The Open Science movement, the standpoint that every aspect of research should be published and
made available to help scientific progress, is gaining popularity. And because of this, the
way research is conducted is changing fundamentally.
This shift towards openness is being driven by a growing recognition of the importance of sharing
knowledge and resources, as seen in the increasing interest in initiatives like the NFDI initiative, which
is funded by the DFG.
However, data sharing is merely a preliminary step in realizing the full potential of Open Science.
To truly facilitate the advancement of scientific knowledge, it is essential that data is shared in a manner
that is discoverable, accessible, and usable by all stakeholders. The FAIR Guidelines, introduced by
Wilkinson et al., provide a structured way to achieve this goal, by outlining certain requirements
making data more Findable, Accessible, Interoperable and Reusable (FAIR).
By adhering to these guidelines, researchers can ensure that their data contributes meaningfully to
the scientific community, rather than remaining an unused resource.

Possible Language The thesis can be written and supervised in English or German.
Supervisor: Alexandro Steinert, M. Sc. alexandro.steinert@offis.de
Evaluator: Prof. Dr.-Ing. Astrid Nieße astrid.niesse@uni-oldenburg.de

If you are interested please contact Alexandro Steinert

The goal of this thesis is to help researchers to make their data available in a FAIR way.
For this possible metrics for a FAIR dataset have to be identified and recommendations to make
datasets more FAIR should be defined based on the metrics.
To achieve this, the landscape of tools that score the FAIRness of datasets is to be examined and the
strength and weaknesses of each approach should be reviewed. Furthermore, the scientific landscape
of what defines a FAIR dataset should be reviewed.
Next, a tool with a REST API that scores the FAIRness of a given dataset, providing a standardized
and reproducible way to evaluate the quality of datasets will be created.
This work can be divided into the following tasks:
• Tool Landscape Analysis: Examine the landscape of tools that score the FAIRness of a
dataset in the scientific literature and practice
• Metric Development: Define metrics that score the FAIRness of a dataset
• Tool Development: Create a tool that scores the FAIRness of a dataset

Basic Experience in programming in the language of your choice