Before you begin, you should go over your research material and think about which parts of it that need to be included so that someone else can understand and reuse the data.
Prepare the data
SND’s services allow you to share digital data from various research disciplines. This means that the research data can be anything from survey data to biological sample data, as long as the material is used for scientific analysis.
It’s important that the data you choose to share are structured and documented in a way that is intuitive to understand for others. The data should also have a file format that is either open and/or commonly used in your field of research. You can read more about how to organise and document data on our pages about data management. We have also compiled a guide for suggested file formats.
If you’re not employed by the University of Gothenburg, you can, at this point, only share anonymous (de-identified) data through SND. This means that all personal data must be removed from the material, and that there can be no remaining code keys. As a rule that applies to everyone, data that are shared through SND cannot contain information that, if shared, could constitute a crime, according to national or international legislation.
If you’re going to make data from a research project accessible elsewhere, you can still describe them with SND so that they become visible and searchable in the SND catalogue. These data will need a PID, or persistent identifier, a long-lasting reference to a landing page where the data are described and where there is information about how to access the data.
Prepare documentation files
In order to make it possible for other people to understand and reuse research data, it’s important to add the documentation that belongs with the data. Reports, variable lists, and README files are some examples of documentation that is of great help if someone wants to do further research on the data you share. You don’t have to share everything, but think about which documentation files that contain information that others need to be able to analyse the data correctly.
Examples of what may need to be explained in documentation:
- what different codes, abbreviations, variable names etc. mean
- which definitions that have been used for coding and markup of the material
- which methods that have been used to collect and process the data
- which legal, ethical, and other restrictions that limit how the data may be reused.
Here you can read some useful recommendations before you make your data accessible.