Services
uniIT
In addition to the file services for units and projects (GFS - group file services) and individuals (PFS - personal file services) at the University of Graz, there is the possibility to store data in the uniCloud, where it can be shared. The uniCloud can also be used to work collaboratively on data. These services provide sufficient storage space, are integrated into the backup management of uniIT and enable adequate user administration. uniCloud can also be used as active storage for projects and work groups.
For data exchange, there is the service uniShare, where it is possible to send data to as well as to receive data from external persons.
Contact: IT Support Portal
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Research management and service
The services of the research management and service division also include legal advice, e.g., regarding the handling of personal data.
Contact: https://forschungsmanagement.uni-graz.at/de/
Additional links:
- uniCloud overview (intranet) and details (intranet), uniCloud terms of use (uniCloud).
- Details uniShare (Intranet)
- Fileservices of the University of Graz (Intranet)
![©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock. RDM Cycle collect and capture ©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock.](https://static.uni-graz.at/fileadmin/_processed_/6/4/csm_RDM_stage_collectCapture_small_right_3ecda03e1f.png)
Help and training
File management
With the amount of data produced in everyday work, it is necessary to approach file management based on certain systematics. In general, but especially in research data management, the goal should always be that the data can be retrieved and understood at any time (even after years) and even by people other than the data producers. To this end, the data must be protected and securely stored, and regular backups need to be made (data storage). In the case of sensitive personal data, attention must also be paid to DSGVO-compliant storage.
Systematic access – regardless of whether a digital versioning system is used – begins with an appropriate folder structure (hierarchical and sequential) and with the meaningful naming of the files. Both the folder names and the file names must contain sufficient semantic information so that the content and purpose can be understood without opening the folder or file. As a rule, this approach needs to be designed individually for each use case.
Appropriate rules should be defined at the beginning of the research project, i.e., before the start of data production. They need to be written down and passed on to all employees, following the rules has to be mandatory. Compliance with these rules must be continuously monitored. When naming the data, the use of certain characters (e.g., %, /, *, !), spaces and upper and lower case letters should also be clearly regulated.
File names consist of the freely assignable name (more accurately: file description) and the file name extension, which designates the format of the file; the latter is assigned either automatically by the software or manually by the user when the file is saved. The following applies: "The file name should be as short as possible and as long as necessary." (translated from Trognitz 2017,45) Nevertheless, the file description, that can be freely assigned, must contain unambiguous and clearly comprehensible information that is understood by all users.
In general, a project team should work with software solutions that automatically document changes to the file: These can be cloud solutions (e.g. uniCloud) for collaborative writing, or version management solutions (git, svn) for other file types. In any case, it is important to ensure that the software solution works reliably and that the employees are trained to use it. Versioning information can of course also be documented manually in the respective file (at the beginning of the file) or in an extra file in a certain file folder. This process should be documented as detailed as possible. Therefore, it needs to be remembered, that versioning information in the file name itself usually does not provide enough details about the changes made to a file.
Literature, resources:
- Datenorganisation. In: forschungsdaten.info. 14.11.2022. https://forschungsdaten.info/themen/organisieren-und-aufbereiten/datenorganisation.
- Frank, Ingo et al. (2022): "Checkliste Datenmanagement", 10.5281/zenodo.6957258. (Zotero)
- Trognitz, Martina / Schäfer, Felix / Heinrich, Maurice (eds.) (2017): IT-Empfehlungen für den nachhaltigen Umgang mit digitalen Daten in den Altertumswissenschaften. IANUS. 10.13149/000.111000-a. (Zotero)
- University of Edinburgh: MANTRA - Research Data Mangement Training. Last updated 10.2022. https://mantra.ed.ac.uk/. (Zotero)
- Verbund Forschungsdaten Bildung (VerbundFDB) (2018): Dateien benennen und organisieren. https://www.forschungsdaten-bildung.de/dateien-benennen. (Zotero)
- Venkatarman, Shanmugasundaram / Moura, Paula (2020): "Raw data, backup and versioning: What you need to know to preserve your research data", 10.5281/zenodo.4041557. (Zotero)
Service:
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If you find an external look at your data management helpful, please feel free to contact us: helmut.klug(at)uni-graz.at
![©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock. RDM cycle collect and capture ©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock.](https://static.uni-graz.at/fileadmin/_processed_/6/4/csm_RDM_stage_collectCapture_small_right_3ecda03e1f.png)