Across the rapid evidence assessment and case studies, the analysis identifies consistent evidence that Open Science can deliver meaningful economic benefits when research outputs are designed and supported for reuse at scale, particularly for data, code, workflows, software, and training resources (see Sections 4.2 and 4.3; Appendix C). Quantified impacts are most robust where Open Science reduces duplication of effort, accelerates research processes, or enables downstream reuse across communities and sectors (see Section 4.2; Tables 3–4; Table 12).

At a macro level, the literature reviewed links Open Science practices to stronger long-run economic performance. The rapid evidence assessment reports macroeconomic modelling evidence indicating that long-run GDP would be approximately 2% lower under more closed research systems, compared with more open conditions that embed Open Science practices (see Section 3; Appendix A). While this estimate is derived from system-level modelling rather than individual case studies, it reinforces the importance of Open Science beyond project-specific impacts.

These findings are consistent with a recent report from PathOS, while adding specificity on how value is realized in practice across different output types and where costs and responsibilities sit across the ecosystem.

Key takeaway: Open Science is not solely a transparency or access measure. When system conditions enable reuse, it functions as a mechanism for improving research productivity and strengthening long-term economic performance (see Sections 3, 4.2–4.3, and 5).

The evidence shows that efficiency gains are the most immediate and consistently quantified economic benefits of Open Science (see Section 4.2). Across multiple case studies, reuse of open outputs reduces duplication, shortens research timelines, and lowers transaction and travel costs, particularly in contexts involving digitized collections and shared computational workflows (see Section 4.2; Tables 3–4; Appendix C).

These efficiency gains are closely linked to the availability of shared infrastructure, common standards, and incentives that support reuse across institutional and disciplinary boundaries (see Sections 4.2 and 5.3).

Key takeaway: Efficiency-driven benefits depend less on individual openness decisions and more on coordinated investment in infrastructure and reuse-enabling conditions (see Section 5.3).

The study finds that Open Science infrastructures can support innovation by enabling downstream reuse, adaptation, and extension of open resources, particularly open-source software, shared workflows, and open models (see Section 4.3; Appendix C). In several cases, estimated downstream value substantially exceeds initial development or training costs, indicating the potential for spillover benefits that extend beyond the original research context (see Section 4.3; Appendix C).

These innovation effects are strongest where adoption is widespread and where open resources are actively maintained and integrated into broader research ecosystems (see Appendix B).

Key takeaway: Open Science infrastructures can act as enabling platforms for innovation, but their economic value depends on sustained adoption and maintenance rather than one-off investment (see Section 4.3; Appendix B).

A cross-cutting insight from the case studies is that many Open Science resources increase in value as more users adopt, contribute to, and reuse them. This dynamic is particularly evident in shared workflows, open-source software ecosystems, and open models, where broader participation enhances reuse and accelerates cumulative innovation (see Section 4.3; Appendix C; methodological discussion in Appendix B).

Key takeaway: The presence of network effects strengthens the case for sustained and coordinated investment in Open Science, rather than fragmented or short-term approaches (see Section 5.3). 

The report shows that the costs associated with Open Science are distributed unevenly across the ecosystem. Researchers bear time and skills costs, responsibility for long-term preservation is carried by libraries as well as other infrastructure providers and repository operators, and publishers invest in platforms and workflows to support a wider range of outputs (see Sections 5.1–5.3). In contrast, many of the benefits accrue broadly across society and across national boundaries, contributing to a mismatch between who pays and who benefits (see Section 5.3).

Key takeaway: Without coordination, this cost–benefit mismatch limits the scalability of Open Science. Funders are well positioned to address this through aligned incentives and shared infrastructure support (see Section 5).

The analysis shows clear evidence of significant economic benefit from Open Science where impacts can be quantified, particularly through reuse and downstream application of open outputs (see Sections 4.2–4.3; Appendix C). At the same time, it highlights gaps in how reuse, network effects, and longer-term spillovers are measured across the ecosystem. Practice-level monitoring efforts already exist, including PLOS’s Open Science Indicators and international initiatives such as UNESCO’s Open Science monitoring work, but the report finds that consistent mechanisms for capturing broader economic and societal value remain limited (see Section 4.1; Appendices A and B). This means that current assessments are likely to capture only part of the value Open Science creates, which in turn contributes to underinvestment in activities that generate the greatest long-term returns (see Sections 4.1 and 5.3).

Key takeaway: Strengthening measurement is critical to expanding and deepening assessment of Open Science value and to aligning policy, funding, and incentives with where economic value is created (see Sections 4.1 and 5.3).