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Storage and publication of research data

Storage of research data

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:

  • Redundantly secured network drives for personal use and for shared data storage for workgroups. (H: workgroups – H: and I: drives)
  • Network drives for confidential data requiring a higher security level: these can only be accessed from the University's internal network or by means of two-factor authentication (S: drive)
  • Data sharing within the University with others who have ZIM credentials; alternatively, external sharing via password-protected links (using Filr/Windat)
  • Cloud storage (50 GB per user) for sharing data or web-based access to one's own data from anywhere in the world (LRZ Sync + Share)
  • Online collaboration via workgroups, blogs, wiki or versioned online documents on a secure online platform (Vibe)
  • Digital Workspace for secure and convenient access to one's own university work environment and research data from anywhere in the world and from any mobile device, secured by two-factor authentication (Citrix)

Wherever possible, research data are published and stored long-term in established, discipline-specific repositories and data centres.

Publication of research data

Research data can be published via data repositories or special data journals. Repositories usually offer more options in defining access and reuse rights.

Institutional, interdisciplinary and discipline-specific repositories are available for data publication in a repository. Due to the heterogeneous nature of conventions across academic disciplines, the use of discipline-specific is recommended.
Directories such as re3data.org are useful for identifying suitable repositories. RISources – the DFG's information portal for research infrastructures – also offers targeted filtering. Some specialised information services [content in German] offer similar functionalities. The best known multidisciplinary repository is Zenodo.

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.

Metadata and standards

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.

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