Bridging the gap between Industry and Academia - An Overview of Data Sharing Possibilities

Bridging the gap between Industry and Academia An Overview of Data Sharing Possibilities Philipp Fritz1[https://orcid.org/0009-0004-0494-3396], Corinna Seiwerth2[https://orcid.org/0009-0004-0779-3186], and Christof Weinhardt1[https://orcid.org/https://orcid.org/0000-0002-7945-4077] 1Karlsruhe Institute of Technology, Germany 2Friedrich-Alexander-Universit¨ at Erlangen-N¨ urnberg, Germany Keywords: Data Sharing, Academia-Industry Cooperation, Best Practices 1 Introduction Data from the Higher Education Barometer shows that working with company data is common practice in academia. Across all types of higher education institutions, two thirds of universities use company data in at least one research project. At state universities and universities of applied sciences, the figure is as high as 91 % in each case [1]. Especially in the energy sector, which depends heavily on data-driven models, collaboration and data sharing between industry and research institutions are essential for fostering innovation. Yet, researchers frequently encounter significant obstacles in obtaining the industrial data needed to tackle complex scientific challenges. These obstacles are particularly based on legal uncertainties when it comes to sharing of sensible data [2]. Data protection concerns and legal reservations often hinder data sharing from the outset, even during the initial planning stages. For non-lawyers involved in project initiation, legal factors can appear abstract and discouraging, widening the gap between the potential and actual availability of data for research. Despite the challenges, collaboration between companies and scientific institutions has a wellestablished tradition. In these partnerships, data exchange is common but is often viewed as a project-specific tool, strictly regulated by contractual usage conditions [2]. Initiatives focused on broader data sharing remain rare, yet they are essential for extending data access beyond traditional collaboration frameworks. This extended abstract explores best practices from existing platforms and services designed to facilitate data sharing between industry and academia, offering valuable insights and potential blueprints for developing a data-sharing service within NFDI4Energy. 2 Foundations of Industry-Academia Collaborations Mutual benefits are a fundamental characteristic of successful industry-academia collaborations. This has been demonstrated by [2] and is also reflected in the outcomes of the initial workshop and surveys conducted within our industry network [3]. Industrial stakeholders are not inherently inclined to share their data; rather, they require a clear, perceived added value from engaging in data sharing with academic research. In this chapter, we thus provide an overview of the key benefits of industry-academia collaborations for both stakeholder groups [4]. These benefits serve as the foundational motivation for the development of a service within the NFDI4Energy Consortium, which aims to facilitate data sharing between industrial and academic stakeholders. P. Fritz et al | Benefits for the Industry • Access cutting-edge research infrastructure and advanced technologies. • Accelerate the development and deployment of innovative products and services. • Attract talented and motivated professionals, including young specialists. • Enhance the company’s reputation and public profile. • Mitigate risks with impartial insights from academic institutions. Benefits for Academic Institutions • Focus on real-world applications to ensure more practical and impactful outcomes. • Improve academic curricula and better prepare students to address industry challenges. • Strengthen the institution’s reputation and standing within the academic community. • Promote efficient data sharing and resource optimization by establishing systematic cooperation models instead of relying on project-specific data exchange. 3 Best Practices In this chapter, we outline Best Practices of Platforms and Initiatives which try to enable a large scale data sharing between Industry and Academia. In this collection, we especially differentiate between efforts within and outside the NFDI association to give an general overview. 3.1 Within NFDI Association KonsortSWD presents an overview of research data centers in economics, social sciences, and behavioral studies, mainly hosting government-mandated survey data [5]. For instance, the Economics & Business Data Center provides innovative datasets on German companies, including balance sheet data from sources such as Amadeus, Orbis, and Hoppenstedt. Within NFDI4Health, the German Health Research Data Portal (FDPG) is continuously developed with the aim of establishing consistent rules for the application process for the subsequent use of personal health data [6]. The envisioned process aims to facilitate access to clinical and epidemiological data while ensuring data protection and usability for future research. PUNCH4NFDI supports collaboration across communities through a marketplace for sharing tools, methods, and services, promoting joint development both within and outside its network [7]. However, the marketplace is not yet further defined. The BERD@NFDI Research Data Marketplace enables collaboration between researchers and organizations by offering secure access to high-quality organizational data [8]. Through a structured proposal process, organizations maintain full control over data access. This process includes summarizing the project’s purpose, scope, research questions, and the relevance of the data to the project. Through this proposalbased approach, the selected research projects are aligned with the organization’s objectives. P. Fritz et al | 3.2 