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Updated: Sep 23, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
ESIC Series: Paper 4 Data sharing and reuse in evidence synthesis: Recommendations from the Evidence Synthesis
1Independent Consultant.
Objective:
Evidence synthesis data processes remain fragmented, with frequent duplication of data extraction, study-quality assessment, and certainty appraisal. These inefficiencies occur within a broader evidence ecosystem shaped by inequities in access, publication practices, language, infrastructure, technology, and representation, which can affect the visibility and reuse of evidence from non-English and low- and middle-income country contexts. The objective of this commentary was to identify practical solutions for strengthening data sharing and reuse so that evidence produced for one synthesis can be reused across multiple contexts.
Study Design And Setting:
WG2 followed the ESIC planning process mapped to the Double Diamond framework. Stage 1 mapped existing capabilities through consultations, interviews, and document review. Stage 2 assessed maturity across 16 core capabilities and identified critical gaps. Stage 3 generated potential strategies through workshops, surveys, and feedback from the Global SDG Synthesis Coalition and other ESIC working groups. Stage 4 prioritised solutions via impact-effort-feasibility analysis, supplemented with strategy bundling, coalition consultation, and indicative costing.
Results:
WG2 identified five high-priority solutions, designed to build on existing repositories, standards, platforms, and quality-assurance practices where successful models already exist: a connected system of synthesis data repositories, interoperable data standards, metadata standards for discoverability, open access standards for equitable reuse, and scalable quality assurance mechanisms. These solutions aim to reduce duplication, improve discoverability, and strengthen transparency, with a cross-cutting strategy on sustainable funding and incentive alignment to ensure long-term viability.
Conclusions:
WG2 provides a structured framework for transforming data sharing and reuse into a reusable global public good. By fostering interoperability, inclusivity, and sustainability, these solutions will reduce duplication, strengthen equity, and ensure that decision makers worldwide benefit from timely, trustworthy, and relevant evidence.
Plain Language Summary:
Evidence reviews often repeat work that has already been done, such as extracting data from the same studies or assessing study quality again. This wastes time and resources and can make it harder to include evidence from non-English and lower-resource settings. This paper identifies five practical ways to make evidence synthesis data easier to find, share, check, and reuse: connected data repositories, common data standards, better metadata for discoverability, open access standards, and quality assurance processes. These changes could make evidence synthesis faster, fairer, and more reliable for decision makers.
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