Envisioning a "science diplomacy 2.0": on data, global challenges, and multi-layered networks

The paper interrogates whether science diplomacy can effectively tackle global challenges and, if so, what would make it more successful. It situates science diplomacy as interactions between scientific and foreign policy communities aimed at international collaboration and advice on transnational issues. While practitioners often portray science diplomacy positively, scholarship highlights its fluid concept, unclear translation into policy, and historically problematic uses (e.g., Cold War power asymmetries and intelligence-gathering). The authors argue that reorienting science diplomacy toward societal and global challenges represents a paradigmatic shift. They propose focusing on the circulation and integration of data—including meta-data—so that science diplomacy can guide investments in international collaborations aligned with responsible innovation and social priorities.

The article reviews critical perspectives on science diplomacy, noting its ambiguous conceptualization and mixed historical legacy (Flink and Schreiterer, 2010; Fähnrich, 2015; Turchetti et al., 2020). It references initiatives such as the Royal Society/AAAS report (2010), EU projects InsSciDE and S4D4C, and the Madrid Declaration (2019), which align science diplomacy with Sustainable Development Goals. The authors discuss the evolution from focusing on knowledge production to circulation, propelled by Open Access/Open Science (Suber, 2012; Vicente-Saez and Martinez-Fuentes, 2018; UNESCO, 2015). They critique “data diplomacy” for deficit-model assumptions that more data automatically yield better decisions, arguing instead for analytical frameworks that integrate data and meta-data. They draw lessons from genomics/omics as a model of integrating data and contextual information, and highlight shortcomings in climate change debates (e.g., limited integration of historical evidence on geoengineering).

This is a conceptual and programmatic paper proposing a framework rather than reporting empirical data. The authors outline a data/meta-data integration approach to inform science diplomacy decisions through: (1) Linked-Data methods and the Semantic Web to structure heterogeneous datasets and enable machine-readable, interlinked queries that dissolve rigid data/meta-data hierarchies; (2) multi-layer network theory to model interactions among different entity types and layers (biophysical, socio-metabolic, socio-cultural; social/semiotic/semantic), enabling analysis of complex interrelations and feedbacks across disciplines and contexts; (3) transdisciplinary collaboration that combines natural and social sciences with humanities, incorporating local and indigenous knowledge; and (4) institutionalization via responsible innovation observatories that consolidate, monitor, and analyze integrated (meta-)data to prioritize research areas aligned with social and global needs. The approach is co-productionist, engaging stakeholders to validate priorities and avoid top-down, monolithic indicators (e.g., bibliometrics).

- A deficit-model focus on simply increasing data availability is insufficient; integrating data with rich contextual meta-data within an analytical framework is necessary to guide responsible, socially aligned innovation. - Linked-Data and multi-layer network modeling offer feasible, standardized ways to structure, connect, and analyze heterogeneous datasets across disciplines and scales, overcoming the data/meta-data divide. - Establishing responsible innovation observatories can operationalize this integration, informing policy-makers about which international scientific collaborations should be prioritized locally and globally. - Existing models such as the WHO Global Observatory on Health R&D and the EU RIO demonstrate how observatories can consolidate information to orient funding toward public needs; national examples (e.g., OSIRIS) show feasibility at different scales. - A science diplomacy 2.0 should channel resources into transdisciplinary, integrative research that combines hard sciences with social sciences and humanities, incorporates stakeholder input, and respects data ethics and privacy.

By reframing science diplomacy as a data-integrative, transdisciplinary endeavor, the proposed approach addresses the core question of how to align international scientific collaboration with societal and global challenges. Linked-Data and multi-layer network methods enable policymakers to interpret diverse, interdependent factors—scientific, social, economic, cultural—when assessing the potential impact of research investments. Responsible innovation observatories provide the organizational mechanism to produce and maintain such integrated analyses, fostering co-production with stakeholders to validate priorities and enhance legitimacy. This model moves beyond rhetorical claims of science diplomacy’s benefits by supplying structured, comparative evidence to guide strategic, operational, and support-level decisions, thereby enhancing the effectiveness and accountability of science diplomacy in practice.

The paper argues that science diplomacy will become genuinely transformative only if concretely aligned with responsible innovation and equipped with mechanisms to demonstrate societal impact. It proposes a science diplomacy 2.0 centered on integrative data analyses—drawing on omics, Linked-Data, and multi-layer network approaches—to identify and prioritize research addressing local and global needs. It calls for establishing and networking responsible innovation observatories at global and regional levels to implement this methodology, support transdisciplinary collaboration, and facilitate the development and execution of prioritized international projects. Future work should focus on building these infrastructures, standardizing interoperable data practices, integrating indigenous and local knowledge through co-production, and ensuring ethical, legal, and privacy safeguards.

- Conceptual paper: no empirical datasets analyzed; recommendations are programmatic. - Imbalance in data production across disciplines, with social sciences and humanities underrepresented, may skew integration efforts. - Legal, ethical, and political issues around data privacy and protection require robust governance. - Risk of reproducing monolithic evaluation models (e.g., bibliometrics) if not carefully designed; the approach emphasizes co-production to mitigate this. - Challenges in integrating indigenous/local knowledge ethically and effectively; efforts must avoid unregulated reuse and ensure respectful inclusion. - Need for education, training, and standardization of data and meta-data practices to support the proposed infrastructure.