Outside NFDI Association In line with the German government’s data strategy, which aims to facilitate the innovative and responsible provision and use of data, the Federal Ministry of Education and Research (BMBF) is funding the development of data trust models. To this end, 18 government-funded research projects are investigating secure and privacypreserving data exchange between industry and academia, focusing on the concept of data trustees [9]. Data trustees act as neutral intermediaries and aim to balance the interests of data providers and users by fostering trust, ensuring data protection, and mitigating monopolization in data markets. For instance, the project TrustNShare [10] develops a data trustee model using techniques like Differential Privacy and Distributed Privacy-Preserving Computing that allow for adjustable data sharing, balancing information flow with re-identification risk. The model is co-designed with data providers and users through participatory research. An energy domain-specific example is the EnDaSpace project [11], which utilizes the International Data Spaces (IDS) infrastructure to facilitate secure SCADA data sharing in wind energy while maintaining data sovereignty. Use cases within the project include anomaly detection and power-to-hydrogen integration, with future plans to expand the types of data exchanged and adopt a data trustee model. The IDS initiative [12] plays a key role in this context, creating a secure, standardized data space that enables and promotes cross-company data exchange. At its core, IDS treats data as a valuable asset and guarantees companies complete control over their data. Its modular structure addresses critical requirements for the data economy, including data ownership, interoperability, security, and trust. 4 Conclusion In conclusion, the outlined best practices showcase a variety of approaches to enabling large-scale data sharing between industry and academia, both within and outside the NFDI association. They share a common emphasis on trust-building, data protection, and ensuring mutual benefits for data providers and users. Platforms like KonsortSWD, NFDI4Health, and BERD@NFDI offer structured processes for data access, ensuring control and alignment with organizational goals. In contrast, projects such as TrustNShare and EnDaSpace explore innovative data trustee models and standardized infrastructures, leveraging privacy-preserving techniques and secure data spaces to balance information sharing with re-identification risks. These best practices provide key insights for NFDI4Energy, emphasizing trust-building, clear processes, and data sovereignty. A successful tool should address domain-specific needs, incentivize stakeholders, and ensure transparent governance. By integrating proposal-based access, data trustee models, and modular data spaces, NFDI4Energy can create a scalable platform for industry-academia collaboration. Author contributions Conceptualization, methodology, writing—original draft preparation, writing—review and editing, P.F; Writing – review & editing: C.S.; Supervision: C.W.; Competing interests The authors declare that they have no competing interests. P. Fritz et al | Funding The authors would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Energy consortium. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501865131. References [1] M. Burk and P. Hetze, Fachkr¨ aftebildung, Stimmungsbarometer, Hochschulpolitik aktuell Energiekrise. Stifterverband f¨ ur die Deutsche Wissenschaft eV, 2023. [2] Stifterverband f¨ ur die Deutsche Wissenschaft eV, Datenaustausch ¨ uber Sektorgrenzen hinweg st¨ arken,https://www.stifterverband.org/sites/default/files/202411/datenaustausch_ueber_sektorgrenzen_hinweg_staerken.pdf, 2023. [3] C. Speck, Z. Pan, S. Foroogh, et al.,D3.1.2.1 Design Thinking Workshop for Industry Partners, 2024. [4] F. Stahl, A. Hamann, K. Hoff, and K. Kockelmann, “Collaboration models between industry and academia,” Section Industry Engagement of the National Research Data Infrastructure (NFDI), University of Mannheim and Stifterverband, Whitepaper, Nov. 2023, On behalf of the Section Industry Engagement of the National Research Data Infrastructure (NFDI). [5] KonsortSWD. “ ¨ Ubersicht Datenzentren.” Accessed: 16 December 2024. (2024), [Online]. Available: https://www.konsortswd.de/angebote/forschende/alle-datenzentren/. [6] NFDI4Health. “Deutsches Forschungsdatenportal Gesundheit (FDPG).” Accessed: 16 December 2024. (2024), [Online]. Available: https://www.nfdi4health.de/service/ deutsches-forschungsdatenportal-gesundheit-fdpg.html. [7] PUNCH4NFDI. “Service Class 1: PUNCH Central Infrastructure.” Accessed: 16 December 2024. (2024), [Online]. Available: https://www.punch4nfdi.de/services/service_ classes/service_class_1/. [8] BERD@NFDI. “Research data marketplace.” Accessed: 16 December 2024. (2024), [Online]. Available: https://researchdatamarketplace.com/. [9] Bundesministerium f¨ ur Bildung und Forschung. “Datentreuhandmodelle: BMBF f¨ ordert Pilotvorhaben.” Accessed: 16 December 2024. (2024), [Online]. Available: https : / / www . bildung - forschung . digital / digitalezukunft / de / technologie / daten / datentreuhandmodelle _ pilotvorhaben / datentreuhandmodelle _ pilotvorhaben . html. [10] Deutsches Zentrum f¨ ur Luftund Raumfahrt (DLR). “Trustnshare.” Accessed: 16 December 2024. (2024), [Online]. Available: https://www.dlr.de/de/dw/forschungtransfer/projekte/trustnshare. [11] Fraunhofer IEE. “Forschungsprojekt EnDaSpace.” Accessed: 16 December 2024. (2024), [Online]. Available: https://www.iee.fraunhofer.de/de/projekte/suche/2021/ endaspace.html. [12] B. Otto, M. t. Hompel, and S. Wrobel, “International data spaces: Reference architecture for the digitization of industries,” Digital transformation, pp. 109–128, 2019.