An experimental approach to data driven process for international research & innovation cooperation

Important results from the project

Through experimental method development, we have investigated the possibilities of developing data-driven environmental monitoring with critical raw materials in the climate transition as an example area. By combining bibliometric analysis with NLP, LLM and expert knowledge, we can identify thematic trends. The method has potential to increase the accuracy of thematic prioritization, create a clearer connection between data and strategy, and complement existing working methods in r&d funding.

Expected long term effects

The method can ultimately contribute to developing more knowledge-based and future-oriented processes for portfolio management and program design in R&D funding. It offers a cost-effective way to follow what is happening in an area and what should be paid attention to. If the methodology is further developed, it could have great significance for how authorities, funders and research organizations work with continuous monitoring of the environment in complex thematic fields.

Approach and implementation

The project was implemented as a method testbed, where we tested different combinations of open and commercial data sources, analysis tools and expert knowledge. The work was carried out iteratively with several rounds of data collection, prompt development, and visualization to understand what is required for the method to be useful in practice. The work has provided both practical insights and strategic reflections on how such a method can be developed further.