Bi-National Academic Funding and Collaboration Dynamics: The Case of the German-Israeli Foundation

TL;DR

This study questions whether bi-national funding schemes like GIF create lasting cross-national scientific bridges. It fuses a large GIF/OpenAlex dataset with DTW-based clustering and ML prediction to map co-authorship trajectories before, during, and after funding, identifying three collaboration archetypes (No, Several, and High-volume co-authorship). The best-performing model (XGBoost) achieves about 0.74 accuracy and 0.81 AUC in predicting cluster membership, yet findings show most new collaborations during GIF are not sustained post-funding. The work argues for policy adjustments—sequential funding, institutional anchoring, and broader networks—and contributes to the design of science diplomacy instruments by highlighting mechanisms and limitations of short-term collaboration boosts.

Abstract

Academic grant programs are widely used to motivate international research collaboration and boost scientific impact across borders. Among these, bi-national funding schemes -- pairing researchers from just two designated countries - are common yet understudied compared with national and multinational funding. In this study, we explore whether bi-national programs genuinely foster new collaborations, high-quality research, and lasting partnerships. To this end, we conducted a bibliometric case study of the German-Israeli Foundation (GIF), covering 642 grants, 2,386 researchers, and 52,847 publications. Our results show that GIF funding catalyzes collaboration during, and even slightly before, the grant period, but rarely produces long-lasting partnerships that persist once the funding concludes. By tracing co-authorship before, during, and after the funding period, clustering collaboration trajectories with temporally-aware K-means, and predicting cluster membership with ML models (best: XGBoost, 74% accuracy), we find that 45% of teams with no prior joint work become active while funded, yet activity declines symmetrically post-award; roughly one-third sustain collaboration longer-term, and a small subset achieve high, lasting output. Moreover, there is no clear pattern in the scientometrics of the team's operating as a predictor for long-term collaboration before the grant. This refines prior assumptions that international funding generally forges enduring networks. The results suggest policy levers such as sequential funding, institutional anchoring (centers, shared infrastructure, mobility), and incentives favoring genuinely new pairings have the potential to convert short-term boosts into resilient scientific bridges and inform the design of bi-national science diplomacy instruments.

Figures (8)

• Figure 1: The temporal distribution of German–Israeli collaborative rates within the GIF funding framework. The X-axis presents a 48-year range from 25 years pre-award year to 22 years post-award year, with year zero marking the award year, highlighted by a red dashed vertical line.
• Figure 2: The temporal variation in the average number of researchers per awarded grant. Error bars represent one standard deviation. No data are available for 2019–2020 due to the absence of new awards during the COVID-19 pandemic.
• Figure 3: Distributions of academic metric diameters across research teams supported by GIF grants. All four measures exhibit right-skewed distributions with Pareto-like tails.
• Figure 4: Temporal cluster analysis of the collaboration dynamics across pre-grant, during-grant, and post-grant periods. The colors highlight prototypical trajectories: blue - funding-oriented dynamics; green - successful sustained collaborations ; red - collapse after funding; yellow - persistently inactive teams; and gray - other transitions.
• Figure 5: Feature importance distribution for each ML-based cluster prediction model.