Advanced societies increasingly rely on expert scientific advice to tackle complex global challenges like climate change and pandemics. Science diplomacy, involving interactions between scientific and foreign policy communities, is promoted as a means to foster international scientific exchanges and constructive relations between nations. While practitioners view science diplomacy positively, scholars highlight its fluid state, lack of a singular approach, and insufficient translation into concrete science policy actions. The authors argue that science diplomacy needs to be more directly linked to addressing social and global challenges. They propose a shift towards a data-driven approach to improve effectiveness and better inform decision-makers on investments in international scientific collaborations.

Existing literature emphasizes the relational aspects of science diplomacy, particularly the need for better coordination. Some scholars advocate for interpersonal measures such as strengthening dialogue between stakeholders. However, the authors critique the overly simplistic view that investing in original science is always beneficial and the lack of clear evidence demonstrating societal impact. They highlight the need to move beyond a focus on data availability to one that emphasizes data integration, particularly the integration of data and metadata, which often provides crucial contextual information.

The authors propose a methodology based on two key advancements: Linked Data and multi-layer network theory. Linked Data, leveraging web technologies and semantic queries, allows the integration of diverse datasets from various sources, breaking down the traditional data/metadata divide. Multi-layer network theory provides a robust framework for analyzing the interrelations between different types of entities and their dynamics. This combination facilitates a flexible, interdisciplinary approach to analyzing the complex interactions between scientific data and contextual information (metadata). The authors suggest a structured approach that involves creating a taxonomy of interconnected layers, similar to approaches used in earth-system sciences and the history of science. The analysis should incorporate data from various disciplines (hard and social sciences) to create truly transdisciplinary assessments. The authors caution against simply using quantitative metrics to determine research priorities and emphasize the importance of critical evaluation and consideration of broader societal impacts.

The authors argue that a successful "science diplomacy 2.0" requires a paradigm shift, moving beyond the historical legacy of using science diplomacy for power projection and towards explicitly addressing societal and global challenges. They cite examples like the EU-sponsored InsSciDE and S4D4C projects, which demonstrate the potential for collaborative engagement across disciplines, as models for positive engagement. They also highlight initiatives promoting open access, open science, and open data as essential for facilitating access to and integration of diverse datasets. The authors emphasize that the integration of data and metadata is critical, particularly in addressing global challenges such as climate change and pandemics. They use genomics as a positive example of successful data and metadata integration, demonstrating the value of considering contextual information. They contrast this with the limitations of non-integrated examinations of climate change issues, highlighting the exclusion of historical data in the 4th IPCC Report. The authors propose a linked-data, multi-layered approach as a solution. This allows for flexible analyses of interconnected data from different sources. The use of multi-layer network theory enables the modeling and analysis of the interactions between various entities and layers, facilitating a more nuanced understanding of the relationship between scientific research, societal needs, and global challenges. The authors acknowledge the challenges in integrating a wide variety of data, but emphasize the potential of advances in graph theory and complex systems analysis to address these challenges. Finally, they advocate for responsible innovation observatories as key instruments for achieving a data-driven science diplomacy 2.0, providing examples such as the WHO Global Observatory on Health R&D and the EU Horizon 2020 Research and Innovation Observatory. These observatories can facilitate better decision-making on research priorities based on societal needs and global challenges.

The proposed "science diplomacy 2.0" directly addresses the limitations of current practices by prioritizing data integration and explicitly linking science diplomacy to societal and global challenges. The multi-layered approach allows for a more nuanced understanding of the complex interactions between scientific research and its social context. The proposed responsible innovation observatories offer a concrete mechanism for implementing this approach. The framework allows for informed decision-making regarding research priorities, facilitating more effective and impactful international collaborations. The authors emphasize the importance of transdisciplinary research, integrating hard and social sciences, and the role of local and indigenous knowledge in this process.

The authors conclude that science diplomacy can become a transformative tool in international relations by explicitly aligning its efforts with responsible innovation and the global challenges agenda. The adoption of a data-driven approach, leveraging linked data and multi-layer network analysis, coupled with the establishment of responsible innovation observatories, are essential steps towards achieving a more effective science diplomacy 2.0. Future research could focus on developing standardized data acquisition methods, addressing the ethical and political aspects of data privacy, and further refining the multi-layered approach for specific global challenges.

The authors acknowledge the considerable challenges in implementing the proposed methodology. The creation of responsible innovation observatories requires substantial resources, inter-institutional coordination, and addressing potential biases in data collection and analysis. The development of standardized approaches to data integration and the consideration of local and indigenous knowledge requires further research and careful consideration.