Q-AKTIV

Analyzing the Scientific, Economic and Social Impact of Research Activities and Research Networks

Project description

The aim of Q-AKTIV is to improve the methods for forecasting dynamics and interactions between research, technology development, and innovation. The network analysis methods will be based on recent developments in Deep Learning. In addition to the emergence of new knowledge areas and networks, we focus on the convergence processes of established sectors. The development and evaluation of the new methods initially takes place in the field of life sciences, which is characterized by high market dynamics. The additional application in economics enables a systematic comparison of the dynamics between the disciplines of science. The new methods will be used to predict the impact of existing research and network structures on the dynamics of knowledge and technologies as well as the future relevance of topics and actors. The result of Q-AKTIV is an implemented and evaluated instrument for the strategic analysis and prognosis of the dynamics in science and innovation. This complements today’s primarily qualitative approaches to early strategic planning and increases the decision-making ability of research institutions, policy makers, and industries. In addition to the analysis of dynamics, also valuable indicators for R & D performance measurement can be derived, e.g., the registration of patents based on scientific publications, the economic development of the companies involved, as well as the outreach of research activities. The practice partner brings in the necessary experience in the field of business valuation and strategy development and ensures a practical testing of the toolkit.

Publications on Q-AKTIV

• Seidlmayer, E., Melnychuk, T., Galke, L. et al. Research topic displacement and the lack of interdisciplinarity: lessons from the scientific response to COVID-19. Scientometrics 129, 5141–5179 (2024). https://doi.org/10.1007/s11192-024-05132-x
• Melnychuk, T., Galke, L., Seidlmayer, E., Bröring, S., Förstner, K. U., Tochtermann, K., & Schultz, C. (2023). Development of Similarity Measures From Graph-Structured Bibliographic Metadata: An Application to Identify Scientific Convergence. IEEE Transactions on Engineering Management, 1–17. https://doi.org/10.1109/TEM.2023.3308008
• Lukas Galke, Eva Seidlmayer, Gavin Lüdemann, Lisa Langnickel, Tetyana Melnychuk, Konrad U. Förstner, Klaus Tochtermann, Carsten Schultz (2021). COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics. Big Data Analysis for COVID-19 Workshop @ IEEE Big Data 2021. IEEE.
• Tetyana Melnychuk, Carsten Schultz, Alexander Wirsich (2021). The effects of university–industry collaboration in preclinical research on pharmaceutical firms’ R&D performance: Absorptive capacity’s role. J. Prod. Innov. Manag., 38: 355-378. https://doi.org/10.1111/jpim.12572
• Tetyana Melnychuk, Lukas Galke, Eva Seidlmayer, Konrad Ulrich Förster, Klaus Tochtermann, Carsten Schultz (2021). Früherkennung wissenschaftlicher Konvergenz im Hochschulmanagement [Translated: Early-detection of scientic convergence in university management]. Hochschulmanagement 16, issue 1.
• Eva Seidlmayer, Jakob Voß, Tetyana Melnychuk, Lukas Galke, Klaus Tochtermann, Carsten Schultz, Konrad U. Förstner (2020). ORCID for Wikidata — Data enrichment for scientometric applications. Wikidata Workshop @ ISWC 2020.
• Eva Seidlmayer, Lukas Galke, Tetyana Melnychuk, Carsten Schultz, Klaus Tochtermann, Konrad U. Förstner (2019). Take it Personally — A Python library for enrichment in informetrical applications. Posters&Demos @ SEMANTICS 2019.
• Lukas Galke, Tetyana Melnychuk, Eva Seidlmayer, Steffen Trog, Konrad U. Förster, Carsten Schultz, Klaus Tochtermann (2019). Inductive Learning of Concept Representations from Library-Scale Bibliographic Corpora. INFORMATIK 2019. GI. https://doi.org/10.18420/inf2019_26

Datasets

• COVID-19++: [dataset], [paper].

Code

• Tools for harvesting raw data
• Toolbox (Python package) for analyzing network dynamics

Project consortium

• Chair of Technology Management, Institute for Innovation Research, Kiel University, Germany.
• ZBW – Leibniz Information Centre for Economics, Kiel and Hamburg, Germany
• ZB MED – Information Centre for Life Sciences, Cologne, Germany

Funding

Q-AKTIV is funded by the Federal Ministry of Education and Research (BMBF).