Data protection during the course of a project
At the end of a research project, the question of how to store research data arises. Ideally, it has already been determined in the data management plan which data should be retained and which should be deleted. This decision is very context-dependent and can vary from project to project. The following questions can help you decide what should be retained:
Raw data are often the basis of analyses. Based on the raw data and information about the subsequent steps, study results can be reconstructed. It is therefore important that raw data are not overwritten or changed. Raw data should therefore be stored write-protected in a separate location.
In the course of a research project, more and more files with different contents are created. It can be difficult to find data if there is no logical folder structure or if the file names are not clear. Therefore, how individual files and folders are labelled should be determined early on and in a uniform manner.
Depending on the size of the data sets and how long they are kept, repositories may charge a fee. These costs should be taken into account at the planning stage. The SNSF reimburses data storage costs for funded projects.
The storage of personal or sensitive data requires special technical and organisational security precautions:
Metadata are data that contain information about characteristics of other data:
Well-documented metadata have several advantages:
There are various standards, for managing metadata. Depending on the subject area, there are different standards.
Metadata Standards Catalog
Metadata standards by subject area
When storing and publishing research data, the FAIR principles should be observed. Adherence to the FAIR principles ensures optimal processing of one's own research data and thus enables the data to be re-used by others for further research.
FAIR stands for Findable, Accessible, Interoperable & Re-usable.
Findable: Machine-readable metadata are needed so that research data can be found easily.
Accessible: Access to data must be regulated, authentication and authorisation must be defined.
Interoperable: In order for data to be linked to other datasets, they must be in an accessible and widely applicable format.
Re-usable: For data to be reusable, they must be comprehensible. For example, the method of data collection must be known. Furthermore, the terms of use of the data must be clearly described.
Ensure that your data are stored in a repository that complies with the FAIR data principles and that is compliant with international data and metadata standards. Examples of such repositories are re3data.org and for Switzerland we recommend DLCM long-term preservation or OLOS.