Bridging the Incentive Gap: From Data Hoarding to Data Sharing

Forschungsthema/Bereich

Wirtschaftsinformatik / Research Data Management

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

-

Bewerbungsschluss

31.07.2026

Dauer der Arbeit

-

Beschreibung

Motivation

High-quality, interoperable data has become a vital cornerstone of the sustainable energy transition. This is primarily due to the increasing use of AI and data-driven approaches in energy research, industry practices, and policy development. While FAIR (Findable, Accessible, Interoperable, and Reusable) data principles are widely recognized as the gold standard for scientific integrity and interdisciplinary collaboration [1], their adoption in the energy sector remains limited. The NFDI4Energy consortium addresses this challenge by offering a comprehensive service portfolio supporting researchers throughout the entire data management lifecycle, from initial planning to final publication.

In research, an overall major barrier is the "incentive gap" for FAIR data sharing. Researchers often perceive the curation and sharing of data as a time-consuming task with little professional gain [2]. Bridging this gap requires moving beyond static guidelines toward identifying specific, actionable incentives, such as enhanced visibility, robust infrastructural support, or educational resources, that truly resonate with the scientific community. Consequently, this thesis aims to align theoretical RDM requirements with the practical motivations of researchers.

Goal of the work

The primary objective of this thesis is to identify and evaluate the efficacy of various incentive mechanisms for data sharing within the NFDI4Energy research data management ecosystem. This will incorporate the following steps:

• Categorize Incentives: Systematize incentive mechanisms through a synthesis of literature and preliminary interview data.
• Survey Development: Design and conduct an online survey to quantify researcher preferences and identify perceived barriers.
• Analysis & Implications: Evaluate the findings to derive concrete recommendations for Research Data Management (RDM) service providers.

References

[1] Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9.

[2] Fecher, B., Friesike, S., & Hebing, M. (2015). What drives academic data sharing?. PloS one, 10(2), e0118053.

Voraussetzung

Voraussetzungen an Studierende

• - Interest in data management, digital platforms, or user-centered system design
• - Basic knowledge of empirical research methods (qualitative or quantitative)
• - Experience with conceptual design, UX, prototyping, or platform thinking is helpful, but not required

Studiengangsbereiche

• Wirtschafts- und Rechtswissenschaften
  Wirtschaftsinformatik
  Technische Volkswirtschaftslehre
  Wirtschaftsingenieurwesen
  Digital Economics

Betreuung

Titel, Vorname, Name

Philipp Fritz

Organisationseinheit

KIT, WIN-IM

E-Mail Adresse

philipp.fritz@kit.edu

Link zur eigenen Homepage/Personenseite

Website

Bewerbung per E-Mail

Bewerbungsunterlagen

• Lebenslauf
• Notenauszug

E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an philipp.fritz@kit.edu


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