If possible, research data are stored in redundantly secured storage systems throughout the entire research process. The ZIM supports the researchers of the University of Passau and advises on data storage and backups.
The services offered to researchers by ZIM include:
Metadata are structured data that describe a resource (such as a research dataset) in greater detail. For instance, this can include a description of the content and technical information, details on the context in which they were created and relationships within and outside the resource. Only a standardised and machine-readable description of research data makes it possible to find, reference and reuse the data as intended by the FAIR principles, making metadata crucial in unlocking the added value of research data.
Owing to the differences in requirements between disciplines, there are numerous metadata standards. Most data repositories support DataCite, which is a generic metadata schema. DataCite provides a Best Practice Guide as a handout.
In addition, the schema forms the basis for a DOI allocation via DataCite. A Digital Object Identifier (DOI) is a persistent identifier that ensures that a record remains permanently discoverable, retrievable and citable.
Once the project has been completed, research data can be archived and published in repositories and research data centers. The research data policy of the University of Passau envisages a retention period of at least ten years. Repositories offer options in defining access and re-use rights, while research data centers usually also offer support services in the entire field of research data management.
Articles that describe research data in detail but do not interpret it can be published in special data journals. The datasets published in the repository can then be linked to the article (ideally including DOI).
Maintaining the authenticity, integrity, accessibility and comprehensibility of research data plays a key role in the long-term usability, which is achieved through open file formats, precise documentation and detailed metadata, among other things.
Any costs potentially incurred for securing and publishing data should already have been estimated at the beginning of the research process e.g. in a third-party funding proposal. Before the end, existing copyright and utilization rights related to the data should be clarified, any necessary consents obtained, and personal references fully anonymized.
If possible, an established, discipline-specific repository or data center should be selected for the publication of research data. The visibility of the research data there is increased by its familiarity in the respective specialised community.
If a research discipline does not yet have a specialized repository, the data can also be published in a generic interdisciplinary or institutional repository. Specific requirements from third-party funders should be given special consideration in this context.
Researchers at the University of Passau can archive and publish their data from completed scientific projects from various disciplines in the university repository RADAR Passau. In the first phase, the repository is primarily open to projects funded by third parties.
to RADAR PassauIf you are interested in archiving or publishing research data in the university repository, please feel free to send us an enquiry to forschungsdaten@uni-passau.de at any time.
Please provide us with the following information:
For an overview of discipline-specific and generic repositories and data centers, a handout [content in German] is available to you.
To identify a suitable repository or to search for reusable research data, re3data (Registry of Research Data Repositories) offers one of the world's largest directories. Individual disciplines can also be specifically searched there.
Additionally, RISources – the DFG information portal for research infrastructures – enables targeted filtering based on geographical, disciplinary, and thematic criteria.
The FAIRsharing platform provides a comprehensive overview and detailed information on various databases, data standards and policies used in different scientific disciplines. It also compares repositories in terms of their compliance with the FAIR principles.
OpenDOAR provides a directory of open-access repositories. It allows repositories to be searched based on a number of characteristics, such as location, software used, or type of material.
DataCite's Repository Finder is a useful tool for identifying suitable repositories that comply with the FAIR principles. You can filter the search results according to various criteria, e.g. certifications (such as CoreTrustSeal) or repository software.
The Open Access Directory: Data Repositories offers a list of repositories and databases for open data research data.
Some specialised information services [content in German] offer similar functionalities.
A non-exhaustive list of data journals can be found on the information pages of forschungsdaten.org [content in German].
When choosing a suitable platform, look for quality standards such as compliance with the FAIR principles. FAIR stands for data that is Findable, Accessible, Interoperable and Reusable.
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