation, and use through to publication and sustainable retention. Good practice in RDM uses discipline-specific, standardised analyses, information network models, metadata templates, comprehensive descriptions of data generation, the implementation of research platforms for the distribution of the generated research data, deposition in publicly accessible repositories as well as standard operating procedures (SOPs) and should draw on examples of best practice.
- Data management plan: To ensure that research data are managed in a sustainable and systematic manner, Julius-Maximilians-Universität recommends that a data management plan (DMP) be developed before the start of each research project. The DMP should identify and describe all relevant datasets expected to be generated by the project and should outline a data management strategy. This strategy should, in particular, discuss measures for the assurance of the quality of the generated data as well as the secure storage, appropriate processing and documentation, long-term archiving, and accessibility of said data. It must be ensured that discipline-specific standards are met and general aspects, such as ethics, data protection, and intellectual property rights, are taken into account. The DMP must be kept up-to-date throughout the course of the project. When the generated research data are being processed, the entire research project, including the methods and tools that were used, must be thoroughly documented according to uniform criteria. Failure to do so will render the data irreproducible and unsuitable for re-use.
- Storage: Julius-Maximilians-Universität Würzburg encourages its researchers to deposit their research data in appropriate repositories and to make them openly available in a timely manner. Research data must be archived for a minimum of ten years.
- Responsibilities and responsible use of research data: The principal investigators for each research project are responsible for the management of all data generated by that project. Their responsibilities include ensuring the long-term archiving of all research data. All research data must be managed in a manner that is compliant with JMU’s guidelines for safeguarding good scientific practice, relevant requirements of funding bodies or collaborators, and, in particular, relevant data protection and intellectual property legislation as well as the provisions of the German Arbeitnehmererfindergesetz (Employee Invention Act, ArbEG). Any legitimate adverse interests of third parties, including legitimate adverse interests of third parties arising from funding agreements, must be considered. The same applies in the case of rights to re-use and publish research data being assigned in the context of publications.
- Institutional guidance: Julius-Maximilians-Universität Würzburg is committed to helping its researchers comply with this policy by providing guidance and support. Information on the support services offered is available on the web pages of JMU.