Frequently Asked Questions

We view data as anything that is used as input for your research project. This includes your protocols, project description/definition, minutes from meetings, logbooks, etc. This data can be in all kinds of formats, for example Word, Excel, PDF, text-based files or code. All these data types play a role in governing your data in a correct way (FAIRifying your data).

In this glossary you can find some of the terms used on the website.

HPCHigh Performance Computing, 'Supercomputer'
DMPData Management Plan
AVGAlgemene Verordening Gegevensbescherming
GDPRGeneral Data Protection Regulation
DACData Access Committee
DABData Access Board
FAIRFindable, Accessible, Interoperable, Reusable
GitFree and open source distributed version control system for code and text-based files.
SVNSubversion, a software versioning and revision control system distributed with an open source Apache License.
UBECUMCU Bioinformatics Expertise Core
MetadataData to describe your data. For example, it can tell you when and by whom the data is created, on what machine, etc.
LicensingA license permits or restricts the use of your dataset or code by others. It is advised to link a license to your data, as it make sure it will not be abused and you will get credits for the creation.

Definitely! 'Data' is a very broad term, and there is no research project without data. If you are working with small scale datasets (for example, one or more Excel sheets, textfiles or a couple of images), there are always ways to apply correct data stewardship. For example, make use of ontologies in your headings and publish your Excel files in a way that makes it possible for non-Excel users to process your data. You can for example do this by exporting your dataset to TSV or CSV. Also describe your data with metadata, so others might know how you've collected the data and how you did your processing. You can also link to protocols and other files that might be of interest when someone wants to reproduce or reuse your data.